A proposed PMU-based voltage stability and critical bus detection method using artificial neural network
Abstract Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical method...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-024-00302-w |
| _version_ | 1797355542077767680 |
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| author | Lesnanto Multa Putranto Izzuddin Fathin Azhar |
| author_facet | Lesnanto Multa Putranto Izzuddin Fathin Azhar |
| author_sort | Lesnanto Multa Putranto |
| collection | DOAJ |
| description | Abstract Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. Data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16 and 18 s per detection which makes the scalability to the real time detection is reasonable. |
| first_indexed | 2024-03-08T14:12:39Z |
| format | Article |
| id | doaj.art-e7d65393cdf448a39dc10fe694c7bb3f |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-03-08T14:12:39Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-e7d65393cdf448a39dc10fe694c7bb3f2024-01-14T12:38:14ZengSpringerOpenEnergy Informatics2520-89422024-01-017112310.1186/s42162-024-00302-wA proposed PMU-based voltage stability and critical bus detection method using artificial neural networkLesnanto Multa Putranto0Izzuddin Fathin Azhar1Department of Electrical and Information Engineering, Engineering Faculty, Universitas Gadjah MadaCenter for Data and Information Technology, Secretariat General, Ministry of TransportationAbstract Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. Data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16 and 18 s per detection which makes the scalability to the real time detection is reasonable.https://doi.org/10.1186/s42162-024-00302-wArtificial neural networkData driven modelPrediction accuracyVoltage stability detection |
| spellingShingle | Lesnanto Multa Putranto Izzuddin Fathin Azhar A proposed PMU-based voltage stability and critical bus detection method using artificial neural network Energy Informatics Artificial neural network Data driven model Prediction accuracy Voltage stability detection |
| title | A proposed PMU-based voltage stability and critical bus detection method using artificial neural network |
| title_full | A proposed PMU-based voltage stability and critical bus detection method using artificial neural network |
| title_fullStr | A proposed PMU-based voltage stability and critical bus detection method using artificial neural network |
| title_full_unstemmed | A proposed PMU-based voltage stability and critical bus detection method using artificial neural network |
| title_short | A proposed PMU-based voltage stability and critical bus detection method using artificial neural network |
| title_sort | proposed pmu based voltage stability and critical bus detection method using artificial neural network |
| topic | Artificial neural network Data driven model Prediction accuracy Voltage stability detection |
| url | https://doi.org/10.1186/s42162-024-00302-w |
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