A new PSS approach to improve reliability of PV inverters : implementing virtual sensor paradigm

Politecnico Di Milano

SCHOOL OF INDUSTRIAL AND INFORMATION ENGINEERING

MASTER OF SCIENCE IN ELECTRICAL

ENGINEERING

A New PSS Approach to Improve Reliability

of PV Inverters: Implementing Virtual

Sensor Paradigm

Internal Supervisor:

Prof. Loredana Cristaldi (Politecnico Di Milano) External Supervisor: Mr. Simone Bernardi (Enertronica Santerno S.P.A) Co-Supervisor:

Prof. Marco Macchi (Politecnico Di Milano)

Mater Thesis by: Amirabbas Mehrafshan (879051)

Acknowledgment

I want to express my sincerest appreciation to my supervisors Prof. Loredana Cristaldi and Mr. Simone Bernardi for their continuous support during this thesis. The door to Mr. Bernardi’s office was always open whenever I ran into a trouble spot or had a question about my research, while Prof. Cristaldi supported me with her patient guidance, encouragement and insightful sug-gestions. It was an honor for me to have the chance to participate in a research with them. Moreover, I wish to show my deep gratitude to Prof. Marco Macchi, the co-supervisor of this thesis, whose professional support and deep advice resulted in a great improvement of this study. I am grateful to him for his invaluable comments on this research.

I would like to offer my special thanks to Enertronica Santerno S.P.A and its experts who made enormous contribution to this research project: Mr. Umberto Simonati, Mr. Mauro Camaggi and Mr. Roberto Spazzoli for their great technical support, comments and suggestions during this research ; Ms. Giorgia Fogli for preparing the necessary data in performing modeling process and Mr. Marco Dalla Rena for facilitating the connection with differ-ent manufacturers and receiving the special information of various products. Without their passionate participation and input, the thesis could not have been successfully conducted.

I would also like to acknowledge IEEE Italy Section PES for offering the “IEEE PES Italy Scholarship Award” to this research. My special world of thanks should go to prof. Tiziana Tambosso, Prof. Carlo Alberto Nucci and their colleagues for selecting me to achieve this award. In addition, it is with immense gratitude that I appreciate Mr. Seyed Moheb Razavi Zadegan for his financial support and help in starting my study in Italy. I am especially indebted to him, as without his support continuing my education would have remained a dream for me.

Finally, I must express my very profound regards to my parents, whose love, encouragement and guidance are with me in whatever I pursue. They are the ultimate role models. Most importantly, I wish to pay my special appreciation to my loving and supportive wife, Sara, who has been a great source of motivation and inspiration; I am forever grateful for her unending patience and sacrifice while I have been far away from her.

Abstract

The recent development in power generation by utilizing solar energy as one of the main renewable resources results in thriving investments in this area which creates a competitive industrial market for manufacturers of photo-voltaic (PV) systems. Accordingly, in order to succeed in attracting cus-tomers, a company needs to improve its position in market not only by focusing on enhancing the products reliability and efficiency, but also by considering the condition of market and customer desires. In this regard, the purpose of this research is to utilize Product Service System (PSS) methods to organize a systematic approach toward improving the reliability of PV inverters as the core element of solar energy generation systems. Further-more, by following the organized plan, ”Reducing the PV plant downtime” is defined as the customer need and various services are suggested with the aim of fulfilling this demand namely as : ”Failure Detection Improvement”, ”Maintenance Prediction”, ”Remaining Useful Life Calculation”, ”PSS Mar-keting” and ”Right Spare Parts preparation”. Moreover, the mentioned PSS approach creates a strategy which helps to use the available resources beside proposing the new mechanisms and implements to be developed. Thereby, employing ”Virtual Sensor” is suggested and realized through this thesis as one of the required designs which results in achieving the defined services. Eventually, the presented methods and techniques are applied to a real PV inverter as the case study of this research.

Sommario

Il recente sviluppo nella produzione di energia elettrica da fonte solare ha comportato investimenti rilevanti in questo ambito ed ha visto consolidarsi un settore industriale competitivo per i produttori di componenti. Il successo commerciale richiede uno sforzo costante di sviluppo dei prodotti, delle loro performance e della loro affidabilit`a ma anche una attenzione continua alle esigenze del cliente, in particolare riferimento ai servizi di valore aggiunto. A questo proposito, la presente ricerca prende spunto dalle metodologie Prod-uct Service System (PSS) perseguendo nello specifico l’ottimizzazione di al-cuni aspetti relativi alla manutenzione degli inverter fotovoltaici. Definita la primaria necessit`a del cliente come ”Ridurre i tempi di fuori servizio dell’impianto fotovoltaico”, i termini di servizio sono elaborati seguendo i seguenti punti chiave: ”Miglioramento della rilevazione dei guasti”, ”Manuten-zione predittiva”, ”Calcolo della vita utile residua”, ”Marketing dei Servizi PSS” e ”Ottimizzazione del set di ricambi”. Attraverso l’approccio PSS `e possibile definire strategie di utilizzo ottimo delle risorse disponibili nello sviluppo di nuove funzionalit`a e di nuovi strumenti. Uno degli elementi di progettazione che emerge dallo studio come essenziale per il conseguimento dei servizi sopra citati `e relativo ai ”Sensori Virtuali”. I metodi e le tecniche presentate vengono applicati ad un inverter fotovoltaico reale come caso di studio di questa ricerca.

