The problem of electricity is enormous, and it will never be completely solved until the fault department (maintenance) is improved to allow for immediate and rapid response to line faults.

In the event of a fault, the current protection scheme only isolates the lines and defines the fault, leaving no proper information about the location of the fault.

Faults must occur on both transmission and distribution lines, but the question is how quickly the fault can be cleared when the maintenance team must patrol the lines (from pole to pole and tower to tower) looking for the fault (s). This study develops fault location-based fuzzy logic. The fact that the input impedance to which distinct motivates this approach.

The procedure Show the line operational data and rules that are simulated in MATLAB using the fuzzy logic toolbox. Faults on both transmission and distribution lines can be easily located and treated (cleared) using the proposed fuzzy-set approach.




The dependability of large power systems with small stability margins is heavily reliant on systems and protection devices. Although the use of microprocessor-based (numeric) relaying protection has improved performance over time, it has had little effect on the speed, sensitivity, and selectivity of primary protective relays. However, decision making based on elements of artificial intelligence (AI) can, in my opinion, have a significant impact in the aforementioned, particularly when it comes to quick fault removal in line maintenance.


Creating a fail-safe power system is neither economically nor technically feasible (Nagrath et al, 1994). Failure of apparatus due to surges or other causes causes power system faults. A fault is defined as any abnormal state of the system. Short circuits are the most common type of fault (Stevenson, 1982). Open circuit faults can be dangerous to personnel, but they are less dangerous than short circuit faults. As a result, short circuit faults must be removed from the system as soon as possible. This short circuit fault removal process is done automatically in modern power systems, and the equipment that does it is known as a ‘protective system’ (Stevenson, 1982). This is a system comprised of transducers, relays, and circuit breakers. Despite the fact that this thesis does not concern The above premises are unavoidable in the event of a fault because fault location is a measure for ensuring quick fault clearing, and fault clearing can only occur if the system is adequately protected.


Over the last two decades, much of the effort in power system analysis (control and protection) has shifted from formal mathematical modeling methodology to less rigorous artificial intelligence (AI) techniques (Agggarwal et al, 1995). The most common AI techniques used in power system applications today are those that use the logic and knowledge representation of expert systems (ES), fuzzy systems (FS), genetic algorithms (GA), artificial neural networks (ANN), and, more recently, evolutionary computing (EC).

The application entails creating a programmable logic (PROLOG) for manipulating symbolic data in a way that mimics human reasoning. It is about constraint satisfaction, but it also provides a detailed look at how to use PROLOG to develop a problem description (Schalkoff, 1990). The application’s overall goal Concerning the development of a reasoning system, it entails creating a strategy for operating (Opening) appropriate circuit breakers in the event of a fault. Fault location is an example of how the above approach to power system fault clearing can be used.


Artificial intelligence (AI) seeks to create intelligent machines that simulate or emulate human intelligence. Artificial Neural Networks, Expert Systems, and Fuzzy Systems are all attempting to achieve these goals. The distinction between them is primarily based on how knowledge is represented in the system and obtained.


The goal of artificial intelligence (AI) is to create intelligent machines that mimic or simulate human intelligence. These objectives are being pursued by Artificial Neural Networks, Expert Systems, and Fuzzy Systems. The distinction between them is primarily based on how knowledge is represented and obtained in the system.

FUZZY SYSTEMS (FS): Fuzzy systems, like expert systems, rely on If-Then-rules. These rules, while superficially similar, allow for fuzzy input, which is more like the natural way humans express knowledge. For instance, we may say the system is ‘somewhat secure’. A fuzzy system can express this linguistic input directly. As a result, the natural format greatly simplifies the interface between the knowledge engineer and the domain expert; thus, a fuzzy system can represent knowledge that an expert system may struggle to represent (or needs a large set of rules). One critical distinction exists between fuzzy and expert systems (Song et al, 1997). Rather than forcing the use of systems, fuzzy systems allow for the natural, logical representation of imprecise human knowledge. Fuzzy systems allow humans to express their judgments using the approximate terms that they almost always use, allowing for more accurate knowledge representations. As a result, fuzzy systems are more robust, compact, and simple.


