A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids
The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal fea...
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/3/636 |
| _version_ | 1797318793598337024 |
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| author | Huimin Zhao Lili He Yelun Peng Zhikang Shuai Zhixue Zhang Liang Hu |
| author_facet | Huimin Zhao Lili He Yelun Peng Zhikang Shuai Zhixue Zhang Liang Hu |
| author_sort | Huimin Zhao |
| collection | DOAJ |
| description | The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions. |
| first_indexed | 2024-03-08T03:57:35Z |
| format | Article |
| id | doaj.art-4f64a5436ef24f538a56ecc5e1d39c85 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T03:57:35Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4f64a5436ef24f538a56ecc5e1d39c852024-02-09T15:11:22ZengMDPI AGEnergies1996-10732024-01-0117363610.3390/en17030636A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in MicrogridsHuimin Zhao0Lili He1Yelun Peng2Zhikang Shuai3Zhixue Zhang4Liang Hu5CRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410012, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410012, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410012, ChinaCRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, ChinaCRRC Zhuzhou Electric Locomotive Institute Co., Ltd., Zhuzhou 412001, ChinaThe transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions.https://www.mdpi.com/1996-1073/17/3/636index terms microgridtransient stability predictiondeep learningtime-varying spatial–temporal correlation |
| spellingShingle | Huimin Zhao Lili He Yelun Peng Zhikang Shuai Zhixue Zhang Liang Hu A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids Energies index terms microgrid transient stability prediction deep learning time-varying spatial–temporal correlation |
| title | A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids |
| title_full | A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids |
| title_fullStr | A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids |
| title_full_unstemmed | A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids |
| title_short | A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids |
| title_sort | refined der level transient stability prediction method considering time varying spatial temporal correlations in microgrids |
| topic | index terms microgrid transient stability prediction deep learning time-varying spatial–temporal correlation |
| url | https://www.mdpi.com/1996-1073/17/3/636 |
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