Continuous commutation failure is very likely to occur in the hybrid Multi-infeed high-voltage direct current (HMIDC) after AC failure. In order to improve the recovery quality after HMIDC failure, an AC-DC voltage-dependent current order limiter (VDCOL) based on system strength index is proposed in this article. Firstly, the control mode transition process and system recovery process after DC failure are analyzed based on the hybrid multi-infeed DC transmission port model. Then, considering the impact of AC voltage and DC voltage input signals of VDCOL on AC voltage recovery and DC power recovery, respectively, the interaction factor and strength index of the hybrid multi-infeed system are constructed. Moreover, the weight coefficient of AC and DC voltage is calculated according to the strength of the multi-infeed system. Finally, a three-infeed hybrid DC transmission simulation model is built in the MATLAB/Simulink digital simulation platform. The simulation results demonstrate that the rapid recovery strategy proposed in this article can effectively suppress continuous commutation failure and improve the recovery speed of AC voltage and DC power.
In order to overcome the difficulty of fault diagnosis in the high-voltage direct current (HVDC) transmission system, a fault diagnosis method based on the categorical boosting (CatBoost) algorithm is proposed in this work. To make the research conform to the actual situation, three kinds of measured fault data in the HVDC system of the Southern Power Grid are selected as the original data set. First, the core role and significance of fault diagnosis in knowledge graphs (KGs) are given, and the characteristics and specific causes of the four fault types are explained in detail. Second, the fault dates are preprocessed and divided into the training data set and the test data set, and the CatBoost algorithm is employed to train and test fault data to realize fault diagnosis. Finally, to verify the progressiveness and effectiveness of the proposed method, the diagnostic results obtained by CatBoost are compared with those obtained by the BP neural network algorithm. The results show that the diagnostic accuracy of the CatBoost algorithm in the three test sets is always higher than that of the BP neural network algorithm; the accuracy rates in the three case studies of the CatBoost algorithm are 94.74%, 100.00%, and 98.21%, respectively, which fully proves that the CatBoost algorithm has a very good fault diagnosis effect on the HVDC system.
Knowledge graph (KG) has good knowledge expression ability and interpretation, and its application to power system fault diagnosis and disposal can effectively integrate data of the whole life cycle of equipment and form a novel knowledge-driven operation and maintenance management mode. This is crucial to assist dispatchers in fault disposal and effectively improve the power system emergency handling capability and dispatch intelligence level. This paper conducts a systematic review and summary of the application of KG in power system fault diagnosis and disposal, so as to provide an adequate and comprehensive guide for further research in this field. Firstly, the definition, status, and classification of KGs are systematically described, and the general process of KG construction is sorted out. Secondly, the basic framework, construction process, key technologies, and typical practices of KG for power system fault diagnosis and disposal are summarized and reviewed in detail. Ultimately, several challenges, opportunities, and perspectives of KG in this field are carefully presented.
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