This thesis presents a complete reliability, availability, and maintainability (RAM) analysis of the variable renewable energy (VRE) systems. Three operating concepts of the wind energy conversion systems (WECS) are considered based on the acceptable speed range of generators, while seven practical layouts of large-scale grid-connected systems are considered for the solar-PV systems. Elaborated RAM analysis of each system associated with each operating concept for the WECS and each layout of the solar-PV systems is presented starting from the subassemblies level to the subsystem level then the overall system.
This thesis is purposed to describe the method of reliability, availability, and maintainability analysis of repairable and non-repairable systems using the exponential PDFs. It is also aimed to explore the method for improving the availability of these systems by managing the effort using availability importance measures of each subassembly. This analysis will also be utilized to studying the criticality of the subassemblies or subsystems of the system in order to continuous improvement. After doing this, this thesis also extends to look into the overall system availability. This analysis is a good tool for helping to identify the critical subsystems or subassemblies of the system that need more attention for improvement.
Table of Contents
Disclaimer
Acknowledgments
Dedication
Table of Contents
List of Tables
List of Figures
List of Abbreviations
List of Symbols
Abstract
Chapter 1 : Introduction
1.1. Reliability (Risk) analysis
1.2. Research problem
1.3. Research Questions
1.4. Purpose of this thesis
1.5. Objective of this thesis
1.6. Organization of the thesis
Chapter 2 : Reliability Engineering Overview
2.1. Reliability engineering
2.1.1. Basic RAM concepts
2.1.2. Hierarchy of the system
2.2. RAM Assessment of VRE systems
2.2.1. System decomposition
2.2.2. Reliability data collection
2.2.3. RAM modelling
Chapter 3 : Practical Layouts of Solar-PV and WECS
3.1. Solar-PV systems
3.2. Wind energy conversion systems
Chapter 4 : Holistic RAM evaluation of WECS
4.1. Introduction
4.2. Survey and analysis of RAM for various WTGs
4.2.1. System decomposition for WTGs
4.2.2. Reliability data collection for WTGs
4.2.3. Reliability modelling for WTGs
4.3. RAM Results for various WTGs
4.3.1. Reliability
4.3.2. Maintainability
4.3.3. Availability
4.4. Weighting and ranking of various WTG system
4.5. RAM comparison before and after applying the weights
4.6. Fuzzy modeling of the RAM for various WECS
4.7. RAM estimate and analysis
Chapter 5 : Holistic RAM evaluation of solar-PV systems
5.1. Literature review of solar-PV systems
5.2. Solar-PV system decomposition
5.3. RAM modelling
5.4. Failure and repair rates of solar-PV systems
5.5. RAM results for subassemblies of solar-PV system
5.6. RAM results for subsystems of solar-PV systems
5.7. Best PDFs for Sub-Assemblies of Solar-PV Systems
Chapter 6 : Availability Importance Measures
6.1. Introduction
6.2. Concept of availability importance measures
6.3. Availability importance measures of solar-PV systems
6.4. Availability importance measures of WECS
Chapter 7 : Discussion and Conclusions
List of Publications
References
Appendix A: Hydrogen-based standalone VRE systems
A.1 Power to Hydrogen to Power (P2H2P) Systems
A.2 Renewable P2H Conversion Systems with Assisted of Nuclear Power
A.3 VRE Systems Based on Conventional Energy Storage and Hydrogen
Appendix B: Failure and repair rate equations
Disclaimer
I hereby declare that this thesis is my own original work and that no part of it has been submitted for a degree qualification at any other university or institute.
I further declare that I have appropriately acknowledged all sources used and have cited them in the references section.
Name: Ahmed Sayed Abd El-Hamid Awad Date:
Signature:
Acknowledgments
First and foremost, I would like to thank God, who gives us the power and hope to succeed. The following document summarizes years’ worth of effort, frustrations, and achievements. However, I am indebted to several people for their contribution to my research and study.
I am taking this opportunity to express my sincere gratitude and appreciation to my supervisory committee for their continuous support and guidance of my studies and related research activities, motivation, and immense knowledge. They were always available with their good advice. I would also like to thank them wholeheartedly for their support during the tough period I had at the end of the last year.
My deepest and sincere gratitude goes to my supervisor, Prof. Dr. Mohamed El-Shimy, for giving me the chance for further studies in the field of reliability engineering and explore the challenging world of research. Particularly for his persistent inspiration, his valuable guidance, his tremendous support, and angelic patience. His inexhaustible pursuit, enthusiasm and high demand of excellence towards research made a very profound impression and set up a model for me to follow. Not only being an excellent supervisor, but he is considered as my father. Without him, I would not be here today, in both the academic world and the real world. It is really a pleasure to have Prof. Dr. Mohamed as my supervisor.
Special thanks go to my family for their continued support during the whole of my academic experience. Without their support, I would not have been able to finish my study program.
Last but not least, many thanks go to all my professors and friends.
