具有高容错能力的电力系统故障诊断的解析模型与方法
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摘要
当电力系统一次设备发生故障时,调度中心将会接收到来自不同监测系统的海量故障警报,调度员要在很短的时间内处理众多信息并准确地判断出故障根源是极其困难的。因此,需要借助故障诊断系统快速准确的识别故障,以确保系统的安全稳定运行并提高其供电可靠性。现有的故障诊断解析模型存在的主要问题在于：所利用的故障信息源单一,信息的冗余度不高,所建立的模型过于依赖故障发生后的保护和断路器动作信息,然而实际故障发生后,保护和断路器可能发生拒动或误动,且在上传警报的过程中,会存在警报上传速度慢、警报错误或警报丢失的情况,导致故障诊断所利用的信息的可靠性下降,从而影响了诊断的结果的正确性。
在此背景下,本论文以现有的基于解析模型的方法为基本思路,充分考虑了故障过程中可能发生的各种不确定因素,通过建立更加复杂精确的模型和增加信息冗余度的方法提高模型的容错能力,发展了更加精确合理的故障诊断数学模型和方法,并取得了一定的研究成果：
1)首先分析了故障诊断系统的目标和功能,并深入了解故障信息源的组成和特点。针对故障信息的分层特性,构建了电力系统故障诊断的分层结构,这种结构能够根据不同的故障状况,对故障信息进行合理的利用,一方面在简单故障时能够提高诊断速度,另一方面在复杂故障时能够提高诊断的准确度。
2)对现有的故障诊断解析模型做了比较详细的介绍。首先,研究了故障区域识别的实现过程,分析了其原理,阐述了其步骤。然后,对解析模型的目标函数进行了介绍,提出了各保护和断路器期望状态的计算式。最后,介绍了在实际应用中,目标函数自动形成所需要解决的问题。
3)为适当处理保护/断路器的拒动或误动以及警报的误报或漏报等不确定性因素,本文充分的考虑了继电保护和断路器的误动和拒动,警报信息的误报和漏报,将机会约束规划模型引入到电力系统故障诊断领域,发展了一类新的故障诊断模型与方法,具有较强的容错能力。之后,采用了基于蒙特卡罗仿真的遗传算法对所建立的模型进行求解。最后,用浙江电力系统实际发生过的复杂故障对所提出的模型和方法进行了测试,得到了与实际情况一致的结果,计算速度也满足在线故障诊断的要求。
4)针对现有故障诊断方法信息源单一,信息冗余度不高的问题,提出了将WAMS获取的电气量信息引入故障诊断解析模型,以提高方法的容错能力。首先,将故障后断路器警报信息和电气量信息相结合,提出了一种快速的故障识别方法,增强了其容错能力。然后,建立了故障诊断的电气量判据,并对其进行处理,使其适用于解析模型,对各判据和保护/断路器的期望状态进行重新建模。通过算例证明,该方法即使在存在大量警报丢失或保护/断路器异常动作的情况下,依然能够快速准确的得到诊断结构。
最后对论文中所作的研究进行简要总结,并指出了这一领域有待进一步深入研究的问题。
When a fault occurs in a section or component of a given power system, the dispatching center will receive a flood of alarms from different monitoring systems, thus it is difficult for the dispatchers to process the information and judge the fault location accurately in a short period of time. Therefore, a fault diagnosis system is introduced to identify faults rapidly and accurately, in order to ensure the security and stability of the power system associated and improve the reliability of power supply. The main problems of the existing analytic models for fault diagnosis include:limited fault information source, low information redundancy, over reliance on the information of protection relays (PRs) and circuit breakers (CBs) after the fault. In an actual situation, PRs and CBs may fail to operate or refuse to act, and some alarms may be false or lost in the transmitting process, which have negative impacts on the accuracy of fault diagnosis result.
Given this background, based on existing analytic model, uncertainties in the fault process are considered to improve the fault tolerance capability by developing a more reasonable and accurate model and increasing the information redundancy. Some research results are obtained as follows:
1) The aim and functions of the fault diagnosis system are first analyzed, and the composition and characteristics of the fault information source are introduced. According to the layered characteristics of the fault information, a layered structure of power system fault diagnosis is constructed. The fault information is used reasonably according to the fault condition:on one hand, the diagnosis process is accelerated in a simple fault; on the other hand, the accuracy can be improved in a complex fault.
2) The existing analytic model is introduced. First, the process of fault area identification is studied, the theory is analyzed and the steps are stated. Then, the objective function of the analytic model is introduced; the formulas of the expected states of PRs and CBs are put forward. Finally, the problems that need to solve in the automatic forming process of the objective function.
3) In order to handle uncertain factors, including the malfunctioning and other improper actions of PRs and CBs, in addition to the false and/or missing alarms, the authors have introduced a chance-constrained programming model into the application of power system fault diagnosis. This thesis presents such a novel type of fault diagnosis model with high fault tolerance. The Monte Carlo simulation based genetic algorithm is employed to solve the model. An actual complex fault scenario at a substation in Zhejiang province, China, is used to test the proposed method. As shown by the case study, the developed model produced a diagnosis result consistent with the real fault. Furthermore, the computation speed of the developed method meets the requirements of on-line fault diagnosis.
