复杂电网连锁故障大停电分析与预防研究
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摘要
随着全球经济的不断发展,电网大规模互联成为电力系统发展的必然趋势,电力网络已成为世界上最复杂的网络之一。该复杂网络的形成,一方面提高了系统的运行效率,另一方面也增加了系统运行的不确定性,同时系统扰动波及范围更广,事故的后果更加严重。近年来国内外发生了多起由连锁故障引起的重大停电事故,造成了巨大损失。连锁故障的分析和预防在各国已作为一个重大的战略问题来研究。
传统的安全分析方法过分注重各原件的个体动态特性,在深入分析电力系统连锁反应事故和大停电机理等系统动态行为方面很难揭示系统整体的动态行为特征。复杂性理论作为系统整体论分析方法,可以自顶向下全面研究停电事故,分析电力连锁故障和大停电的全局性质。本论文结合复杂性理论及连锁故障模型,对和电力系统连锁故障大停电密切相关的网络拓扑、保护故障以及大停电的预测和控制进行了研究,并提出预防连锁故障大停电的整体和局部措施。本文的具体工作如下:
第一章介绍了电力系统连锁故障大停电的研究背景和意义;介绍了连锁故障分析和预防的研究现状和发展前景,特别强调了采用复杂性理论分析和预防电网连锁故障大停电的方法;最后介绍了本文的主要工作以及章节安排。
第二章考虑了线路相继开断的动作特性,首先提出了一个模拟线路连锁跳闸过程的连锁故障动态模型。为比较不同电网拓扑对连锁故障大停电的影响,分别建立了具有小世界、无标度特性的人工电网模型,并基于提出的连锁故障动态模型进行仿真,验证了模型关于连锁故障动态演化过程的合理性。进一步对两种网络拓扑进行比较,发现连锁故障在小世界电网中造成的负荷损失比无标度电网大得多,并且连锁故障传播的速度远比无标度电网迅速,结论从电网结构角度为预防和减小大停电事故提供了指导。
第三章结合电力系统本身的快速增长特性,提出了一种考虑电网演化的改进OPA模型来模拟电网在不同时间尺度上的连锁故障过程。模型在电网连锁故障慢动态过程中考虑了电网结构的演化,揭示了在不同电网演化模式下,人们追求经济利益最大化的目的总会驱使系统自组织到临界状态。模型比较了小世界电网和无标度电网演化方式的连锁故障特性,进一步验证了小世界电网的平均损失负荷大于无标度电网,而无标度电网能通过小规模停电释放系统压力,减少发生大停电的概率和规模。最后,结合进化后网络负载率的不均衡性,提出了潮流熵的概念,揭示了潮流熵的大小是决定电网是否处于临界态的关键因素。
第四章基于风险理论和隐性故障模型,提出了一种复杂电力系统的风险评估方法用于辨识对系统停电影响最大的一系列关键线路。模型考虑了线路过负荷保护、保护隐性故障、控制策略及系统运行等参数,并且考虑了线路连锁跳闸的级联特性,对连锁故障过程中先后开断的线路赋予不同权重。在重负荷运行状态和临界运行状态对系统关键线路的辨识结果验证了模型的有效性。本章从电网局部角度提出了预防和减小连锁故障大停电事故的措施。
第五章基于自组织理论中的协同学原理,提出了一种反映电力系统大停电的普适性模型-协同学模型。指出大停电过程中,总是伴随着电网各子系统的竞争和合作,具有自组织临界特性。进而根据伺服原理、序参量原理建立了大停电演化的序参量方程。计算实例结果表明了大停电过程存在着自组织特性,提出的模型在美加大停电中得到了很好验证,该模型可望为进一步深入分析电力系统连锁反应事故和大停电机理开拓新的研究方向。
第六章为弥补复杂性理论对大电网连锁故障实时安全控制的不足,从预防线路连锁跳闸角度提出了一种大规模电网在线分布式计算的控制方法,提出了最佳切机切负荷控制策略来预防连锁故障。本方法不再把分布式计算局限于分层分区控制,而是把每个电网节点定义为一个智能体。模型中每个智能体在各自邻域内采用预测控制算法,经智能体间的协作求解最优控制措施。利用预测控制的滚动优化,在每个控制步内对系统控制变量和约束集进行筛选,以线性规划求解。每步优化控制算法中,各智能体数据由实际电网状态测量值得到。仿真结果表明了本方法的快速有效性。
With the development of global economy, the interconnected power grid has become one of the largest and most complex modern artifical networks in the world. In this large-scale network, the capabilities that allow power to be transferred over hundreds of miles also enable the propagation of local failures into grid-wide events and even trigger serious consequence. Recently, a series of large blackouts caused by cascading failures have taken place and bring catastrophes to the national economy. Analysis and prevention of cascading blackouts have therefore attracted great attention and been treated as an important strategic problem to study all over the world.
