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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.

With the development of distribution network, distributed generation such as wind and photovoltaic (PV) power will become increasingly prominent in the near future. PV is widely constructed because of advantages it has. However, the volatility and randomness of PV makes it more complex than traditional energy in the security assessment of distribution network. Based on risk theory, considering the randomness of PV, node low voltage risk index and line overload risk index are established in this paper. Also, K (N - 1 + 1) principle for distribution network which is developed from traditional (N-1) deterministic principle is applied to reflect the flexible structure of distribution network. IEEE three-feeder example system is utilized to investigate the influence of PV power on the security assessment of distribution network.

In allusion to the randomness of output power of distributed generation (DG), a reliability evaluation model based on sequential Monte Carlo simulation (SMCS) for distribution system with DG is proposed. Operating states of the distribution system can be sampled by SMCS in chronological order thus the corresponding output power of DG can be generated. The proposed method has been tested on feeder F4 of IEEE-RBTS Bus 6. The results show that reliability evaluation of distribution system considering the uncertainty of output power of DG can be effectively implemented by SMCS.

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]