This text discusses sensitivity parametric analysis for the single tuned filter parameters and presents an optimization-based method for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. It also highlights the importance of artificial intelligence techniques such as water cycle algorithms in solving power quality problems such as over-voltage and harmonic distortion. Features: Presents a sensitivity parametric analysis for the single tuned filter parameters. Discusses optimization-based methods for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. Highlights the importance of artificial intelligence techniques (water cycle algorithm) for solving power quality problems such as over-voltage and harmonic distortion. Showcases a procedure for harmonic mitigation in active distribution systems using the single tuned harmonic filters. Helps in learning how to determine the optimal planning of the single tuned filters to mitigate the harmonic distortion in distorted systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, Power systems planning and analysis.

This text discusses sensitivity parametric analysis for the single tuned filter parameters and presents an optimization-based method for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. It also highlights the importance of artificial intelligence techniques such as water cycle algorithms in solving power quality problems such as over-voltage and harmonic distortion. Features: Presents a sensitivity parametric analysis for the single tuned filter parameters. Discusses optimization-based methods for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. Highlights the importance of artificial intelligence techniques (water cycle algorithm) for solving power quality problems such as over-voltage and harmonic distortion. Showcases a procedure for harmonic mitigation in active distribution systems using the single tuned harmonic filters. Helps in learning how to determine the optimal planning of the single tuned filters to mitigate the harmonic distortion in distorted systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, Power systems planning and analysis.

This book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India, during September 5–6, 2020. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers selected papers in the area of computer vision. This book covers new tools and technologies in some of the important areas of medical science like histopathological image analysis, cancer taxonomy, use of deep learning architecture in dental care, and many more. Furthermore, this book reviews and discusses the use of intelligent learning-based algorithms for increasing the productivity in agricultural domain.

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties. Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering. Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies. Highlights include: Power quality enhancement by PV-UPQC for non-linear load Energy management of a nanogrid through flair of deep learning from IoT environments Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis AC power optimization techniques Artificial intelligence and machine learning techniques in power systems automation

This proceeding discuss the latest solutions, scientific findings and methods for solving intriguing problems in the fields of data mining, computational intelligence, big data analytics, and soft computing. This gathers outstanding papers from the fifth International Conference on “Computational Intelligence in Data Mining” (ICCIDM), and offer a “sneak preview” of the strengths and weaknesses of trending applications, together with exciting advances in computational intelligence, data mining, and related fields.

This book provides a brief insight of various challenges and its mitigation techniques in microgrid due to power quality (PQ) issues. The central concept of this book revolves around the PQ issues in microgrid. The main objective of this book is to make aware of the power and control engineers with different innovative techniques to mitigate the challenges due to PQ issues in microgrid. The topics covered in this book are PQ disturbances in microgrid and different recent and innovative schemes to mitigate them. The book emphasizes technical issues, theoretical background, and practical applications that drive postgraduates, researchers, and practicing engineers with right advanced skills, vision, and knowledge in finding microgrid power quality issues, various technical challenges and providing mitigation techniques for the future sustainable microgrids.

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. - Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems - Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms - Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub