Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

APPLICATION OF ANN AND GA FOR TRANSFORMER WINDING/ INSULATION FAULTS

This report presents an application of Artificial Neural Network and Genetic Algorithm for transformer winding/insulation faults diagnosed using Dissolved Gas in Oil Analysis. A back propagation training method is applied in neural network to detect the faults without cellulose involvement. While, heuristic method of Genetic Algorithm is used to locate the optimal values to enhance the accuracy of fault detection. The dissolved gas in oil analysis is chosen to diagnosis the transformer faults in this project as the method is known to be an early fault detection method and enables to carry out during online operation of the transformer. Besides, the condition of the transformer could be monitored continuously by time to time. The project outcome is analyzed using Neural Network and Genetic Algorithm MATLAB Toolbox. Comparison between the real fault and predicted fault is made as to observe the accuracy rate of the system. As transformer faults detection concentrated more in conventional method such the stability of the voltage and current of the transformer. Therefore, hopefully the transformer winding and insulation faults could be studied from new point ofview and method

Measurements, Models, Systems and Design

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Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

Diagnosing faults in power transformers with autoassociative neural networks and mean shift

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores (Área de Especialização de Energia). Faculdade de Engenharia. Universidade do Porto. 201

Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

A novel ANN fault diagnosis system for power systems using dual GA loops in ANN training

Fault diagnosis is of great importance to the rapid restoration of power systems. Many techniques have been employed to solve this problem. In this paper, a novel Genetic Algorithm (GA) based neural network for fault diagnosis in power systems is suggested, which adopts three-layer feed-forward neural network. Dual GA loops are applied in order to optimize the neural network topology and the connection weights. The first GA-loop is for structure optimization and the second one for connection weight optimization. Jointly they search the global optimal neural network solution for fault diagnosis. The formulation and the corresponding computer flow chart are presented in detail in the paper. Computer test results in a test power system indicate that the proposed GA-based neural network fault diagnosis system works well and is superior as compared with the conventional Back-Propagation (BP) neural network.published_or_final_versio

Intelligent management of substation assets

This article discusses the advantages and challenges of using artificial intelligence for substation transformer management. It focuses on the use of various techniques for qualitative assessment of substation transformers through cost-effective means