分布式电源规模化接入,使配电网拓扑结构和组成元素越来越复杂,传统矩阵算法由于馈线开关处故障电流方向难以确定已不再适用于配电网相间故障定位。为有效将复杂配电网的拓扑结构简化,基于深度优先原则对配电网进行解耦,使其变成由若干个树干状结构组成的网络;针对复杂配电网故障定位矩阵算法计算量大、步骤复杂的难题,提出了一种新的复杂配电网故障定位矩阵算法,与传统矩阵算法相比,该方法对配电网T型区段及末梢区段故障的定位更加精确。最后,通过典型园区配电网模型与改进的 IEEE 33系统案例,验证了所提方法既能实现配电网的故障定位,又能简化复杂配电网结构,与以往的行波法相比,文章提出的方法缩短了定位时间,具有较好的工程应用价值。

Since distributed power supply is connected to distribution networks on a large scale, its topology and components are becoming more and more complex. As the direction of fault current at feeder switches is difficult to determine, a new matrix algorithm for equivalent decoupling fault location in complex distribution networks is proposed in this paper. In order to simplify the topology of complex distribution network effectively, the distribution network is decoupled according to the principle of depth priority to make it a network composed of tree trunks. A new fault-location matrix algorithm for complex distribution network is presented to solve the problem of large computation and complex steps in fault-location matrix algorithm for complex distribution network. Compared with traditional matrix algorithm, this method is more accurate for T-section and terminal section of power network fault location. Finally, through the typical park distribution network model and the improved IEEE 33-node system case, the method proposed in this paper can not only locate the faults in the distribution network, but also simplify the complex distribution network structure. Compared with the traveling wave method, the proposed method shortens the locating time and has better application value.

When a fault arises in an active distribution network, both the grid upstream of the fault point and the distributed generation (DG) downstream of the fault point contribute short-circuit currents, posing challenges for existing centralized feeder automation (FA) technologies in accurately locating and isolating the fault. Moreover, the radial network structure of the distribution network can lead to power outages for downstream loads when the DG downstream of the fault point ceases operation due to islanding effects. To tackle this issue, this paper introduces a communication-based distributed FA strategy that employs interlocking signals between adjacent switches to pinpoint and isolate faults in active distribution networks, effectively minimizing the scope of fault-induced power outages. Furthermore, to guarantee uninterrupted power supply to the non-fault zones located downstream of the fault point, an inverter control strategy integrating voltage negative feedback control, reactive power negative feedback control, and quasi-synchronization grid-connection control is proposed. This strategy allows for seamless switching between grid-following and grid-forming inverter control modes, enabling adaptive grid-connection/islanding control of DGs. Consequently, non-fault areas downstream of the fault point maintain uninterrupted power supply, significantly enhancing the reliability of power supply in distribution networks. Simulation models have been constructed using MATLAB/Simulink to validate the efficacy of the proposed approach.

Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.

The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs.

Effective diagnosis of distribution network faults is crucial to ensuring the reliability of power systems. However, the bidirectional power flow caused by the integration of new energy limits the effectiveness of traditional detection methods. Although data-driven approaches are not restricted by power flow direction, their performance is heavily dependent on the quantity and quality of training samples. In addition, factors such as measurement noise, variable fault impedance, and volatile photovoltaic output complicate fault information. To address this, we present a new fault diagnosis model named the dynamic, adaptive, and coupled dual-field-encoding graph neural network (DACDFE-GNN), which introduces a dynamic aggregation module to assign different weights to reduce noise interference and fully integrates information from observable nodes. On this basis, the coupled dual-field-encoding module is proposed, which encodes topological information and physical–electrical domain information as part of the initial features, thereby capturing fault features and learning the law of feature propagation. The experimental results for the IEEE 34- and IEEE 123-node feeder systems indicate that the proposed model surpasses recent fault diagnosis methods in detection performance, particularly regarding its low training sample rate.