Volume 21, Issue 4 (JIAEEE Vol.21 No.4 2024)
Journal of Iranian Association of Electrical and Electronics Engineers 2024, 21(4): 123-133 |
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Hatami A, GanjKhani I. Long-term Voltage Stability Assessment of an Integrated Transmission Distribution System based on LSTM recurrent neural network. Journal of Iranian Association of Electrical and Electronics Engineers 2024; 21 (4) :123-133
URL: http://jiaeee.com/article-1-1543-en.html
URL: http://jiaeee.com/article-1-1543-en.html
Department of Electrical Engineering, Bu-Ali Sina University
Abstract: (893 Views)
In this paper, a new method based on long-short-term memory neural networks is proposed for predicting long-term voltage instability in interconnected transmission and distribution networks. Using the online information of the phasor measurement units, the neural network estimates the stability of the network voltage and in case of any event in the network, it estimates the long-term voltage stability status using the information before and after the event. This structure can be used as an auxiliary tool to quickly inform the network operator of possible risks caused by voltage instability after any contingency in the network. The simulation results of different case studies in offline-mode have been used to create the training dataset. In order to have different case studies, considering load growth in areas prone to voltage instability, (N-1) and (N-1-1) contingency have been simulated. Nordic extended network has been used to evaluate the proposed neural network performance. By using the appropriate time shift, the occurrence of all contingencies has been moved to the tenth second so that the neural network only learn the trajectory of the features. The accuracy of the neural network in the 17th second (7 seconds after contingency) is 99.05%. Finally, the effect of reducing the input dimensions by clustering the data has been investigated.
Type of Article: Research |
Subject:
Power
Received: 2022/12/26 | Accepted: 2023/07/4 | Published: 2025/01/11
Received: 2022/12/26 | Accepted: 2023/07/4 | Published: 2025/01/11
Extended Abstract [PDF 302 KB] (23 Download)
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