A comprehensive research of machine learning algorithms for power quality disturbances classifier based on time-series window

Akkaya, Sıtkı; Yüksek, Emre; Akgün, Hasan Metehan

doi:10.1007/s00202-023-02177-8

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Wei et al [23] employed a principal component analysis with a support vector machine to monitor disturbances, reducing the curse of dimensionality in the original data, and also used an extreme learning machine to classify PQ events. Due to the traditional solution to PQ disturbance being time-consuming and not feasible, the hyperparameter optimization of machine learning algorithms was executed for detection and classification, in which noise was randomly prepared, and the simulation outperformed the other algorithms in accuracy in [24]. Gaussian mixture models were used to detect anomalies in PQ disturbance events to predict the occurrence of unusual clusters in weather condition in [25].…”

Section: Introductionmentioning

confidence: 99%

Dynamics Power Quality Cost Assessment Based on a Gradient Descent Method

Zhang,

Sheng,

et al. 2024

Energies

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The escalating demand for power load is increasingly prone to triggering power quality (PQ) issues, leading to severe economic losses. Aiming at reducing the economic losses, this paper focuses on the coordinated relationship between PQ and economic costs. Firstly, a multilayer multiple linear stepwise regression method is employed to screen PQ indicators, identifying harmonic and voltage deviation as the primary influencing factors of PQ. Secondly, a gradient descent optimization algorithm based on the Least Absolute Shrinkage and Selection Operator (LASSO) is proposed, enabling rapid computation of the minimum PQ cost. Finally, through validations of two case studies, the results confirm that the proposed method can rapidly calculate the minimum PQ cost based on real-time load demands, enabling the dynamic adjustment of PQ cost to meet the evolving needs of power system development.

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Section: Introductionmentioning

confidence: 99%