article · Fractal and Fractional
Balancing power generation with fluctuating consumer demand is vital for maintaining system security and stability across multi-area power networks. A proportional-integral-derivative controller tuned using an artificial rabbits algorithm addresses load frequency control in interconnected power grids, specifically two-area non-reheat thermal systems. The controller incorporates a derivative filter designed to suppress accompanying signal noise. Evaluated through time-domain simulations, the tuning method minimises the integral time-multiplied absolute error across three distinct operational disturbance scenarios. Comparative evaluations demonstrate that this optimisation approach outperforms established benchmarks, including particle swarm optimisation, differential evolution, and variants of the JAYA algorithm. The largest gains occur during simultaneous disturbances across multiple network areas, where error reductions reach up to sixty percent compared to alternative optimisation techniques.
Electrical grids require rapid, precise control to keep frequency steady as consumer demand shifts unpredictably. Failure to balance generation and load can compromise grid dependability and equipment safety. Demonstrating that nature-inspired optimisation can improve controller tuning under complex disturbance conditions provides electrical engineers with practical strategies to boost power system resilience and power delivery quality.
This simulation-based control method targets power generation companies and grid operators managing interconnected thermal networks. By improving frequency stabilisation during unexpected load fluctuations, the algorithm could be integrated into automated generation control software. Because the findings rely entirely on time-domain computer simulations without physical hardware or field validation, the technology is at an early research stage, requiring real-time testing on physical power testbeds before industrial deployment.
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A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller’s design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA.
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DOI: 10.3390/fractalfract7010097
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