Contents

Acknowledgement i

Abstract ii

Sommario iii

Contents iv

List of Figures vii

List of Tables ix

List of Abbreviations x

Introduction xii

1 Product Service System 1

1.1 Introduction to Service Engineering . . . 2

1.2 PSS on Photovoltaic Inverters . . . 4

1.2.1 Failure Detection Improvement . . . 5

1.2.2 Maintenance Prediction . . . 6

1.2.3 Remaining Useful Life Calculation . . . 6

1.2.4 Right spare part preparation . . . 7

1.2.5 PSS Marketing . . . 8

1.2.6 SRT and QFD Matrix . . . 8

2 Design Requirements and Methods 15 2.1 Failure Mode, Effects and Criticality Analysis (FMECA) . . . 16

2.1.2 Criticality Matrix . . . 21

2.2 Degradation Models . . . 23

2.2.1 Capacitor . . . 23

2.2.1.1 Metallized Polypropylene Film Capacitors . . 23

2.2.1.2 Capasitor Failure Mode and Life Expectancy 24 2.2.1.3 Temperature . . . 26

2.2.1.4 Voltage . . . 26

2.2.1.5 Humidity . . . 26

2.2.1.6 Capacitor Remaining Useful Life . . . 27

2.2.2 Insulated Gate Bipolar Transistor (IGBT) . . . 28

2.2.2.1 IGBT Degradation Mechanisms . . . 28

2.2.2.2 IGBT Lifetime Expectancy . . . 30

2.2.2.3 Thermal Cycle Counting . . . 32

2.2.3 Ventilation System (Fan) . . . 34

2.2.3.1 Fan Degradation and Lifetime Expectancy . 34 2.3 Virtual Sensors . . . 37

2.3.1 Nonlinear Regression . . . 38

2.3.2 System Identification ToolboxTM _{. . . 39}

3 Industrial Case Study 42 3.1 Photovoltaic Inverter . . . 42

3.2 FMECA on PV Inverter . . . 44

3.3 Degradation Model of Critical Components . . . 51

3.3.1 Capacitor . . . 51

3.3.1.1 Hot-spot Temperature Calculation . . . 52

3.3.2 Insulated Gate Bipolar Transistor (IGBT) . . . 55

3.3.2.1 Thermal Mission Profile (TMP) . . . 56

3.3.2.2 Power Loss Calculation for IGBT . . . 58

3.3.3 Ventilation System (Fan) . . . 63

3.3.3.1 Inverter Ventilation System . . . 63

3.4 Implementation of Virtual Sensors . . . 67

3.4.1 Thermal Test Setup . . . 67

3.4.2 Data Analysis and Model Creation . . . 69

4 Conclusion 78

A FMECA Table 82

B Gamma Function 100

List of Figures

1 Simple Layout of PV System . . . xiii

1.1 Service Requirement Tree . . . 2

1.2 QFD matrix . . . 3

1.3 SRT Part1 (Failure Detection Improvement) . . . 9

1.4 SRT Part2 (Maintenance Prediction) . . . 10

1.5 SRT Part3 (RUL Calculation) . . . 11

1.6 SRT Part4 (Right Spare Part Preparation) . . . 12

1.7 SRT Part5 (PSS Marketing) . . . 13

1.8 QFD Matrix for Evaluation of DRs Priority . . . 14

2.1 Sample Diagram of RPN Pareto . . . 20

2.2 Sample of Criticality Matrix . . . 22

2.3 Simplified Schematic of DC-Link Capacitors of the PV Inverters 24 2.4 MPPFCs Design and Self-healing Process . . . 25

2.5 IGBTs in a Three Phase DC-AC Inverter . . . 28

2.6 IGBT Configuration . . . 28

2.7 CTE of Materials in Power Modules . . . 29

2.8 Bond Wire lift-Off, Cracks in Solder layer . . . 30

2.9 IGBT UCE, Tj(t) as a Function of Power Cycles . . . 31

2.10 Example of Rainflow Cycle Counting . . . 33

2.11 System Identification ToolboxTM _{. . . 40}

2.12 Flowchart of Process to Create Virtual Sensors . . . 41

3.1 PV Inverter: Sunway TG1800 1500V TE . . . 43

3.2 Pareto Diagrams for Inverter of the Case Study . . . 45

3.3 RPN Pareto Diagram . . . 46

3.4 Criticality Matrix of the Investigated Inverter . . . 47

3.6 Schematic of Bus-Link Capacitors of the Investigated inverter 52

3.7 Thermal Cycling Capability of the IGBT . . . 55

3.8 Thermal Cycling Capability for Industrial Modules according to the Case temperature . . . 56

3.9 Heat transfer Between junction, Case and Heatsink . . . 57

3.10 Position of IGBT and Diode in the IGBT Module . . . 58

3.11 Conduction and Switching Power Loss . . . 58

3.12 V0CE and R0I of IGBT Infineon FF650R17IE4 . . . 59

3.13 EOn, EOf f of IGBT Infineon FF650R17IE4 . . . 61

3.14 V0D of the IGBT Infineon FF650R17IE4 . . . 62

3.15 Air Flow through the Inverter Cabinet . . . 63

3.16 Mounted Fans ON the Converter Case . . . 64

3.17 Large AC and DC Cabinets Fans . . . 65

3.18 Schematic of the Thermal Test Setup . . . 68

3.19 Thermal Test Setup for Sunway TG1800 1500V . . . 69

3.20 Thermal Test Probes . . . 71

3.21 Embedded Thermal Sensors . . . 72

3.22 VSs on Capacitor Case . . . 73

3.23 VSs for Operating Temperature of the CPU Fan . . . 73

3.24 VSs for Operating Temperature of the IGBT heatsink Fan . . 74

3.25 VSs Used as CPU Thermal Sensor Redundancy . . . 74

3.26 VSs Used as Redundancy for Thermal Sensor of IGBT heatsink 75 3.27 VSs Used as Redundancy for Thermal Sensor of Cabinet Input Air . . . 75

3.28 VSs Used as Redundancy for Thermal Sensor of Cabinet Out-put Air . . . 76

List of Tables

2.1 FMECA Sample Table . . . 16

2.2 Severity Criteria . . . 18

2.3 Occurrence Criteria . . . 18

2.4 Detection Criteria . . . 19

2.5 Input/Output Example Data for Nonlinear Regression . . . . 38

3.1 Technical Specifications of Sunway TG1800 1500V TE . . . . 43

3.2 The Extracted Critical Failure Modes . . . 49

3.3 The Results of Life Experiment for DC Fan . . . 66

3.4 Electrical Characteristic of TG1800 1500V TE-600 OD . . . . 70

3.5 The Environmental Condition During Thermal Test . . . 70

List of Abbreviations

CA Critical Analysis

CDF Cumulative Distribution Function CTE Coefficient of Thermal Expansion

D Detection

DC Direct Current

DR Design Requirement

DS Design Specification

EOL End Of Life

FMEA Failure Modes and Effects Analysis

FMECA Failure Mode, Effects, and Criticality Analysis IEC International Electrotechnical Commission IGBT Insulated Gate Bipolar Transistor

MPPFC Metallized Polypropylene Film Capacitor MTTF Mean Time To Failure

NASA National Aeronautics and Space Administration

O&M Operation & Maintenance PDF Probability Density Function PEM Prediction-Error Minimization

PS Physical Sensor

PSS Product Service Systems

PV Photovoltaic

QFD Quality Function Deployment RPN Risk Priority Number

RUL Remaining Useful Life

S Severity

SE Service Engineerin

SEEM SErvice Engineering Methodology SRT Service Requirement Tree

TMP Thermal Mission Profile

Introduction

Nowadays by development of human society and increasing demand for en-ergy, renewable energy resources are attracting more attention thanks to their principles which are environmentally friendly and inexhaustible. Moreover, the global demand for energy has a growing tendency which shows to have more than 25% of increasing until 2040, while renewable energy resources are expected to provide 40% of the total global energy [1].

Among the variety of renewable energy resources, photovoltaic (PV) source is well-known as the safest and cleanest technology which is capable of pro-viding electrical energy even up to GW production scales, hence PV systems are considered to play an important role in generation of power in compare to other renewable sources [2]. However, the growing desires for investment in PV plants necessitates improvements in more stable and reliable power generation systems [3].

Generally, the system reliability can be described as the ability of the system, or even its sub-system, in performing the stated required function adequately during a specific time [4]. Indeed, the reliability of a system is identified as a fundamental aspect which contributes to its value in mar-ket, thus improving a system reliability can creates more valuable product regarding customers perspective [5].

Moreover, the cost of reliability is another factor which affects the cos-tumers decision. In other word, providing high reliable product at lowest possible costs is the main value that can result in more cost-effective product to compete in market. Besides, customer supporting through offered ser-vices by the manufacturers also can create value to provide more efficient products and compensate the reliability limits [6]. For instance, providing system maintenance is known as one of the common services which is offered by manufacturers to keep and improve the product performance. Thus, the phenomenon of integrating services and products which has the potential

to improve performance and efficiency with subsequent results in positive economic effects is a growing trend among manufacturers [7], [8].

Regarding the particular consideration on photovoltaic energy genera-tion, a respective focus is attracted to the inverter as the core element of PV grid connected system. A PV inverter is responsible to convert the output Direct Current (DC) of PV modules to the Alternative Current (AC) which is needed for transferring the generated electricity to the grid Fig. 1. The concentrated attention on PV inverters are due to the fact that, although these element account for a remarkable portion of PV system costs, they are still recognized as a bottleneck for system reliability and efficiency. There-fore, to maintain the further spread of photovoltaic systems in the market, investigation of PV inverters can leads to valuable improvements [9]–[12].

