Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-Driven and Model-Driven Methods

doi:10.12204/j.issn.1000-7229.2021.05.008

Abstract

Abstract:

Commutation failure (CF) is one of the most common faults in traditional HVDC system. Effective prediction of CF is beneficial to the safety and stability of the power system. The physical-driven prediction method can effectively reflect the causal law but it is difficult to establish a precise model. Data-driven prediction method has the advantage of efficient training, but the prediction accuracy depends on a large number of high-quality training samples. Combining the advantage of physical-driven and data-driven methods, a CF prediction method is proposed. In the part of physical-driven, the inherent response of the power system is transformed from time-domain to frequency-domain to obtain the predicted commutation voltage. Then the predicted DC current can be obtained according to the superposition theorem. Finally, the predicted extinction angle can be calculated according to the commutation mechanism. In the part of data-driven, the amplitude and phase of each harmonic of the commutation voltage are taken as the input characteristics, and the extinction angle predicted by the physical-driven method can be modified. According to the results of the test system built in electromagnetic transient simulation software, the validation of the proposed method is verified.

Key words: HVDC transmission, commutation failure prediction, integration of physical-driven and model-driven method

CLC Number:

TM73

TANG Yi, GU Rui, DAI Jianfeng, ZHENG Chenyi, ZHANG Chaoming, DANG Jie. Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-Driven and Model-Driven Methods[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 69-80.

Figures/Tables 19

Fig.1 The topology of three-phase full-wave bridge inverter

Fig.2 Commutation voltage and corresponding valve current during the commutation

Fig.3 Commutation failure prediction by integrating physical-driven and model-driven

Fig.4 The diagram of extinction angle prediction

Fig.5 The flow chart of commutation voltage prediction

Fig.6 The equivalent and simplified equivalent circuit during the commutation process

Fig.7 The equivalent circuit of steady-state and transient response of DC current

Fig.8 The commutation voltage and the corresponding extinction angle during the failure

Fig.9 The topology of test system one

Fig.10 The topology of test system 3

Table 1 Relevant fault setting parameters of test system 2 and 3

Fig.11 Comparison between the predicted and actual values of three-sequence components of commutation voltage

Table 2 Comparison of prediction errors of commutation voltage

Table 3 Parameters for DC current prediction

Fig.12 Comparison of predicted and actual values of the DC current time domain response

Fig.13 Predicted and actual values of min_γ under different prediction methods

Fig.14 Comparison of prediction accuracy of three kinds of methods under different scenes

Fig.15 The influence of training samples on the physical and data-driven prediction method

Fig.16 The influence of samples on the physical and data-driven prediction method

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