基于拓扑的大电网暂态失稳模式智能辨识算法研究

发布时间：2018-10-09 11:01

【摘要】：目前我国大电网采用的是由预防控制、紧急控制和恢复控制组成的三层安全防御体系。预防控制策略的搜索和决策必须建立在暂态稳定评估的基础之上。目前对于暂态稳定的评估只能通过枚举事故，并针对每个事故逐个求解微分方程组，观察输出变量的变化关系来进行判断。对于运行方式变化与稳定水平变化之间的关联关系尚未建立起任何规则或模型。这就导致目前的预防控制多数依靠运行人员的经验，参考大量预想工况的离线稳定仿真结果，以试探的方式调整运行方式安排。随着新能源发电的加入，系统结构和运行工况日益复杂，大电网的不可预测性不断增强，单纯依靠人的经验进行判断和决策已经难以驾驭大电网的安全运行。调度中心每天针对运行方式的变化不断进行着在线和离线稳定评估。这些评估结果实际上已经为研究运行方式与稳定水平的关联关系提供了大量的样本。广域相位量测信息的加入也使得电网状态的可观测性不断增强。应用数据挖掘和模式识别技术，结合稳定机理分析，研究和建立电网拓扑和运行方式与电网失稳模式和稳定水平的关联关系，将为大电网的智能预防控制决策提供有效的支撑，也是电网调度智能化和电网的大数据挖据的重要应用。主导失稳机群的运动是决定暂态稳定水平的主要因素，本文提出了一种主导失稳机群辨识算法，通过电网运行方式信息和电网拓扑，结合故障时刻的短路电压，实现对主导失稳机群的辨识。在此基础上，构建了基于拓扑和运行方式信息的在线暂态稳定评估模型和算法，，通过一系列稳态电气特征量可以有效预测当前运行点的暂态稳定水平。所提算法的有效性在IEEE10机39节点系统和南方电网系统上得到了检验。
[Abstract]:At present, a three-layer security defense system is adopted in China's large power grid, which consists of preventive control, emergency control and restoration control. The search and decision of preventive control strategy must be based on transient stability assessment. At present, the evaluation of transient stability can only be judged by enumerating the accidents, solving the differential equations one by one for each accident, and observing the variation of the output variables. No rules or models have been established for the relation between the change of operation mode and the change of stable level. This leads to the majority of current prevention and control rely on the experience of the operators, referring to a large number of expected conditions of off-line stability simulation results, and adjust the operation mode arrangements in a trial manner. With the addition of new energy generation, the system structure and operation conditions are becoming more and more complex, and the unpredictability of large power grid is increasing. It is difficult to control the safe operation of large power grid simply by relying on human experience to judge and make decisions. The dispatching center continuously evaluates the stability of operation mode online and offline every day. These results have actually provided a large number of samples for the study of the relationship between operation patterns and stable levels. With the addition of wide area phase measurement information, the observability of power grid state is enhanced. Applying data mining and pattern recognition technology, combining with stability mechanism analysis, the paper studies and establishes the relationship between power network topology and operation mode and power network instability mode and stability level, which will provide an effective support for intelligent preventive control decision of large power grid. It is also an important application of intelligent dispatching and big data of power grid. The movement of dominant unstable cluster is the main factor to determine the level of transient stability. In this paper, a dominant unstable cluster identification algorithm is proposed, which combines the short-circuit voltage at fault time through the operation mode information and topology of power network. The identification of dominant unstable cluster is realized. On this basis, an on-line transient stability evaluation model and algorithm based on topology and operation mode information are constructed. A series of steady state electrical characteristics can effectively predict the transient stability level of the current operating point. The validity of the proposed algorithm is tested on IEEE10 39 bus system and Southern Power Grid system.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM712

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