4) The existing analytic model for power system fault diagnosis with a single data resource is insufficient in information redundancy and has difficulty in getting the correct diagnosis in a complex fault. The development of WAMS based on PMU makes real-time and accurate electrical data information of the system available. Therefore, electrical data information is introduced to improve the existing analytic model. Fault area is rapidly confirmed using states of circuit breakers combined with electrical data. Then, fault section is diagnosed using the developed analytic model. Finally, a fault scenario is served for demonstrating the feasibility and efficiency of the developed model. It is verified by simulation results that the developed model has high fault tolerance and can locate fault even with a large amount of malfunctions and lost alarms.
Finally, several conclusions are obtained based on the research outcomes, and directions for future research indicated.
在此背景下,本论文以现有的基于解析模型的方法为基本思路,充分考虑了故障过程中可能发生的各种不确定因素,通过建立更加复杂精确的模型和增加信息冗余度的方法提高模型的容错能力,发展了更加精确合理的故障诊断数学模型和方法,并取得了一定的研究成果：
1)首先分析了故障诊断系统的目标和功能,并深入了解故障信息源的组成和特点。针对故障信息的分层特性,构建了电力系统故障诊断的分层结构,这种结构能够根据不同的故障状况,对故障信息进行合理的利用,一方面在简单故障时能够提高诊断速度,另一方面在复杂故障时能够提高诊断的准确度。
2)对现有的故障诊断解析模型做了比较详细的介绍。首先,研究了故障区域识别的实现过程,分析了其原理,阐述了其步骤。然后,对解析模型的目标函数进行了介绍,提出了各保护和断路器期望状态的计算式。最后,介绍了在实际应用中,目标函数自动形成所需要解决的问题。
3)为适当处理保护/断路器的拒动或误动以及警报的误报或漏报等不确定性因素,本文充分的考虑了继电保护和断路器的误动和拒动,警报信息的误报和漏报,将机会约束规划模型引入到电力系统故障诊断领域,发展了一类新的故障诊断模型与方法,具有较强的容错能力。之后,采用了基于蒙特卡罗仿真的遗传算法对所建立的模型进行求解。最后,用浙江电力系统实际发生过的复杂故障对所提出的模型和方法进行了测试,得到了与实际情况一致的结果,计算速度也满足在线故障诊断的要求。
4)针对现有故障诊断方法信息源单一,信息冗余度不高的问题,提出了将WAMS获取的电气量信息引入故障诊断解析模型,以提高方法的容错能力。首先,将故障后断路器警报信息和电气量信息相结合,提出了一种快速的故障识别方法,增强了其容错能力。然后,建立了故障诊断的电气量判据,并对其进行处理,使其适用于解析模型,对各判据和保护/断路器的期望状态进行重新建模。通过算例证明,该方法即使在存在大量警报丢失或保护/断路器异常动作的情况下,依然能够快速准确的得到诊断结构。
最后对论文中所作的研究进行简要总结,并指出了这一领域有待进一步深入研究的问题。
When a fault occurs in a section or component of a given power system, the dispatching center will receive a flood of alarms from different monitoring systems, thus it is difficult for the dispatchers to process the information and judge the fault location accurately in a short period of time. Therefore, a fault diagnosis system is introduced to identify faults rapidly and accurately, in order to ensure the security and stability of the power system associated and improve the reliability of power supply. The main problems of the existing analytic models for fault diagnosis include:limited fault information source, low information redundancy, over reliance on the information of protection relays (PRs) and circuit breakers (CBs) after the fault. In an actual situation, PRs and CBs may fail to operate or refuse to act, and some alarms may be false or lost in the transmitting process, which have negative impacts on the accuracy of fault diagnosis result.
Given this background, based on existing analytic model, uncertainties in the fault process are considered to improve the fault tolerance capability by developing a more reasonable and accurate model and increasing the information redundancy. Some research results are obtained as follows:
1) The aim and functions of the fault diagnosis system are first analyzed, and the composition and characteristics of the fault information source are introduced. According to the layered characteristics of the fault information, a layered structure of power system fault diagnosis is constructed. The fault information is used reasonably according to the fault condition:on one hand, the diagnosis process is accelerated in a simple fault; on the other hand, the accuracy can be improved in a complex fault.
2) The existing analytic model is introduced. First, the process of fault area identification is studied, the theory is analyzed and the steps are stated. Then, the objective function of the analytic model is introduced; the formulas of the expected states of PRs and CBs are put forward. Finally, the problems that need to solve in the automatic forming process of the objective function.
3) In order to handle uncertain factors, including the malfunctioning and other improper actions of PRs and CBs, in addition to the false and/or missing alarms, the authors have introduced a chance-constrained programming model into the application of power system fault diagnosis. This thesis presents such a novel type of fault diagnosis model with high fault tolerance. The Monte Carlo simulation based genetic algorithm is employed to solve the model. An actual complex fault scenario at a substation in Zhejiang province, China, is used to test the proposed method. As shown by the case study, the developed model produced a diagnosis result consistent with the real fault. Furthermore, the computation speed of the developed method meets the requirements of on-line fault diagnosis.
4) The existing analytic model for power system fault diagnosis with a single data resource is insufficient in information redundancy and has difficulty in getting the correct diagnosis in a complex fault. The development of WAMS based on PMU makes real-time and accurate electrical data information of the system available. Therefore, electrical data information is introduced to improve the existing analytic model. Fault area is rapidly confirmed using states of circuit breakers combined with electrical data. Then, fault section is diagnosed using the developed analytic model. Finally, a fault scenario is served for demonstrating the feasibility and efficiency of the developed model. It is verified by simulation results that the developed model has high fault tolerance and can locate fault even with a large amount of malfunctions and lost alarms.
Finally, several conclusions are obtained based on the research outcomes, and directions for future research indicated.
引文
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