Investigation of an Output Voltage Harmonic Suppression Strategy of a Power Quality Control Device for the High-End Manufacturing Industry

Wan, Chengkuan; Li, Kai; Xu, Lin; Xiong, Chao; Wang, Lingang; Tang, Hao

doi:10.3390/mi13101646

Cited by 1 publication

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References 12 publications

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“…The power quality problem has become increasingly prominent, so the main task is to guarantee voltage stability in power generation and distribution systems [6]. In this context, guaranteeing a high-quality power supply has become one of the tasks to be resolved urgently [7]. The architectures where the quality of electric power is most unstable are in microgrids because these types of schemes often use renewable distributed generators, which depend on natural resources [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

et al. 2023

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Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.

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Section: Introductionmentioning

confidence: 99%