A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Measurements, Models, Systems and Design

531 s.

A comparison framework for distribution system outage and fault location methods

Finding the location of faults in distribution networks has been a long standing problem for utility operators, and an interesting subject for researchers as well. In recent years, significant research efforts have been devoted to the development of methods for identification of the faulted area to assist utility operators in expediting service restoration, and consequently reducing outage time and relevant costs. Considering today's wide variety of distribution systems, a solution preferred for a specific system might be impractical for another one. This paper provides a comparison framework which classifies and reviews a relatively large number of different fault location and outage area location methods to serve as a guide to power system engineers and researchers to choose the best option based on their existing system and requirements. It also supports investigations on the challenging and unsolved problems to realize the fields of future studies and improvements. For each class of methods, a short description of the main idea and methodology is presented. Then, all the methods are discussed in detail presenting the key points, advantages, limitations, and requirements

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Design and implementation of ANN based phase comparators applied to transmission line protection

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The reliability and efficiency of ANN based distance relays is improving with the developing digital technologies. There are, however, some inherent deficiencies that still exist in the way these relays are designed. This research addresses some of these issues and proposes an improved protective relaying scheme. The traditional ANN distance relay designs use parameter estimation algorithms to determine the phasors of currents and voltages. These phasors are used as inputs to determine the distance of a fault from relay location. The relays are trained and tested on this criterion; however, no specific relay characteristic has been defined. There is a need for development of a new methodology that will enable designing of an ANN that works as a generic distance relay with clearly defined operating boundary. This research work presents a modified distance relaying algorithm that has been combined with a neural network approach to eliminate the use of phasors. The neural network is trained to recognize faults on basis of a specific relay characteristic. The algorithm is flexible and has been extended for the design of other relays. The neural network has been trained using pure sinusoidal values and has been tested on a 17-bus power system simulated in PSCAD. The training and testing of the neural network on different systems ensures that the relay is generic in nature. The proposed relay can be used on any transmission line without re-training the neural network. The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries

Fast Fault Location Method for a Distribution System with High Penetration of PV

Distribution systems with high levels of solar PV may experience notable changes due to external conditions, such as temperature or solar irradiation. Fault detection methods must be developed in order to support these changes of conditions. This paper develops a method for fast detection, location, and classification of faults in a system with a high level of solar PV. The method uses the Continuous Wavelet Transform (CWT) technique to detect the traveling waves produced by fault events. The CWT coefficients of the current waveform at the traveling wave arrival time provide a fingerprint that is characteristic of each fault type and location. Two Convolutional Neural Networks are trained to classify any new fault event. The method relays of several protection devices and doesn’t require communication between them. The results show that for multiple fault scenarios and solar PV conditions, high accuracy for both location and type classification can be obtained