Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach
In this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Electricity |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4826/4/4/22 |
| _version_ | 1797381331883130880 |
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| author | Oswaldo Cortes-Robles Emilio Barocio Ernesto Beltran Ramon Daniel Rodríguez-Soto |
| author_facet | Oswaldo Cortes-Robles Emilio Barocio Ernesto Beltran Ramon Daniel Rodríguez-Soto |
| author_sort | Oswaldo Cortes-Robles |
| collection | DOAJ |
| description | In this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines faulted), islanding events, sudden load variations, and generation tripping. The proposed LSTM-based method was trained and tested using the signals produced by the events simulated in a study system with distributed generation sources via PSCAD<sup>®</sup>. Then, noise with different levels was added to the testing set for a thorough assessment, and the results were compared with other well-known methods such as convolutional and simple recurrent neuronal networks. The LSTM-based method with multi-input proved to be effective for event classification, achieving remarkable classification performance even in noisy conditions. |
| first_indexed | 2024-03-08T20:49:54Z |
| format | Article |
| id | doaj.art-88a8cbee7a8c49ee98c557d205f907de |
| institution | Directory Open Access Journal |
| issn | 2673-4826 |
| language | English |
| last_indexed | 2024-03-08T20:49:54Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electricity |
| spelling | doaj.art-88a8cbee7a8c49ee98c557d205f907de2023-12-22T14:04:43ZengMDPI AGElectricity2673-48262023-12-014441042610.3390/electricity4040022Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor ApproachOswaldo Cortes-Robles0Emilio Barocio1Ernesto Beltran2Ramon Daniel Rodríguez-Soto3Electrical Engineering Department, Universidad de Guadalajara, Guadalajara 44430, MexicoElectrical Engineering Department, Universidad de Guadalajara, Guadalajara 44430, MexicoElectrical Engineering Department, Universidad de Guadalajara, Guadalajara 44430, MexicoElectrical Engineering Department, Universidad de Guadalajara, Guadalajara 44430, MexicoIn this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines faulted), islanding events, sudden load variations, and generation tripping. The proposed LSTM-based method was trained and tested using the signals produced by the events simulated in a study system with distributed generation sources via PSCAD<sup>®</sup>. Then, noise with different levels was added to the testing set for a thorough assessment, and the results were compared with other well-known methods such as convolutional and simple recurrent neuronal networks. The LSTM-based method with multi-input proved to be effective for event classification, achieving remarkable classification performance even in noisy conditions.https://www.mdpi.com/2673-4826/4/4/22distributed generation sourcesevents classificationdeep learninglong short-term memory networksmulti-input |
| spellingShingle | Oswaldo Cortes-Robles Emilio Barocio Ernesto Beltran Ramon Daniel Rodríguez-Soto Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach Electricity distributed generation sources events classification deep learning long short-term memory networks multi-input |
| title | Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach |
| title_full | Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach |
| title_fullStr | Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach |
| title_full_unstemmed | Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach |
| title_short | Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach |
| title_sort | events classification in power systems with distributed generation sources using an lstm based method with multi input tensor approach |
| topic | distributed generation sources events classification deep learning long short-term memory networks multi-input |
| url | https://www.mdpi.com/2673-4826/4/4/22 |
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