Figure 1: Simple layout of PV system

The aim of this research is to integrate managerial concept with engineer-ing approaches for the purpose of recognizengineer-ing significant demands of indus-trial market regarding PV inverters, as well as, determining and evaluating the methods of addressing them. Furthermore, by employing engineering technique, it is tried to deal with the established strategy of fulfilling cus-tomer desires in order to improve a manufacturer position in the competitive market.

This thesis is done in collaboration with Enertronica Santerno S.P.A which is an Italian manufacturer in power electronics fields. Inverters for renewable resources and industrial application are the main product of this

company, where a specific product has been investigated as the case study of this research which is a PV inverter known as ”SUNWAY TG TE”.

The thesis structure is organized in the manner that the first chapter is dedicated to the management prospective, where the Product Service System (PSS) along with Service Engineering (SE) are determined as an approach to investigate the services in which the PV inverters customers are interested in. Moreover, the evaluation of services in this chapter results in framing a plan to follow in satisfying customers desires.

In order to provide the services which are defined in the first chapter the required methods are studied in the second chapter, where the procedures of performing ”Failure mode, effects, and criticality analysis”, constructing components ”lifetime models” and designing ”Virtual Sensors” are demon-strated.

Correspondingly, through the third chapter, by considering the PV in-verter, ”SUNWAY TG TE”, as the case study of this thesis, the introduced methods of the former chapter is implemented. Accordingly, Critical and sensitive components of the inverter are recognized by utilizing FMECA, in subsequent, the lifetime model of these components are created. Besides, the lack of essential measurement data for specific points of the inverter is compensate by designing virtual sensors. Finally, a summary of the main steps of the thesis together with suggestions for future work are denoted in the conclusion chapter.

Chapter 1

Product Service System

In recent mature stage of industries, saturated industrial market together with commoditization have led manufacturing companies to seek strategies with competitive advantage. Moreover, customers’ expectations and needs have created a new source of value for manufacturer companies to re-position themselves in market by changing their traditional prospective and turning from simply providing products to the integration and provision of industrial product services which leads to manifestation of ”Product Service Systems” (PSS) [13].

Taking the steps toward implementing PSS by integrating related services to products, known as servitisation of manufacturing, is attracting more attentions among manufacturers, however, a noticeable number of companies were not successful in pursuing the transition from ”pure products” toward integrating product services [14].

For instance, “service paradox” can be mentioned as one of the ineffective ways of delivering PSS where a company invested in increasing its service offering but has not realized any return from investment in extending its services [15]. Hence, the lack of suitable models and methods to design proper services can leads to failure in development of a marketable PSS. Consequently, the needs to approach PSS in a systematic process creates Service Engineering (SE) methodologies [16].

1.1

Introduction to Service Engineering

Considering the needs of integrating suitable services to products in perform-ing PSS, ”Service Engineerperform-ing” (SE) has emerged as a technical discipline which focus on adoption of engineering methods in development of proper services matched with products. More in detail, SE aims at analyzing cus-tomers expectations to address them while considering both the internal and external performances of a manufacturer [16], [17].

Analysis of customer needs can be know as the first step of SE with the purpose of understanding the requirements of customers in terms of services, product and performance, in order to compare them with current company offerings and find out the existing or potential gaps [18].

Subsequent to identifying the customer needs, process prototyping have to be done with the purpose of suggesting the associated PSSs and design requirements of the services. In this regard, Service Requirement Tree (STR), Fig. 1.1, can be employed to put in to evidence the relationships between customer needs, PSSs and required resources to satisfy the needs [19].

Figure 1.1: Service Requirement Tree (STR) [19]

The construction of SRT which is represented in Fig. 1.1 is composed of four main level namely as needs, wishes, design requirements, and design specification. These four levels can be described as follow:

• Wishes: the solutions to satisfy the needs are expressed by wishes • Design Requirements (DRs): they represent the designs which are

re-quired to perform by the company in order to fulfill the wishes

• Design Specification (DSs): they contain activities and related re-sources which are essential in providing DRs

The assessment of the created SRT with the purpose of identifying the most relevant DRs in satisfying wishes and consequently customer needs can be done by an approach based on Quality Function Deployment (QFD). In other word, employing QFD as a management tool in designing services can support design teams on a structural approach to match the market demand with appropriate technical requirements and consequently develop PSS planing to achieve higher-quality products [20], [21].

One of the benefits of using QFD approach is that by assigning weights to the SRT elements make them more understandable to all involved people. More in detail, the importance of lower lever elements in fulfilling the upper ones is quantified by assigning them weight numbers which are defined by PSS design team [16]. These weights can be scaled in various manners, for instance, they can be introduced as ten-point [22], five-point [23], [24], three-point [21], [25], [26], proportions of 1 [27], etc.

The above mentioned quantitative scaling of the effectiveness of lower el-ements in satisfying the upper elel-ements can be collected together and results in QFD matrix, Fig. 1.2, which is used in this thesis to rank the importance of DRs to focus on in developing more proper and customer-oriented PSS.

Figure 1.2: QFD matrix used for ranking the importance of design require-ment

The relevance scaling and creation of QFD matrix among this thesis is carried out according to the method known as ”SErvice Engineering Method-ology” (SEEM) [16], [28] which is compatible to industrial cases. In this regard, three-point scaling is utilized to define three expressions of ”funda-mental”, ”important but not fundamental” and ”not essential” by assigning three numbers of ”1”, ”2” and ”3” respectively, in order to identify the strength of relation of each DR with its upper level wish. In addition, the last two columns of the QFD matrix, Fig. 1.2, demonstrate the importance of each DR which is calculated according to Eqs. 1.1 and 1.2.

DRIj = X i∈W W Ii·Iji (1.1) DRIj% = DRIj P k∈DRDRIk (1.2) Following this chapter, the aforementioned method of service engineering is employed to help in planning product service system properly and clear the important points to focus among this thesis.

1.2

PSS on Photovoltaic Inverters

The attempts toward servitisation of Photovoltaic (PV) Inverters as one of the products of the investigated company contribute to bring attentions to strategies related to the development of PSS. In this regard, service engineer-ing is suggested and employed in this thesis in order to orient the product services toward more up-to-date and customer satisfying services.

Regarding the first phase of the SE approach, it is essential to identify the most fundamental and significant needs of customers in relation to the pur-chased product. Moreover, the customer demands and needs can be realized by considering product utilization, its operation condition and effects on cos-tumer business. Accordingly, to determine the essential needs of customers of PV inverters as the case study of this thesis, it is necessary to study the related needs and effects to which the customers are faced.

Reliability and availability of inverters as the core of solar energy pro-duction system is known as an indispensable issue to be considered in de-velopment of PV systems, since, compensation of a large amount of initial investment of the plant installation is considered to payed back by selling

electricity during lifetime of the systems. Likewise, the failure of PV in-verters can leads to interruption in plant production and consequently loss of potential revenues. Hence, ”Reducing plant downtime” has been always among crucial concerns of PV inverters customers.

By considering ”Reducing plant downtime” as a well-known need of in-verter customers, various bellow-mentioned wishes are proposed in order to address this necessity:

• Failure Detection Improvement • Maintenance Prediction

• Remaining Useful Life (RUL) Calculation • Right Spare Part Preparation

• PSS Marketing

Subsequently, different design requirements (DRs) have to be provided by the company in order to support the above-mentioned wishes, however, the DRs effects in satisfying the wishes are not the same. Following this section, each of the named wishes and their related requirements are described.