Dedication
To my family
List of Tables
Table 4.1. Subsystems and subassemblies for different WTG systems
Table 4.2. Subassemblies classification
Table 4.3. Influence factors of secondary subassemblies
Table 4.4. Failure rate for various WTGs
Table 4.5. Repair rate for various WTGs
Table 4.6: Subsystems failure and repair rates of WTGs
Table 4.7. Weight value for WTs years of operation
Table 4.8. Weight value of WTs numbers
Table 4.9. Failure rate for various WTGs after applying the corrective weights
Table 4.10. Repair rate for various WTGs after applying the corrective weights
Table 4.11. Availability for various WTGs after applying the corrective weights
Table 4.12. Impact of various subassemblies on the RAM performance for various WTG systems
Table 4.13. Impact of various subsystems on the RAM performance of various WTG systems
Table 5.1: Failure and repair rates for various subassemblies of solar-PV systems
Table 5.1: Cont
Table 5.2: Median failure and repair rates for solar-PV systems
Table 5.3: Number of sub-assembly for each PV system 69
Table 5.4: Sub-assembly failure rate (yr-1)
Table 5.5: Sub-assembly repair rate (yr-1)
Table 5.6: Subassemblies reliability for the PV systems for a period of one year of operations (in %)
Table 5.7: Subassemblies reliability for the PV systems for a period of 20 years of operations [in %]
Table 5.8: Sub-assembly availability of the PV systems (in %)
Table 5.9: The expected lifetime for various subsystems of solar-PV system (year)
Table 5.10: Subsystem failure rate (yr-1)
Table 5.11. Subsystem repair rate (yr-1)
Table 5.12: Summary of PDFs failure rates for the subassemblies of the solar-PV system
Table 5.13: Comparison between the proposed technique and some other related techniques
Table 5.14: Comparison between the reliability results when applying Reliability, availability, and maintainability (RAM) analysis using reliability block diagram (RBD) method in the proposed technique and the FTA method that discussed in 64 67
Table 6.1: Subsystem Availability [in %]
Table 6.2: Subsystem reliability for the PV systems for a period of one year of operations [in %]
Table 6.3: Subsystem reliability for the PV systems for a period of 20 years of operations [in %]
Table 6.4: Availability importance measures
Table 6.5: Failure rate importance measures
Table 6.6: Repair rate importance measures
Table 6.7: Critical subsystem priorities
Table 6.8: Sub-system Availability of WTGs [in %]
Table 6.9: Availability importance measures ( ) for the WTGs
Table 6.10: Failure rate importance measures ( ) for the WTGs
Table 6.11: Repair rate importance measures ( ) for the WTGs
Table 6.12: Critical sub-system priorities for the WTGs
List of Figures
Figure 1.1: Example of energy sources comprising variable renewable systems considering various modes of operation and load types
Figure 1.2: Reliability assessment of power system with variable resource
Figure 2.1: Bathtub curve
Figure 2.2: Hierarchical levels of a system
Figure 2.3: The difference between the median value and the average value of some of the collected data
Figure 2.4: Various connections in RBD method
Figure 3.1: Electrical energy storage requirements for various layouts of renewable systems
Figure 3.2: Various layouts of solar-PV systems: (a) Grid-connected solar-PV system with a reliable grid; (b) Grid-connected solar-PV system with non-reliable grid; (c) Off-grid solar-PV system with deferrable loads; (d) Off-grid solar-PV system with non-deferrable loads
Figure 3.3: Various layouts of WECS: (a) Grid-connected WECS with reliable grid; (b) Grid-connected WECS with non-reliable grid; (c) off-grid WECS with deferrable loads; (d) Off-grid WECS with non-deferrable loads
Figure 4.1: WTG system decomposition into subsystems and subassemblies
Figure 4.2: A typical WTG system: (A) overall configuration; (B) subsystems and subassemblies
Figure 4.3: Median failure rates for various subassemblies of various WTG systems. (a) Failure rates of electrical subassemblies, (b) Failure rates of mechanical subassemblies, and (c) Failure rates of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.4: The simple RBD for main sub-systems of the WTGs
Figure 4.5: Failure rates of various WTG systems: (a) Failure rates of different subsystems for various WTG systems, (b) Failure rates of whole WTG systems
Figure 4.6: Median repair rates for various subassemblies of various WTG systems. (a) Repair rates of electrical subassemblies, (b) Repair rates of mechanical subassemblies, and (c) Repair rates of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.7: Repair rates of various WTG systems: (a) Repair rates of different subsystems for various WTG systems, (b) Repair rates of whole WTG systems
Figure 4.8: Availability for various subassemblies of various WTG systems. (a) Availability of electrical subassemblies, (b) Availability of mechanical subassemblies, and (c) Availability of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.9: Availability of various WTG systems: (a) Availability of different subsystems for various WTG systems, (b) Availability of whole WTG systems
Figure 4.10: Percentage of weight value for WTs years of operation
Figure 4.11: Percentage of weight value of WTs numbers
Figure 4.12: Median failure rates for various subassemblies of various WTG systems after applying the corrective weights. (a) Failure rates of electrical subassemblies, (b) Failure rates of mechanical subassemblies, and (c) Failure rates of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.13: Median repair rates for various subassemblies of various WTG systems after applying the corrective weights. (a) Repair rates of electrical subassemblies, (b) Repair rates of mechanical subassemblies, and (c) Repair rates of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.14: Median availability for various subassemblies of various WTG systems after applying the corrective weights. (a) Availability of electrical subassemblies, (b) Availability of mechanical subassemblies, and (c) Availability of others subassemblies. 1: Generator, 2: Converter, 3: Electrical parts (fuses, switches, and cables/connections), 4: Gear box, 5: Yaw system, 6: Blades, 7: Pitch control, 8: Mechanical brake, 9: Air brake, 10: Main shaft, 11: Hydraulic system, 12: Anemometer, 13: Sensors, 14: Hub, 15: Tower, and 16: Other items
Figure 4.15: Failure rates of various WTG systems after applying the corrective weights: (a) Failure rates of different subsystems for various WTG systems, (b) Failure rates of whole WTG systems
Figure 4.16: Repair rates of various WTG systems after applying the corrective weights: (a) Repair rates of different subsystems for various WTG systems, (b) Repair rates of whole WTG systems
Figure 4.17: Availability of various WTG systems after applying the corrective weights: (a) Availability of different subsystems for various WTG systems, (b) Availability of whole WTG systems
Figure 4.18: Failure rate of different subassemblies for FS-SCIG before and after applying the suggested weights
Figure 4.19: Repair rate of different subassemblies for FS-SCIG before and after applying the suggested weights
Figure 4.20: Availability of different subassemblies for FS-SCIG before and after applying the suggested weights
