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  • Open Access


    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

    Yanjun Yan, Kai Chen*, Hang Geng, Wenqian Fan, Xinrui Zhou

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252

    Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start More > Graphic Abstract

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

  • Open Access


    Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

    Abdul Quawi1,*, Y. Mohamed Shuaib1, M. Manikandan2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3469-3486, 2023, DOI:10.32604/iasc.2023.035001

    Abstract In this work, an Artificial Neural Network (ANN) based technique is suggested for classifying the faults which occur in hybrid power distribution systems. Power, which is generated by the solar and wind energy-based hybrid system, is given to the grid at the Point of Common Coupling (PCC). A boost converter along with perturb and observe (P&O) algorithm is utilized in this system to obtain a constant link voltage. In contrast, the link voltage of the wind energy conversion system (WECS) is retained with the assistance of a Proportional Integral (PI) controller. The grid synchronization is… More >

  • Open Access


    Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

    Feng Zhao1,2, Di Liao1,*, Xiaoqiang Chen1,2, Ying Wang1,2

    Energy Engineering, Vol.120, No.5, pp. 1133-1148, 2023, DOI:10.32604/ee.2023.025900

    Abstract Aiming at the problems of multiple types of power quality composite disturbances, strong feature correlation and high recognition error rate, a method of power quality composite disturbances identification based on multi-resolution S-transform and decision tree was proposed. Firstly, according to IEEE standard, the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given, and the disturbance waveform samples are generated in batches. Then, in order to improve the recognition accuracy, the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis. On this… More >

  • Open Access


    Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

    Nagendra Kumar Swarnkar1, Om Prakash Mahela2, Baseem Khan3,*, Mahendra Lalwani1

    Energy Engineering, Vol.119, No.2, pp. 473-497, 2022, DOI:10.32604/ee.2022.017703

    Abstract A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST… More >

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