电力系统暂态脆弱性评估及连锁故障跳闸预测研究
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摘要
国内外频发的各类大停电事故表明,如何应对因系统失稳造成的电力系统大面积停电,保证电网正常运行,已成为明确且紧迫的研究课题。评估电力系统的脆弱程度和稳定程度,以及加强对电网可能存在的连锁跳闸事件进行预测分析,为制定正确的应对措施提供理论依据,是防范和减少电网发生恶性大面积停电事故的一项重要的基础性工作。
论文从电网可能发生的短路故障入手,在电网的脆弱性评估、暂态稳定评估及不发生暂态失稳却可能由于过负荷等因素引起的连锁跳闸事件方面展开深入研究,主要研究成果如下:
建立了计及气候因素的故障概率模型,提出了从暂态能量裕度角度对电网脆弱性进行评估的思想。为避免严重程度高但发生概率小的事故被忽略掉,提出使用概率和能量裕度两个指标来评估系统的脆弱性。借助暂态能量函数定义了事故的脆弱性能量裕度指标,它能反映负荷水平的影响。对各事故的脆弱性裕度指标进行排序,可以识别出系统的薄弱环节。借助概率理论和范数理论,利用各事故脆弱性的概率指标和能量裕度指标分别推导出计算整个系统综合脆弱性的概率指标和能量裕度指标的模型,并在此基础上提出了电网脆弱性的评估算法。
提出了将提升型贝叶斯分类器应用于电力系统暂态稳定评估。为减少不必要属性变量的收集,采用灰色关联聚类法对反映电力系统暂态过程的相关变量进行约减,筛选出12个特征量作为贝叶斯分类器的属性变量,把稳定还是不稳定作为类变量。由于贝叶斯分类器只能处理离散变量,因此对连续的属性数据采用信息熵和粗糙集理论进行离散化处理。采用AdaBoost算法对单个朴素贝叶斯分类器进行提升,构造了提升型暂态稳定评估贝叶斯联合分类器,有效降低了分类器的误分类率。
在剖析线路过负荷引发连锁故障原因的基础上,提出一种连锁跳闸序列事件的预测方法。为了避免盲目的线路开断选择,快速筛选出后果严重且较易发生的连锁故障模式,对初始故障进行指定,采用直流潮流模型进行潮流计算,定义了过负荷严重度指标,利用启发式搜索策略提出了一种基于线路过负荷严重度并计及隐性故障影响的预测方法。该方法在原有隐性故障模型的基础上提出了广义的隐性故障模型,该模型综合考虑了由于继电保护隐性失败引起的故障线路和与初始故障线路在同一输电断面上的隐性故障线路,形成隐性故障线路集。继电保护隐性失败引起的隐性故障线路由与初始故障线路两端相连的线路组成;而与初始故障线路在同一输电断面上的隐性故障线路,运用图论知识对网络拓扑图进行分区处理,采用图搜索算法予以实现。
Blackouts happened recently around the world showed how to deal with large-scale power system blackout so as to ensure the normal operation of power grids has become a clear and urgent research issue. To assess the vulnerability and stability of power systems, as well as to strengthen to analyze and predict cascading trip events which probably exist in power system, providing the theoretical basis for formulating correct counter measures, is an important fundamental work that prevent and reduce the occurrence of vicious large-scale blackouts in power grid.
This thesis, starting from the fault may occurred in power grid, launches deep researches on assessing vulnerability of power grid, assessing transient stability of power system and cascading trip events caused by overload and other factors on condition that the power system did not lose transient stability, the main research work are as follows:
Contingent faults’probability models taking the weather factor into account are set up, and the idea of evaluating a power system’s vulnerability based on the system’s transient energy margin is proposed. In order to avoid a serious fault of smaller occurring probability is ignored, the idea of using both probability index and energy margin index to evaluate a system’s vulnerability is put forward. The formula to calculate the energy margin index of a fault is defined based on the transient energy function, which can reflect the influence of load demand change to the system's vulnerability. The weak components in the system can be found by means of energy margin indices relating to faults. Moreover, based on probability theory and norm theory, the models to calculate compositive probability index and energy margin index of a power system are derived. The models’parameters are probability indices and energy margin indices of all possible faults in the system. Based on the models, an assessing algorithm for power system vulnerability is proposed.
A method of applying Boostting Bayesian classifier to power system transient stability assessment is proposed. In order to avoid collecting the unnecessary attribute variables, the variables reflecting power system transient process are extracted by using grey relational clustering method, and then 12 characteristic variables are selected to be attribute variables of Bayesian classifier, at the same time take stability or instability as class variables. Because the Bayesian classifier can only handle the discrete variables, continual attribute data is dispersed based on information entropy and rough set theory. According to AdaBoost algorithm, single Naive Bayesian classifier is boosting, and then a boosting united Bayesian classifier using to assess transient stability is constructed which reduces the misclassification rate effectively.
At the basis of analyzing the cause of cascading failures triggered by line overload, a method of predicting chain trip sequence event is proposed. In order to avoid breaking line blindly and screen the cascading failures mode which causes serious consequences and is prone to happen rapidly, assign the initial breakdown, calculate the power flow using DC power flow model, define the index which reflect the severity of overload, and then a prediction method is proposed based on the severity of line overload considering the impact of hidden failures according to the heuristic search strategy. This method proposes generalized hidden fault model based on the original fault model, and this model forms the hidden failure set taking into account the fault lines caused by hidden failures of relay protection and the hidden failure line which is in the same transmission section as the original fault line. The fault lines caused by hidden failures of relay protection are composed with the lines connected to the both sides of original fault line; the hidden failure lines which are in the same transmission section as the original fault line can be find out by chart searching algorithm after dividing the network topology map into several regions according to graph theory knowledge.