1.2.1

Failure Detection Improvement

Diagnosis faults in PV inverters is a vital issue in minimizing the downtime and consequently maximize the energy generation of a solar plant. Thus, to secure the energy generation and related economical profits, customers are interested in effective ways of detecting every failures in order to maintain them and resume the generation of energy.

The approach by which the company aims to achieve ”Failure Detection Improvement” is to provide several DRs namely as employing ”Monitoring System”, creating ”Degradation Models” for critical components and equip-ping the inverters by ”Virtual Sensors”. Moreover, with the purpose of find-ing out the critical point of inverters which may cause failures, the ”FMECA” is advantageous to be perform on the inverters. latter, these DRs are evalu-ated and some of them are developed during the thesis so as to improve the detection of the inverter failures.

1.2.2

Maintenance Prediction

The efficiency and continuity of energy generation necessitate improvement in system reliability. Besides, keeping the operation of a system in an optimum condition creates a requirement for an effective and well-timed maintenance [29]. Accordingly, predicting the machine failures helps companies to provide necessary services to reduce the possibility of machine breakdown, and con-sequently improves service quality in addition to mitigate the maintenance costs [30].

There are various approaches to predict failure or maintenance time. Data-driven methods and application of artificial intelligence in model-based approaches are among outstanding methods in predictive maintenance tech-nology [31]–[34]. However, selecting one of these methods to be utilized in order to predict the system failures is depending on the available models and data, operating condition and failure history of the system [35].

Complexity of the investigated inverter in this thesis and lack of data about each component operating conditions as well as inadequate historic records for each single components of the inverter to use for training a model so as to employ artificial intelligent methods, bring attention to the approaches based on components life time and failure models along with analyzing the overall receiving data from inverter.

In order to actualize maintenance prediction regarding the above-mentioned condition, obtaining the critical components lifetime model, monitoring the system operation condition and employing virtual sensors to collect data which are not received normally can be considered as some of the funda-mental DRs. Furthermore, performing FMECA can be profitable in order to recognize the critical failure modes.

1.2.3

Remaining Useful Life Calculation

By increasing demands for high reliable power conversion, smart system con-trolling to monitor the system operation stability is attracting attentions. Monitoring the End Of Life (EOL) of inverters as a PV plant subsystem with high requests for receiving maintenance and operation services, is an approach to improve the reliability of power generation [36].

Regarding estimation of EOL of inverters, RUL is introduced as the time between estimation of EOL to the end of inverter life [37]. Complexity of systems and available operating data are factors that can constrain possible

methods of calculating RUL. Accordingly, approaches which mostly match to the inverts under analysis of this thesis are based on evaluating inverter RUL by utilizing individual components failure rates which are available through literature and and manufacturers’ data [38].

Therefore, to calculate RUL of the investigated inverter, FMECA can sup-port with recognizing sensitive individual components of the inverter which are possible elements that can limit the life-time of the whole system. Fur-thermore, the other DRs of achieving RUL calculation system can be pro-posed as: Degradation model of the critical elements, Monitoring system for controlling the components statue and employing virtual sensors with the purpose of obtaining special operational parameters.

1.2.4

Right spare part preparation

The uncertainty in failures and demand of spare parts contributes to a chal-lenge in on-time preparation of the right quantity of spare parts which results in spare parts management issues [39]. Indeed, improving the efficiency of supply chain of spare parts can results in more effective management of ma-chine failures which has crucial effect on reducing product downtime and availability [40].

Demands for spare parts of the investigated inverter can be divided to three categories:

• Commissioning: There are demands for spare parts due to the infant failures during starting up the inverter on the site.

• Service contracts: This category is related to the spare part require-ments during ”Operation & Maintenance” (O&M) contracts, contrac-tual guarantees and extraordinary maintenance.

• After guarantee repair: The demands for spare parts after the product guarantee period is in this category. The capability to provide spare parts to customers of this category is commercially valuable for the company.

Dealing with the first catagory is basically related to the preliminary tests which is out of context of this study, however,the second and third category of demands are addressed instead, by diagnosing the sensitive components of the investigated inverter through FMECA beside analyzing the lifetime models

of these components. In addition, an optimize and competitive proposal for preparing spare parts is a key factor for the company which is reported to be obtained through active operating of marketing& sale department.

1.2.5

PSS Marketing

By increasing the technical similarity of products in market, decision making of customers are mostly affected by the services which are introduced by PSS [41]. Thus, PSS marketing is known as an effective tactics which transfers the designed services properly to the market by making its value recognizable for the customers and enhancing the product value in competitions [42].

Marketing& Sales play a fundamental role in communicate the service values to the customers. Accordingly, ”PSS Marketing” attempts to design a business plan by representing in a valuable manner all services which are contributing to the main goal of PSS which is specifically ”Reducing Plant Downtime” in this thesis.

1.2.6

SRT and QFD Matrix

Considering the aforementioned wishes and related design requirements, cre-ating SRT and utilizing QFD matrix can result in obtaining the priority of actions and efforts to address the main need of customer which is introduced as ”Reducing Plant Downtime”. Due to the fact that SRT of the mentioned service requirements contains too many branches and elements it is divided in to some portions in order to facilitate its presentation in this thesis which are shown in Figs. 1.3 - 1.7.

Furthermore, in order to evaluate the priority and importance of require-ments to be focused on, QFD matrix is created as demonstrated in Fig. 1.8. The assigned weight to each cells of the matrix is done according to the afore-mentioned definition of each wish and related DRs by PSS team. According to the last column of the QFD matrix, Fig. 1.8, the importance of DRs are evaluated which shows the priority of requirements in satisfying the proposed wishes to reduce plant downtime.

Figure 1.8: QFD Matrix for evaluation of DRs priority

In summary, in this chapter Product Service System (PSS) is introduced. Besides, Service Engineering (SE) is presented as an approach in perform-ing PSS. Accordperform-ingly, the main need of photovoltaic inverter customers is assessed to be ”Reducing the plant downtime”, in this manner, the proper services and actions to satisfy the need is proposed through SRT and evalu-ated by QFD matrix. The next chapter is allocevalu-ated to describe comprehen-sively the methods and Design Requirements (DRs) which are suggested and evaluated in this chapter.

Chapter 2

Design Requirements and

Methods

In previous chapter, PSS concept is developed to be employed through this thesis by utilizing Service Engineering (SE) approach which establishes a structural process to satisfy inverter customers’ needs. Wherefore, the main need of PV inverter customers introduced as ”Reducing Plant downtime” and the wishes to address this need are proposed. Furthermore, various Design Requirements (DRs) to realize the proposed wishes are suggested.

The defined need, wishes and requirements are organized in SRT struc-ture, likewise, the importance of each DRs is declared through QRT matrix. Therefore, in order to accomplish PSS, the next step is to focus on setting up the DRs and methods to achieve the main purpose.

The represented DRS in previous chapter are ”FMECA”, ”Virtual Sen-sors”, ”Degradation model”, ”Monitoring system” and ”Marketing & Sales”. Due to the fact that ”Monitoring system” is currently available among the services of investigated company, its transmitted data can be used without any need to improve the system. Furthermore, ”Marketing & Sales” is com-posed of business concepts which needs to be studied according to marketing arguments which is out of technical context of this thesis. In Consequence, following this chapter three remaining DRs namely as ”FMECA”, ”Degra-dation model” and ”Virtual Sensors” are going to be under consideration to be analyzed and improved for the thesis Case study.

