Condition monitoring of Electrical power equipment has attracted considerable attention for years. The aim of this work is to use Fuzzy logic (FL) Tool Box in building a simulation system that will diagnose all kinds of incipient faults, phase to phase fault and overloading in a transformer and monitor its conditions. Current and rate of change of current with time have been identified as the input variables, duly represented in the programme as “Error” and “Error-Dot”. These variables have their universe of discourse from -1.5 to 1.5 and from -10 to 10 respectively. Fuzzy logic sensor is designed to monitor the current(i) conditions of the transformer at both ambient and full load. The results from the research show that whenever the output response is zero the current in transformer is normal. This is obtained when input values of  and  are injected into the system to produce a response of “6e-017” which is approximately zero. Whereas if the output response is greater than zero it implies that the transformer current is rising beyond normal and protection scheme should be alerted. This condition is achieved when input values of [-1.5] and  are used on the system to give a response of “+5”. However, if the response is less than zero then the transformer current is below normal, hence the protection scheme should be alerted. To investigate this, input values of [1.5] and [-5] give a response of “-5”. Fuzzy logic is used as an expert system that assesses all information keyed in at the front panel to analyze and predict the condition of the transformer at any time.
TABLE OF CONTENTS
Approval Page ————————————————————————————— i
Abstract ———————————————————————————————- iv
Table of Contents ———————————————————————————— v
List of Tables —————————————————————————————- vii
List of Figures ————————————————————————————— viii
List of Symbols ————————————————————————————– ix
CHAPTER ONE: INTRODUCTION
- Background of the Study———————————————————————– 3
- Statement of the Problem———————————————————————– 4
- Objective of the Study ————————————————————————- 4
- Significance of the Study ———————————————————————– 4
- Scope of the Study —————————————————————————– 5
CHAPTER TWO: LITERATURE REVIEW
2.1.0 The Transformer ——————————————————————————- 6
2.1.1 Information on name plate of the three phase
Double wound transformer ——————————————————————- 8
2.1.2 Transformer losses—————————————————————————— 9
2.1.3 Test on Transformer—————————————————————————- 12
2.1.4 Faults in Transformer ————————————————————————– 14
2.1.5 Constructional features to reduce faults and
increase Efficiency ————————————————————————— 16
2.1.6 Conventional fault detection and protection scheme—————————————— 18
2.2.0 Fuzzy Logic———————————————————————————— 24
2.2.1 Fuzzy sets————————————————————————————– 29
2.2.2 Membership function ————————————————————————– 30
2.2.3 Rule base————————————————————————————— 31
2.2.4 Inference————————————————————————————— 32
2.2.5 De-fuzzification ——————————————————————————- 32
CHAPTER THREE: METHODOLOGY
3.1 Identification of input and output variables—————————————————– 38
3.2 Construction of control rules——————————————————————— 39
3.3 Rules——————————————————————————————— 39
3.4 Membership functions ————————————————————————— 41
3.5 Fuzzification ————————————————————————————- 44
3.6 Selection of compositional Rule of Inference—————————————————- 46
CHAPTER FOUR: SIMULATION AND RESULTS
4.1 Simulation software used ———————————————————————— 49
4.2 Fuzzification————————————————————————————- 50
4.3 Rule number determination———————————————————————- 51
4.4 Rules ——————————————————————————————— 54
4.5 Inference Engine——————————————————————————— 54
4.6 Rule firing ————————————————————————————— 54
4.7 Defuzzification———————————————————————————– 55
4.8 Result analysis———————————————————————————— 61
CHAPTER FIVE: CONCLUSION
5.1 Summary—————————————————————————————– 63
5.2 Conclusion————————————————————————————— 64
5.3 Contribution to knowledge———————————————————————- 65
REFERENCES —————————————————————————————————– 66
Appendix A: Input I (Error)————————————————————————- 68
Appendix B: Input II (Del-error)——————————————————————– 69
Appendix C: Output I——————————————————————————– 70
Appendix D: Rules Structure———————————————————————— 71
LIST OF TABLES
2.1 Interpretation of the Fault Behaviour ———————————————————– 27
2.2 Example of fuzzy sets—————————————————————————- 30
2.3 Fuzzy set speed———————————————————————————- 30
3.1 The Rule Structure——————————————————————————- 40
3.2 The Rule Matrix———————————————————————————- 40
Transformers are static electromagnetic machines designed for transformation of one alternating voltage to another with different voltage and current characteristics . Large power transformers belong to a class of very expensive and vital components in electrical power system. In practice, they are protected against internal short circuit and over-heating of which capacity percentage differential relays are universally adopted for internal short circuit protection .
A transformer can be single or multiphase depending on the primary and secondary windings. There are so many faults and losses which can occur in transformer both on load and off load. Most of the losses like Eddy current and hysteresis in the core and the I2R loss in the windings result to over current may culminate to other dangerous conditions like reduction of dielectric strength, earth fault and finally burning of the windings. Tests are carried out during commissioning and while in service to ensure reliability.
