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Risk Grading of Distribution Network Equipment Group Based on Fuzzy Clustering Factor Analysis

机译:基于模糊聚类因子分析的配电网设备群风险分级

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The power system, however, has shifted from a regulated system to a competitive, uncertain market environment. Various risks threaten the safe and stable operation of the distribution network at any time. At present, the research objects of risk assessment are mainly single equipment and overall system. However, it is difficult for these two research results to directly reflect the risk situation, and give suggestions for operation and maintenance improvement based on the results. At the same time, traditional risk assessment methods rely too much on expert advice. The actual evaluation efficiency is low and the application is difficult to promote. This paper proposes a method of risk grading for distribution network equipment group based on fuzzy clustering factor analysis. It can flexibly allocate network equipment groups and use equipment groups as research units. Based on multi-factor and multi-level distribution network equipment risk information, we refine the risk assessment index information by refining public factors. This method can not only optimize the risk assessment index information of distribution network equipment, but also improve the accuracy of assessment. And from the perspective of the equipment group, it is more convenient for the risk assessment conclusion to be applied to the distribution network, which has great practical significance. Some feeder data of Guangzhou are used to verify this method. The experimental results indicated that this method can be more easily applied to the actual distribution network operating environment.
机译:但是,电力系统已从规范系统转变为竞争激烈,不确定的市场环境。各种风险随时威胁着配电网络的安全稳定运行。目前,风险评估的研究对象主要是单个设备和整个系统。但是,这两个研究结果很难直接反映风险情况,并难以根据结果给出运行和维护改进的建议。同时,传统的风险评估方法过于依赖专家的建议。实际评估效率低,难以推广。提出了一种基于模糊聚类因子分析的配电网设备群风险分级方法。它可以灵活地分配网络设备组,并将设备组用作研究单位。基于多因素,多层次的配电网设备风险信息,通过细化公共因素,细化风险评估指标信息。该方法不仅可以优化配电网设备的风险评估指标信息,而且可以提高评估的准确性。从设备组的角度出发,将风险评估结论应用于配电网更为方便,具有重要的现实意义。使用广州的一些馈线数据来验证该方法。实验结果表明,该方法可以更容易地应用于实际的配电网运行环境。

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