Load frequency control of autonomous power system using adaptive fuzzy based PID controller optimized on improved sine cosine algorithm

• Published in: Journal of ambient intelligence and humanized computing 2019-06, Vol.10 (6), p.2361-2373
• Main Authors: Rajesh, K. S., Dash, S. S.
• Format: Article
• Language: English
• Subjects: Adaptive control; Adaptive systems; Algorithms; Alternative energy sources; Artificial Intelligence; Computational Intelligence; Controllers; Diesel fuels; Dynamic stability; Eigenvalues; Electrical loads; Energy industry; Energy resources; Energy storage; Engineering; Exploitation; Flywheels; Fossil fuels; Frequency control; Fuel cells; Fuzzy control; Fuzzy logic; Hybrid systems; Mathematical functions; Optimization techniques; Original Research; Parameter robustness; Parameter sensitivity; Pattern search; Proportional integral derivative; Renewable resources; Robotics and Automation; Robust control; Sensitivity analysis; Stability analysis; Systems design; Trigonometric functions; Turbogenerators; User Interfaces and Human Computer Interaction; Wind turbines
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-018-0834-z

An autonomous power generation system contains numerous autonomous generation units like diesel energy generator, solar photovoltaic units, wind turbine generator, fuel cells along with energy storing units such as the flywheel energy storage system and battery energy storage system. These renewable sources are typically varying in nature. Therefore, the system components either run at lower/higher power output or may turned on/off at different instant of their operation. Due to the above mentioned uncertainties, the conventional controllers are not able to provide desired performance under varied operating conditions. Owing to this challenge, this paper proposes an adaptive fuzzy logic PID controller (AFPID) optimized by improved sine cosine algorithm (ISCA) for the load frequency control (LFC) of an autonomous power generation system. Proposed ISCA algorithm is evaluated using standard test functions and compared with original sine cosine algorithm (SCA) to authenticate the competence of algorithm. It is found from the statistical results that the proposed ISCA algorithm outperform original SCA, Hybrid Improved Firefly-Pattern Search, gravitational search, and grey wolf optimization algorithms. The proposed AFPID controller optimized by ISCA is used for the load frequency control of the autonomous power generating system. The results show that the ISCA tuned AFPID controller has superior performance over conventional PID controller. The proposed AFPID controller is again examined by the sensitivity analysis by introducing different hybrid power system parameters and the robustness of the control approach with the dynamic change of power system parameters is evaluated. Finally, the stability of the proposed control system is tested using Eigen value analysis.