:Multi-terminal high voltage DC (MT-HVDC) grid has broad application prospects in connecting different energy sources, asynchronous interconnection of power grids, remote load power supply, and other fields. At present, the key technologies that affect the development of MT-HVDC transmission system include swift fault identification and location in the DC line and its rapid isolation. Traditional fault monitoring relies on line communication, which cannot guarantee the rapidity and reliability of protection; moreover, it may even cause device damage. A fault identification scheme based on a single-terminal transient is presented in this paper. This scheme calculates the line inductance by using the rise rate of fault current at the initial stage of the fault, and determines the occurrence of the fault by comparing the observed line inductance with the set value, which lays a foundation for calculating the location of the fault point using distance protection. A simulation model on the PSCAD/EMTDC platform is built; the simulation example verifies that the proposed scheme can identify faults under dissimilar conditions while maintaining a low error level on the premise of no communication lines so as to meet the protection requirements of the MT-HVDC grid.
In order to improve the accuracy of fault diagnosis of the wind turbine's pitch system, an improved stack autoencoder network is proposed. Based on the Supervisory Control And Data Acquisition (SCADA) data of the wind turbine's electric pitch system, the batch normalization (BN) algorithm was introduced for the gradient dispersion problem in the feature extraction of ordinary autoencoder networks when there are many parameters. This article uses the Adam optimizer to iteratively update the neural network weights based on the training data. Then calculate the cross-entropy loss function and train the network with the minimum loss function as the goal. Finally, the Softmax classifier is used, and its output is the diagnosis and probability of each component of the pitch system. The data set in the pitch control system SCADA is selected. This paper selects the verification set in the pitch control system SCADA and substitutes it into the ordinary stack autoencoder and improved stack autoencoder network (SAE) for comparison and verification. The verification results show that the batch-standardized SAE network has a more optimized network model and higher recognition accuracy, and also provides a strategy for fault diagnosis of wind turbines.
As a bridge of the transmission system, flexible DC transmission line plays an important role. If a fault occurs, it will bring serious consequences to the entire transmission system. It is very important to study the fault location of the flexible DC transmission line. According to the system structure of flexible DC transmission line, the mathematical model, operation mode and control strategy of MMC are analyzed. According to the characteristics of transmission line with bipolar short circuit fault, a fault location method based on BP neural network is proposed. Combined with PSCAD/EMTDC, a twoterminal flexible DC transmission system is built, and the fault data of the flexible DC transmission line is obtained by simulation. After preprocessing the data, a sample set is constructed. The neural network analysis uses the two-terminal fault location method, and finally a high-precision fault location model is obtained, which verifies the accuracy and effectiveness of the location method proposed in this paper.
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