An improved fault location on power system transmission lines using fuzzy logic approach

Thesis Overview

<p> </p><p><strong>INTRODUCTION</strong><br></p><p><strong>1.1. BACKGROUND</strong></p><p>The reliable operation of large power systems with small stability margin is highly dependent on systems and protection devices. The application of microprocessor based (numeric) relaying protection has improved performance over time but, this has not led to a major impact on speed, sensitivity, and selectivity of primary protective relays. However decision making based on elements of artificial intelligence (AI) can, in my view, lead to a major impact in the aforementioned especially as quick fault removal in lines maintenance is concerned here.</p><p><strong>1.2. POWER SYSTEM PROTECTION</strong></p><p>Designing a fail-free power system, is neither economically justifiable nor technically feasible (Nagrath et al, 1994). Failure of apparatus due to surges or other causes leads to faults on a power system. Strictly speaking, a fault is any abnormal state of the system. In general faults consist of ‘short circuit’ (Stevenson, 1982). Open circuit faults pose potential hazards to personnel, but, they are less server that short circuit faults. Hence they (short circuit faults) must be removal from the system as fast as possible. In modern power systems, this short circuit fault removal process is done automatically, and the equipment that does it is called ‘protective system’ (Stevenson, 1982). This is a combination of transducers, relays and circuit breakers. Although this thesis does not concern the isolation of any part of the system in event of fault, the above premises is inevitable since fault location is a measure for ensuring speedy fault clearing, and fault clearing can only take place if the system is adequately protected.</p><p><strong>1.3. ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES IN POWER SYSTEM PROTECTION</strong></p><p>In the last two decades, much of the efforts in power system analysis (control and protection has moved from the methodology of formal Mathematical modeling to the less rigorous techniques of artificial intelligence (AI) (Agggarwal et al, 1995). Today the main AI techniques found in power system applications are those utilizing the logic and knowledge representation of expert system (ES), fuzzy systems (FS), Genetic Algorithm (GA), Artificial Neural Networks (ANN) and recently, evolutionary computing (EC).<br>The application involves developing a programmable logic (PROLOG) for the manipulation of symbolic information in a manner that emulates human reasoning. It concerns, constraints satisfaction, and nevertheless, provides a rich in-depth look at the use of PROLOG to develope a problem description (Schalkoff, 1990). The overall goal of the application concerns the development of a reasoning system involves developing a strategy to operate (Open) appropriate circuit breakers in the event of fault. Sample application of the above approach to power system fault clearing include fault location.</p><p><strong>1.4. COMPARISON OF AI TECHNIQUES.</strong></p><p>The goal of artificial intelligence (AI) is to produce intelligent machines which simulate or emulate human being’s intelligence. Artificial Neural Networks, Expert Systems and Fuzzy Systems all attempt to meet these objectives. The difference in them lies essentially in the way knowledge is represented in the system, and how it is obtained.<br>· ARTIFICIAL NEURAL NETWORKS (ANN)<br>ANNs and fuzzy systems are similar in many ways; first they both store knowledge and use it to make decisions on new inputs. Both can generalize and produce correct responses despite minor variations in input vector. However, there are some fundamental differences. ANN has a major advantage of acquiring knowledge through training. Often the training set can be composed of actual observations of the physical world, rather than being formed from the human opinions used for fuzzy (or expert) systems. In other words, the neural network lets the data speak for itself; they cannot directly handle fuzzy information (Song et al, 1997).<br>· FUZZY SYSTEMS (FS): Like expert systems, fuzzy systems rely on If-Then- rules. These rules, while superficially similar, allow the input to be fuzzy, i.e. more like the natural way that humans express knowledge. For instance, we may say the system is ‘somewhat secure’. This linguistic input can be expressed directly by a fuzzy system. Therefore the natural format greatly eases the interface between the knowledge engineer and the domain expert, thus, a fuzzy system can represent knowledge in which an expert system may have difficulty (or needs a large set of rules). Fuzzy and expert system differ in one critical respect (Song et al, 1997). Fuzzy systems allow the representation of imprecise human knowledge in a natural, logical way, rather than forcing the use systems. Fuzzy systems allow the approximate terms that are nearly always employed by humans to express their judgments, thereby permitting more accurate knowledge representations. Thus fuzzy systems are more robust, more compact and simpler.