This book highlights an intelligent framework for detecting and classifying partial discharge in solid insulation, a quiet but persistent threat to the reliability of high-voltage power systems. It presents the complete diagnostic pipeline, from capturing discharge signals with a custom, low-cost Rogowski coil sensor to characterising their phase-resolved patterns and distilling them into informative features through statistical analysis and principal component analysis. At its core lies a hybrid classifier that pairs a feedforward backpropagation neural network with a genetic algorithm, improving accuracy, efficiency, and generalisation beyond conventional methods. Bridging high-voltage insulation engineering and machine learning, the book explains specialist concepts in accessible terms while preserving technical rigour, offering researchers, graduate students, and practising engineers a practical route toward smarter insulation diagnostics.

Ir. Dr. Mohamad Kamarol Mohd Jamil obtained his D.Eng. from Kyushu Institute of Technology, Japan. His research interests include the insulation properties in oil palm, solid dielectric material, insulation properties of environmentally benign gas, and PD detection technique for insulation diagnosis of power apparatus and electrical machine. He is also involved in temperature rise and short-circuit electromagnetic study of busbar system and HVDC system.

Dr. Norfadilah Rosle received her Ph.D. in Electrical Engineering from Universiti Sains Malaysia, Penang. Her research interest is partial discharge detection and measurement on solid insulation, signal processing and artificial intelligence. She is now working as a senior lecturer in Universiti Malaysia Perlis, Malaysia.

Dr. Mohamad Nur Khairul Hafizi Rohani obtained his Ph.D from the University Malaysia Perlis. His research interest is partial discharge detection and measurement on solid and liquid insulation, signal and image processing and partial discharge sensor development for high voltage equipment applications.