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• W4386314106 startingPage "109807" @default.
• W4386314106 abstract "This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score." @default.
• W4386314106 created "2023-09-01" @default.
• W4386314106 creator A5018905527 @default.
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• W4386314106 date "2023-11-01" @default.
• W4386314106 modified "2023-09-27" @default.
• W4386314106 title "Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks" @default.
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• W4386314106 doi "https://doi.org/10.1016/j.epsr.2023.109807" @default.
• W4386314106 hasPublicationYear "2023" @default.
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