Prediction method for power outage faults in distribution network areas containing high penetration photovoltaic power sources
ZHANG Shuhan
BAI Xue
WANG Yanting
WANG Jing
Abstract：[Objective]With the global energy transition and the rapid development of clean energy,the penetration rate of high-penetration photovoltaic(PV)sources in distribution networks is increasing.However,PV output power exhibits significant fluctuations and uncertainty due to factors such as solar irradiance and temperature.When a large number of such sources are integrated into distribution networks,they can cause voltage fluctuations,frequency variations,and other issues,presenting significant challenges for power outage fault prediction.Traditional fault prediction methods struggle to accurately capture fault characteristics in complex distribution networks with high PV penetration,leading to reduced prediction accuracy and efficiency,which fails to meet the stability requirements for distribution network operation.[Methods]To improve prediction accuracy and efficiency,this study proposed a fault prediction method for distribution networks with high PV penetration.First,a PV-integrated grid model was built to analyze the impact of PV sources on fault current characteristics in distribution networks.This model clarified how PV sources influence fault current magnitude and distribution under different operating conditions,providing a theoretical basis for subsequent fault zone identification.Next,potential outage zones were inferred by combining grid topology and load imbalance features.The grid topology reflected the connectivity of components,while load imbalance indicated regional load variations.By integrating these factors,the method more accurately localized the fault zone.In addition,power flow entropy was introduced to assess whether circuit loads were in a critical state.Key fault-related power flow features were then extracted from the identified zones.These features were fed into an optimized SA-SAE for training,allowing the system to automatically learn underlying patterns from large datasets and achieve precise outage prediction.[Results]Experimental results demonstrate that the proposed method achieves high prediction accuracy in fault localization for distribution networks with high PV penetration,correctly identifying fault zones(sections 3-6 of the K5-K8 lines)and fault types.Moreover,the average prediction time is only 2.236 seconds,significantly outperforming comparative methods in both accuracy and efficiency.[Conclusion]By comprehensively considering PV integration effects,grid topology,load characteristics,and leveraging power flow entropy and SA-SAE,the proposed method enables high-precision and high-efficiency outage prediction in distribution networks.This method not only enhances prediction accuracy and timeliness,reducing outage risks and economic losses,but also provides robust support for grid planning,operation,and maintenance.It ensures stable distribution network operation and facilitates large-scale integration of clean energy.
Keywords：operation of distribution network stationhigh-penetration photovoltaic power supplypower flow entropyload statusfeature extractioncurrent value characteristicself-attention mechanismpower outage fault prediction
Publication Date：2025-09-25
Online Publishing Date：2025-10-31(First online date of this platform, not the publication date of the document)
Pages：9( 566-574 )