Smart deployment of IoT-TelosB service care StreamRobot using software-defined reliability optimisation design

Okafor, K. C.; Longe, Omowunmi Mary

doi:10.1016/j.heliyon.2022.e09634

Cited by 9 publications

(3 citation statements)

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“…Here, an IEEE standard four-machine network is selected to test the stabilizers mentioned above, and the types of stabilizers in question are applied to the system machines according to Figures 5 and 6 [63], [66]. To demonstrate the effectiveness of these optimization methods for improving power system performance, the proposed technique was tested on a standard two-area, four-machine power system.…”

Section: Resultsmentioning

confidence: 99%

“…A huge amount of literature is available on non-traditional optimization tools. These methods include, genetic programming (GP) [55], evolution strategies (ES) [56], differential evolution (DE) [56], cultural algorithm (CA) [57], evolutionary programming (EP) [57], whale optimization algorithm (WOA) [24], grasshopper optimization algorithm (GOA) [58], kidney-inspired algorithm (KA) [58], salp swarm algorithm (SSA) [58], sine cosine algorithm (SCA) [59], bat algorithm (BA) [59], general relativity search algorithm (GRSA) [60], farmland fertility algorithm (FFA) [61], artificial bee colony (ABC) [62], cuckoo search optimization (CSO) [63], interior search algorithm (ISA) [63], teaching-learning-based optimization (TLBO) [64], harmony search (HS) [64], biogeography-based optimization (BBO), seeker optimization algorithm (SOA) [65], moth search algorithm (MSA) [66], hybrid pattern search-sine cosine algorithm (HPS-SCA) [67], modified version of multi-objective particle swarm optimization (MOPSO) [68], MSA [68], gray wolf optimization (GWO) [69], hybrid of genetic algorithm and pattern search (GA-PS) [68], brainstorm optimisation algorithm (BSOA) [70], asexual reproduction optimization (ARO) [70], and others were developed for multi-machine PSSs design.…”

mentioning

confidence: 99%

“…The optimization algorithm determines the decision variables in such a way as to ensure that the eigenvalues are within the allowable range in different operating conditions. Research by Okafor and Longe [63] the hybrid BFOA-PSO algorithm has been applied to the IEEE 14 bus test system under normal, light, and heavy load conditions. BSO combines both algorithms, BFOA and PSO, thus using the advantages of both techniques.…”

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confidence: 99%

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Designing a power system stabilizer using a hybrid algorithm by genetics and bacteria for the multi-machine power system

et al. 2023

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This research creates an optimal power grid stabilizer for four machines. Rotor speed signals are system inputs in the proposed design. To improve response, this system uses a conventional power system stabilizer (CPSS) and an optimal CPSS (OCPSS). The genetic optimization hybrid has a new structure, and the network generators use the bacterial foraging algorithm (BFA) with stabilizer system. The system set is tested by MATLAB software under various conditions to evaluate the designs. The experiment starts with a three-phase fault in line 3 that is fixed by breaking the line after 0.2 seconds. The simulation results show that after a short circuit in line 3, the proposed OCPSS design reaches damping after about a cycle of oscillation in 4 seconds. However, the conventional CPSS design achieves damping after four oscillations in six seconds. Simulations show that the proposed method is better than genetic algorithm (GA) and BFA. Power system oscillations are dampened faster and with lower amplitude when power system stabilizer (PSS) coordinate with the proposed optimization method. It also improves power system dynamics. We demonstrate with the proposed OCPSS stabilizer that advanced optimization systems can maximize system control capacity by utilizing conventional CPSS system advantages.

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Section: Resultsmentioning

confidence: 99%