2.1

Failure Mode, Effects and Criticality

Anal-ysis (FMECA)

The first step to study the reliability of a product is to study and analyze the system to identify the potential failure modes. The method of Failure Modes and Effects Analysis (FMEA) was firstly used to study the failures in US military systems in 1940 [43]. Later this analysis improved to Failure Mode, Effects and Criticality Analysis (FMECA) and used to increase the reliability of space program hardware by National Aeronautics and Space Administration (NASA) [44]. In other word, Critical Analysis (CA) is the extension for FMEA to develop into FMECA [45]. Nowadays, FMEA and FMECA are systematic proactive approaches to identify the failures cause and effect of a system and evaluate the parts which need more attention to prevent malfunction or failure of the system.

Various renewable energy fields take advantage of FMEA and FMECA methods, e.g. solar modules [46], [47], and wind energy [48], [49]. In this thesis this technique is employed to photovoltaic (PV) inverters with the aim of recognizing the critical point of the inverter to focus on, so as to increase the reliability of them and reducing the probability of long machine down-time.

The process of recognizing the critical points and failure modes starts with classification of different possible failures that would threaten the system performance in the form of FMEA table Tab. 2.1.

Table 2.1: A sample table for FMEA

To perform FMEA analysis, a cross-functional team with different exper-tise from diverse section of the company have to compose their experience and knowledge about the analyzed system to gather information about fail-ure modes, cause, effect, severity, occurrence, detection etc. According to the International Electrotechnical Commission (IEC) [50], failure modes in

considered; moreover, the most probable causes for each potential failure mode should be remarked. Hence, as a result of the introduced failure modes and causes, the effects of each failure can be defined.

The evaluation of failures, causes and effects is done by risk calculation of FMEA technique which is the base of critical analysis and FMECA. A very frequent method for evaluating the criticality is calculation of Risk Prior-ity Number (RPN). Furthermore, other method of critical analysis could be added in order to improve this analysis.

The RPN of a component failure is determined by multiplication of three factors: severity of the failure effect, probability of the failure mode occur-rence and the possibility of its detection Eq. 2.1. Thus, a failure mode with higher RPN is considered to have more priority and importance than a one with lower RPN [51], [52].

RP N = (Severity) × (Occurrence) × (Detection) (2.1) Following, the three constituent factors of RPN (”Severity”, ”Occur-rence”, ”Detection”) and their ranking are mentioned:

Severity: The severity and seriousness of a failure effect is ranked ac-cording to defined margins of effects on system or users. Ranking ’1’ for severity represents a failure which is so insignificant that most customers may also not even notice and dose not affect any other parts. The ’2’ rank-ing is related to the partial loss of performance. Rankrank-ing ’7’ corresponds to the system shutdown, while the severity with the ranking of ’10’ revealed the failure modes which are affecting the safety and are life-threatening [51], [53]. In Tab. 2.2 the criteria and ranking for Severity is described.

Occurrence: The occurrence of a defined failure mode is estimated as a value between ’1’ to ’10’ to manifest the likelihood of a failure mode as a result of a defined cause. The occurrence ranking of ’1’ is related to the failures which are so unlikely to happen, while the ranking of ’10’ corresponds to a failure with high probability of happening [51]. More detailed criteria for occurrence can be found in Tab. 2.3.

Detection: The possibility of recognizing a failure mode related to a specific cause before its occurrence is called Detection. The associate value to detection is in opposite order of Occurrence or Severity value. Hence, the probability of recognizing a failure is higher, if the Detection is ranked lower. Therefore, the more is the possibility of failure detection, the less is the RPN

Effect Criteria: severity of effect Rank Hazardous

Failure is hazardous and occurs without warning. It suspends operation of the system and/or involves

noncompliance with government regulations

10

Serious

Failure involves hazardous outcomes and/or noncompliance with government regulations or

standards

9 Extreme Product is inoperable with loss of primary function.

The system is inoperable 8

Major Product performance is severely affected but functions.

The system may not operate 7

Signicant Product performance is degraded. Comfort or convince

functions may not operate 6

Moderate Moderate effect on product performance. The product

requires repair 5

Low Small effect on product performance. The product does

not require repair 4

Minor Minor effect on product or system performance 3 Very minor Very minor effect on product or system performance 2

None No discernible effect 1

Table 2.2: Failure mode Severity evaluation, criteria and ranking [50], [54].

Probability of failure Criteria: Possible failure rates Rank Extremely high: Failure almost inevitable >1 in 2 10 Very high 1 in 3 9 Repeated failures 1 in 8 8 High 1 in 20 7 Moderately high 1 in 80 6 Moderate 1 in 400 5 Relatively low 1 in 2,000 4 Low 1 in 15,000 3 Remote 1 in 150,000 2 Nearly impossible <1 in 1,500,000 1

Detection Criteria: likelihood of detection by design control Rank Absolute

uncertainty

Design control does not detect a potential cause of failure or subsequent failure mode; or there is no design control

10 Very remote Very remote chance the design control will detect a

potential cause of failure or subsequent failure mode 9 Remote Remote chance the design control will detect a

potential cause of failure or subsequent failure mode 8 Very low Very low chance the design control will detect a

potential cause of failure or subsequent failure mode 7 Low Low chance the design control will detect a potential

cause of failure or subsequent failure mode 6 Moderate Moderate chance the design control will detect a

potential cause of failure or subsequent failure mode 5 Moderately

high

Moderately high chance the design control will detect

a potential cause of failure or subsequent failure mode 4 High High chance the design control will detect a potential

cause of failure or subsequent failure mode 3 Very high Very high chance the design control will detect a

potential cause of failure or subsequent failure mode 2 Almost certain Design control will almost certainly detect a potential

cause of failure or subsequent failure mode 1 Table 2.4: Failure mode detection evaluation, criteria and ranking [50], [54].

of that failure [47]. Tab. 2.4 contains detail information about the criteria and ranking of the detection.

Through this thesis a table of all failure modes and the related value of Severity (S), Occurrence (O), Detection (D) and linked RPN is provided by a team including experts from different department of Enertronica Santerno. In this regard, the FMEA analysis started by filling the first parts of table shown in Fig. 2.1 to calculate all RPN. Applying the FMECA on the thesis Case study and results are reported in Section 3.2.

Having a table of all possible failure modes, their associated S, O, D and RPN, the Criticality Analysis (CA) can be started to extend the analysis from FMEA to FMECA so as to prioritize the failure modes for which the appropriate corrective action is in prime concern. Following, these methods of CA are described.

2.1.1

RPN Ranking and Pareto Diagram

RPN ranking and Pareto diagram are very common and straight forward methods to assess the most critical risk events [51], [55]. In this approach failures are ordered from the highest RPN to the lowest and a cumulative graph of failure percentage is plotted. In Fig. 2.1 a sample diagram of RPN Pareto is illustrated.

Figure 2.1: Sample diagram of RPN Pareto

A threshold value for RPN can be decided by the FMEA team consider-ing the fact that every project has its own characteristic, consequently, in a project a certain RPN value may be assume moderate, while this value can be related to a crucial risks in another project [45], [56]. Moreover, accord-ing to Pareto principle, 80% of failures happen due to 20% of causes [57], accordingly, 20% of the highest RPN can be considered as the critical failures which require urgent corrective action.

2.1.2

Criticality Matrix

Considering the fact that the ranking of S, O, D and RPN are qualitative assessments and are not numeric in the sense that, for instance, the detection of 4 is twice as detection ranked by 2, therefore, these ranking numbers are just for ordering and works as a label [51].

