Improvement of HVDC CFPREV based on a three phase simultaneity sampling values algorithm
DOI：DOI: 10.19783/j.cnki.pspc.191469
Key Words:commutation failure  CFPREV  fault detection  three phase simultaneity sampling values algorithm
Author NameAffiliation
LI Xiaohua School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
ZHANG Chenzeyu School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
CAI Wangyan School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
TAN Zhanpeng School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
CHEN Zhensheng School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
CAI Zexiang School of Electric Power, South China University of Technology, Guangzhou 510640, Chin 
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Abstract:Commutation failure is an important factor restricting the safe operation of a line-commutated converter-based high voltage direct current (LCC-HVDC) transmission system. From the perspective of prevention, up to now practical projects mainly adopt the commutation failure prevention control (CFPREV). According to the field fault records, the problem of insufficient fault detection speed in the existing predictive control is analyzed, and improving the sensitivity of the fault detection algorithm is the key to the improvement of predictive control. This paper compares the advantages and disadvantages of the voltage RMS algorithm based on the three phase simultaneity sampling values and the current fault detection algorithms, and improves the current CFPREV by the voltage RMS algorithm. The simulation results show that in the single phase grounding fault or two phase grounding fault, the fault detection speed of the voltage RMS detection algorithm is faster than the current algorithms, and the improved CFPREV can also effectively reduce the probability of commutation failure. This work is supported by State Grid Smart Grid Joint Fund Project (No. U1766213) and National Natural Science Foundation of China (No. 51677073).
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