Figure 4.21: Failure rate of different subassemblies for DFIG-PRC before and after applying the suggested weights
Figure 4.22: Repair rate of different subassemblies for DFIG-PRC before and after applying the suggested weights
Figure 4.23: Availability of different subassemblies for DFIG-PRC before and after applying the suggested weights
Figure 4.24: Failure rate of different subassemblies for DDSG-FRC before and after applying the suggested weights
Figure 4.25: Repair rate of different subassemblies for DDSG-FRC before and after applying the suggested weights
Figure 4.26: Availability of different subassemblies for DDSG-FRC before and after applying the suggested weights
Figure 4.27: Fuzzy failure rates of primary subassemblies for various WTG systems
Figure 4.28: Fuzzy failure rates of secondary subassemblies for various WTG systems
Figure 4.29: Fuzzy repair rates of primary subassemblies for various WTG systems
Figure 4.30: Fuzzy repair rates of secondary subassemblies for various WTG systems
Figure 4.31: Fuzzy failure rates of subsystems for various WTG systems
Figure 4.32: Fuzzy repair rates of subsystems for various WTG systems
Figure 4.33: Proposed flow chart of calculating failure rate, repair rate, and availability with fuzzy representation
Figure 4.34: Fuzzy output of availability of subsystems for various WTG systems
Figure 5.1: System decomposition of a generic solar-PV system
Figure 5.2: Simple reliability block diagram of grid-connected solar-PV system
Figure 5.3: Median failure and repair rates of various sub-assemblies of solar-PV system
Figure 5.4: Impact of failure of various solar PV systems (a) for a period of one year of operations and (b) for a period of 20 years of operations
Figure 5.5: Reliability of subsystem and overall solar-PV system
Figure 6.1: Availability results for the studied systems
Figure 6.2: Overall system availability (a) before and (b) after inverter redundancy
Figure 6.3: Percentage availability for various sub-systems of WTGs
Figure 6.4: Percentage availability importance measures for various sub-systems of WTGs
Figure 6.5: Failure rate importance measures for various sub-system of WTGs
Figure 6.6: Repair rate importance measures for various sub-system of WTGs
Figure 6.7: Critical sub-system priorities for various sub-systems of WTGs
Figure A.1: Energy storage options for VRE systems
Figure A.2: Power-To-Fuel process based integrating variable renewable sources and biomass
Figure A.3: Reliability enhancement of VRE systems by using the hydrogen as an energy storage and energy carrier in P2H2P or P2F2P layouts
Figure A.4: Hydrogen and synthetic natural gas production based on integrated variable renewable sources and nuclear power
Figure A.5: Standalone VRE System based on short-term and long-term energy storage systems
List of Abbreviations
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List of Symbols
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Abstract
Variable renewable energy (VRE) systems, especially in the form of solar-PV and wind energy conversion systems (WECS), are significantly contributed to the growing electricity demand because the conditions for large-scale utilization of solar and wind energies are excellent. These systems, which matured to an extreme level of development, have become a generally accepted utility generation technology. However, recent surveys and reports have been appeared the delusion of PV and wind systems being reliable systems. This is according to the recorded data of such system components failures and the associated degradation of their output power. Thus, the reliability and availability improvement of such VRE systems has become a critical area of interest for researchers. Reliability, availability, and maintainability (RAM) is an engineering tool used to address operational and safety issues of systems. It aims to identify the weak areas of a system which will improve the overall system performance. This thesis presents a complete RAM analysis of the VRE systems.
Three operating concepts of the WECS are considered based on the acceptable speed range of generators, while seven practical layouts of large-scale grid-connected systems are considered for the solar-PV systems. Elaborated RAM analysis of each system associated with each operating concept for the WECS and each layout of the solar-PV systems is presented starting from the subassemblies level to the subsystem level then the overall system. Based on their impact on energy production, the wind turbine generator (WTGs) subassemblies are classified to primary and secondary elements. A failure in a primary element causes interruption of the energy production while the energy production efficiency is degraded due to failures in secondary components. Mathematically, various WTGs subassemblies are given weights according to their classifications. Further, an improved Reliability Block Diagram (RBD) is presented to estimate the RAM performance of the studied systems. The system is considered in the various sequential blocks combinations including the failure and repair rates in order to find all realistic ways in which undesired events can occur. The confidence of the results is increased by collecting huge amounts of data of failures, and repair for various subassemblies comprising various meteorological conditions. A novel statistical method is applied in the collected data in order to obtain an accurate value for failure and repair rates of the subassemblies. This method aims to compute the median values of the collected data instead of the average value. The median failure or repair rate is the middle value in the sorted list of the collected data. The usage of the median will reduce the uncertainties arisen from the unexpected values introduced by assumptions in some cases of the collected data. Furthermore, the proposed RAM approach can identify areas that the planned maintenance should focus on. The monitoring of the critical subsystems of the system will increase the possibility not only for improving the overall availability of the system, but also to optimize the maintenance costs. Additionally, it will inform the operators about the status of the various subsystems of the system. The secure operation of the system will be achieved by this approach. The implementation demonstrates the efficiency and the effectiveness of the proposed approach which may significantly contribute to putting more appropriate maintenance plans and diagnostic strategies for the given systems.
Although the proposed statistical method, which applies to the collected data, gives accurate values of the failure and repair rates of the subassemblies it is difficult to have an exact estimation of the failure and repair rates of the WTGs subassemblies. To overcome this disadvantage, RBD based on the fuzzy set theory is proposed and triangle or trapezoidal fuzzy number instead of the accurate values is used to represent the input parameters in the system. However, to the best of authors’ knowledge, the fuzzy representation method for failure and repair rates of WTGs subassemblies has not been applied for the reliability evaluation of WECS.