Correspondingly, this kind of qualitative ranking may cause some peculiar results in prioritization. For example, considering a failure mode with very high severity of S=9, very high probability of Occurrence O=10 and high possibility of detection D=1, results in RPN=90, besides that, another failure mode with medium severity of S=5, moderate occurrence of O=4 and average detection possibility of D=6, lead to RPN=120. As a result, considering just the RPN value, the former failure mode shows less importance and priority in compare with the latter failure mode, although the former failure mode has so high severity and probability of occurrence and accordingly it is so risky comparing to the later.

Another method to compare failure mode can be through employing a Criticality Matrix, which indicates the severity of failure events on the verti-cal axis and the occurrence of them on the horizontal one [45]. This method was introduced also in some references with different axes position [58], [59]. In Fig. 2.2 a sample of Criticality Matrix for failure modes is illustrated. In this matrix the failure modes are plotted according to their Severity and Occurrence. The priority of these failure modes is increased from down-right through up-left cells of this matrix. Regarding the most critical zone of upper left part of the matrix, colored red, these failures are risky and in priority for corrective actions.

Figure 2.2: Sample of Criticality Matrix for the failure modes

To sum up, Failure Mode, Effects and Criticality Analysis is described in this section. The methods to collect and provide failure modes and subse-quently analysis of them through RPN ranking and Pareto diagram, more-over, Critically Matrix are defined. The above mentioned approaches are implemented within this thesis in the industrial case study and reported in section 3.2 to extract the critical components which are essential to be studied in order to improve the system reliability.

2.2

Degradation Models

Degradation of components is known as one of the failure causes in electrical systems. Analysis of a system reliability can effectively be based on multiple degradation modeling of its components which is obtained through study of degradation mechanism [60].

Among this section the failure mechanism and degradation model of three electrical components of photovoltaic inverters is analyzed, which are Capac-itors, IGBT and Fans. Choosing these three components for preliminary study in this chapter is based on the experience of the designers, however, in section 3.2 the result of failure mode analysis will confirm this components as critical components.

2.2.1

Capacitor

DC-link current in pv inverters includes high frequency osculations and har-monics thanks to switching behavior of the inverter. DC-link capacitors are connected to the inverters DC-link Fig. 2.3 in order to absorb and reduce the voltage ripples by providing low impedance path to the current ripples [61]. Following, in this section the structure of these capacitors, degradation mechanism and their lifetime model are studied.

2.2.1.1 Metallized Polypropylene Film Capacitors

Attempts to improve PV inverters reliability and performance leads to us-ing of Metallized Polypropylene Film Capacitors (MPPFCs) instead of elec-trolytic capacitors in new designs [62]. The investigated inverter in this thesis belongs to the latest version and is equipped with this type of Capaci-tors which offer more longer lifetime and can withstand higher levels of surge voltage in compare to electrolytic capacitors.

MPPFCs capacitors are designed by Polypropylene Films as dielectric which are coated with a layer of zink or aluminum with the thickness of several tens of nanometers creating the electrodes. The metallization of elec-trodes is done in a mosaic pattern Fig. 2.4a and each segments interconnect with each other through a narrow path of current gate to perform as a fuse Fig. 2.4b. Thus in case of arc discharges, the vaporization of these metallized gates, disconnects the defected areas from the other parts in order to clear the fault Fig. 2.4c. Consequently, by removing the fault area, the capacitor

Figure 2.3: Simplified schematic of DC-Link Capacitors in the three phase inverter

can continue to function, although with a small reduction of its capacitance. This method of fault clearance, is named as self-healing property which is provided by the Metallized film capacitors [63], [64].

2.2.1.2 Capasitor Failure Mode and Life Expectancy

Following the technology of MPPFCs and the self-healing process, the end of life of these capacitors is when their capacitance drops bellow a required tolerance. Moreover, although the mechanism of self-healing makes MPPFCs safer than electrolytic, the clearing events increase the vaporized metal presure inside the case which augments the rate of degradation. Thus, the reduction of capacitance is usually accompanied by increasing in loss factor [66], [67].

Different intrinsic and extrinsic factors can affect capacitors failures both catastrophic, due to single-event over-stress, or wear out failure as a result of long time degradation. Design defect, operating temperature, voltage, cur-rent and humidity are the main failure factors [63], [68]. The solutions which is followed in this research is based on improving the detectabality of the system to recognize the failures before they happen. Accordingly, observ-ing the failure factors helps to monitor the over-stress condition and prevent

(a) Mosaic pattern mettallization (b) Mettalized current gates as fuses

(c) Fault clearance by isolation of defected area

Figure 2.4: Metallized Polypropylene Film Capacitors(MPPFCs) design and self-healing process [65].

mend to control some main factor namely as: Hot-spot temperature, Voltage and Humidity. Moreover, it is worth mentioning that these parameters are essential parameters in calculating the degradation and remaining useful life of the component.

2.2.1.3 Temperature

One of the main effective factors in failure rates of a vast number of systems and components is temperature [69], [70]. The Arrhenius equation Eq. 2.2 is one of the most well-known equations to analyze and calculate the effect of operating temperature on life time of components [66], [71]. This equation has been extended and used for different electrical components including capacitors and fans which will be discussed in this thesis.

t(T ) = tTnexp  Ea KB  1 T − 1 Tn  (2.2) Eq. 2.2 is the exponential form of Arrhenius law, where tTn is the expected

lifetime at a reference hot-spot temperature Tn, KB is the Boltzmann

con-stant, Ea is an activation energy and finally t(T ) is the expected lifetime at

operating hot-spot temperature T which is calculated as a function of case temperature of a capacitor and it’s dissipated power. The relation between case and hot-spot temperature is described in Section 3.3.1.

2.2.1.4 Voltage

The life-time of capacitors is also sensitive to the operating voltage and op-eration in voltages higher than nominal concludes in reduction of capacitor remaining life. The power law equation Eq. 2.3, or the exponential law Eq. 2.4 are used as lifetime multipliers as a function of the ratio between the rated and applied voltage. [66], [72].

t = tUn  U Un −n (2.3) t = tUnexp  −α(U − Un) Un  (2.4) where tUn is the expected lifetime at the nominal voltage or reference

voltage and t_/

t_Unis the voltage acceleration factor.

2.2.1.5 Humidity

Humidity is another effective factors in degradation of capacitors. The humidity-dependent lifetime factor, indeed, is a concern for plastic-boxed

materials [66], [73]. The focus of this thesis is on power capacitors operating as DC-link capacitors Fig. 3.5 which are made in metal boxes, therefore, the humidity effect can be neglected in this case.

2.2.1.6 Capacitor Remaining Useful Life

Considering the above-mentioned factors, combining Eq. 2.2 and Eq. 2.3, the general lifetime model for capacitors as a function of operating voltage and hot-spot temperature will be obtained as Eq. 2.5.

t(T, U ) = tTn,Unexp  Ea KB  1 T − 1 Tn   U Un −n (2.5) Finally, for estimating the remaining useful lifetime of the capacitor, which has operated in different hot-spot temperatures and voltages, the Eq. 2.6 can be used in order to calculate the percentage of remaining useful life-time of capacitor (RU Lcap%).

RU Lcap% = 100 ∗ " 1 −X i tTi,Vi T (Ti, Vi) # (2.6) Where:

- tTi,Vi is the operating time duration at hot-spot temperature Tiand voltageVi.

- T (Ti, Vi) is the calculated lifetime of capacitor at hot-spot temperature Ti

and voltage Vi which is obtained from Eq. 2.5.