</p><p><strong>1.5. FUZZY LOGIC FOR POWER SYSTEM PROTECTION</strong></p><p>Fuzzy logic can be said to be a problem-solving control system methodology, which provides a simple way to arrive at definite conclusion based upon vague, blurred, ambiguous, noisy, imprecise input information (Kaeler,2005). It was first conceived by professor Lofti Zadeh of the University of Clifornia in 1965. It incorporates a simple rule-based “IF X and Y THEN Z” approach to solving control problem rather than attempting to model a system mathematically. Fuzzy logic allows complex system design directly from engineering experience and experimental results, thus quickly rending solutions that can effectively describe the vagueness of the real world. It uses an imprecise but very descriptive language to deal with input data in a way that mimics a human operator.<br>Mathematical formulations of real-world problems are derived under certain restrictive assumptions. Conversely, there are many uncertainties in various power system problems because power systems are large, complex geographically widely distributed and influenced by unexpected new challenges. These facts make it difficult to effectively deal with many power system problems through strict mathematical formulations alone. Fuzzy logic, among others is a powerful AI tool in meeting challenging power system problems.<br>The following are the uncertainty and imprecision in power systems which pose significant difficulties when applying conventional techniques:<br>· Imprecise information caused by human beings involved in the planning, management, operation and control of power systems.<br>· Changing power system operating conditions such as changes in load or generation and changes in the topology of power systems.<br>· Inaccuracies caused by voltage and current transducers or SCADA measurements/state estimations or noise introduced through electromagnetic interference,<br>· Many fault conditions, include fault inception, fault location, fault types and fault path resistance.<br>The aforementioned problems are compounded by their random nature. In this respect, fuzzy logic (FL) has been investigated as a powerful tool in the development of novel protective relays for transmission systems (Aggarwal et al, 1997).<br>SOME FUZZY LOGIC BENEFITS INCLUDE<br>ØFuzzy logic is based on natural languages and is conceptually easy to understand<br>ØFL can resolve conflicting objectives<br>ØFL is tolerant of imprecise data and can handle ambiguity<br>ØFL is flexible and relatively easy to implement<br>ØFL can be built on top of the experience of experts or can be implemented with other techniques.<br>When developing a fuzzy logic control system, there are eight tasks we need typically to perform in an iterative development cycle (Song et al, 1997).<br>· Define the problem<br>· Define the linguistic variables<br>· Define control surface (fuzzy sets)<br>· Define the behavior of the control surface (Fuzzy rules)<br>· Define reasoning mechanism (Fuzzy inference)<br>· Build the system<br>· Test the system<br>· Tune and validate the system.</p><p><strong>1.6. STATEMENT OF PROBLEM</strong></p><p>Uncertainty imprecision, and ambiguity of power system conditions pose serious challenges of complex mathematical modeling when we apply conventional relay based protection techniques. These challenges are compounded by the fact that the input parameters needed for the design of protective systems are not always clear and distinct.</p><p><strong>1.7. OBJECTIVE OF THE STUDY</strong></p><p>This thesis is aimed at:<br>vImproving power efficiency by providing alternative and improved solutions to the challenges in the convetional maintenance scheme for high tension transmission lines and even distribution lines.<br>vRemoving accident risks that are always encountered by maintenance crew when location faults.</p><p><strong>1.8. THE SCOPE OF THE STUDY</strong></p><p>This thesis covers the existing conventional relay based fault location schemes and the application of artificial intelligence (Fuzzy logic) concept to provide improved solutions.</p><p><strong>1.9. LIMITATIONS OF STUDY</strong></p><p>This thesis does not seek to design a completely new fault location scheme. It uses operational and experimental results to improve on the existing fault location scheme in transmission lines, subject to availability of software that is compatible with fuzzy logic techniques.</p><p><strong>1.10. ORGANIZATION OF THESIS</strong></p><p>This thesis is organized in six chapters. A general introduction and background information about power system protection and review of artificial intelligence techniques in power system protection is presented in chapter One. In chapter two, a detailed review of related literatures is presented. This includes power system faults, protection zones, elements of protective system, distance relaying concepts and operations, an typical applications of fuzzy logic to line fault location. Chapter three deals with the design methodology (hardware and software strategy), sources of data and flow diagram of design implementation. The design presentation and simulation, using appropriate software techniques, along with the basis for the design is in chapter four. The results and discussion are presented in chapter five. While the conclusion derivable from the thesis, some fuzzy logic objections and suggested recommendations are presented in chapter six.</p> <br><p></p>