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

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

Bushing diagnosis using artificial intelligence and dissolved gas analysis

This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings

Development of Voltage Controller and Fault Analysis of Self Excited Induction Generator System

Increasing fuel cost and attempt to get pollution free environment, renewable sources of energy such as the wind, solar, micro-hydro, tidal wave, and biomass, etc. have grabbed recently the attention of researchers. Among these available energy resources, the use of wind energy is growing rapidly to generate and supply electricity as grid connected or stand alone mode. To generate electric power from such non-conventional sources, self-excited induction generator (SEIG) is found to be a suitable option for either using in grid connected mode or isolated mode. Selection of SEIG in these areas depends on its advantages such as low cost, less maintenance, and absence of DC excitation. High maintenance and installation costs including transmission losses of conventional power supply to remote or isolated place by means of power grid can be reduced by installing stand-alone wind driven SEIG system at those places. In the year of 1935, self-excitation concept in squirrel cage induction machine with capacitors at their stator terminals was introduced by Basset and Potter. But the problems associated with SEIG are its poor voltage and frequency regulation under load and prime mover speed perturbations which put a limit on the use of SEIG for a long time. By controlling active and reactive power accurately, it is possible to regulate frequency and voltage of SEIG terminal during load and speed perturbations. Various efforts have been put by researchers in developing SEIG voltage and frequency controller but these control schemes demand multiple sensors along with complex electronic circuits.This dissertation presents some studies and development of new voltage controller of the SEIG system for balanced resistive, R − L and induction motor (IM) load that is used in isolated or remote areas. So in this context, an attempt is taken to develop an optimized voltage controller for SEIG using Generalized Impedance Controller (GIC) with a single closed loop. Stable zones of proportional and integral gains for GIC based SEIG system are computed along with parameter evaluation of the GIC based SEIG system. Further, Particle Swarm Optimization(PSO) technique is used to compute the optimal values of proportional and integral gains within the stable zone. The research work on SEIG system is extended to develop a voltage controller for SEIG with minimum number of sensors to make the system less complex and cost effective. Here, a voltage peak computation technique is developed using Hilbert Transform and computational efficient COordinate Rotation DIgital Computer (CORDIC) which requires only one voltage sensor and processed to control SEIG voltage for GIC based SEIG system. This voltage control scheme is implemented on commercially available TMS320F2812 DSP processor and performed laboratory experiment to study the performance of GIC based SEIG system during load switching. The work of this thesis is not confined only to study an optimal and simple voltage controller for SEIG system but also extended to investigate the fault identification methodologies of SEIG system. Here, the features of non-stationary SEIG signal with faults are extracted using Hilbert-Huang Transform (HHT). Further, different classifiers such as MultiLayer Perceptron (MLP) neural network, Probabilistic Neural Network (PNN), Support Vector Machine (SVM), and Least Square Support Vector Machine (LS-SVM) are used to identify faults of SEIG system. In this study, it is observed that LS-SVM among above classifiers provides higher classification accuracy of 99.25%

The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems

Includes bibliographical references (leaves 128-129).The thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. The mathematical and signal processing tools which are explored are wavelet analysis and a non-stationary adaptive signal processing algorithm. Four specific applications are identified for the research. These applications were specifically chosen to encapsulate important issues in condition monitoring of variable speed drive systems. The main aim of the project is to highlight the need for fault detection during machine transients and to illustrate the effectiveness of incorporating and adapting these new class of algorithms to detect faults in electrical machine drive systems during non-stationary conditions

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section