To sum up, in this section the construction and life expectancy of bus-link capacitors is described. The hot-spot temperature and operating voltage are recognized as the effective factors on these capacitors and their relation to the remaining lifetime is reported, however, to use the final formula of remaining useful life, Eq. 2.5 and Eq. 2.6, different sensors are needed to provide data. As already stated, these parameters are not measured usually. Therefore, the approach of this thesis is to design virtual sensors in order to obtain this data. Virtual sensors and their models are described in Sections 2.3 and 3.4 in order to measure all essential parameters which are distinguished as degradation models input during this section.

2.2.2

Insulated Gate Bipolar Transistor (IGBT)

The Insulated Gate Bipolar Transistors (IGBTs) are semiconductor switching devices which are playing as the core of inverters. Each inverter contain six IGBTs which transform the DC input to the three-phase AC output Fig. 2.5. The configuration of an IGBT is illustrated in Fig. 2.6.

Figure 2.5: Six IGBTs in a three phase DC-AC inverter

Figure 2.6: IGBT Configuration [74]

2.2.2.1 IGBT Degradation Mechanisms

dent parameter which describe this expansion. Fig. 2.7 shows the CTE for some common materials used in power modules [75].

Figure 2.7: Coefficient of thermal expansion of the most commonly used materials in power modules [76].

Three main interfaces inside module, bellow mentioned, are affected by temperature changes which is usually cyclic due to the load cycles:

• The bonding between the chip metalization and the bond wire • The Chip solder

• The Baseplate solder

The thermal expansion of the cheap and bond wire can generate a stress which initiates bond wire lift-off. The stress causes a rupture which progress laterally, Fig. 2.8a and finally leads to bond wire lift-off. Furthermore, regarding the intermetallic layers between base plate and chip, the solder layers are affected by higher stress due to the difference in CTE of joint materials which results in cracks in these layers Fig. 2.8b [77] .

(a) (b)

Figure 2.8: (a) Bond wire lift-off, (b) Cracks in the solder layer [77] . It is worth mentioning that each degradation can accelerate the other one, for instance, solder degradation causes increase in thermal resistance and leads to rise cheap temperature which consequently accelerates bond wire lift-off [77].

Considering the aforementioned thermomechanical interactions, degrada-tion of IGBTs are evaluated according to the thermal cycles. Power cycling is one of the main origin of thermal stress on IGBTs and used in the calculation of IGBT lifetime [78], [79].

By delamination of solder layers and starting of bond wire lift-off the total resistance between collector and emitter and consequently the collector-emitter voltage (VCE) will increase. this degradation also rises the thermal

resistance inside the IGBT and leads to increase the junction temperature (Tj) [77], [80]. Fig. 2.9 illustrates the change in VCE and Tj due to the

degradation of IGBT.

2.2.2.2 IGBT Lifetime Expectancy

There are various models suggested by different researchers and manufactur-ers for calculation of remained useful life of IGBTs. The most common used models are based on Arrhenius model Eq. 2.2. Power-law which showed by Eq. 2.7 is one of these models, where Nf is nominal cycle count, Q[J/mol] is

the activation energy, R = 8.31J/(K · mol)is the universal gas constant and T [K] is the absolute temperature [81].

Nf = e(

Q

R.T) (2.7)

Figure 2.9: Collector-emitter voltage UCE and junction temperature Tj(t) of

two IGBTs as a function of power cycles [77].

Nf = A · (∆Tj)α·e  Ea KB .Tm  (2.8) where ∆Tj is Cycle Amplitude, kB = 1.38 · 10−23J/K is Boltzmann

con-stant, Ea[J] is activation energy, Tm[K] is the mean temperature, A and α

are constants which are acquired by curve fitting on experimental lifetime tests [82]. The model of Eq. 2.8 is one of the accepted model in researches and designs, however, it can be developed in order to consider cycling fre-quency [83]. Moreover, there are other modification of the Eq. 2.8 taking in to account the heating time, heating current, blocking voltage, bond wire diameter [84]. In addition to the above mentioned models, some manufac-turers provide graphs which demonstrates the capability of their produced IGBT in performing thermal cycles in various operating temperature.

in consequence of obtaining the cyclic capability of the IGBT, the total cycles lifetime can be calculated by summation of partial cycles lifetimes Eq. 2.9. This approach of evaluating the lifetime consumption is known as ”Miner Linear Damage Accumulation Rule” [85]. Each partial cycles lifetime is the number of cycles in a specific temperature divided by the total number of cycles which IGBT is capable to perform in that temperature.

LC = n X i,j=1 N(i,j) Nf (i,j) (2.9) In Eq. 2.9, N(i,j)is the number of performed cycles, i and j are the indices

for medium Temperature Tm[K] and Cycle Amplitude ∆T respectively and

Nf (i,j) is the total number of cycles which IGBT is capable to perform within

condition of i and j, while the failure occurs if LC = 1. Another form of Eq. 2.9 can be used in order to be able to obtain the percentage of remained useful lifetime (RUL) using Eq. 2.10.

RU L% = " 1 − n X i,j=1 N(i,j) Nf (i,j) # ×100 (2.10)

The RUL percentage have to be calculated both for junction cycles and base-plate, then, the minimum gained RUL is considered as the remaining lifetime of the module. According to Eq. 2.10, in order to calculate the RUL percentage of IGBTs, the number of thermal cycles, the average and amplitude of each thermal cycle are needed for both junction and case of the IGBT. Thus, in the first step it is needed to have the Thermal Mission Profile (TMP) of junction and case. Providing the TMP depends on the sensors and data log of the systems. In Section 3.3.2 creating the TMP for the case study of this thesis is described. The subsequent step after obtaining TMP, is to count the cycles according to their average and amplitude. In this regard, the next section deal with the methods of counting the thermal cycles from the obtained TMP.

2.2.2.3 Thermal Cycle Counting

As mentioned before, the next step after creating the TMP is to analyze it with the aim of counting the number of cycles and evaluating the cycles amplitude and medium. Different cycle-counting methods have been used in fatigue analysis, such as peak counting, level cross counting, apices-crossing, simple range counting and rainflow. These algorithms are used to analyze spectrum and reduce them to simple uniform histograms [86], [87].

In this thesis, the Rainflow algorithm is used which is one of the popu-lar method for counting which is used in failure analysis of semiconductor lifetime estimation [88]. The Rainflow algorithm is capable of counting the

half- and full-cycles [89]. This method was developed by M. Matsuishi and Tatso Endo in 1968 and its basic idea came from the water down of a series of pagoda roofs [90]. Going into details of the procedure of rainflow algorithms is not in the scope of this thesis.

To apply the Rainflow algorithm on created TMP the Rainflow toolbox of Matlab is employed. An example of implementing rainflow on the heatsink temperature of an IGBT which is collected during one week is shown in Fig. 2.10. In this figure the histogram of temperature cycles, the range and mean value of cycles are demonstrated. This is the last step in which all the required data can be obtained to complete the calculation of remaining useful life of the IGBTs.

Figure 2.10: example of Rainflow cycle counting for recorded heat-sink tem-perature in 7 days

2.2.3

Ventilation System (Fan)

Thermal-related issues are among the common origin of failures in electrical products [91]. Moreover, according to the previous sections, the main effec-tive parameters on the lifetime of the components is the temperature. Hence, the importance of the ventilation system and fans is remarkable in thermal management of electric products and reliability of the electrical systems de-pends significantly on the ventilation system efficiency [91]. Thus, studying and evaluating life expectancy of the fans can results in improving the reli-ability of the electrical devices [92]. Following this section, the degradation mechanism and the lifetime expectancy of the fans are analyzed.

