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.

为了解决分布式电源（distributed generation，DG）接入情况下对配电网故障定位及电压质量的影响，分析了各种类型分布式电源对短路电流的影响，得出了依靠故障电流分布进行故障定位的传统配电自动化系统的适应范围，提出了一种利用重合闸与分布式电源脱网特性协调配合的改进故障处理策略，以应对更大规模分布式电源接入的挑战。分析了分布式电源接入配电网后对电压偏差和电压波动的影响，提出了一种获得不必进行控制就能满足电压质量要求的判断条件，对于不符合该判断条件的分布式电源可以通过配电自动化系统进行监控以满足电压质量的要求。实例表明所建议的方法是可行的。

To solve the influence of distributed generation （DG） on the fault location and voltage quality of distribution network, the influence of various DGs on the short-circuit current were analyzed. The adaptation range of traditional distribution automation system was obtained, in which the fault location was based on the fault current distribution. An improved fault process strategy was proposed to meet the requirement of much larger amount of DGs connection, based on the coordination of the reclosing procedure and DG escaping after faults. The influences of DGs on the voltage deviation and voltage fluctuation of distribution network were analyzed. The judging condition to meet the requirements of voltage quality without the control of DG was proposed. The DG that could not accord with the judging condition could be monitored by distribution automation system to meet the requirements of voltage quality. The examples show that the proposed method is feasible.

在智能配电系统快速发展的背景下，分布式电源（distributed generation，DG）越来越多地接入配电系统，传统的针对单端电源的配电系统故障诊断方法不再适用。在此背景下，针对含有DG的配电系统，提出一种基于故障暂态分量和遗传算法的配电系统故障诊断方法。针对DG接入使故障点的暂态零序电流方向复杂化的问题，通过给定开关上下游电源的方式改善了终端的状态编码环节，且考虑了故障线路诊断的优先级。采用小波分析方法提取故障暂态分量，能够很好地适应我国中压配电系统广泛采用的小电流接地方式，在相当程度上解决了单相接地短路稳态故障信息不明显的问题。最后，用算例说明了所提出方法的可行性，分析了其对故障信息畸变和丢失的容错性能。

With the fast development of smart distribution systems, more and more distributed generation （DG） sources are connected, so traditional fault diagnosis approaches for distribution systems with single power source are no longer applicable. Given this background, this paper proposes a fault diagnosis method based on fault transient components and genetic algorithm for distribution systems with DGs. Considering that the DG connections make the transient zero-sequence current direction complicated at fault point, an improved coding method for feeder terminal units is presented by specifying the upstream and downstream power sources for each switch, and the priority level of a faulted line to be diagnosed is also taken into account. We adopt a wavelet based method to extract fault transient components, which is well applicable for the widely-implemented non-effective grounding in middle-voltage distribution systems in China, and can solve, to some extent, the problem of inconspicuous steady fault current with single-phase ground faults. Finally, we demonstrate the proposed method with a sample system, and analyze its fault tolerance performance for distorted and missing fault information.

由于电力变压器发生故障时油色谱在线监测数据无标签，工程现场往往会得到大量无标签故障样本，而传统的故障诊断方法在对变压器故障类型进行判别时往往无法充分利用这些无标签故障样本。该文基于深度学习神经网络（deep learning neural network，DLNN），构建了相应的分类模型，分析并用典型数据集对其分类性能进行测试。在此基础上提出一种电力变压器故障诊断新方法，它能够有效利用大量电力变压器油色谱在线监测无标签数据和少量故障电力变压器油中溶解气体分析（dissolved gas-in-oil analysis，DGA）实验数据进行训练，并以概率形式给出故障诊断结果，具有更优的故障判别性能，能够为变压器的检修提供更为准确的参考信息。工程实例测试结果表明，该方法正确可行，诊断性能优于三比值、BP神经网络和支持向量机的方法，适用于电力变压器的故障诊断。

As oil chromatography online-monitoring data is unlabeled during power transformer failure, project sites tend to get a large number of unlabeled fault samples. However, traditional diagnosis methods often fail to make full use of those unlabeled fault samples in judging transformer fault types. Based on deep learning neural network （DLNN）, a corresponding classification model was established, whose classification performance was analyzed and tested by typical datasets. On this basis, a new fault diagnosis method of power transformer was further proposed, in which a large number of unlabeled data from oil chromatogram on-line monitoring devices and a small number of labeled data from dissolved gas-in-oil analysis （DGA） were fully used in training process. It could generate fault diagnosis result in the form of probabilities, and provide more accurate information for the maintenance of power transformer because of its better performance in fault diagnosis. Testing results from engineering example indicate that the proposed method is correct and feasible, and its diagnosis performance is better than that of three radio, BP neural network and support vector machine, which is suitable for the fault diagnosis of power transformer.