Hybrid ANN for Transformer Fault Detection

In "Hybrid ANN for Transformer Fault Detection", author K. Balan presents a comprehensive study of the use of machine learning and neural networks for detecting faults in power transformers. Transformers are a critical component of power systems, and their failure can have significant economic and safety consequences.

The book focuses on the use of a hybrid artificial neural network (ANN) model for transformer fault detection, which combines the strengths of supervised and unsupervised learning techniques. Balan provides a detailed overview of the process of data analysis, feature extraction, and pattern recognition used in the model, as well as the use of time-frequency analysis and signal processing techniques.

One of the key topics in the book is the detection of inrush current and fault current, which are two common types of electrical faults in power transformers. Balan explains how the hybrid ANN model can accurately distinguish between these two types of faults, leading to more efficient and effective fault diagnosis.

The book also covers other important aspects of transformer fault detection, including fault classification, fault identification, fault localization, and fault mitigation. Balan discusses the use of fault simulation and testing, as well as the importance of condition monitoring and predictive maintenance for improving transformer reliability and performance.

Overall, "Hybrid ANN for Transformer Fault Detection" is an essential resource for electrical engineers, power system professionals, and researchers interested in the application of machine learning and neural networks to transformer fault detection. It provides a thorough overview of the latest techniques and methods for detecting and mitigating faults in power transformers, and offers insights and recommendations for improving transformer performance and reliability.