Springer Applications of Artificial Intelligence Techniques in Engineering: SIGMA 2018, Volume 1: 698 (Advances in Intelligent Systems and Computing, 698)

Product Description Chapter 1: Annual Energy Savings with Multiple DG and D-STATCOM Allocation using PSO in DNO Operated Distribution Network.- Chapter 2: Optimized 2DOF PID for AGC of Multi Area Power System using Dragon-Fly.- Chapter 3:Wide Area Monitoring System using Integer Linear Programming.- Chapter 4: Fault Classification and Faulty Phase Selection Using Symmetrical Components of Reactive Power for EHV Transmission Line.- Chapter 5: Optimal Bidding Strategy in Deregulated Power Market Using Krill Herd Algorithm.- Chapter 6: A Novel Intelligent Bifurcation Classification Model Based on Artificial Neural Network (ANN).- Chapter 7: Teaching Learning Based Optimization for Frequency Regulation in Two Area Thermal-Solar Hybrid Power System.- Chapter 8: An Approach to Minimize the Transmission Loss and Improves the Voltage Profile of Load Bus Using Interline Power Flow Controller (IPFC).- Chapter 9: A Novel Intelligent Transmission Line Fault Diagnosis Model Based on EEMD And Multiclass PSVM. From the Back Cover The book is a collection of high-quality, peer-reviewed innovative research papers from the International Conference on Signals, Machines and Automation (SIGMA 2018) held at Netaji Subhas Institute of Technology (NSIT), Delhi, India. The conference offered researchers from academic and industry the opportunity to present their original work and exchange ideas, information, techniques and applications in the field of computational intelligence, artificial intelligence and machine intelligence. The book is divided into two volumes discussing a wide variety of industrial, engineering and scientific applications of the emerging techniques.  About the Author Mr. Hasmat Malik is an Assistant Professor of Electrical Engineering at the Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), Delhi, India. He is a fellow (M.Tech.) of the National Institute of Technology (NIT), Hamirpur and is pursuing his Ph.D. in the field of power systems at the Department of Electrical Engineering, Indian Institute of Technology (IIT) Delhi. He has published several research papers in leading international journals and conference proceedings, and presented papers at several prestigious academic conferences such as IEEE, Elsevier, etc. His major areas of interest are condition monitoring and fault diagnosis (CMFD), noise and vibration analysis, signal processing of power systems & machines, intelligent techniques for condition monitoring and control of power systems and power quality studies, renewable energy and high voltage engineering. Prof. Smriti Srivastava received her B.E. degree in Electrical Engineering and M.Tech. degree in Heavy Electrical Equipment from Maulana Azad College of Technology [now Maulana Azad National Institute of Technology (MANIT)], Bhopal, India, in 1987 and 1991, respectively, and her Ph.D. degree in Intelligent Control from the Indian Institute of Technology, New Delhi, India, in 2005. From 1988 to 1991, she was a faculty member at MANIT, and since August 1991, she has been with the Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, New Delhi, India, where she has worked as a professor at the Instrumentation and Control Engineering Division at NSIT, New Delhi since 2008, and is Dean of Undergraduate Studies. She also worked as Associate Head of Instrumentation and Control Engineering Division at NSIT, New Delhi from April 2004 to November 2007 and from September 2008 to December 2011. She is the author of a number of publications in transactions, journals and conferences in the areas of neural networks, fuzzy logic, and control systems. She has given a number of invited talks and tutorials, mostly in the area of fuzzy logic, process control, and neural networks. Her current research interests include neural networks, fuzzy logic, and hybrid methods in modeling, and