Design of ANFIS for Hydrophobicity Classification of Polymeric Insulators with Two-Stage Feature Reduction Technique and Its Field Deployment

Abstract

Hydrophobicity of polymeric insulator plays a vital role in determining the insulation quality in outdoor overhead electrical transmission and distribution lines. Loss of hydrophobicity increases the leakage current and leads to flashover. Monitoring hydrophobicity becomes a fundamental requirement to ensure continuity of power line operations. Hydrophobicity of polymeric insulator is classified according to STRI (Swedish Transmission Research Institute) guidelines. This paper proposes an intelligent ANFIS (Adaptive Neuro-Fuzzy Inference System) based classifier to determine the hydrophobicity quality using the digital image of the insulator. Ten statistical features are extracted from the digital images. Two stages of feature reduction are employed to reduce the number of features. Pre-design stage uses PCA (Principal Component Analysis) and reduces the number of features to six from ten and the post-design stage analyzes the accumulation effect to reduce the number of features to four. Various ANFIS classifiers are trained using these reduced features extracted from the image. The performance of these ANFIS classifiers is evaluated in both field and laboratory specimens. Results indicate classification accuracy of 96.4% and 93.3% during the training and testing phase when triangular membership function with linear output function is employed in ANFIS. A GUI (Graphical User Interface) has also been designed to facilitate the use of the proposed system by field operators.

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

“…Initially, the model quality is determined using a fuzzy inference system (FIS), which is capable of representing human knowledge efficiently in the form of a rule‐base 30 . The FIS uses data quality ( ) and model fit percentage ( ) as inputs to determine the model quality.…”

“…Initially, the model quality is determined using a fuzzy inference system (FIS), which is capable of representing human knowledge efficiently in the form of a rule-base. 30 The FIS uses data quality (Q d ) and model fit percentage (Q fp ) as inputs to determine the model quality. The data quality is a manually evaluated metric that describes the severity of manual interventions and the efficiency of the acquired data in describing the HE dynamics.…”

Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

“…Statistical features are extracted from the grey‐scaled image. Various statistical features including fractal dimension, standard deviation, entropy, maximum intensity, kurtosis, skewness, variance, homogeneity, contrast, correlation and energy [15] are widely used. Grey‐scale intensity level is the vital parameter to determine the quality of these features, which depends on the ambient light.…”

Hydrophobicity classification of RTV silicone rubber-coated insulators using deep learning algorithms

“…The back propagation neural network (BPNN) was used as a classifier and achieved 96.6% accuracy. Principal component analysis (PCA) was used to reduce the number of features along with the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in [34]. They found that the best features selected by PCA are average intensity, correlation, skewness, and homogeneity, with a classification accuracy of 94.85%.…”