Chapter 1 : Introduction
An increase of carbon dioxide emissions (CDE) in the environment, rising energy demands, and the liberalization of the electricity sector represent the recent concerns in the electrical energy production from conventional ways. These concerns, as well as the developments of renewable technologies, increased the attention of the global community to renewable energy technologies 1. In fact, renewable sources not only provide energy to remote locations but also significantly contributed to the reduction of fossil fuel consumption in recent time. Thus, the renewable industry expected to grow up to three times within the next two decades 2–5. This growing requires more concerted efforts to exert to ensure that such systems generate energy as predicted.
The available renewable energy, such as solar, wind, hydro, biomass, geothermal, wave or tidal, is an “energy obtained from natural and persistent flows of energy occurring in the immediate environment”. This energy can be used as a huge alternative for electricity generation. However, renewable energy sources face significant problems when compared with conventional energy sources 6, 7. These problems arise from the nature of renewable resources which characterized by variability, intermittency, and non-predictability. These problems pose a major challenge in electricity energy production, especially, in the case of used renewable energy as the main source for electrical power production. These challenges are extended to grid stability, security, power quality, and behavior during fault conditions in addition to the grid reliability. So, the integration of intermittent renewable energy (RE) generations is receiving increasing attention around the world for improvement of power quality and voltage profile, enhancement of voltage stability and reliability, and grid support, etc. 8.
Wind and solar energies are represented the most popular renewable sources, and therefore act as the leading potential sources of alternative energies. They come in the second and the third place after the hydropower sources in the total worldwide installed capacity. The spectacular improvement in the wind turbine and PV system technologies represents the main reason behind increasing the large scale wind and solar-PV systems around the world. The world installed capacity of the onshore wind turbines and the solar-PV systems, by the end of 2016, approximately reached 472 GW and 303 GW respectively, by an additional value of 52.4 GW for the wind and 75 GW for the Solar-PV 9.
This thesis will present a detailed reliability analysis of renewable sources such as wind and solar-PV systems and determines some recommendations for improving the reliability and the availability of these systems.
1.1. Reliability (Risk) analysis
Reliability is the probability of a system or a subsystem or even subassembly/component to perform its function adequately, under the given operating conditions, for an intended period. This intended period is typically the lifetime of the system. The reliability of renewable sources seemed relatively lower than the conventional energy sources. This is due to the fact that these systems are usually operated in harsher environment. Therefore, the performance evaluation of renewable sources was considered the most important issue for the system planning and long-term operation, and was given a larger attention in reliability studies in recent times.
Generally, the performance of the renewable power plants depends on the availability of the primary resource, outside the variable renewable energy (VRE) system, and/or the availability of each plant subassemblies 10, inside the VRE system, as shown in Figure 1.1. Therefore, two trends have been taken in order to assess the reliability of the renewable systems in the relevant studies; reliability assessment of the system vulnerable components, and reliability evaluation of the whole systems considering the variable resources as illustrated in Figure 1.2. Although, the reliability issues have been identified since three decades, the reliability assessment of the whole power production is still under controversy due to the complex nature of these systems. The most literature focuses only on reliability assessment of the system vulnerable components (trend one) by different methods, whereas literatures that discussed the reliability evaluation of the whole systems (trend two) are much fewer. From reliability point of view, the main reason behind the lower reliability of the renewable systems is the higher failure rates of some subassemblies in the system 11. Moreover, the system failure rate represents one of the main negative impacts on the overall cost of energy (CoE) where higher failure rates potentially lead to a higher CoE.
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Figure 1.1: Example of energy sources comprising variable renewable systems considering various modes of operation and load types
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Figure 1.2: Reliability assessment of power system with variable resource
There are two categories of faults lead to loss of energy production from renewable systems. The first category is wear-out failures whereas the second category is the temporary random faults. In Wear-out failures, which specified as a long-term and permanent events, repairing or replacing action of a failed subassemblies in specific time are needed. Although, this action needs additional costs, it is very important in the operational stage. Consequent failures of other subassemblies and even the entire system may occur, if the failed subassembly is not identified and repaired or replaced in time. The temporary random fault is defined as a short-term, temporary events caused by external factors such as grid disturbances, temporary wrong sensor readings, wind speed fluctuation, thermal issue, etc. This type of fault can usually be cleared by temporarily shutting down and restarting the faulty components or the whole system. 11. Due to inaccessibility issues, the defective components may not be repaired or replaced for a long period of time, the failure rates of some subassemblies become even more important 12. Thus, the developers try to select a system with low failure rates.
On the other hand, it is important to point out that the availability depends on more factors than just reliability and the availability studies are necessary for assessment the system performance, specially, when the issue of accessibility is considered.
1.2. Research problem
There are several factors affect the operational reliability and availability of renewable systems. These factors must be optimized, by achieving the maximum or minimum value of the operation parameters, to achieve a good operation of such systems. In order to obtain good profitability, a high reliability and availability level of system components needed. Although the optimal benefits may be realized when reliability is designed into a part of the system, it is also important to improve reliability, availability, and maintainability (RAM) throughout the life of the system to meet RAM goals and objectives. The operators can identify undesirable alternatives and predict what is affordable for the system through the reliability model. Of course, RAM and the capability of the system to perform will affect the effectiveness of the system. High reliability and availability level will be achieved by addressing several issues. Among them, engineering design, material, manufacturing, operation, spare parts, and maintenance, which requires information, modeling, analyzing, testing, data, additional analysis, often additional testing, reengineering when necessary, and so forth in order to address these issues.
As a result of continual increasing the size and complexity of the renewable systems, the implications of the system failure become ever more important. The failure consequences, which highly rely on the system items, are many and varied and every failure has an economic impact. A failure in an item of the system items not only affect this item itself but in some cases will extend to other items or even the entire system and hence loss of energy production. However, failure prevention cannot be achieved entirely. Thus, minimization of the probability of occurrence the failure as well as the impact of failures when it does occur represent the main challenge in the operation phase and this is one of the principal roles of reliability analysis and maintenance.
