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Aiming at the difficulty of accurately identifying latent mechanical faults inside high-voltage shunt reactors (HVSRs), this paper proposes a new method for HVSR state feature extraction and intelligent diagnosis. The method integrates a modified complementary ensemble empirical mode decomposition (CEEMD)–permutation entropy–CEEMD (MCPCEEMD) method, mutual information theory (MI), multiscale fuzzy entropy (MFE), and an improved grasshopper optimization algorithm to optimize the probabilistic neural network (IGOA-PNN) model. First, we introduce MCPCEEMD for suppressing modal aliasing to decompose the HVSR raw vibration signals. Then, the correlation degree between the obtained intrinsic mode function (IMF) components and the HVSR original vibration signals is judged through MI, and the IMF with the highest correlation is selected for feature extraction. Furthermore, this study uses MFE to quantify the selected IMF. Finally, we employ piecewise inertial weights to improve GOA to select the best smoothing factor for PNN, and use the optimized IGOA-PNN model to identify feature subsets. The experimental results show that the proposed method can successfully diagnose different types and degrees of HVSR mechanical faults, and the identification accuracy rate reaches more than 98%. The high recognition accuracy of the proposed method is helpful for the state detection and field application of HVSRs.

Compared with L-type filter, LCL-type filter is more suitable for high-power low-switching frequency applications with reducing the inductance, improving dynamic performance. However, the parameter design for the LCL filter is more complex due to the influence of the controller response performance of the converter. If the harmonic current around switching frequency can be fully suppressed, it is possible for inverter to decrease the total inductance as well as the size and the cost. In this paper, the model of the LCL filter is analyzed and numerical algorithms are adopted to analyze the stability of the closed-loop control system and stable regions are deduced with different parameters of LCL filter. Then, the minimum sampling frequencies are deduced with different conditions. Simulation and experimental results are provided to validate the research on the generating mechanism for the unstable region of sampling frequency.

Unlike traditional load, pulsed load typically features small average power and large peak power. In this paper, the mathematic models of microgrid consisting of synchronous generator and pulsed load are established. Average Magnitude Difference Compensate Function (AMDCF) is proposed to calculate the frequency of synchronous generator, and, based on AMDCF, relative deviation rate (RDR) which characterizes the impact of pulsed load on the AC side of grid is firstly defined and this paper describes calculation process in detail. Insulated Gate Bipolar Transistor (IGBT) is used as DC switch to control the on/off state of resistive load for simulating pulsed load, the period and duty-cycle of the pulsed load are simulated by setting the gate signal of IGBT, and the peak power of the pulsed load is simulated by setting the resistance. The system dynamic characteristics under pulsed load are analyzed in detail, and the influence of duty-cycle, period, peak power, and filter capacitance of the pulsed load on system dynamic indicators is studied and validated experimentally.

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This paper summarizes the advantages of application of micro grid, analyzes the structure of microgrid, and discusses the factors, which are important to the stable operation of micro grid. The factors include the technology of power matching, harmonic suppression and the stability of electronic cascaded systems etc.