Optimal design of full order state observer for active surge control in centrifugal compressors using genetic algorithm

Mohammed, Salisu; Sha’aban, Yusuf A.; Umoh, Ime J.; Salawudeen, Ahmed Tijani

doi:10.12928/telkomnika.v20i2.17440

Cited by 2 publications

(1 citation statement)

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“…The coding process can easily handle multiple modal types of parameter optimization, which is rather challenging to handle with a classical optimization method [ 30 ]. The GA can substantially run for a reasonable number of iterations and terminates with desired chosen ending conditions such as fitness convergence when individuals meet target fitness, the maximum number of generation limits and the maximum number of stall generations [ 31 ]. In SOS, the natural interaction for survival between symbiotic organisms within an ecosystem is adapted in the search for an optimal solution to the optimization problem [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

Mohammed¹,

Sha’aban

Umoh

et al. 2023

PLoS ONE

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This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

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Section: Introductionmentioning

confidence: 99%