A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion
DOI：10.19783/j.cnki.pspc.260248
Key Words:HVDC transmission system  fault identification  converter station control  DC line fault
Author NameAffiliation
SUN Kezhen 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
XIAO Taiyu 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
YU Bing 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
OUYANG Jinxin 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
SUN Jiaqi 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
CHEN Qidi 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
YANG Yaming 1. EHV Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
3. State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830018, China
4. Urumqi Vocational University, Urumqi 830002, China 
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Abstract:To address the difficulty of accurately identifying the causes of HVDC transmission line faults due to the homogenization of transient features caused by the nonlinear reshaping of electrical quantities by converter-station control systems, a fault cause identification method is proposed based on control response decoupling and adaptive multi-source information fusion. First, the differences in the evolution transient characteristics of DC line faults caused by lightning strikes, wildfires, wind-induced conductor deviations, and foreign objects are analyzed, and the underlying causes of fault characteristic homogenization are revealed. Second, an analytical model is constructed considering the dynamic evolution of the firing angle. An extraction method for inherent fault characteristic quantities of DC voltage and current while considering the control response is proposed. Furthermore, an identification model is constructed by fusing a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network, and a spatial attention mechanism (SAM). Multi-scale spatial details and bidirectional global temporal evolution characteristics of the inherent fault electrical quantities are mined by the constructed model. On this basis, a Bayesian probability model is introduced based on meteorological environment priors, and a dynamic fusion strategy of multi-source information is proposed based on Jensen-Shannon divergence to achieve adaptive weight coordination between data-driven features and external physical constraints. Case studies demonstrate that the proposed method achieves high-precision identification for different fault causes, effectively addressing the challenge of fault cause identification under severe feature homogenization.
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