Fault Detection on Distribution Line Using Fuzzy Logic

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021

Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults.

Journal 4 Research - J4R Journal, 2016

Transmission line among the other electrical power system component suffers from unexpected failure due to various random causes. Because transmission line is quite large as it is open in environment. A fault occurs on transmission line when two or more conductors come in contact with each other or ground. This paper presents a proposed model based on MATLAB software to detect the fault on transmission line. Fault detection has been achieved by using Fuzzy Logic based intelligent control technique. The proposed method aims in presenting a fast and accurate fault diagnosis method to classify and identify the type of fault which occurs on a power transmission system. In this paper, some of the unconventional approaches for condition monitoring of power systems comprising of relay Breaker, along with the application of soft computing technique like fuzzy logic. Results show that the proposed methodology is efficient in identifying fault in transmission system.

2020

In this study, the fuzzy logic is employed to Fault Identification in grid Network by Using Rule-based mathematical logic. Power reliability of power supply is generally stricken by the occurrence of faults within the transmission or distribution network. Recent advances within the field of fuzzy logic systems and variety of successful applications in electric power (wattage) systems show that logic is efficiently applied to house imprecision, ambiguity and probabilistic information in the input file to scale back the time of power outages and for the immediate restoration, the fault identification method must be very effective. Under faulty condition system operator receives very complex information difficult to form the choice on restoring a tripped feeder to normal operation. Hence, to create the decision-making ability effectively, the Fuzzy logic-based fault diagnosis method is presented during this project. This method can classify and identify the kinds of faults that occur t...

Science and Technology of Nuclear Installations, 2012

Economic constraints are driving the electric power industry to seek improved methods for monitoring, control, and diagnostics. To increase plant availability, various techniques have been implemented in industry to assess equipment condition to prevent system inoperability. The availability of a large number of measured signals and additional component information and the increasing number of signal processing options to analyze sampled data motivate the assimilation of such diverse information into a plantwide condition monitor. The use of fuzzy logic is described herein for the purpose of performing the decision making regarding the system status and the possible need for component maintenance. Fuzzy-logic-based diagnostic monitoring is applied to data acquired from instrumentation within operating facilities.

this paper introduces the general aspects of smart grid, which is the combination of many latest technologies for effective energy distribution and usage. Fault occurrence in power grid is one event which is completely unexpected. Out of many Computational Intelligence methods to deal with such an event Fuzzy Logic techniques are of most use. Fuzzy Logic helps us to incorporate the knowledge of human experts into the expert systems using qualitative expression. Fuzzy Logic can effectively be applied to the electric system fault detection because of its ability to deal with imprecision, incomplete data and its strong knowledge base.

This paper presents an investigation into neuro-fuzzy techniques for the accurate detection, classification and location of an electric power fault in a distribution network. Ten different types of faults were studied with respect to a real network. These include: line-to-ground faults (on each of phases A, B and C); line-to-line faults (on phases A-B, B-C and A-C); line-to-line-to-ground faults (on phases A-B, B-C and A-C) and three phase fault. A Mandami-type fuzzy controller was also applied to fault type determination. The results reveal that the developed models detect, identify and locate fault incidences to a high degree of accuracy.

Przeglad Elektrotechniczny, 2013

Load balancing is an important feature that keeps the power system safe from overloading. Load details can be obtained from Circuit Breaker's Relay Meter and it is connected to computer. Power balancing and failure detection will be done using Fuzzy Rule, which is helpful for the operator to recover the power fault quickly. Because of this power sharing between the substations, in turn between feeders, the load balancing of the power system is maintained. This is applied in the real time system using tree topology to get the result more quickly than the existing Petri Net Model. Streszczenie. Przedstawiono możliwości wykorzystania logiki rozmytej do analizy obciążę i nierównowagi mocy systemu energetycznego. Analizowany jest rozdział mocy między podstacjami oraz sposoby równoważenia rozdziału mocy żeby zapobiec przeciążeniom. (Wykrywanie nieprawidłowości w systemie energetycznym bazujące na logice rozmytej)

2011

2005

Quick restoration after a power outage occurrence is Later, Improved Escalation Algorithm [13], Tabu Search very important for utilities. In storm conditions the restoration [14], Ordered Binary Decision Diagram (OBDD) [15] [17] process is more complicated because the number of outages and have been appeared to resolve these shortages. affected customers is high and finding position of the operated protective devices is hard. This paper presents a new method for in Escalation Algorithm applying heuristic rues have result trouble call analysis and outages determination in low and in some improvements butthese rules are crisp and mayresult medium voltage distribution networks base solely on customers in incorrect responses. cail and network topology. Combination of fuzey set theory and * 1 a neural networks is used to do this work. Also to find the I [ a f parametsof fuzzy inference system, adaptive learnng protective devices (PDs) in network, their selectivity and their parameters of operation possibility is created then by Tabu search algorithm techniques applied. the most probable OPDs are determined. Tabu search Keywords; trouble call; outage management; fuzZy set; neural algorithm works based on iteration and random selection and networks; ANFIS aims to minimize a target function. In this method there is no guarantee we get best response in limited time. OBDD calculates the outage degree of each protective Many different factors may result in interruption of device in tracing path. This calculation is done based on calls electricity distribution, for example natural events, customrers of underlying customers and the level of the protective device abuse, animals and so on. Prevention of some of these events is in network. Then all possible responses are extracted from impossible, but in times of outage occurrences, detection of OBDD and eventually the most probable responses are operated protective devices (OPDs) positions in least time represented. This method doesn't use information of the PDs

2019 4th International Electrical Engineering Conference (IEEC 2019)

In distribution network of smart grid there are various type of fault occur in the network, which are challenge for the control system to identify its type of fault, location and restore the network automatically. In this paper we applied neural network to determine type of fault in order to do predictive maintance and recover the network as early as possible depending on the fault type. The paper also presents the accuracy level of neural network model to determine the type of fault accurately at any mentioned condition and environment.