Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

[1]  Tjeerd W. Boonstra,et al.  Fuzzy Entropy and Its Application for Enhanced Subspace Filtering , 2018, IEEE Transactions on Fuzzy Systems.

[2]  Qiang Wang,et al.  Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques , 2018, Energy.

[3]  Marimuthu Palaniswami,et al.  Fuzzy c-Means Algorithms for Very Large Data , 2012, IEEE Transactions on Fuzzy Systems.

[4]  Peter Oluseyi,et al.  Fault diagnosis in an extra-high voltage power line , 2017, 2017 IEEE PES PowerAfrica.

[5]  Jan Adamowski,et al.  Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model , 2018, Stochastic Environmental Research and Risk Assessment.

[6]  Jing Liu,et al.  Robust learning of large-scale fuzzy cognitive maps via the lasso from noisy time series , 2016, Knowl. Based Syst..

[7]  Chandram Karri,et al.  Electricity Price Forecasting in Deregulated Power Markets using Wavelet-ANFIS-KHA , 2018, 2018 International Conference on Computing, Power and Communication Technologies (GUCON).

[8]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[9]  Witold Pedrycz,et al.  Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Hisao Ishibuchi,et al.  Adaptive fuzzy rule-based classification systems , 1996, IEEE Trans. Fuzzy Syst..

[11]  Eugenia Minca,et al.  Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources , 2015 .

[12]  Shyi-Ming Chen,et al.  Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques , 2019, Inf. Sci..

[13]  Francisco Martínez-Álvarez,et al.  A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting , 2015 .

[14]  Abdulhamit Subasi,et al.  A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA , 2016, Neural Computing and Applications.

[15]  Abdullah Al-Badi,et al.  Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm , 2019 .

[16]  Zahid Ullah,et al.  Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed , 2018, The Journal of Engineering.

[17]  Miin-Shen Yang,et al.  A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy , 2018, IEEE Transactions on Fuzzy Systems.

[18]  Paulo Gil,et al.  Adaptive Neuro–Fuzzy Control for Discrete-Time Nonaffine Nonlinear Systems , 2019, IEEE Transactions on Fuzzy Systems.

[19]  Sivaji Chakravorti,et al.  A Novel Leakage Current Index for the Field Monitoring of Overhead Insulators Under Harmonic Voltage , 2018, IEEE Transactions on Industrial Electronics.

[20]  Yung-Ruei Chang,et al.  Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System , 2014 .

[21]  Mohammad Lutfi Othman,et al.  An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network , 2018 .

[22]  Deng Honglei,et al.  Ultrasonic guided wave-based detection of composite insulator debonding , 2017 .

[23]  Yijia Cao,et al.  A Uniform Fault Identification and Positioning Method of Integrated Energy System , 2018 .

[24]  Constantine J. Hatziadoniu,et al.  Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees , 2018 .

[25]  Linong Wang,et al.  Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data , 2018 .

[26]  Feng Li,et al.  Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change , 2018, Energies.

[27]  Ming Dong,et al.  Joint Visualization Diagnosis of Outdoor Insulation Status With Optical and Acoustical Detections , 2019, IEEE Transactions on Power Delivery.

[28]  Ademir Nied,et al.  Evaluation of Methods for Electric Field Calculation in Transmission Lines , 2018, IEEE Latin America Transactions.

[29]  Ademir Nied,et al.  Analysis of the Electric Field in Porcelain Pin-Type Insulators via Finite Elements Software , 2018, IEEE Latin America Transactions.

[30]  Panos M. Pardalos,et al.  Analysis of Images, Social Networks and Texts , 2017, Lecture Notes in Computer Science.

[31]  Jing Liu,et al.  Time-Series Forecasting Based on High-Order Fuzzy Cognitive Maps and Wavelet Transform , 2018, IEEE Transactions on Fuzzy Systems.

[32]  Jörg Verstraete.  The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System , 2017, IEEE Transactions on Fuzzy Systems.

[33]  Witold Pedrycz,et al.  Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design , 2017, IEEE Transactions on Fuzzy Systems.

[34]  S. Chiu.  Method and software for extracting fuzzy classification rules by subtractive clustering , 1996, Proceedings of North American Fuzzy Information Processing.

[35]  Auli Damayanti.  Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation , 2017, 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

[36]  Bo Jin,et al.  Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.

[37]  Stéfano F. STEFENON,et al.  REAL TIME AUTOMATED DIAGNOSIS OF INSULATING SYSTEM EMPLOYNG ULTRASOUND INSPECTION , 2015 .

[38]  Leandro dos Santos Coelho,et al.  Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines , 2017 .

[39]  Yu Cheng,et al.  Early Fault Detection Approach With Deep Architectures , 2018, IEEE Transactions on Instrumentation and Measurement.

[40]  M. A. Abido,et al.  Power Quality Classification Using Neuro Fuzzy Logic Inference System , 2017, 2017 9th IEEE-GCC Conference and Exhibition (GCCCE).

[41]  Ademir Nied,et al.  Fault diagnosis of insulators from ultrasound detection using neural networks , 2019, J. Intell. Fuzzy Syst..

[42]  Aoife Foley,et al.  Random Forest Based Approach for Concept Drift Handling , 2016, AIST.

[43]  Pravat K. Rout,et al.  Fault Detection and Classification Using Wavelet Transform and Neuro Fuzzy System , 2018, 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT).

[44]  Honglei Deng,et al.  Ultrasonic guided wave-based detection of composite insulator debonding , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[45]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[46]  Chih-Min Lin,et al.  Wavelet-TSK-Type Fuzzy Cerebellar Model Neural Network for Uncertain Nonlinear Systems , 2019, IEEE Transactions on Fuzzy Systems.

[47]  Jong Rak Yoon,et al.  Acoustic Fault Detection Technique for High-Power Insulators , 2017, IEEE Transactions on Industrial Electronics.

[48]  Roohollah Fadaeinedjad,et al.  Generator Coherency and Network Partitioning for Dynamic Equivalencing Using Subtractive Clustering Algorithm , 2018, IEEE Systems Journal.

[49]  Johan A. K. Suykens,et al.  Artificial neural networks for modelling and control of non-linear systems , 1995 .

[50]  Ranran Li,et al.  Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model , 2019, Energies.

[51]  Luiz Henrique Meyer,et al.  Diagnostic of Insulators of Conventional Grid Through LabVIEW Analysis of FFT Signal Generated from Ultrasound Detector , 2017, IEEE Latin America Transactions.

[52]  Roberto Zanetti Freire,et al.  Optimized Ensemble Extreme Learning Machine for Classification of Electrical Insulators Conditions , 2020, IEEE Transactions on Industrial Electronics.

[53]  Jiao Cheng,et al.  Prediction of industrial wastewater quality parameters based on wavelet de-noised ANFIS model , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).