Abstract
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).
Highlights
A power network generally comprises several areas or power systems, interconnected through tie-lines
We have considered the plus integral (PI) controller for a load frequency control (LFC) analysis of the hydrothermal power system
To understand the efficacy of this algorithm, the results are compared with particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA), and firefly algorithm (FA), which can be used as a benchmark for performance evaluation
Summary
A power network generally comprises several areas or power systems, interconnected through tie-lines. Distribution systems, transmission lines, and generation systems that may include renewable energy sources are some of the prime constituents of the power network [1]. The real-time integration of these components and their operation in the dynamic environment cause differences in the active and reactive power demands. The variations in these quantities produce undesired oscillations in the system. For a power system comprising multiple areas, the issue of optimal generation and distribution has been addressed in [5]. Researchers have modernized the power system using smart grid technologies with the objective to make the power system reliable, resilient, secure, and stable [7,8,9,10]
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