Conventional safety analysis constructs detailed model of every component of the system, and pays attention to dynamic behavior of individual component. Therefore, it is difficult to uncover the global dynamic characteristic while tend to deeply study the cascading failures and the mechanism of large blackouts. The complex theory, as a holism that emphasizes the whole rather than their constituent parts, can provide global and top-down perspectives of cascading blackouts. This dissertation focuses on the complex theory and cascading failure models, and employs them to make systematic and thorough studies on the topology of power grid, protection device failure as well as blackout prediction and preventive control. The main work is as follows.
Firstly, the background and significance of cascading failure and blackout in power systems are introduced. In detail, the situation of current research on cascading failures analysis and prevention is summarized, and a novel approach which studies cascading failures via complex theory is emphasized especially. In addition, the main work and the chapter arrangement of this dissertation are briefly mentioned.
In the second chapter, for the different time-scale of line outage, a dynamical cascading failure model for simulating lines outage is proposed at first. To compare the influence of different topology on cascading failures, two representative complex power grids, small-world network and scale-free network, are performed for line cascading failures. Simulation results prove the rationality of the dynamical evolution process of cascading failures. Moreover, according to the comparison results of the two power grids, the load loss caused by cascading failures in small-world network is much larger than that in scale-free network, and the spreading speed of cascading failures in small-world network is much faster than that in scale-free network. These results can be beneficial not only in areas such as major disturbance mitigation but also in system planning and upgrading.
Traditional OPA model can simulate the cascading failures well but lack for considering the change of topology of the power grid. In the third chapter an improved OPA model is proposed, in which the size growing of the power grid is added to the slow dynamics. Simulation results from two typical complex networks show power grid always organizes itself to criticality in growing model of small-world network as well as scale-free network. Moreover, from the comparison results between two power grids with different growth ways, we find that the characteristic of small-world power grid makes larger load loss than that in scale-free power grid, and cascading failures in scale-free power grid often lead to small-scale lines outage and end with little load loss, which can help to release the system stress and decrease the probability of large blackouts. Furthermore, a novel conception of power flow entropy is defined to display the large heterogenous loading rate of the two grown power grids. The index of power flow entropy is one of the most important factors to judge whether the power system is at critical state.
The fourth chapter presents a solution to find out the key lines affecting the cascading failures most. Based on the Hidden Failure model and risk theory an approach to estimate the risk of line outage and identify key lines causing blackouts is proposed for large-scale power systems. This model considers line overload, hidden failure, control strategy, operation condition and weights of tripped lines. Simulation results indicate that improving a few key lines identified at high loading can reduce the probability of blackouts, especially large blackouts; improving a few key lines identified at critical state can take the system out of critical state and avoid large blackouts.
The fifth chapter investigates the mechanism of large blackouts and points out that in the process of blackouts, subsystems of the power grids are always competitive and cooperative. This process behaves in a manner of self-organized criticality and can be forecasted. Based on the synergetic theory of self-organized criticality, a syergetic model for power system blackout is proposed and an order parameter evolving equation for blackout is described by the order parameter theory and the slaving principle. Simulation results show that power system blackouts reveal self-organized criticality, and the proposed model has satisfying performance in the case study of North America blackouts and can be used to analyze the mechanism of cascading failures and blackouts.
At last, to overcome the shortage of complex theory's application to real-time control of cascading failures in large-scale power grids, an online distributed computing control approach is proposed, and an optimal principle of load shedding and generator tripping is also given in the sixth chapter. In this dissertation, agent is placed at each bus of the power grid differing from traditional distributed method that takes subarea or substratum as an agent. Each agent employs model predictive control to solve its local problem based on locally available data and the global solution can be acquired with agents' cooperation. Further, a filtering method for control variable and constraint set is applied, and linear program method is used in every varying receding-horizon. The data for next varying receding-horizon control is acquired from the measured data of the real power grid. Simulation results on the IEEE118-bus system verify the fleetness and effectiveness of this method.