论文从电网可能发生的短路故障入手,在电网的脆弱性评估、暂态稳定评估及不发生暂态失稳却可能由于过负荷等因素引起的连锁跳闸事件方面展开深入研究,主要研究成果如下:
建立了计及气候因素的故障概率模型,提出了从暂态能量裕度角度对电网脆弱性进行评估的思想。为避免严重程度高但发生概率小的事故被忽略掉,提出使用概率和能量裕度两个指标来评估系统的脆弱性。借助暂态能量函数定义了事故的脆弱性能量裕度指标,它能反映负荷水平的影响。对各事故的脆弱性裕度指标进行排序,可以识别出系统的薄弱环节。借助概率理论和范数理论,利用各事故脆弱性的概率指标和能量裕度指标分别推导出计算整个系统综合脆弱性的概率指标和能量裕度指标的模型,并在此基础上提出了电网脆弱性的评估算法。
提出了将提升型贝叶斯分类器应用于电力系统暂态稳定评估。为减少不必要属性变量的收集,采用灰色关联聚类法对反映电力系统暂态过程的相关变量进行约减,筛选出12个特征量作为贝叶斯分类器的属性变量,把稳定还是不稳定作为类变量。由于贝叶斯分类器只能处理离散变量,因此对连续的属性数据采用信息熵和粗糙集理论进行离散化处理。采用AdaBoost算法对单个朴素贝叶斯分类器进行提升,构造了提升型暂态稳定评估贝叶斯联合分类器,有效降低了分类器的误分类率。
在剖析线路过负荷引发连锁故障原因的基础上,提出一种连锁跳闸序列事件的预测方法。为了避免盲目的线路开断选择,快速筛选出后果严重且较易发生的连锁故障模式,对初始故障进行指定,采用直流潮流模型进行潮流计算,定义了过负荷严重度指标,利用启发式搜索策略提出了一种基于线路过负荷严重度并计及隐性故障影响的预测方法。该方法在原有隐性故障模型的基础上提出了广义的隐性故障模型,该模型综合考虑了由于继电保护隐性失败引起的故障线路和与初始故障线路在同一输电断面上的隐性故障线路,形成隐性故障线路集。继电保护隐性失败引起的隐性故障线路由与初始故障线路两端相连的线路组成;而与初始故障线路在同一输电断面上的隐性故障线路,运用图论知识对网络拓扑图进行分区处理,采用图搜索算法予以实现。
Blackouts happened recently around the world showed how to deal with large-scale power system blackout so as to ensure the normal operation of power grids has become a clear and urgent research issue. To assess the vulnerability and stability of power systems, as well as to strengthen to analyze and predict cascading trip events which probably exist in power system, providing the theoretical basis for formulating correct counter measures, is an important fundamental work that prevent and reduce the occurrence of vicious large-scale blackouts in power grid.
This thesis, starting from the fault may occurred in power grid, launches deep researches on assessing vulnerability of power grid, assessing transient stability of power system and cascading trip events caused by overload and other factors on condition that the power system did not lose transient stability, the main research work are as follows:
Contingent faults’probability models taking the weather factor into account are set up, and the idea of evaluating a power system’s vulnerability based on the system’s transient energy margin is proposed. In order to avoid a serious fault of smaller occurring probability is ignored, the idea of using both probability index and energy margin index to evaluate a system’s vulnerability is put forward. The formula to calculate the energy margin index of a fault is defined based on the transient energy function, which can reflect the influence of load demand change to the system's vulnerability. The weak components in the system can be found by means of energy margin indices relating to faults. Moreover, based on probability theory and norm theory, the models to calculate compositive probability index and energy margin index of a power system are derived. The models’parameters are probability indices and energy margin indices of all possible faults in the system. Based on the models, an assessing algorithm for power system vulnerability is proposed.
A method of applying Boostting Bayesian classifier to power system transient stability assessment is proposed. In order to avoid collecting the unnecessary attribute variables, the variables reflecting power system transient process are extracted by using grey relational clustering method, and then 12 characteristic variables are selected to be attribute variables of Bayesian classifier, at the same time take stability or instability as class variables. Because the Bayesian classifier can only handle the discrete variables, continual attribute data is dispersed based on information entropy and rough set theory. According to AdaBoost algorithm, single Naive Bayesian classifier is boosting, and then a boosting united Bayesian classifier using to assess transient stability is constructed which reduces the misclassification rate effectively.
At the basis of analyzing the cause of cascading failures triggered by line overload, a method of predicting chain trip sequence event is proposed. In order to avoid breaking line blindly and screen the cascading failures mode which causes serious consequences and is prone to happen rapidly, assign the initial breakdown, calculate the power flow using DC power flow model, define the index which reflect the severity of overload, and then a prediction method is proposed based on the severity of line overload considering the impact of hidden failures according to the heuristic search strategy. This method proposes generalized hidden fault model based on the original fault model, and this model forms the hidden failure set taking into account the fault lines caused by hidden failures of relay protection and the hidden failure line which is in the same transmission section as the original fault line. The fault lines caused by hidden failures of relay protection are composed with the lines connected to the both sides of original fault line; the hidden failure lines which are in the same transmission section as the original fault line can be find out by chart searching algorithm after dividing the network topology map into several regions according to graph theory knowledge.
引文
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