Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based M
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Detection and Classification of Incipient Faults in Three‑Phase Power Transformer Using DGA Information and Rule‑based Machine Learning Method Mohsen Savari Katooli1 · Amangaldi Koochaki1 Received: 28 December 2019 / Revised: 9 May 2020 / Accepted: 27 June 2020 © Brazilian Society for Automatics--SBA 2020
Abstract Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the subsystems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure. Keywords BWO · Fuzzy rules · Item selection · Learning algorithm · Transformer List of symbols C2H6 Ethane C2H4 Ethylene CH4 Methane C2H2 Acetylene H2 Hydrogen σ Sigma ARMT Association Rules Mining Technique ANFIS Adaptive Neuro-Fuzzy Inference System BRO Bayesian Regularization Optimization BWO Black Widow Optimization BGAFG Binary GA With Feature Granulation BGAFG Binary GA With Feature Granulation BMOPSO Binary Multi-Objective Particle Swarm Optimization * Amangaldi Koochaki [email protected] 1
Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
CFR Composition of Feature Relevancy ConvNet Convolutional Neural Networks CFNN Cascade-Forward Neural Network CHC-GA GA with cataclysmic mutation, heterogeneous recombination and cross-generational elitist selection BPA Back Propagation Algorithm DT Decision Tree DGAT Dissolved Gas Analysis Technique DEU Discriminative Embedded Unsupervised DRA Doernenburg’s Ratio Approach ED Energy Discharges FKN Fuzzy K-Nearest Neighbors GA Genetic Algorithm GWN GA-Based Wavelet Networks KRR Kernel Ridge Regression LM Levenberg–Marquardt MLPNN Multilayer Perceptr
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