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To solve the commutation failure (CF) issues in high voltage direct current (HVDC) transmission systems based on line commutated converter (LCC), this paper proposes a hybrid solution combining the LCC inverter with the continuously controlled IGCT‐based full‐bridge submodules (FBSMs). The scheme can provide the LCC inverter with an additional commutation area, and compensate for the phase‐shift of the grid voltage under faults. Therefore, it shows better CF resistance than the existing scheme. The characteristics of the proposed hybrid converter are modelled and analyzed in both normal and fault conditions. Then a decoupling control strategy is presented for it. With the ability to counteract negative‐sequence grid voltage, the strategy brings good voltage stability to the dc transmission. And when the grid fault occurs, it can switch to suppress CF by rapidly increasing the output voltage of the FBSMs and applying commutation failure prevention (CFPREV) control to the LCC. As a result, the CF issues can be maximally solved with a smaller number of FBSMs than the existing scheme. Simulations in PSCAD verify the effectiveness of the strategy. Furtherly, an experimental platform is built to show the capacity of the proposed scheme to solve the CF issues. [ABSTRACT FROM AUTHOR]

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users' energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatiotemporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms. [ABSTRACT FROM AUTHOR]