Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity
Highly penetrated distributed generations exacerbate over-voltage and increase the difficulty of voltage safety analysis in distribution networks. This study proposes a method for over-voltage probabilistic analysis and risk traceability based on global probabilistic voltage sensitivity (GPVS). Firs...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004041 |
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| _version_ | 1849390122336780288 |
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| author | Ren Zhang Jian Wang Zhihao Yang Haoming Liu Funian Hu |
| author_facet | Ren Zhang Jian Wang Zhihao Yang Haoming Liu Funian Hu |
| author_sort | Ren Zhang |
| collection | DOAJ |
| description | Highly penetrated distributed generations exacerbate over-voltage and increase the difficulty of voltage safety analysis in distribution networks. This study proposes a method for over-voltage probabilistic analysis and risk traceability based on global probabilistic voltage sensitivity (GPVS). First, a global voltage sensitivity analytical (GVSA) model is constructed based on the node-shared impedance. The Gaussian mixture models (GMMs) of forecasting errors are affine transformed based on the GVSA model to construct the probability models of voltage fluctuation components. Then, based on the characteristic function of GMM, the GPVS model for voltage fluctuation under the influence of source-load uncertainty is derived, which provides an analytical perspective for explaining the impact mechanism of source-load uncertainty on node voltage. Meanwhile, a comprehensive contribution degree index for node voltage deviation is constructed using the parameters of the GPVS model, which quantifies the influence weight of power uncertainty on node voltage deviation. Finally, the probability of node over-voltage is calculated using the GPVS model, and the key nodes that cause the risk of node voltage deviation are traced. The case study results show that the proposed method can characterize the non-Gaussian distribution characteristics of voltage fluctuations, quickly calculate the probability of node over-voltage, and trace the key risk nodes that cause node voltage deviation. |
| format | Article |
| id | doaj-art-c92730e72d6f4aa290a4071b67c375dd |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-c92730e72d6f4aa290a4071b67c375dd2025-08-20T03:41:46ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011085610.1016/j.ijepes.2025.110856Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivityRen Zhang0Jian Wang1Zhihao Yang2Haoming Liu3Funian Hu4School of Electrical and Control Engineering, Xuzhou University of Technology, Xuzhou, China; Corresponding authors.School of Electrical and Power Engineering, Hohai University, Nanjing, China; Corresponding authors.College of electrical, energy and power engineering, Yangzhou University, Yangzhou, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaSchool of Electrical Engineering and Automation, Jangsu Normal University, Xuzhou, ChinaHighly penetrated distributed generations exacerbate over-voltage and increase the difficulty of voltage safety analysis in distribution networks. This study proposes a method for over-voltage probabilistic analysis and risk traceability based on global probabilistic voltage sensitivity (GPVS). First, a global voltage sensitivity analytical (GVSA) model is constructed based on the node-shared impedance. The Gaussian mixture models (GMMs) of forecasting errors are affine transformed based on the GVSA model to construct the probability models of voltage fluctuation components. Then, based on the characteristic function of GMM, the GPVS model for voltage fluctuation under the influence of source-load uncertainty is derived, which provides an analytical perspective for explaining the impact mechanism of source-load uncertainty on node voltage. Meanwhile, a comprehensive contribution degree index for node voltage deviation is constructed using the parameters of the GPVS model, which quantifies the influence weight of power uncertainty on node voltage deviation. Finally, the probability of node over-voltage is calculated using the GPVS model, and the key nodes that cause the risk of node voltage deviation are traced. The case study results show that the proposed method can characterize the non-Gaussian distribution characteristics of voltage fluctuations, quickly calculate the probability of node over-voltage, and trace the key risk nodes that cause node voltage deviation.http://www.sciencedirect.com/science/article/pii/S0142061525004041Distribution networkGaussian mixture modelGlobal probabilistic voltage sensitivityOver-voltage probabilityRisk traceability |
| spellingShingle | Ren Zhang Jian Wang Zhihao Yang Haoming Liu Funian Hu Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity International Journal of Electrical Power & Energy Systems Distribution network Gaussian mixture model Global probabilistic voltage sensitivity Over-voltage probability Risk traceability |
| title | Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity |
| title_full | Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity |
| title_fullStr | Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity |
| title_full_unstemmed | Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity |
| title_short | Probabilistic analysis and risk traceability of over-voltage for distribution networks based on global probabilistic voltage sensitivity |
| title_sort | probabilistic analysis and risk traceability of over voltage for distribution networks based on global probabilistic voltage sensitivity |
| topic | Distribution network Gaussian mixture model Global probabilistic voltage sensitivity Over-voltage probability Risk traceability |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004041 |
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