面向过程测量数据的暂态稳定算法研究
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摘要
暂态稳定分析与控制是电力系统运行中的重要问题之一,目前在这方面已经进行了较为深入的研究,并且取得了丰硕的研究成果。但是,近年来的事故记录表明,传统基于时间断面和系统模型的暂态稳定分析方法存在着较大误差,导致控制措施难以适应现代电力系统。广域测量系统(WAMS)在电力系统中的发展和应用,为电力系统分析与控制提供了新的技术支持。本文正是在这种条件下,提出了一种利用WAMS过程测量数据进行快速暂态稳定分析与控制的方法。该方法不仅体现了“基于测量,面向过程”的电力系统计算思想,而且对于提高暂态稳定计算的准确性与适应性具有重要意义。
本文首先从分析基于时间断面和系统模型算法的误差入手,借助于矩阵分析理论,研究了传统电力系统计算方法中存在的误差来源以及它们对计算结果的影响。根据对计算的不同需求,本文将电力系统计算划分为面向短期目标的实时计算、面向中期目标的离线计算和面向长期目标的规划计算,并指出了各种计算对测量、模型以及算法的依赖性。其中,实时计算应该更多地依赖测量,离线计算应该更多地依赖模型,而规划计算则应更多地依赖算法。
在研究如何利用WAMS过程数据进行快速暂态稳定分析之前,本文探讨了WAMS构建与PMU配置问题。分析了构建WAMS可能遇到的诸如通信、存储、管理、应用等技术难点;探讨了电力系统运行控制对WAMS的应用需求;得出了在当前经济技术水平下,构建WAMS应该考虑与现有SCADA系统互为补充的结论。讨论了在WAMS,SCADA互补系统中的PMU配置以及数据处理方法。传统暂态稳定分析方法是从故障前稳定平衡点开始的,然而实际系统总是处在或大或小的扰动之中,因此,这种分析方法不能准确地表现系统真实的运行情况。针对这种现状,本文提出了一种基于WAMS过程数据的快速暂态稳定预估方法。该方法利用故障前系统实际的WAMS过程测量数据,通过数值处理方法获得系统运行状态的初值和高阶导数,从而利用状态量的高阶Taylor级数展开式对预想故障后的系统状态进行快速预测。由于时间过程不仅是系统动态轨迹的体现,而且还是计算效率的累积,因此,该方法除了能够较好地符合系统实际情况外,还能够获得比常规高阶Taylor级数法暂态稳定快约一个数量级的计算速度。通过误差分析和计算量统计,证明了此方法的正确性和高效性。本文还给出了该方法在采用发电机双轴二阶模型和五阶实用模型时的具体实现步骤。
Transient stability analysis and control is one of the most austere problems in power system operation. Although many efforts have been taken to study the problem and plentiful and substantial results have been achieved in many respects. More and more evidence shows that there are errors in the traditional method, which is based on the time-fraction snapshot data. Even with the support of WAMS, the correctness of the result cannot improve radically. Based on this condition, this dissertation proposes a transient analysis and control method based on the WAMS process measurement. This method incarnates a new ideologies of Measurement based and process oriented in power system calculation, and can improve the veracity and adaptability of transient stability analysis.
Starting with analyzing the error of algorithm based on time-fraction and system model, the dissertation investigates the inherent limitation of traditional power system calculation methods. According to the demand for the power system calculation, the calculations are classified as real time calculation orienting short-term objects, off-line calculation orienting medium-term objects and planning calculation orienting long-term objects. The dependence on measurement and model of each calculation is pointed out, that is, the real time calculation should depend more on measurement, the off-line calculation should depend more on system model, and the planning calculation should depend more on algorithm.
Firstly, This dissertation discusses the problem of WAMS construction and PMUs (Phase Measurement Units) placement. After analyzing in detail the technical difficulties of constructing WAMS and the requirements of power system operation and control to it, the paper points out that the construction of WAMS should complement the existing SCADA (Supervisory Control And Data Acquisition) under present economic level, and the principle should also be conformed to during the placement of PMUs. The information extending method and curve fitting method, which deals with data in the complementary system of WAMS and SCADA, are discussed.
Secondly, traditional transient stability analysis method starts at the static
本文首先从分析基于时间断面和系统模型算法的误差入手,借助于矩阵分析理论,研究了传统电力系统计算方法中存在的误差来源以及它们对计算结果的影响。根据对计算的不同需求,本文将电力系统计算划分为面向短期目标的实时计算、面向中期目标的离线计算和面向长期目标的规划计算,并指出了各种计算对测量、模型以及算法的依赖性。其中,实时计算应该更多地依赖测量,离线计算应该更多地依赖模型,而规划计算则应更多地依赖算法。
在研究如何利用WAMS过程数据进行快速暂态稳定分析之前,本文探讨了WAMS构建与PMU配置问题。分析了构建WAMS可能遇到的诸如通信、存储、管理、应用等技术难点;探讨了电力系统运行控制对WAMS的应用需求;得出了在当前经济技术水平下,构建WAMS应该考虑与现有SCADA系统互为补充的结论。讨论了在WAMS,SCADA互补系统中的PMU配置以及数据处理方法。传统暂态稳定分析方法是从故障前稳定平衡点开始的,然而实际系统总是处在或大或小的扰动之中,因此,这种分析方法不能准确地表现系统真实的运行情况。针对这种现状,本文提出了一种基于WAMS过程数据的快速暂态稳定预估方法。该方法利用故障前系统实际的WAMS过程测量数据,通过数值处理方法获得系统运行状态的初值和高阶导数,从而利用状态量的高阶Taylor级数展开式对预想故障后的系统状态进行快速预测。由于时间过程不仅是系统动态轨迹的体现,而且还是计算效率的累积,因此,该方法除了能够较好地符合系统实际情况外,还能够获得比常规高阶Taylor级数法暂态稳定快约一个数量级的计算速度。通过误差分析和计算量统计,证明了此方法的正确性和高效性。本文还给出了该方法在采用发电机双轴二阶模型和五阶实用模型时的具体实现步骤。
Transient stability analysis and control is one of the most austere problems in power system operation. Although many efforts have been taken to study the problem and plentiful and substantial results have been achieved in many respects. More and more evidence shows that there are errors in the traditional method, which is based on the time-fraction snapshot data. Even with the support of WAMS, the correctness of the result cannot improve radically. Based on this condition, this dissertation proposes a transient analysis and control method based on the WAMS process measurement. This method incarnates a new ideologies of Measurement based and process oriented in power system calculation, and can improve the veracity and adaptability of transient stability analysis.
Starting with analyzing the error of algorithm based on time-fraction and system model, the dissertation investigates the inherent limitation of traditional power system calculation methods. According to the demand for the power system calculation, the calculations are classified as real time calculation orienting short-term objects, off-line calculation orienting medium-term objects and planning calculation orienting long-term objects. The dependence on measurement and model of each calculation is pointed out, that is, the real time calculation should depend more on measurement, the off-line calculation should depend more on system model, and the planning calculation should depend more on algorithm.
Firstly, This dissertation discusses the problem of WAMS construction and PMUs (Phase Measurement Units) placement. After analyzing in detail the technical difficulties of constructing WAMS and the requirements of power system operation and control to it, the paper points out that the construction of WAMS should complement the existing SCADA (Supervisory Control And Data Acquisition) under present economic level, and the principle should also be conformed to during the placement of PMUs. The information extending method and curve fitting method, which deals with data in the complementary system of WAMS and SCADA, are discussed.
Secondly, traditional transient stability analysis method starts at the static
引文
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