Evolutionary Algorithms based AGA and BPSO Computing for Hybrid Renewable Energy System
Sampath Kumar V. Patil1, S. B. Shiva kumar2
1Mr. Sampath kumar V. Patil, Pursuing Ph.D, Vishweshwaraya Technological university, Belagavi, Karnataka, India.
2Dr. S. B. Shivakuar, Head, Department in RYMEC, Bellary, Karnataka, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3348-3352 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5716018520/2020©BEIESP | DOI: 10.35940/ijrte.E5716.018520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Solar and wind Renewable system have more advantages compared to Conventional energy sources and it is environment friendly and also from economic point of view…For overcoming the problems of Conventional Energy sources, this paper proposed an hybrid renewable energy system using Different evolutionary algorithm approaches. This proposed method hybrids the three systems namely, solar PV, wind and Fuel Cell which have the biggest potential to provide power. The battery and loads are also integrated with this hybrid system. Then, the charging and discharging of this system is controlled by an Optimized PID controller for balancing the generated power and load power. Here, the PID controller works based on the rules to provide an efficient system. Also, the tuning parameters of the PID controller are enhanced by Adaptive Genetic Algorithm and Particle Swarm Optimization. Finally, the performance of the Combined Solar and wind system is tunedwith the Evolutionary algorithm based PID controller and is compared with the existing hybrid renewable systems based on Generated Power, Load side Power, Speed Controller and Torque.
Keywords: Hybrid Renewable Energy system, Solar, Wind, Fuel Cell, Adaptive Genetic Algorithm, Particle Swam Optimization, PID Controller.
Scope of the Article: Data Analytics Modelling and Algorithms.