传统的安全分析方法过分注重各原件的个体动态特性,在深入分析电力系统连锁反应事故和大停电机理等系统动态行为方面很难揭示系统整体的动态行为特征。复杂性理论作为系统整体论分析方法,可以自顶向下全面研究停电事故,分析电力连锁故障和大停电的全局性质。本论文结合复杂性理论及连锁故障模型,对和电力系统连锁故障大停电密切相关的网络拓扑、保护故障以及大停电的预测和控制进行了研究,并提出预防连锁故障大停电的整体和局部措施。本文的具体工作如下:
第一章介绍了电力系统连锁故障大停电的研究背景和意义;介绍了连锁故障分析和预防的研究现状和发展前景,特别强调了采用复杂性理论分析和预防电网连锁故障大停电的方法;最后介绍了本文的主要工作以及章节安排。
第二章考虑了线路相继开断的动作特性,首先提出了一个模拟线路连锁跳闸过程的连锁故障动态模型。为比较不同电网拓扑对连锁故障大停电的影响,分别建立了具有小世界、无标度特性的人工电网模型,并基于提出的连锁故障动态模型进行仿真,验证了模型关于连锁故障动态演化过程的合理性。进一步对两种网络拓扑进行比较,发现连锁故障在小世界电网中造成的负荷损失比无标度电网大得多,并且连锁故障传播的速度远比无标度电网迅速,结论从电网结构角度为预防和减小大停电事故提供了指导。
第三章结合电力系统本身的快速增长特性,提出了一种考虑电网演化的改进OPA模型来模拟电网在不同时间尺度上的连锁故障过程。模型在电网连锁故障慢动态过程中考虑了电网结构的演化,揭示了在不同电网演化模式下,人们追求经济利益最大化的目的总会驱使系统自组织到临界状态。模型比较了小世界电网和无标度电网演化方式的连锁故障特性,进一步验证了小世界电网的平均损失负荷大于无标度电网,而无标度电网能通过小规模停电释放系统压力,减少发生大停电的概率和规模。最后,结合进化后网络负载率的不均衡性,提出了潮流熵的概念,揭示了潮流熵的大小是决定电网是否处于临界态的关键因素。
第四章基于风险理论和隐性故障模型,提出了一种复杂电力系统的风险评估方法用于辨识对系统停电影响最大的一系列关键线路。模型考虑了线路过负荷保护、保护隐性故障、控制策略及系统运行等参数,并且考虑了线路连锁跳闸的级联特性,对连锁故障过程中先后开断的线路赋予不同权重。在重负荷运行状态和临界运行状态对系统关键线路的辨识结果验证了模型的有效性。本章从电网局部角度提出了预防和减小连锁故障大停电事故的措施。
第五章基于自组织理论中的协同学原理,提出了一种反映电力系统大停电的普适性模型-协同学模型。指出大停电过程中,总是伴随着电网各子系统的竞争和合作,具有自组织临界特性。进而根据伺服原理、序参量原理建立了大停电演化的序参量方程。计算实例结果表明了大停电过程存在着自组织特性,提出的模型在美加大停电中得到了很好验证,该模型可望为进一步深入分析电力系统连锁反应事故和大停电机理开拓新的研究方向。
第六章为弥补复杂性理论对大电网连锁故障实时安全控制的不足,从预防线路连锁跳闸角度提出了一种大规模电网在线分布式计算的控制方法,提出了最佳切机切负荷控制策略来预防连锁故障。本方法不再把分布式计算局限于分层分区控制,而是把每个电网节点定义为一个智能体。模型中每个智能体在各自邻域内采用预测控制算法,经智能体间的协作求解最优控制措施。利用预测控制的滚动优化,在每个控制步内对系统控制变量和约束集进行筛选,以线性规划求解。每步优化控制算法中,各智能体数据由实际电网状态测量值得到。仿真结果表明了本方法的快速有效性。
With the development of global economy, the interconnected power grid has become one of the largest and most complex modern artifical networks in the world. In this large-scale network, the capabilities that allow power to be transferred over hundreds of miles also enable the propagation of local failures into grid-wide events and even trigger serious consequence. Recently, a series of large blackouts caused by cascading failures have taken place and bring catastrophes to the national economy. Analysis and prevention of cascading blackouts have therefore attracted great attention and been treated as an important strategic problem to study all over the world.
