Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

• Published in: Mathematics (Basel) Vol. 9; no. 7; p. 712
• Main Authors: Gupta, Deepak Kumar, Soni, Ankit Kumar, Jha, Amitkumar V., Mishra, Sunil Kumar, Appasani, Bhargav, Srinivasulu, Avireni, Bizon, Nicu, Thounthong, Phatiphat
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2021
• Subjects: automatic generation control; Business metrics; Case studies; Control systems design; Controllers; Frequency control; Genetic algorithms; Heuristic methods; Integrals; interconnected power system; load frequency control; Mathematics; multisource power system; Optimization techniques; Particle swarm optimization; Performance evaluation; Performance measurement; Search algorithms; Smart grid technology; Test systems
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9070712

The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).