Monitoring and Control of Electrical Power Systems using Machine Learning Techniques 1st Edition

Discover Monitoring and Control of Electrical Power Systems using Machine Learning Techniques, 1st Edition — a practical, forward-looking guide by Emilio Barocio Espejo, Felix Rafael Segundo Sevilla, and Petr Korba. This essential resource bridges electrical engineering and data science, showing how machine learning transforms power system monitoring, fault detection, load forecasting, and control for modern grids.

Clear, well-structured chapters walk readers through advanced algorithms, sensor data interpretation, and real-world applications that improve reliability and efficiency across transmission and distribution networks. Emphasizing actionable insights, the book covers supervised and unsupervised learning, neural networks, anomaly detection, and predictive maintenance strategies tailored to smart grids, renewable integration, and microgrid operation.

Engineers, grid operators, researchers, and graduate students will appreciate the book’s focus on practical problem-solving: reducing downtime, enhancing situational awareness, and optimizing energy flow in regional and international power systems—from North America and Europe to Asia-Pacific and Latin America. Case studies and comparative analyses highlight how machine learning techniques can be deployed in diverse regulatory and climatic contexts.

Whether you’re modernizing utility infrastructure or advancing academic research, this authoritative first edition equips you with the methodologies and technical clarity needed to implement data-driven monitoring and control solutions. Stay ahead in the evolving energy landscape with a title that combines rigorous theory and pragmatic application.

Order your copy of Monitoring and Control of Electrical Power Systems using Machine Learning Techniques today and begin transforming grid performance with cutting-edge machine learning expertise.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.