Artificial Intelligence for Renewable Energy Systems

Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman, Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman

• Publisher: Wiley-Scrivener
• Year: 2022
• Print ISBN: 9781119761693
• eBook ISBN: 9781119761716
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS

Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.

Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business.

Audience

The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

1
Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation

Arif Iqbal¹* and Girish Kumar Singh²

¹Department of Electrical Engineering, Rajkiya Engineering College Ambedkar Nagar, Akbarpur, India

²Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India

Abstract

Owing to meet the incremental need of energy with exhaustion of fossil fuel in few upcoming years, renewable power generation has emerged as a potential and permanent solution in present scenario. In this regard, research has diverted toward exploration and development of various new techniques of renewable power generation for last few decades, and various systems have been adopted in both isolated and grid connected modes. Among various available options (solar, wind, biomass, tidal, etc.), the wind power generation system has a major market share due to its pollution-free operation together with its economic viability and mature technology. Presently, wind power generation system is increasing exponentially, particularly in on-shore sites of India and European subcontinents. Wind power generation system works on a successful operation and coordination of various parts, where an electric generator is an important component. Hence, the selection of suitable electrical machine (as generator) is of paramount importance for reliable operation of complete wind power generation system. Conventionally, three-phase electrical machine is employed. But, in last two decades, the multiphase (more than three-phase) machine is replacing the conventional one. This is because of various inherent potential advantageous features present in multiphase machines, when compared with its three-phase equivalent. This includes the elimination of lower order space, resulting in lower torque pulsation, enhanced power handling capability in the same frame (approximately 175%), and higher degree of freedom with improved reliability. Hence, multiphase machines have to be explored and investigated in various operational aspects for power generation. In this chapter, a six-phase synchronous machine is selected as a potential option as generator in grid connected mode for wind power generation system. An exhaustive dynamic analysis has been presented during various working conditions. Moreover, generator has been further investigated under steady state with the inclusion of small disturbance (i.e., small signal stability) through linearized model using dq0 approach. Linearized model was used to determine the absolute stability using eigenvalue criteria wherein, the effect of parametric variation is presented, related with both stator and rotor side. It was noted that the stability of generator operation can be enhanced with increased values of stator resistance. On rotor side, with higher value of leakage reactance of field winding circuit and/or by increased resistance of damper winding along q axis.

Keywords: Wind power generation, six-phase synchronous generator, small-signal stability, dynamic analysis

1.1 Introduction

The development of human civilization resulted in a tremendous demand of electrical power with a fear of f...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation
• 2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
• 3 Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network
• 4 Artificial Intelligence for Modeling and Optimization of the Biogas Production
• 5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression
• 6 Deep Learning Algorithms for Wind Forecasting: An Overview
• 7 Deep Feature Selection for Wind Forecasting-I
• 8 Deep Feature Selection for Wind Forecasting-II
• 9 Data Falsification Detection in AMI: A Secure Perspective Analysis
• 10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques
• 11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy
• Index
• End User License Agreement