Fuzzy logic is a problem-solving control system methodology that provides a simple way to reach definite conclusions based on vague, blurred, ambiguous, noisy, and imprecise input data (Kaeler,2005). Lofti Zadeh of the University of California first proposed it in 1965. Rather than attempting to model a system mathematically, it employs a simple rule-based “IF X and Y THEN Z” approach to solving control problems. Fuzzy logic enables complex system design directly from engineering experience and experimental results, allowing for the rapid generation of solutions that effectively describe the ambiguity of the real world. It mimics a human operator by using an imprecise but descriptive language to deal with input data.

Real-world problems are mathematically formulated.

derived under certain restrictive assumptions. In contrast, because power systems are large, complex, geographically dispersed, and influenced by unexpected new challenges, there are many uncertainties in various power system problems. Because of these facts, it is difficult to effectively address many power system problems using strict mathematical formulations alone. Fuzzy logic, among other things, is a powerful AI tool for dealing with difficult power system problems.

The following are examples of power system uncertainty and imprecision that pose significant challenges when using conventional techniques:

Humans involved in the planning, management, operation, and control of power systems produce imprecise information.

Changing power system operating conditions, such as changes in load or generation, as well as changes in power system topology.

Inaccuracies due to voltage and current transducers

or noise introduced by electromagnetic interference, or SCADA measurements/state estimations,

Many fault conditions exist, including fault initiation, fault location, fault types, and fault path resistance.

The aforementioned issues are exacerbated by their randomness. Fuzzy logic (FL) has been investigated as a powerful tool in the development of novel protective relays for transmission systems in this regard (Aggarwal et al, 1997).


Fuzzy logic is based on natural languages and is conceptually simple.

FL can reconcile competing goals.

FL can deal with ambiguity and is tolerant of imprecise data.

FL is adaptable and relatively simple to implement.

FL can be built on top of expert experience or combined with other techniques.

In an iterative development cycle, we typically need to complete eight tasks when developing a fuzzy logic control system (Song et al, 1997).

Define the problem.

Identify the linguistic variables.

Define the control surface (fuzzy sets)

Determine the control surface’s behavior (Fuzzy rules)

Define the reasoning mechanism (Fuzzy inference)

Construct the system.

Run the system through its paces.

Fine-tune and validate the system.


When using traditional relay-based protection techniques, the uncertainty, imprecision, and ambiguity of power system conditions pose serious challenges to complex mathematical modeling. These difficulties are exacerbated by the fact that the input parameters required for protective system design are not always clear and distinct.


The purpose of this thesis is to:

Improving power efficiency by offering alternative and improved solutions to the challenges of conventional maintenance schemes for high tension transmission lines and even distribution lines.

vRemoving accident risks that are always encountered by maintenance crews when faults are located.


This thesis examines existing conventional relay-based fault location schemes as well as the use of artificial intelligence (Fuzzy logic) concepts to provide improved solutions.


This thesis does not aim to create an entirely new fault location scheme. It improves on the existing fault location scheme in transmission lines using operational and experimental results, subject to the availability of software compatible with fuzzy logic techniques.


This dissertation is divided into six chapters. Chapter One provides a general introduction and background information about power system protection, as well as a review of artificial intelligence techniques in power system protection. A detailed review of related literatures is presented in chapter two. This includes power system faults, protection zones, protective system elements, distance relaying concepts and operations, and typical fuzzy logic applications to line fault location. The third chapter discusses design methodology (hardware and software strategy), data sources, and design implementation flow diagrams. Chapter four contains the design presentation and simulation using appropriate software techniques, as well as the design basis. Chapter five contains the findings and discussion. While the thesis leads to the conclusion, In chapter six, some fuzzy logic objections and recommendations are presented.


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