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The digital controller of the grid-connected converter achieves closed-loop control by sampling key voltage and current signals. High-frequency switching of power electronic switches introduces high-frequency ripples into the signals, which may cause sampling aliasing. Severe distortion may happen in the output current of the converter because of the sampling aliasing. This paper explains how the aliasing problem arises by analyzing the spectrum of sampling-controlled signals. The explanation shows the severity of the aliasing problem and the necessity of adopting anti-aliasing methods. Next, this paper analyzes the impact of commonly used anti-aliasing low-pass filters on current control stability. From the conclusions, adopting a low-pass anti-aliasing filter reduces the stability margin of the current loop, which needs improvement. So this paper proposes a novel hybrid anti-aliasing filter based on the combination of the notch and low pass analog filters. An experiment on a 6kVA prototype verifies the proposed anti-aliasing method. This paper shows distortion and step-response oscillation of the output current when proposed and traditional anti-aliasing methods adopted. The comparison shows the advantages of the proposed anti-aliasing method.

Current residual current action protection device cannot detect the type of residual current in the electric shock current. In this paper, an electric shock test platform is built and the residual current data is obtained by experiment. Based on the above data, this paper presents a residual current classification method based on the AdaBoost algorithm, which firstly obtains the characteristic components of different types of residual current components, and then maps these component features to the AdaBoost algorithm. The type of electric shock current in total residual current is detected by AdaBoost algorithm. Compared with SVM and random forests, the results show that the proposed method has some advantages, which can provide theoretical basis and support for the development of the subsequent adaptive residual current action protection device.

The access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG. [ABSTRACT FROM AUTHOR]

Accurate and dynamic identification of key nodes that can efficiently improve the safety and economy of active distribution network (ADN) is of great significance for optimising the operation level of ADN with time‐varying characteristics. In this study, a dynamic identification method for the key nodes of ADN operation optimisation requirements is proposed. Firstly, considering the influence of nodes on ADN operation performance, the importance of nodes in the network structure, and the controllable potential of nodes, a multi‐angle key node identification index system is constructed. Secondly, considering the influence of the ADN operation state on node importance, the classification boundary of ADN operation state was set to determine the operation state of the ADN at different times. Thirdly, the order relation analysis method and variation coefficient method were used to comprehensively evaluate the key nodes of the ADN under different operating states. Finally, the technique for order preference by similarity to an ideal solution is used to sort the comprehensive evaluation results of key nodes to achieve the effective identification of key nodes. The effectiveness and feasibility of the proposed method are verified on 33‐bus and 118‐bus test systems considering the time sequence variation in distributed generation and load. [ABSTRACT FROM AUTHOR]