Dispersed harmonic control makes a critical guarantee for safe and stable operation of distribution networks. However, conventional point-to-point local control method has limitations. In addition, harmonic current injection has volatility and time-variability, and the residual capacity (RC) of multi-functional grid-connected inverter (MFGCI) employed for auxiliary harmonic compensation is uncertain, which challenges mitigation decision-making. In this study, a grid-side distributed mitigation strategy based on distributed model predictive control (DMPC) is proposed. Firstly, the voltage distortion observation node is selected based on node clustering, and sensitivity analysis of harmonic mitigation for observation nodes is conducted to divide the whole network into multiple control areas. Secondly, for global optimal of voltage distortion, the long time scale distributed optimization model for mitigation parameters orienting partitional coordination is established. Finally, a rolling optimization model based on DMPC for correction of conductance parameters is developed from regional autonomy perspective. The results show that: (1) Distributed mitigation scheme can improve the total harmonic distortion of voltage (THDV) of the whole network node; (2) Compared with control references, the corrected parameters can reduce the decision deviation and further lower the THDV by about 2%; (3) In multi-uncertain scenarios, multi-time-scale optimization can alleviate the uncertainty in harmonic control.

Due to the influence of the external environment and the internal noise of the inverter, the noise harmonic injection may not be obvious in the initial stage of photovoltaic DC arc generation, resulting in the arc fault with strong concealment and difficult to detect. To solve this problem, an arc fault detection method based on improved empirical wavelet transform (IEWT) and improved singular value decomposition (ISVD) is proposed in this paper. Firstly, the improved empirical wavelet transform is used to realize the accurate segmentation of the noise spectrum, and then the construction dimension of the corresponding matrix of each component is reduced by improving the singular value decomposition, which not only achieves accurate noise reduction but also effectively reduces the amount of calculation of the algorithm. It makes the normal signal and the arc fault signal show obvious differentiation after noise reduction. In order to realize accurate detection of hidden arc, the appropriate transition interval is set according to each characteristic index, and the suspected arc fault signal is judged by the ratio of high and low frequency characteristic indexes. Finally, the improved entropy weight method (IEWM) is used to construct the fusion characteristic index to realize a fast and accurate diagnosis of arc fault. Through a series of comparative experiments, it can be proved that, compared with the commonly used AI method, the detection algorithm in this paper achieves 99.47% detection accuracy and improves the average calculation speed by 56.7%.

The large-scale integration of high-penetration distributed photovoltaic systems into distribution networks can result in significant grid voltage fluctuations within a short period. However, centralized regulation instructions for passive/reactive compensation, by themselves, are insufficient for effectively suppressing these fluctuations. Thus, this study used the grid-forming and grid-following control characteristics of modern power electronic inverters to propose an optimal allocation strategy for reactive power compensation equipment. This strategy aimed to address the insufficient proactive support capacity in the reactive power equipment used to suppress short-time grid voltage fluctuations. After establishing uncertain operation scenarios for the distribution network, we analyzed the respective multi-timescale behavioral characteristics of traditional, grid-forming, and grid-following reactive power compensation devices. The primary and auxiliary objectives were to minimize the investment cost of the special equipment and voltage deviation of the entire network, respectively. To achieve these objectives, we established a collaborative optimal allocation model for grid-forming and grid-following reactive power equipment. A multi-timescale cooperative allocation strategy was proposed to decompose the total reactive power demand curves at the equipment installation nodes into reactive power curves for different response levels and then collaboratively allocate the multiple devices. A comparative analysis of the three schemes in IEEE 33-node and 69-node systems shows that the proposed strategy guarantees lower overall network voltages while reducing the cost by at least 20% compared to those of other schemes.