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The vigorous development of renewable energy has induced multiple sources connected to regional distribution networks. However, the high impedance ground fault that appeared in the distribution network can induce an intermittent arc accompanied by extinction and reignition, which may damage renewable energy generation devices severely. Therefore, the detection of arcing faults is profoundly necessary for practice, but the high impedance arcing fault is easily confused with switches of motor or capacitor bank. In order to solve high impedance arcing fault detection, this paper proposes a high impedance arcing fault detection method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN algorithm is used to extract the intrinsic mode function (IMF) of transient zero‐sequence current (TZSC). According to the correlation coefficient and waveform similarity, which can select maximum correlation coefficient value as the characteristic component of TZSC; Then, TEO is utilized to track the change of signal in real‐time, meanwhile, wavelet singular entropy (WSE) is applied to calculate the characteristic component of TZSC. In particular, high impedance arcing fault (HIAF), motor switching (MS), and capacitor bank switching (CBS) can be effectively identified by comparing WSE. The effectiveness of CEEMDAN‐TEO detection method is verified by simulation, which owns advantages in extraction accuracy, calculation efficiency, and detection accuracy of feature components. [ABSTRACT FROM AUTHOR]

Summary: This paper designs a novel adaptive fractional‐order PID (AFOPID) control of a permanent magnetic synchronous generator (PMSG)‐based wind energy conversion system, which attempts to extract the maximum wind power by using a linear perturbation observer. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, and stochastic wind speed variation is aggregated into a perturbation, which is then estimated in real time by a linear extended‐state observer called high‐gain state and perturbation observer. Besides, the perturbation estimate is used as an auxiliary control signal which is fully compensated by a fractional‐order PID (FOPID) controller to achieve a globally robust control consistency, simple structure and high reliability, as well as an improved tracking performance compared to that of PID control. In addition, AFOPID does not require an accurate PMSG model while only the measurement of d‐axis current and mechanical rotation speed is required, in which parameter is optimally tuned by particle swarm optimization. Four case studies are carried out, including step change of wind speed, low‐turbulence stochastic wind speed, high‐turbulence stochastic wind speed, and generator parameter uncertainties, respectively. Simulation results verify the effectiveness and superiority of AFOPID compared to that of PID, FOPID, and nonlinear control. [ABSTRACT FROM AUTHOR]