Solar Photovoltaic Power Plants

1. Front Matter

   Pages i-xvii

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2. Adaptive Control Techniques for Three-Phase Grid-Connected Photovoltaic Inverters

   • Wanshi Hong, Gang Tao, Hong Wang

   Pages 1-24

3. Application of Sliding-Mode Control for Maximum Power Point Tracking of PV Systems

   • M. R. Mojallizadeh, M. A. Badamchizadeh

   Pages 25-43

4. Predictive Control of Four-Leg Converters for Photovoltaic Energy Systems

   • Venkata Yaramasu, Marco Rivera, Apparao Dekka, Jose Rodriguez

   Pages 45-69

5. A Novel Maximum Power Point Tracking Method for Photovoltaic Application Using Secant Incremental Gradient Based on Newton Raphson

   • Saber Arabi Nowdeh, Mohammad Jafar Hadidian Moghaddam, Manoochehr Babanezhad, Iraj Faraji Davoodkhani, Akhtar Kalam, Abdollah Ahmadi et al.

   Pages 71-96

6. Study on Control of Hybrid Photovoltaic-Wind Power System Using Xilinx System Generator

   • Nadjwa Chettibi, Adel Mellit

   Pages 97-120

7. Artificial Intelligence for Photovoltaic Systems

   • Rami Ghannam, Paulo Valente Klaine, Muhammad Imran

   Pages 121-142

8. Applications of Improved Versions of Fuzzy Logic Based Maximum Power Point Tracking for Controlling Photovoltaic Systems

   • R. Boukenoui, A. Mellit

   Pages 143-164

9. A New Method for Generating Short-Term Power Forecasting Based on Artificial Neural Networks and Optimization Methods for Solar Photovoltaic Power Plants

   • Tugce Demirdelen, Inayet Ozge Aksu, Burak Esenboga, Kemal Aygul, Firat Ekinci, Mehmet Bilgili

   Pages 165-189

10. Evaluation on Training Algorithms of Back Propagation Neural Network for a Solar Photovoltaic Based DSTATCOM System

    • Nor Hanisah Baharudin, Tunku Muhammad Nizar Tunku Mansur, Rosnazri Ali, Muhammad Irwanto Misrun

    Pages 191-207

11. Power Extraction from PV Module Using Hybrid ANFIS Controller

    • Tata Venkat Dixit, Anamika Yadav, S. Gupta, Almoataz Y. Abdelaziz

    Pages 209-232

12. An Online Self Recurrent Direct Adaptive Neuro-Fuzzy Wavelet Based Control of Photovoltaic Systems

    • Syed Zulqadar Hassan, Tariq Kamal, Sidra Mumtaz, Laiq Khan

    Pages 233-250

This book discusses control and optimization techniques in the broadest sense, covering new theoretical results and the applications of newly developed methods for PV systems. Going beyond classical control techniques, it promotes the use of more efficient control and optimization strategies based on linearized models and purely continuous (or discrete) models. These new strategies not only enhance the performance of the PV systems, but also decrease the cost per kilowatt-hour generated. 

• Type-I and Type-II fuzzy control
• Fuzzy sliding control
• Fuzzy coupled hopfield neural network
• Adaptive fuzzy sliding control
• Double-integral sliding-mode control
• Second-order sliding-mode control
• adaptive neuro-fuzzy control
• Model predictive neuro-fuzzy control
• Optimizing PV plants

• Faculty of Automation and Computers, Politehnica University of Timișoara, Timișoara, Romania

  Radu-Emil Precup

• Department of Electrical and Electronics Engineering, Sakarya University, Serdivan/Sakarya, Turkey

  Tariq Kamal

• School of Electrical Engineering, Chongqing University, Chongqing, China

  Syed Zulqadar Hassan

Radu-Emil Precup received his Dipl.Ing. degree in Automation and Computers from the Traian Vuia Polytechnic Institute of Timisoara, Romania, in 1987, his diploma in Mathematics from the West University of Timisoara in 1993, and his Ph.D. degree in Automated Systems from the Politehnica University of Timisoara in 1996, where he is currently working.  He has been an Editor-in-Chief of the International Journal of Artificial Intelligence since 2008 and also serves on the editorial boards of several other prestigious journals including Applied Soft Computing (Elsevier), Evolving Systems (Springer) and Cogent Engineering (Taylor & Francis). He is the author/coauthor of more than 300 papers published in various scientific journals, conference proceedings, and book chapters. His research interests mainly include the development and analysis of new control structures and algorithms, theory and applications of soft computing, computer-aided design of control systems,and applications to mechatronic systems (including automotive systems and mobile robots). Among other achievements, he was listed as one of the top 10 researchers in Artificial Intelligence and Automation (IIoT World July 2017).


Tariq Kamal holds doctorates in Energy and Sustainability Engineering from the University of Cadiz, Spain; and in Electrical Power and Control Engineering from Sakarya University, Turkey. His research interests include advanced intelligent control techniques for power systems; hybrid renewable energy systems; plug-in hybrid electric vehicles and their integration in power systems; distributed generation; smart grid applications; and the application of signal processing in power systems. Currently, he is working with the research group on Electrical Technologies for Sustainable and Renewable Energy, University of Cadiz, Spain. He is the author/co-author of more than 50 papers published in various international scientific journals and conference proceedings. He has been involved in major joint projects around the globe, including Spain, Turkey, Australia, China, the UK, Pakistan and Estonia, and has been a regular reviewer for many leading international journals, including IEEE Transactions on Industrial Electronics, IEEE access, and Applied Energy.


Syed Zulqadar Hassan received his Ph.D. degree in Electrical Engineering from Chongqing University, China. His research interests include fuzzy logic, neural networks, optimal control, adaptive control, integration of renewable energies, smart grids and the application of signal processing (wavelets) in power systems. He has contributed more than 45 publications in the field of Electrical Engineering, and frequently serves as a reviewer for IEEE journals and international conferences. 

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