Power System Stability Analysis and Control Using Statistical Machine Learning Techniques

使用统计机器学习技术的电力系统稳定性分析与控制

• 批准号: RGPIN-2016-05734
• 负责人: Chung, ChiYung
• 金额: $ 3.28万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689072
• 关键词: Power; System; Stability; Analysis; Control

Simulation methods based on conventional offline models have been used widely in power system stability analysis and control design and they constitute effective tools for ensuring system stability. Effectiveness of these stability analysis and design methods is declining steadily because of the constant evolution of the power grid environment, the changes being largely attributable to increased variations in power flow and the difficulties in acquiring accurate offline models for various power-electronics-based devices. Advancement in information and communications technologies have facilitated transfer of massive data in real time and implies an opportunity for wider applications of advanced real time monitoring systems, allowing the acquisition of data of real time conditions and dynamics of various components of power systems. This makes the whole system more observable. Meanwhile, data-driven methods such as statistical machine learning techniques have developed significantly in recent times and have been successfully applied in various areas. Therefore, real time stability analysis and control using statistical machine learning techniques has become an important research direction since it aims to perceive the system's operational situation directly through real time data and provide insights into optimal operations and controls. This has the potential to resolve the problems of biased parameters when using offline models which, in most cases, do not fit real time operating conditions in the power grid. The significance of this research motivates this research program to combine statistical machine learning with domain knowledge in power systems and make them applicable to stability analysis and control in real power systems. The long-term goal of this research program is to develop new approaches for power system stability analysis and effective online model-free and self-optimization control strategies. To achieve this ultimate goal, the short-term goals are (i) to develop new approaches for prediction, control and optimization of power systems to resolve the problem of bias in offline models used in the conventional power system simulation; and (ii) to apply the general approaches proposed in this program to various problems related to power system stability and develop new online control strategies for the same. The outcomes of this research are expected to not only constitute milestones in power system stability analysis and control, but also contribute to the development of a more reliable and stable power system in the future.*** **

基于传统离线模型的仿真方法在电力系统稳定性分析和控制设计中得到了广泛的应用，是保证系统稳定的有效工具。由于电网环境的不断变化，这些稳定性分析和设计方法的有效性正在稳步下降，这种变化主要归因于潮流变化的增加以及各种电力电子设备难以获得准确的离线模型。信息和通信技术的进步促进了实时传输大量数据，并意味着有机会更广泛地应用先进的实时监测系统，从而可以获取电力系统各组成部分的实时情况和动态数据。这使得整个系统更易于观察。与此同时，数据驱动的方法，如统计机器学习技术，近年来有了显著的发展，并已成功地应用于各个领域。因此，利用统计机器学习技术进行实时稳定性分析与控制已成为一个重要的研究方向，因为它旨在通过实时数据直接感知系统的运行情况，并提供最优操作和控制的见解。这有可能解决使用离线模型时的参数偏差问题，在大多数情况下，离线模型不适合电网的实时运行条件。本研究的意义促使本研究计划将统计机器学习与电力系统领域知识相结合，并将其应用于实际电力系统的稳定性分析与控制。本研究计划的长期目标是开发电力系统稳定性分析的新方法以及有效的在线无模型和自优化控制策略。为了实现这一最终目标，短期目标是：(1)开发电力系统预测、控制和优化的新方法，以解决传统电力系统仿真中使用的离线模型的偏差问题；（ii）将本方案提出的一般方法应用于与电力系统稳定性有关的各种问题，并开发新的在线控制策略。本研究成果不仅在电力系统稳定性分析和控制方面具有里程碑意义，而且对未来发展更加可靠和稳定的电力系统也有重要意义。* * * * *