Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm

• Published in: IEEE/CAA journal of automatica sinica Vol. 6; no. 2; pp. 503 - 515
• Main Authors: Jagatheesan, K., Anand, B., Samanta, Sourav, Dey, Nilanjan, Ashour, Amira S., Balas, Valentina E.
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Automatic control; Automatic generation control; Control systems design; Controllers; Genetic algorithms; Heuristic methods; Optimization; Particle swarm optimization; Perturbation; Power consumption; Power efficiency; Power flow; Power system stability; Proportional integral derivative; Tuning; Turbines
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2017.7510436

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control &#x0028 AGC &#x0029 plays a crucial role. In this paper, multi-area &#x0028 Five areas: area 1, area 2, area 3, area 4 and area 5 &#x0029 reheat thermal power systems are considered with proportional-integral-derivative &#x0028 PID &#x0029 controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm &#x0028 FFA &#x0029. The experimental results demonstrated the comparison of the proposed system performance &#x0028 FFA-PID &#x0029 with optimized PID controller based genetic algorithm &#x0028 GA-PID &#x0029 and particle swarm optimization &#x0028 PSO &#x0029 technique &#x0028 PSO-PID &#x0029 for the same investigated power system. The results proved the efficiency of employing the integral time absolute error &#x0028 ITAE &#x0029 cost function with one percent step load perturbation &#x0028 1 &#x0025 SLP &#x0029 in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot &#x002F undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.