Modified sine cosine algorithm-based fuzzy-aided PID controller for automatic generation control of multiarea power systems

• Published in: Soft computing (Berlin, Germany) Germany), 2020-09, Vol.24 (17), p.12919-12936
• Main Authors: Sahu, P. C., Prusty, R. C., Sahoo, B. K.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automatic control; Computational Intelligence; Control; Controllers; Engineering; Fuzzy control; Fuzzy logic; Genetic algorithms; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Optimization techniques; Particle swarm optimization; Proportional integral derivative; Robotics; Robust control; Trigonometric functions
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-020-04716-y

The research paper presents implementation of a fuzzy rule and membership function-based fuzzy-aided PID controller for automatic generation control (AGC) in multiarea nonlinear power system. At the initial stage of this proposed work, a three-area nine-unit installed interconnected network is considered for developing different dynamic responses in response to AGC analysis. A modified approach named modified sine cosine algorithm (M-SCA) is proposed for tuning the gain parameters of the above-proposed fuzzy controller to produce close optimum gain values. The proposed modified algorithm is developed from its original sine cosine algorithm by improving and updating few equations which is capable of making the balance between exploration and exploitation levels of this algorithm and improving the updating quality of iteration. To impose supremacy of M-SCA technique, it is examined through convergence curves and its performance is compared with host sine cosine algorithm, genetic algorithm, and particle swarm optimization algorithm. For controller supremacy analysis, the performance of the proposed fuzzy-aided PID controller is compared with conventional I, PI, and PID controllers, and it has been revealed that proposed M-SCA-tuned fuzzy-aided PID controller exhibits better performances through different deviated responses for AGC analysis. To demonstrate most standard and supremacy of proposed approaches, finally these are tested through a five-area ten-unit system considering some physical nonlinear constraints like generation rate constraint, governor dead band, boiler dynamics and time delay. At the final observation level, the proposed fuzzy controller has gone through different sensitivity analyses with variation of different system parametric conditions and different load conditions.