2.2.3.1 Fan Degradation and Lifetime Expectancy

Fans are electromechanical systems. Electronic components, control circuitry and motor constitute the electrical parts, while the fan mechanical parts are consisting of shaft, bearing, lubricant, fan blades and fan housing. Therefore, their failures can be due to electrical or mechanical degradation [93]. In general, the degradation in fans can leads to noise, vibration, rubbing of blades, reduction in rotational speed, increasing of input current, failure to start and locked rotor, etc. From an aerodynamic point of view, a fan fails not only when it stops rotation, but also when the rotational velocity is decreased by about 10% of its nominal speed [92].

Thanks to the low failure rate of modern electronic components, electrical failures of the fans is rare during its life, besides, most of the electronic failures of the fans happen in the infant period. Thus, these failures can be found out before delivery to customers by performing burn-in tests. On the other hand, vibration, noise and rotational speed drop are known as the symptoms of the mechanical degradation in the fans. Accordingly, an experimental analysis at StorageTek illustrated the lubricant breakdown as the dominant cause of the failures on all tested fans [91], [92].

In normal operating condition, the fans life expectancy is too long, thus, it is not possible to test them until they stop working. Consequently, the methods of accelerate testing is used to shorten product life and speeding up its degradation [94]. The accelerate testing provide manufacturers to investi-gate the failure mechanism of their products and analyze their reliability. The stress which are used in the accelerate testing can be such as temperature, voltage, current, humidity, pressure, loading, etc.; however, temperature is

known as the most effective stress which has been broadly used in accelerate testing [91].

In order to investigate the life expectancy of the fans by performing ac-celerate testing, a number of M fans are provided to be tested at higher specified temperature with respect to the nominal temperature. The fans are kept in operation to record the number of damaged fans during the test time according to various defined temperature. Weibull distribution is one of the most used distribution model to fit the fan life and failure analysis [92], [95].

The Probability Density Function (PDF) of Weibull distribution is shown in Eq. 2.11, where t represents time, α known as characteristic life and β is the shape parameter [91].

f (t) = β α  t α β−1 e−(t/α)β t, α, β > 0 (2.11) According to Eq. 2.11, Weibull Cumulative Distribution Function (CDF) can be calculating by integrating the PDF which is shown in Eq. 2.12

F (t) = 1 − e−(t/α)β

(2.12) Generally, CDF can be used in order to calculate Q as Weibull percentile to evaluate the percentage of failure probability for a specified operating time. In other words, Weibull percentile is introduced to present the fail-ure probability in percentage scale. Accordingly, by letting F (t) = Q/100 and solving it for t to obtain LQ, Eq. 2.13 will be achieved; where, LQ is

introduce to show the lifetime of fan with failure less than Q%. It is worth mentioning that, L10 and L2 are the commonly used reliability metrics by

fan manufacturers which are calculated in Eq.2.14.

LQ= α[−ln(1 − Q/100)]1/β (2.13)

L10 ∼= α(0.10536)1/β , L2 ∼= α(0.02020)1/β (2.14)

Another metric which is used by vendors for fan reliability is known as Mean Time To Failure (MTTF) based on Weibull distribution. Eq. 2.15 shows the relation between MTTF and Weibull model parameters α and β, where Γ is called Gamma function which is calculated according to the shape parameter β, in Appendix B Gamma function is calculated for various β.

M T T F = αΓ(1 + 1/β) (2.15) In conclusion, according to the available data based on fan experiment report and datasheet, Weibull parameters can be extracted and consequently, the remaining life LQ can be evaluated based on desired accuracy, for

dif-ferent working temperature, for instance, a lookup-table of L10 for various

temperature can be generated. Furthermore, the final relation for remain-ing useful life (RUL) of fan, Eq. 2.16, can be obtained accordremain-ing to L10

of operating temperature and the duration of operating in that operating temperature. In Eq. 2.16, Tn is the nominal operating temperature of the

fan and Ti represents different temperature in which fan L10_Ti and operating

duration tTi are calculated.

RU LL10T n = l10T n " 1 −X i tTi L10_Ti !# (2.16) Implementation of the Eq. 2.16 and providing the required data to fulfill it, depend on the case study and condition of fans. Accordingly, in Section 3.3.3 the position and working condition of fans which are installed in the inverter of the case study will be discussed in detail.

2.3

Virtual Sensors

According to PSS analysis which is done in Chap. 1, Virtual Sensors is proposed as an important Design Requirement which is essential to fulfill ”Calculating RUL”, ”Improving Failure Detection” and ”Predicting Mainte-nance” of the inverter in order to ”Reduce the Plant Downtime”.

Physical Sensor (PS) and Virtual Sensors (VS) can be known as two categories of sensors. PSs are real sensors which are placed near to a physical phenomenon to measure it directly, while VSs are developed to calculate the value of desire physical phenomenons without putting real sensors there and only by utilizing other physically sensed data and suitable models [96], [97]. In other words, VS is a model which can estimate a parameter value without direct measurement of that and just by exploiting other sensors output [98], [99].

VSs are used in various areas, namely as Communication, Remote sensing, Computer science and Chemistry. Moreover, some applications of VS can be mentioned as bellow [100]:

• Using instead of a temporarily installed sensor

• Employing periodic PSs measurements to obtain continuous output • Providing predictive control by predicting the sensing data in advance • Providing robustness and redundancy in case of failure in PS or during

its maintenance

Regarding the procedure of creating a virtual sensor for a physical pa-rameter, specific tests and analysis of data are required. Considering “a(t)” as a desired parameter to be measured, it is needed to perform various tests to collect a set of physical values including parameters which are in corre-spondence with a(t) and the value of a(t) itself by using PSs in a laboratory environment.

Measuring set: a(t), b(t), c(t)...

The next step after collecting the mentioned set of data is to use them to create a model which can estimate the desired parameter, a(t), by utilizing other measured values Eq. 2.17.

The function F in Eq. 2.17 represents a function that is constant in the time domain, without memory (no past points are included in the domain). The unavoidable error is expressed by e(t), which is tried to minimize with different approach for each virtual sensor.

The approaches which are applied to create the virtual sensor models in this thesis are “Nonlinear Regression” and “System Identification Toolbox” of MATLAB [101], [102]. It is worth mentioning that by utilizing “Nonlinear Regression” It is possible to introduce freely a nonlinear model, However, “System Identification Toolbox” would suggest itself some nonlinear models such as Hammerstein-Wiener models and nonlinear ARX model. Following this two approach are described.

2.3.1

Nonlinear Regression

Nonlinear regression is a statistical technique that helps describe nonlinear relationships in experimental data. The first step in this method is to define the inputs and output. The input variables are set of data which are measured by real sensors during the test except those sensors which are aimed to replace by VSs. Besides, the output is defined as the collected measured values with real sensor for the parameters which are intended to estimate later by VSs. For instance, considering a test for creating a redundancy for inverter CPU temperature. During this test all parameters namely as CPU temperature, cabinet input and output air flow temperature, output power and DC voltage are measured for a period. The CPU temperature collected data is defined as the output of the model while other parameters are considered as the input Tab. 2.5.

Table 2.5: Example of input and output data for Nonlinear Regression

Subsequent to the definition of inputs and output, a model needs to be introduced, including all inputs and coefficients, defined by β, which will be calculated by regression as the results of this method. Eq. 2.18 shows an example of a model which is introduced for the aforementioned example of CPU temperature. It is worth mentioning that, although in this example we