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电力大数据:2019,22(9):-
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基于Hash算法的大数据架构下电力系统风险评估研究
钟臻1, 张楷旋2
(1.国网重庆市电力公司江北供电分公司;2.国网重庆市电力公司南岸供电分公司)
Power System Risk Assessment Based on Hash Algorithm in Big Data Architecture
Zhong Zhen1, Zhang Kaixuan2
(1.Jiangbei Power Supply Company,Chongqing Electric Power Corp,State Grid Corporation of China;2.Nanan Power Supply Company,Chongqing Electric Power Corp,State Grid Corporation of China)
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投稿时间:2019-05-26    修订日期:2019-06-25
中文摘要: 为解决电力系统风险评估传统方法存在的处理数据耗时长、实时交互性能欠缺、难以全面反应电网状态等问题,本文提出一种基于Hash算法的大数据架构下电力系统风险评估方法,利用Hash算法将电力系统基础数据关联至服务平台和风险评估系统,采用架空线路停运模型、变压器时变停运模型对电力系统进行大数据建模,融合静态和暂态安全性风险指标,依托多维度数据源,形成电力系统风险评估关联大数据分析体系。该方法有效解决了电力系统风险评估所涉及的多样性和不确定性问题,提升了风险评估实效。将其应用到重庆市某区域电网全年风险评估中,得到的评估结果符合实际情况,验证了所述方法能够综合反应电网设备状态演变和电网外部环境变迁,提高风险评估的准确性和实时性。
Abstract:In the power system risk assessment research, traditional methods have shortcomings such as long processing time and lack of real-time interaction performance. It is difficult to comprehensively reflect operating status of the grid. This paper proposes a big data architecture method based on Hash algorithm to improve the effectiveness of power system risk assessment. The Hash algorithm is used to correlate the basic data of the power system to the service platform and the risk assessment system. The overhead line outage model and the transformer time-varying outage model are used to model the big data of the power system, and the static and transient security risk indicators are integrated. On the basis of multi-dimensional data sources, the power system risk assessment associated big data analysis system was formed. This method effectively solves the diversity and uncertainty involved in power system risk assessment and improves the effectiveness of risk assessment. Apply it to the annual risk assessment of a regional power grid in Chongqing, and the evaluation results obtained are in line with the actual situation. It is verified that the method can comprehensively reflect the evolution of the state of the grid equipment and the external environment changes of the grid, and improve the accuracy and real-time of the risk assessment.
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