Conventional safety analysis constructs detailed model of every component of the system, and pays attention to dynamic behavior of individual component. Therefore, it is difficult to uncover the global dynamic characteristic while tend to deeply study the cascading failures and the mechanism of large blackouts. The complex theory, as a holism that emphasizes the whole rather than their constituent parts, can provide global and top-down perspectives of cascading blackouts. This dissertation focuses on the complex theory and cascading failure models, and employs them to make systematic and thorough studies on the topology of power grid, protection device failure as well as blackout prediction and preventive control. The main work is as follows.
Firstly, the background and significance of cascading failure and blackout in power systems are introduced. In detail, the situation of current research on cascading failures analysis and prevention is summarized, and a novel approach which studies cascading failures via complex theory is emphasized especially. In addition, the main work and the chapter arrangement of this dissertation are briefly mentioned.
In the second chapter, for the different time-scale of line outage, a dynamical cascading failure model for simulating lines outage is proposed at first. To compare the influence of different topology on cascading failures, two representative complex power grids, small-world network and scale-free network, are performed for line cascading failures. Simulation results prove the rationality of the dynamical evolution process of cascading failures. Moreover, according to the comparison results of the two power grids, the load loss caused by cascading failures in small-world network is much larger than that in scale-free network, and the spreading speed of cascading failures in small-world network is much faster than that in scale-free network. These results can be beneficial not only in areas such as major disturbance mitigation but also in system planning and upgrading.
Traditional OPA model can simulate the cascading failures well but lack for considering the change of topology of the power grid. In the third chapter an improved OPA model is proposed, in which the size growing of the power grid is added to the slow dynamics. Simulation results from two typical complex networks show power grid always organizes itself to criticality in growing model of small-world network as well as scale-free network. Moreover, from the comparison results between two power grids with different growth ways, we find that the characteristic of small-world power grid makes larger load loss than that in scale-free power grid, and cascading failures in scale-free power grid often lead to small-scale lines outage and end with little load loss, which can help to release the system stress and decrease the probability of large blackouts. Furthermore, a novel conception of power flow entropy is defined to display the large heterogenous loading rate of the two grown power grids. The index of power flow entropy is one of the most important factors to judge whether the power system is at critical state.
The fourth chapter presents a solution to find out the key lines affecting the cascading failures most. Based on the Hidden Failure model and risk theory an approach to estimate the risk of line outage and identify key lines causing blackouts is proposed for large-scale power systems. This model considers line overload, hidden failure, control strategy, operation condition and weights of tripped lines. Simulation results indicate that improving a few key lines identified at high loading can reduce the probability of blackouts, especially large blackouts; improving a few key lines identified at critical state can take the system out of critical state and avoid large blackouts.
The fifth chapter investigates the mechanism of large blackouts and points out that in the process of blackouts, subsystems of the power grids are always competitive and cooperative. This process behaves in a manner of self-organized criticality and can be forecasted. Based on the synergetic theory of self-organized criticality, a syergetic model for power system blackout is proposed and an order parameter evolving equation for blackout is described by the order parameter theory and the slaving principle. Simulation results show that power system blackouts reveal self-organized criticality, and the proposed model has satisfying performance in the case study of North America blackouts and can be used to analyze the mechanism of cascading failures and blackouts.
At last, to overcome the shortage of complex theory's application to real-time control of cascading failures in large-scale power grids, an online distributed computing control approach is proposed, and an optimal principle of load shedding and generator tripping is also given in the sixth chapter. In this dissertation, agent is placed at each bus of the power grid differing from traditional distributed method that takes subarea or substratum as an agent. Each agent employs model predictive control to solve its local problem based on locally available data and the global solution can be acquired with agents' cooperation. Further, a filtering method for control variable and constraint set is applied, and linear program method is used in every varying receding-horizon. The data for next varying receding-horizon control is acquired from the measured data of the real power grid. Simulation results on the IEEE118-bus system verify the fleetness and effectiveness of this method.
引文
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