The main research problem addressed in this thesis can be formulated as:
- How to improve system availability of the renewable systems by use of reliability and maintainability analysis?
1.3. Research Questions
As mentioned before, for estimating the effectiveness of system production, three important measures named reliability, availability, and maintainability (RAM) are used. These measures are essential for the system planning and long-term operation in order to ensure an accurate prediction of energy production. RAM analysis aims to identify critical items which have the greatest impact for improving the overall system reliability and/or availability. Thus, this analysis predicts the behavior of such systems over time, as well as devises appropriately timed maintenance plans. Hence, RAM analysis of renewable energy sources represents a serious challenge in worldwide development and economy 13. Although, RAM analysis represents the crucial issue for such system planning and long-term operation, it is limited due to the unavailability of robust data or even due to the complex nature of these systems. Therefore, a major part of the existing literature is focused only on the reliability assessment of vulnerable subsystems. Such as the inverter (INV) 14, PV module (PVM) 15–19, balance of systems (BOS) 17 considering the failure information only, and analyzing the reliability of the major subassemblies in wind energy conversion systems (WECS) 20. Much fewer studies, discuss the reliability evaluation of the whole system by using oversimplified assumptions. These assumptions may lead to controversial observations between simulated and real results as stated in more detail in 21.
At the same time, the reliability of the overall system is determined using the failure rates of each sub-assembly, and thus every failure is very important. Commonly, failure rates are assumed constant in the most of the reliability studies. Some studies gives approach for analyzing the reliability with failure rates described by time-dependent probability density functions. However, this approach does not rely on actual field values or the best probability density functions of each sub-assembly of the repairable and non-repairable systems.
The important characteristics of the repairable or the non-repairable systems are reliability and availability. More efforts must be conducted to improve the reliability and the availability of the renewable system, when they are low, by reducing the failure rate or increasing the repair rate for each subassembly and/or subsystem. According to the discussion in the previous sections, the main concern now is how to meet the goals of the reliability and availability of the system, in case of referring to that the estimated reliability and availability are inadequate. This is called the reliability and maintainability allocation problem at the subassembly level. Reliability and availability engineers are usually responsible for making decisions about a certain component or components in order to achieve better results.
Based on the basis of the research problem, this thesis will attempt to answer the following questions:
- How to overcome the unavailability of robust reliability data of some of the subassemblies?
- How to use the reliability and maintainability analysis to find the criticality of each subsystem or subassembly in the repairable and non-repairable systems such as wind and solar-PV systems respectively?
- Based on the reliability and maintainability analysis, How to improve the overall availability of these systems?
- What is the best maintenance strategy based on reliability and maintainability analysis?
- What is the best probability density function of each subassembly?
- What is the expected lifetime of each subsystem based on the reliability analysis?
1.4. Purpose of this thesis
This thesis is purposed to describe the method of reliability, availability, and maintainability analysis of repairable and non-repairable systems using the exponential PDFs. It is also aimed to explore the method for improving the availability of these systems by managing the effort using availability importance measures of each subassembly. This analysis will also be utilized to studying the criticality of the subassemblies or subsystems of the system in order to continuous improvement. After doing this, this thesis also extends to look into the overall system availability. This analysis is a good tool for helping to identify the critical subsystems or subassemblies of the system that need more attention for improvement.
1.5. Objective of this thesis
The main objectives of this thesis are:
- To present an up-to-date dataset of reliability in order to solve the problem of lacking robust reliability data. The confidence of the results is increased by collecting huge amounts of data, failure rate, and repair rate for each subassembly of the considering systems. These data that cover various large scale system configurations, and meteorological conditions (i.e. stress factors) are analyzed and represented by their confidence median values.
- To give a complete detailed RAM analysis for the all sub-assemblies of solar-PV and wind system, considering the failure information and repair interval (period of detection and replacement of the faulty part).
- To show the criticality of the subassemblies or subsystems of the studied systems which need more attention for improvement.
- To improve the availability of the systems under studied using importance measures.
- To find the best probability density function of each subassembly for the studied systems.
- To determine the expected lifetime for each subsystem for the studied system and the overall system.
1.6. Organization of the thesis
The remainder of this thesis organized as follows:
Chapter 2 illustrates the reliability engineering overview as well as the RAM assessment of the VRE systems.
Chapter 3 demonstrates the practical layouts of the solar-PV and wind energy conversion systems.
Chapter 4 gives the holistic RAM evaluation for various configurations of wind energy conversion systems.
Chapter 5 gives the holistic RAM evaluation for seven practical solar-PV systems and shows the expected lifetime of each subsystem as well as the pest probability function of the failure rate of each subassembly.
Chapter 6 gives the availability importance measures for the solar-PV and wind energy conversion system as shows the subsystem that have a greater effect on the overall availability of the system as well as shows a method for improving the availability of the overall system.
A separated section for the conclusion is inserted in order to give the salient conclusions of this thesis.
Chapter 2 : Reliability Engineering Overview
2.1. Reliability engineering
Reliability engineering aimed to compute and evaluate the performance index of systems by the way that ensures their capabilities to perform their required tasks successfully. In this regard, the main concern of reliability engineers is putting proper modeling of systems through seeking tools and methodologies.
In fact, the model is used for the presentation of the real system’s behaviors towards real phenomena. The mathematics plays a very important role, in the modeling, for simplifying the real matters to a greater or lesser extent. Of course, ignoring details which have an impact on the studied phenomena and will affect the model success.
