Integration of distributed energy resources (DER), such as wind and solar power systems is increasing day-by-day to meet growth of power demand. Distributed generation (DG) integration has numerous technological advantages such as enhanced performance, security of system and quality of power. In addition to the benefits of integration of DG to grid we need to face some challenges that are caused due to the isolation nature of DGs. One of the major issues identified in the integration of DGs is islanding, which leads to power quality issues, damage of equipment and keeps the lives of utility works at risk. “To overcome this speed and accurate detection of islanding become necessary for the smooth operation of Micro Grid (MG) which can be achieved by implementing suitable Islanding detection technique”. In this paper a novel intelligent islanding detection method (IIDM) is implemented by integrating convolution neural network (CNN) with long short-term memory (LSTM) by using Empirical wavelet transform (EWT) technique for feature extraction of uncertainty signals at point of coupling of DGs to grid. To improve the accuracy and performance of the classifier CNN-LSTM (CL) network optimal weight selection is done by using Particle swarm Optimization algorithm (PSOA). The objective of the proposed classifier is to classify non-islanding and islanding events under various fault scenarios. The proposed classifier illustrated better performance in view of Signal Noise Ratio (SNR) when tested with various noise levels. The results of MATLAB/Simulink clearly showed that implemented method categorized the state of islanding and non-islanding state more accurately when compared with existing islanding detection techniques.


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    Titel :

    Detection of Islanding Using Intelligent Classifiers for Integrated Solar Wind Power System


    Beteiligte:
    Vadlamudi, Bindu (Autor:in) / Damarla, Indira (Autor:in) / Vital, MLN (Autor:in) / Dawn, Subhojit (Autor:in)


    Erscheinungsdatum :

    31.07.2024


    Format / Umfang :

    1689878 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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