采用模糊理论的覆冰灾害风险评估
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摘要
为评估覆冰灾害对电网可能的风险,建立一个基于气象信息的电网冰灾风险评估模型。首先利用模糊理论建立基于气象信息的线路覆冰厚度预测模型,给出模糊推理系统各项参数具体的确定过程。其次分析风力载荷,冰力载荷对线路失效率影响,讨论融冰装置的投入情况与线路覆冰厚度关系,同时确定冰灾对线路修复时间的影响。最后给定风险指标,综合上述内容建立电网冰灾风险评估模型。算例仿真结果表明,覆冰厚度预测值与实际值的误差小于4 mm,覆冰厚度预测模型可行且有效;冰灾风险评估模型综合考虑了多因素对电网冰灾风险的影响,论证融冰机等措施的实行能极好降低冰灾风险,并通过分析风险损失费用,确定最优初始融冰厚度。
To evaluate risk of ice disaster for power system, a model based on weather data is made for risk evaluation. The ice prediction fuzzy logic model is built at first. Firstly, the prediction model of ice cover thickness was established by using fuzzy theory. Then the influence of ice cover thickness and ice melting device on line failure efficiency and line repair time was analyzed. Finally, the risk assessment model of ice disaster was established comprehensively. The simulation results show that the error between the predicted value and the actual one is less than 4 mm, and the prediction model is feasible and effective. The risk assessment model takes into account the influence of many factors on the ice disaster risk of power grid.
To evaluate risk of ice disaster for power system, a model based on weather data is made for risk evaluation. The ice prediction fuzzy logic model is built at first. Firstly, the prediction model of ice cover thickness was established by using fuzzy theory. Then the influence of ice cover thickness and ice melting device on line failure efficiency and line repair time was analyzed. Finally, the risk assessment model of ice disaster was established comprehensively. The simulation results show that the error between the predicted value and the actual one is less than 4 mm, and the prediction model is feasible and effective. The risk assessment model takes into account the influence of many factors on the ice disaster risk of power grid.
引文
[1]胡志鹏. 输电线路多因素风险评估及巡检策略研究[D]. 武汉:武汉大学, 2018.
[2]YING Z, SU X. Icing thickness forecasting of transmission line based on particle swarm algorithm to optimize SVM[J]. Journal of Electric Power,2014(29): 6-9.
[3]陈百炼,胡欣欣,吴息,等. 基于过程判别的雨雾凇导线覆冰气象模式[J]. 应用气象学报,2018, 29(3): 354-363.
[4]晏鸣宇,周志宇,文劲宇,等. 基于短期覆冰预测的电网覆冰灾害风险评估方法[J]. 电力系统自动化,2016, 40(21): 168-175.
[5]刘宏伟,陆佳政,赖旬阳,等. 输电线路覆冰厚度短期多变量灰色预测模型研究[J]. 高电压技术,2015, 41(10): 3372-3377.
[6]张子翀. 输电线路综合荷载等值覆冰厚度预测模型与应用研究[D]. 广州:华南理工大学, 2016.
[7]吴昊. 电网连锁故障筛选及风险分析[J]. 电力安全技术,2018, 20(4): 19-23.
[8]黄新波,李佳杰,欧阳丽莎,等. 采用模糊逻辑理论的覆冰厚度预测模型[J]. 高电压技术,2011, 37(5): 1245-1252.
[9]吕健双,李健.特高压输电线路覆冰断线张力计算与分析[J].电力科学与工程,2013,29(8):10-15.
[10]石辛民,郝整清. 模糊控制及其MATLAB仿真[M]. 北京:清华大学出版社, 2008.
[11]尹世锋,卢勇,王闸等.基于CACA-WNN的恶劣气候条件下架空输电线路覆冰厚度的预测[J].电力科学与工程,2012,28(11):28-31.
[12]张勇军,许亮,吴成文. 计及多因素的电网冰灾风险评估模型研究[J]. 电力系统保护与控制,2012, 40(15): 12-17.
[13]宋宏佺. 输电线路导线及地线复用新型直流融冰装置技术研究综述[J]. 南方能源建设,2018, 5(3): 72-76.
[14]李文沅. 电力系统风险评估——模型、方法和应用[M]. 北京:科学出版社, 2006.
[2]YING Z, SU X. Icing thickness forecasting of transmission line based on particle swarm algorithm to optimize SVM[J]. Journal of Electric Power,2014(29): 6-9.
[3]陈百炼,胡欣欣,吴息,等. 基于过程判别的雨雾凇导线覆冰气象模式[J]. 应用气象学报,2018, 29(3): 354-363.
[4]晏鸣宇,周志宇,文劲宇,等. 基于短期覆冰预测的电网覆冰灾害风险评估方法[J]. 电力系统自动化,2016, 40(21): 168-175.
[5]刘宏伟,陆佳政,赖旬阳,等. 输电线路覆冰厚度短期多变量灰色预测模型研究[J]. 高电压技术,2015, 41(10): 3372-3377.
[6]张子翀. 输电线路综合荷载等值覆冰厚度预测模型与应用研究[D]. 广州:华南理工大学, 2016.
[7]吴昊. 电网连锁故障筛选及风险分析[J]. 电力安全技术,2018, 20(4): 19-23.
[8]黄新波,李佳杰,欧阳丽莎,等. 采用模糊逻辑理论的覆冰厚度预测模型[J]. 高电压技术,2011, 37(5): 1245-1252.
[9]吕健双,李健.特高压输电线路覆冰断线张力计算与分析[J].电力科学与工程,2013,29(8):10-15.
[10]石辛民,郝整清. 模糊控制及其MATLAB仿真[M]. 北京:清华大学出版社, 2008.
[11]尹世锋,卢勇,王闸等.基于CACA-WNN的恶劣气候条件下架空输电线路覆冰厚度的预测[J].电力科学与工程,2012,28(11):28-31.
[12]张勇军,许亮,吴成文. 计及多因素的电网冰灾风险评估模型研究[J]. 电力系统保护与控制,2012, 40(15): 12-17.
[13]宋宏佺. 输电线路导线及地线复用新型直流融冰装置技术研究综述[J]. 南方能源建设,2018, 5(3): 72-76.
[14]李文沅. 电力系统风险评估——模型、方法和应用[M]. 北京:科学出版社, 2006.