Description
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
Table of Contents
List of Contributors
1 Faults Diagnosis Using AI and ML
HAMEED KHAN, KAMAL KUMAR KUSHWAH, PRADEEP KUMAR JHINGE, GIREESH GAURAV SONI, RAVI KANT CHOUBEY, AND RUPESH KUSHWAH
2 Load Frequency Control for Multi-Area Power System Using PSO-Based Technique
SIDDHARTH SHUKLA, AMIT GUPTA, AND RUCHI PANDEY
3 Power Quality Enhancement by PV-UPQC for Non-Linear Load
EKNATH BORKAR AND NAGENDRA SINGH
4 A Hybrid Energy Management for Stand-Alone Microgrids Using Grey Wolf Optimization System
SIDDHARTH SHUKLA AND AMIT GUPTA
5 Energy Management of Nanogrid through Flair of Deep Learning from IoT Environments
VANDANA SONDHIYA, KAUSTUBH DWIVEDI, SHEKH KULSUM ALMAS, AND NAGENDRA SINGH
6 An Elitism-Based SAMP-JAYA Algorithm for Optimal VA Loading of Unified Power Quality Conditioner
SWATI GADE AND RAHUL AGRAWAL
7 Applications of Artificial Intelligence
ANUPRITA MISHRA AND ANITA SONI
8 Role of Artificial Intelligence and Machine Learning in Power Systems with Fault Detection and Diagnosis
ANJALI NIGHOSKAR, SHIVANI GAUTAM, AND KAMINI LAMBA
9 AC Power Optimization Technique
ANITA SONI AND ANUPRITA MISHRA
10 Data Transformation: A Preprocessing Stage in Machine Learning Regression Problems
AKSHAY JADHAV, DEVASHISH DHAULAKHANDI, SHISHIR KUMAR SHANDILYA, LOKESH MALVIYA, AND ARVIND MEWADA
11 Predicting Native Language with Machine Learning: An Automated Approach
DASANGAM VENKAT NIKHIL, RUPESH KUMAR DEWANG, BUVANEISH SUNDAR, AYUSH AGRAWAL, ANANTA NARAYAN SHRESTHA, AKASH TIWARI, AND ARVIND MEWADA
12 Artificial Intelligence and Machine Learning Techniques in Power Systems Automation
AMAR NAYAK AND RACHANA KAMBLE
Index