Transformers are designed such that as little energy as possible is wasted inside it, thus ensuring that its efficiency is as high as possible . Hence:
- Low resistance copper coils are used so that internal energy (I2R) losses in the windings are small.
- Laminated core is used to reduce eddy current losses.
- The core is made of soft magnetic materials in order to reduce the energy required to bring about magnetic reversal (hysteresis loss).
- Efficient core design is adopted to ensure that all the primary flux is linked with secondary.
Despite all engineering design and constructional efforts, an ideal transformer called “lossless transformer” cannot be practically obtained because of the inherent and unavoidable losses and faults. Sequel to this, detection schemes are deviced to monitor and sieve out the occurrence of such faults. The inception of the faults introduces abrupt changes of amplitude and phase in voltage and circuit signals. Faults allow abnormal large currents to flow, resulting in over heating of power system components. If the fault is typically a short circuit, it can exist as an electrical arc in a fluid (such as air). The extremely high temperature in arcs will vaporize any known substances causing equipment destruction and fire . Faults can cause three-phase system voltages to rise above their acceptable ranges or to be unbalanced causing three-phase equipment to operate improperly. They can cause the system to become unstable and loose synchronism. There are conventional fault detection schemes which include typical electrical sensors, which are of bimetal-strip, switch or thermostat type or thermocouple, thermo-resistor detectors and thermistor. Non electrical sensors are of gas or fluid filled type. Other constructional features are employed for the detection such as bulchholz relay and transformer breather with desiccant.
A fuzzy logic as an alternative method of fault detection and protection on power transformer has been adopted for this project research.
Soft computing has been proposed as a method to solve real-world problems, which defy conventional approaches. In fact, even when expert knowledge is available, it is often more easily stated in descriptive form i.e. as statement like “IF a sign of certain type appears THEN one or more faults must be present”.
From a practical point of view, diagnostic knowledge often comes in form of a kind of compendium of descriptive expert knowledge and relevant raw system data.
Fuzzy logic incorporates a simple rule-base if “X AND Y THEN Z” approach to solving problems rather than attempting to model a system used which rely on the operator and experience rather than technical understanding of the system. Design of a fuzzy logic sensor needs qualitative knowledge about the system under consideration.
Unlike most conventional and modern detection schemes, fuzzy logic sensors are capable of tolerating uncertainties and imprecision to a greater extent. Hence they produce good results under changing operating conditions and uncertainties or imprecision in system parameters.
1.1 BACKGROUND OF THE STUDY
Fuzzy logic is a subset of conventional (Boolean) logic that has been extended to handle the concepts of partial truth-values, between ‘completely true’ and ‘completely false’.
The ideas of fuzzy logic dates all the way back to Plato who proposed that there is a third region between true and false. Fuzzy logic is a technique of making a choice answer to question other than yes or no. It resembles human reasoning in the use of approximate information and uncertainty to generate decision. The fuzzy logic is a designed technique used in providing formalized tools for dealing with imprecision that are intrinsic to many problems. The fuzzy set theory implements clauses, of data that are not sharply defined. To that effect, since transformer is a vital component in power system, it needed such precision technique in its fault detections and protections.
1.2 STATEMENT OF THE PROBLEM
High voltage transformers are unavoidably subject to various faults and relays like differential, bulchholz and directional relays with sensors are usually adopted to detect and transmit (relay) the decision to a circuit breaker which trips or opens the power system. These modern detection schemes do not tolerate uncertainties or impression under changing operating conditions in a given system parameters, hence fuzzy logic sensors were investigated.
1.3 OBJECTIVE OF THE STUDY
The objective of this work is to use fuzzy logic method of sensing and protection on a high voltage transformer in place of the conventional protective scheme.
1.4 SIGNIFICANCE OF THE STUDY
One advantage that fuzzy sets offer to constructors of experts system is the ability to work in terms of familiar words. This eases the task of system construction, the domain expert can sketch out rules in English language terms, which are familiar, and this facilitates communication between expert and knowledge engineer .
It is more than relevant, contrary to opinions, that it lacks precision. However, the logic solves problems related to non-linear system in the same manner human beings do. For instance, the way we eat requires no measurement of our mouth size, and the ‘bolus’ size, yet we achieve this by more than 95% success every time. Similarly, fuzzy logic based appliances really do things with high degree of adaptability, self-adjustability and robustness. To this effect, many video and digital cameras do not need manual focusing ability, which make the object invariably focused. Fuzzy logic is found in a variety of control applications including chemical process control, manufacturing and in some consumer products like washing machine, video cameras and automobiles. Fuzzy logic can be used for condition monitoring of electrical power equipment and also in the design of a simple proportional temperature controller with an electric heating element and a variable-speed cooling fan, and so on.
1.5 SCOPE OF THE STUDY
The scope of this work is limited to the study of the most prevalent faults, their detection and protection in high-voltage transformers of 200MVA power rating using fuzzy sensors.[email protected].[email protected].