From the construction point of view, modern systems become more complex and contain a mixture of different subsystems which connected together with each other to perform a chain of tasks. Thus, Designers are always trying to pick perfect systems that ensure the proper operation and keeping the objectives with minimum failures during its expected life. However, this view considers an ideal and unfortunately difficult to execute. This is due to the operational and economic constraints associated with the system’s operation. So, in such complex systems; the probability of their satisfactory operation period of time can be used to express to the performance. This measure is what is called reliability; it reflects how successful the design and the operation performance of the system are. Generally, there are some points, expecting to achieve through reliability engineering such as 22:
- Avoiding potential failures or even reducing their frequency of occurrence by applying the knowledge of reliability of systems,
- Presenting solutions to prevent failure causes after identifying the roots of them,
- Determining the causes of failures that cannot be voided and proposing methods to cope with them, and
- Estimating the possible reliability of new designs and analyzing the reliability data by applying some techniques.
2.1.1. Basic RAM concepts
Reliability is defined, according to the IEC definition, as the ability of the system, subsystem, or even sub-assembly to perform its required function adequately, without failure, for a given time interval, under given conditions. Accordingly, there are three crucial elements covered by this definition; intended function, an interval of time and specified conditions.
It is clear from the reliability definition that, the reliability relies on the intended function of the item that can be either success or failure. The item, in this case, is what is called a binary state item. On the other hand, the intended function of the item may be a complete success, partial success or failure, and in this case; the item is denoted as multistate or degraded one.
The time interval is related to the design life, warranty period or any other periods of interest. The unit used for the time interval is expressed by operating cycle, calendar time or other relevant time units. The factors that affect reliability are expressed by the given conditions. These factors/conditions such as modes of operation, maintenance policy, and levels of stress.
There are several metrics used to express the reliability of the system. This is due to the fact that it is hard to consider one metric as the best to cover all probable circumstances. Under certain conditions, some of these metrics can be less appropriate than others. Based on reliability theory 22, reliability function, cumulative distribution function, probability density function, mean time to failure, and hazard rate are considered the most common reliability metrics and probably most recognized indices for specifying reliability.
Reliability function, which is called also survivor function, is interpreted as the probability of a system’s success and can be written as follows.
The cumulative distribution function (CDF), which is called also failure probability or unreliability and denoted F(t), is the probability of the product’s failure within a specific time t. it is depicted that CDF is the compliment of R(t) such that; The probability density function (PDF), denoted f(t), indicates the distribution of the failure over the entire time range. Equations (2.1) and (2.2) can be expressed with the density function f(t) as:
The larger is the value of F(t) , the more failures that occur in the small interval time around t. If the time to failure, T, in Equation (2.1) has a Probability Density Function (PDF), then The mean time to failure (MTTF) for the sub-assembly, which expresses the expected life for the sub-assembly, represents the most common method for specifying reliability of non-repairable items. It can be calculated by:
The hazard function or hazard rate may be evaluated from the conditional probability of failure in the interval t to (t+dt), considering no failure occurs at the t divided by the time interval dt, it is calculated from The very important index for the system designers, field engineers, and maintenance groups is the hazard rate. This is due to that it allows to evaluate the time between failures or time to failure and determine the warranty cost in addition to calculating the necessary spare parts stocks and size of the repair crew. Furthermore, it is useful for studying the behavior of the system’s failures versus time; the system’s hazard rate might be decreasing, increasing or constant with time. The bathtub curve presents the typical relationship between the hazard rate and the time as shown in Figure 2.1.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2.1: Bathtub curve
In reliability studies, the life cycle of any complex system may be divided into three distinct stages where the chances of the failures are much different as shown in Figure 2.1. These stages are the start-up / commissioning stage, normal operation stage, and the end of life stage. The failure rates characteristics in each stage vary according to the nature of each stage. For example, during the start-up stage; the transition rate (or the hazard function) decreases with time due to the adjustment of the operating settings, correction of designs errors, and other debugging actions. So, this stage also may be called "debugging stage". In contrast, during the end of life stage; transition rate will be increased continuously. This increasing in failure rate is mainly due to the aging of the components. This stage is also named "wear-out stage". During the normal operation stage which named also useful-life stage, the failure rate is quasi-constant. In this stage, the exponential distribution can be used to evaluate the system reliability.
In order to assessing the reliability of a system, it is an important to distinguish between repairable and non-repairable within this system. For non-repairable components, reliability is considered as the survival probability, when only one failure can occur, over a certain period. The hazard rate of this failure can be determined by a relationship between its behaviors versus time. Such hazard rate can be analyzed with the aid of PDF. Another characteristic here, which indicates the life value, is called The Mean Time to Failure (MTTF). On the contrary, the Mean Time between Failure (MTBF), the availability, and the maintainability are used to characterize the repairable items.
During the useful-life stage of the repairable component, the failure rate (λ) can be defined as the number of random (unscheduled) occurrences of failure of the system to perform its intended function divided by the length of time the system was functioning 23–26. The failure rate can be considered as a measure of the basic design of the system as well as the operating and maintenance practices employed. The reliability of a system is the probability of the system to successfully perform its intended function(s) over a specified period of time. Thus, during this stage, the reliability can be expressed by:
However, the reliability is usually demonstrated by either the failure rate or the MTBF, which equals to the 1/λ. The mean time to failure (MTTF) for the sub-assembly, which expresses the expected life for the sub-assembly, represents the most common method for specifying reliability of non-repairable items. It can be calculated by:
Maintainability is the measure of the ability of an item under given condition of use, to be restored to a state in which it can perform its intended function, when maintenance is performed. The maintainability is usually demonstrated by either the mean time to repair (MTTR) or the repair rate. The repair rate is the reciprocal of the MTTR. It is a common mistake to say that maintainability is the same as maintenance, even though maintainability is a design parameter, while maintenance consists of actions to correct or prevent a failure event.
The availability is defined as the percentage of time that the system is available to perform its required function 27. Typically, the steady state availability is measured by:
Because of the above definition of reliability that denotes to a simple device or equipment and also in order to assess the reliability of complex systems, it is very crucial to split these complex systems into several hierarchical levels. Accordingly, the RAM evaluation will start from the lowest level in the system hierarchy (the subassembly level), then proceeds through the intermediate level, which is subsystems, until the whole system reliability is evaluated.
