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Summary: This paper proposes a two‐stage planning approach for the efficient utilization of distributed generation (DG) and capacitor banks (CBs) for the simultaneous grid‐connected and islanded operations of the balanced distribution networks. The first stage determines the optimal installation locations and capacities of DGs and CBs using an improved variant of the Jaya optimization algorithm (IJaya) to minimize the power losses and voltage deviation during the grid‐connected operation of the distribution network. The second stage identifies the optimal operating point of the DG and CB combination to carry the maximum load during the islanded network operation under power supply‐demand imbalance conditions. For the second stage, an analytical approach is proposed to identify the best operating power factor for the DG‐CB combination, calculating the power loss, and under‐utilization of the installed real‐reactive power sources. Simulation results on the IEEE 33‐bus grid‐dependent distribution network demonstrate that the proposed IJaya significantly improved the network performance and outperforms various existing optimization methods. In addition, during the network's islanded operation, maintaining the network power factor equal to the source power factor (pfsource) makes the DG‐CB combination operate at their rated capacities. [ABSTRACT FROM AUTHOR]

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]