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

Featured Application This work describes the design of an intelligent tool for hydrophobicity classification of polymeric insulators used in electrical transmission lines. It eliminates the manual inspection of insulators, which involves a significant amount of labor work load. This automated tool can be integrated to an unmanned aerial vehicle to provide autonomous inspection of insulators. 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.