2.1.2. Hierarchy of the system
Complete system denotes to the complete installation of all equipment, which can be very small or gigantic in size, that serve the system. Hierarchical levels, according to the hierarchy theory, are levels contain entities whose properties characterize the level in question.
Generally, any system may be broken to three hierarchical levels; systems, sub-systems, and sub-assemblies/components as shown in Figure 2.2.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2.2: Hierarchical levels of a system
As shown in Figure 2.2, the lowest level of the system’s structure is the sub-assembly which regarded as an indivisible unit once a failure occurs. The subassemblies are connected with each other to form one subsystem in order to achieve a certain function. The complexity of the system is related to the functions performed by the overall system and structural complexity; such as the number of the subassemblies, the dimension of each subsystem, and the connections or links between subsystems 28. Since most of the engineering systems are described as complex systems, it is very important to understand the operational relationship among the sublevels and subassemblies in order to assess the overall system reliability. The following points can summarize the general procedure of reliability evaluation of complex systems 29.
- Defining the system’s operating condition, its functionality, its performance criteria, and the guarantee values in terms of success or failure.
- Compiling and arranging the parts list for the overall system.
- Assigning failure and repair rates of each part of the system.
- Selecting the suitable reliability approach in order to evaluate the reliability of each part of the system.
- Developing the system model using a suitable methodology such as the reliability block diagram or fault tree methods in order to evaluate the reliability of the overall system.
2.2. RAM Assessment of VRE systems
Reliability, availability, and maintainability are three important measures for estimating the effectiveness of system production. RAM analysis has many multi-faceted objectives in operations and safety issues. It aims to identify critical items which have the greatest impact for improving the overall system reliability. Thus, this analysis not only predicts the behavior of such systems over time, but also devises appropriately timed maintenance plans. Hence, RAM analysis of VRE systems represents a serious challenge in worldwide development and economy 13.
RAM analysis represents the crucial issue for the VRE systems’ planning and long-term operation. However, it’s limited due to the unavailability of the failure and repair rates data. For instance, there are no recorded reliability data of failure and repair rates for the subassemblies in Gabal EL-Zayt wind farm, in Egypt. Although this wind farm is considered as the biggest wind farm in Egypt, the absence of these data represents an important issue for executing a complete RAM analysis and enhances the overall performance for this wind farm. Thus, we recommended that new and renewable energy authority (NREA) in Egypt tries, in future, to record such data due to its importance for enhancing the overall performance of the VRE systems.
Generally, RAM evaluations are executed throughout three stages; system decomposition, reliability data collection, and RAM modelling as follow.
2.2.1. System decomposition
This stage represents the first stage in RAM analysis. It involves decomposing the main system into subsystems according to their functions, then each subsystem is then divided into subassemblies. Previously, in order to avoid the lower availability of data for the subassemblies of VRE systems, only one subsystem was considered for RAM analysis. Therefore, many cases have been focused on the major subassemblies in VRE systems. This is not only due to the lower availability of data for all subassemblies of the VRE system but also to overcome the complicity that arises from connecting more than one subsystem. On the other hand, some cases considered only the failure rate of the subassemblies, and such studies were limited only on the reliability analysis of such systems. This thesis offers a complete decomposition of the VRE systems considering all subassemblies in order to obtain accurate results in holistic RAM assessment.
2.2.2. Reliability data collection
This stage represents the second stage of RAM analysis. Collecting accurate reliability data of VRE systems is considered as a main challenging in RAM analysis. This is not only due to the recorded data in some sites are not available, but also because the retrieval of these data is too expensive. Even if field reliability data were available, these usually don’t satisfy the assumptions of the selected model for analysis. Therefore, various assumptions were introduced in literature to overcome this problem as in 30. These assumptions don’t give a satisfactory result for reliability analysis. A general three-parameter Weibull failure rate function is introduced in 31 in order to explain the reliability growth in case of incomplete data. Thus, in order to obtain satisfactory input data for the proposed RAM model, a methodology for collecting and obtaining the best value of the reliability data of each subassembly is introduced in this thesis. This methodology relies on collecting a huge amount of the reliability data, failure and repair rates, for each subassembly. These data have been collected from different sites and different scan time considering the same technology used in the studied systems. These data have been collected from several reliable research which used these data for estimating the reliability of VRE systems. However, the median value is computed after collecting these data in order to obtain more accurate value for reliability data of each subassembly. These median values of failure and repair rates will be used in RAM analysis. The median failure or repair rate is the middle value in the sorted list of the collected data. So, usage of the median instead of the average value will reduce the uncertainties arisen from the unexpected values introduced by assumptions as shown in Figure 2.3.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2.3: The difference between the median value and the average value of some of the collected data
In order to validate the quality of the collected data, the obtained median values of the failure rates of some sub-assemblies are compared with the failure rates of the same sub-assemblies that obtained from real field data as shall see later in the next chapters.
2.2.3. RAM modelling
RAM analysis has been carried out, as seen in reliability work, using several reliability methods such as fault tree analysis (FTA) and reliability block diagram (RBD). Based on the concept of FTA, the physical layout is interpreted into a logical diagram whereby each block represents a system component, and the failure rate is used for describing each block. On the other hand, especially with considering the failure and repair rates, the usage of RBD is preferable. In RBD, the system components interpreted by either sequential or parallel blocks which link with each other depending on their effects on the whole system. Each block of each component is described by the failure and repair rates of this component. The overall failure and repair rates of two series blocks, shown in Figure 2.4 (a), are given by Equation (2.11) and Equation (2.12). Whereas, Equation (2.13) and Equation (2.14) give the total failure and repair rates of two parallel components shown in Figure 2.4 (b).
