| 引用本文: | 王 建,姚江宁,刘泽青,欧阳金鑫,熊小伏.恶劣天气下配电网故障统计分析及其概率分布拟合[J].电力系统保护与控制,2022,50(17):143-153.[点击复制] |
| WANG Jian, YAO Jiangning, LIU Zeqing, OUYANG Jinxin, XIONG Xiaofu.Fault statistical analysis and probability distribution fitting for a power distributionnetwork in adverse weather conditions[J].Power System Protection and Control,2022,50(17):143-153[点击复制] |
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| 摘要: |
| 为了提升配电网防灾减灾决策的科学性，需要准确掌握配电网的故障统计参数。为此，采用时间和环境相依的配电网故障统计分析方法，按不同月份、不同天气条件、不同线路类型，计算配电网故障率、停运率等参数在历史同期月份的时间分布特征。针对不同天气下的故障停运时间，提出了概率密度分布拟合方法。对南方沿海地区某大型配电网的实例分析表明：电缆故障的月际分布不明显，受天气影响较小。架空线路的故障受雷雨天气影响较大，分布集中在5—9月，呈现明显的单峰特性，可用高斯分布拟合。雷雨天气下的故障率和停运率明显高于基础故障率和停运率，并且停运持续时间更长，强迫停运时间可用威布尔或伽马分布拟合。所提方法对配电网精细化故障率统计分析具有借鉴意义，可用于指导配电网规划、运行风险评估和恶劣天气下的故障恢复策略优化。 |
| 关键词: 恶劣天气 配电网 时变故障率 强迫停运时间 概率分布拟合 |
| DOI：DOI: 10.19783/j.cnki.pspc.211536 |
| 投稿时间：2021-11-16修订日期：2022-03-02 |
| 基金项目:国家自然科学青年基金项目资助(51707018)；重庆市出站留(来)渝博士后择优项目资助(2020LY23) |
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| Fault statistical analysis and probability distribution fitting for a power distributionnetwork in adverse weather conditions |
| WANG Jian,YAO Jiangning,LIU Zeqing,OUYANG Jinxin,XIONG Xiaofu |
| (1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University,
Chongqing 400044, China; 2. Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China) |
| Abstract: |
| To improve the scientific basis of distribution network disaster prevention and mitigation decisions, accurate fault statistics parameters of the network are needed. To this end, weather- and time-related fault statistical analysis methods for distribution networks are used. Distribution network fault parameters such as failure and outage rates in the same historical period are calculated according to the different months and weather conditions as well as different line types. The probability density distribution fitting method is proposed for the forced outage time in different weather conditions. The case study of a large distribution network in the southern coastal area of China shows that the monthly distribution of cable faults is not obvious, and the cable faults are less affected by weather. Overhead line faults are greatly affected by thunderstorms and rainstorms, and their distribution is concentrated in May to September, showing obvious single-peak characteristics, and it can be fitted by a Gaussian distribution. The failure and outage rates in thunderstorms and rainstorms is significantly higher than the basic failure and outage rates; and the forced outage time is longer, and can be fitted by Weibull or Gamma distributions. The proposed method has implications for refined fault rate statistical analysis of distribution networks. It can guide the planning and operational risk assessment of distribution networks, and the optimization of fault recovery strategies in adverse weather.
This work is supported by the Youth Fund of National Natural Science Foundation of China (No. 51707018). |
| Key words: adverse weather power distribution network time-varying failure rate forced outage time probability distribution fitting |
