Tuned support vector regression by modified particle swarm optimization for online power system static security evaluation

Rastgoufard, Samin; Charalampidis, Dimitrios

doi:10.1109/tpec.2018.8312063

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…PSO is proposed by Kennedy and Eberhart. The main idea is to find the optimal solution iteratively after random initialization in the solution space [31]. PSO simulates the behavior of birds and fish.…”

Section: Methodsmentioning

confidence: 99%

Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Bao¹,

Nakamura²

2019

dtetr

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This paper proposed a method that combines wavelet entropy with particle swarm optimized support vector machine to detect hearing loss (HL). The dataset for this task contains 75 images: 25 healthy controls, 25 left-sided hearing loss patients, and 25 right-sided hearing loss patients. The results shows that our method has higher accuracy than some traditional method. The sensitivities over healthy control, left-sided HL patients, and right-sided HL patients are 85.20± 3.79, 85.20± 4.64, and, 86.40± 5.06, respectively. The overall accuracy is 85.60± 0.84.

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Section: Methodsmentioning

confidence: 99%

Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Bao¹,

Nakamura²

2019

dtetr

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“…Modified PSO [275], binary PSO [276] Reactive power and voltage control adaptive discrete binary PSO [277], standard PSO [278], PSO with artificial physics optimization [279], hybrid PSO [280] Short-Term load forecasting switching delayed PSO [281], hybrid PSO [282] VII. PSO DRAWBACKS Despite the excellent performance of its variants, PSO suffers, in general, from some weaknesses that can be alleviated by introducing new modifications to the current PSO variants.…”

Section: State Estimationmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…The SVM method has been combined with particle swarm optimization (PSO) in Reference 76. In this study, the PSO has been used for optimal tuning the inertial weighting factors of the SVM.…”

Section: Categorization Based On Power System Implementationmentioning

confidence: 99%

Static security assessment of power systems: A review

Gholami

Sanjari

Safari

et al. 2020

Int Trans Electr Energ Syst

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Summary The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.

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Tuned support vector regression by modified particle swarm optimization for online power system static security evaluation

Cited by 6 publications

References 24 publications

Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Particle Swarm Optimization: A Comprehensive Survey

Static security assessment of power systems: A review

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