Abbildung in dieser Leseprobe nicht enthalten
Figure 2.4: Various connections in RBD method
Chapter 3 : Practical Layouts of Solar-PV and WECS
Recently, the installations of environment-dependent renewable energy systems are increased into the electrical grid in order to meet the national strategic plans of most countries. As a consequence, the electrical grid became highly decentralized and vulnerable due to the injection of intermittent power. This will increase the necessity to limit such installations, which is opposite to the strategic plans of the countries or search for solutions to limit the severity of increasing these installations. One of the most important problems that faced the increasing of these installations is the lower reliability of the renewable systems.
This part of this chapter will introduce an overview of practical layouts of solar-PV and wind energy conversion systems in order to select the appropriate layout of the renewable systems that helping to enhance the reliability of the overall system. This overview is essential for understanding the weak areas that affect the reliability and performing a detailed reliability analysis and hence will help in putting the appropriate maintenance plans that enhance the overall reliability.
The selection of an appropriate layout of the systems represents the important issue in RAM analysis and has a significant impact on reliability. Generally, many factors have been used for obtaining the appropriate layout. Among them, the operation and control capabilities, and operating mode (i.e. Grid-connected, or off-grid).
The grid is directly fed from the grid-connected systems which are ideally located close to it. In grid-connected systems, the presence of storage devices is related to the degree of grid reliability. There will an increasing need for storage devices with grid-connected systems provided that the reliability of the grid is not sufficient for supplying local loads. Whereas, the needing for storage devices with grid-connected systems will decrease with grid-connected systems that fed a reliable grid. In the latter, the grid, which acts as an energy storage with unlimited capacity, will secure supplying the local load by the power balance constraint. As with the grid reliability level, grid-connected systems usually fall into one of two categories; grid-connected systems connected to a grid with insufficiently low reliability and grid-connected systems connected to a grid with sufficiently high reliability.
According to the consequent impacts of power interruptions, power system loads can fall into one of the three categories; non-essential, essential, and critical loads. The techno-economic impact of interrupting a specific load represents the main reason behind this classification. In the case of essential loads, very short interruptions of power are allowed. Whereas long interruptions of power are acceptable in the case of the non-essential load, while, with any reason and for even very short durations, the critical loads shouldn't be interrupted. Consequently, when the grid-connected solar-PV or wind systems used with non-reliable grid, the energy storage is utilized for providing the power for essential and critical loads in the case of the grid outage 32–36. So, the overall system, in this case, acts as the Uninterruptable Power Supply (UPS).
On the other hand, off-grid renewable systems cover distinct situations for the far loads which the traditional sources of electricity (the utility grid) fail to deliver electricity to them. The load instantaneous power balance constraint plays an important role in the requirements of the energy storage of these systems. The non-deferrable loads require instantaneous power balance for their proper operation. Hence, the off-grid systems that supply non-deferrable loads require an energy storage system. Whereas the off-grid systems that feed deferrable loads, which refer to a load type at which its energy requirements can be postponed to another nearby time, do not require electrical an energy storage system. A popular example of deferrable loads is a water irrigation pumping systems 37. In such systems, storage tanks may be used for utilizing the surplus power for water storage in irrigation water pumping systems. Figure 3.1 demonstrates the energy storage requirements for various operational modes, and load types.
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Figure 3.1: Electrical energy storage requirements for various layouts of renewable systems
3.1. Solar-PV systems
Driven by the previous discussions about the various layouts of renewable systems, Figure 3.2 illustrates the various layouts of solar-PV systems.
The DC-DC converter (CON) acts as Maximum Power Point Tracker (MPPT) in layouts without electrical energy storage, while it also acts as a charge controller in layouts with battery storage. The Automatic Static Transfer Switch (ASTS) is used with the grid-connected solar-PV system that feed non-reliable grid in order to provide the immediate islanding the solar-PV system through its sensing, and switching control logics. The grid is disconnected, in the island mode, due to either an outage, or a severe power quality problem. As a result, the non-essential loads are isolated from the solar-PV system which used in this case for providing only the required energy for the essential, and critical loads. In the island mode, the power balance is secured by the battery energy storage.
Abbildung in dieser Leseprobe nicht enthalten
Figure 3.2: Various layouts of solar-PV systems: (a) Grid-connected solar-PV system with a reliable grid; (b) Grid-connected solar-PV system with non-reliable grid; (c) Off-grid solar-PV system with deferrable loads; (d) Off-grid solar-PV system with non-deferrable loads
3.2. Wind energy conversion systems
The same concepts of the layouts presented in this section are applied to solar-PV as well as the wind energy system. This chapter classifies the wind turbine generator WTG into three main configurations; according to the operation modes, and operating concept. These configurations are fixed speed squirrel cage induction generator (FS-SCIG), the doubly fed induction generator which utilizes a partially rated converter (DFIG) and direct drive electrically excited synchronous generator with a fully rated converter (DDSG-FRC). According to the previous discussions as well as Figure 3.1, Figure 3.3 illustrates the various layouts of wind energy conversion systems WECS.
As stated before, the VRE systems may be operated in either grid-connected or off-grid (i.e. standalone) modes. In the latter, the requirements for energy storage rely on load characteristics. For instance, the requirement of energy storage is essential for supplying non-deferrable loads while supplying deferrable loads doesn't require energy storage. Generally, there are two options of energy storage; the electrochemical storage system and non-electrical storage system. First option of the energy storage that known as electrochemical energy storage is provided by various types of batteries. Whereas the second option, the non-electrical energy storage, can be provided with many options such as pumped storage, hydrogen, and compressed air energy storage (CAES). Various energy storage options are discussed in details at reference 38. The hydrogen based energy storage is considered as the main core in the future of this thesis due to its major benefits as energy storage and energy carrier medium. More details about the renewable-based hydrogen storage systems are available in Appendix A.
[...]
- Quote paper
- Doctor Ahmed Sayed (Author), 2020, Reliability analysis of power systems with variable renewable resources, Munich, GRIN Verlag, https://www.grin.com/document/922535
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