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.*** **

基于常规离线模型的仿真方法已在电力系统稳定性分析和控制设计中广泛使用，它们构成了确保系统稳定性的有效工具。由于电网环境的不断发展，这些稳定性分析和设计方法的有效性正在稳步下降，因此，这些变化在很大程度上归因于功率流的增加以及为各种基于电力电子设备的精确离线模型获得准确的离线模型的困难。信息和通信技术的进步已经促进了大量数据的实时传输，并意味着有机会更广泛地应用高级实时监控系统，从而允许获取实时条件的数据和电源系统各个组件的动态。这使整个系统更加可观察到。同时，诸如统计机器学习技术之类的数据驱动方法近来已经显着发展，并已成功应用于各个领域。因此，使用统计机器学习技术实时稳定性分析和控制已成为重要的研究方向，因为它旨在通过实时数据直接感知系统的运行状况，并为最佳操作和控制提供见解。当使用离线模型时，在大多数情况下，这可能不符合电源网格中的实时操作条件时，有可能解决偏见参数的问题。这项研究的意义激发了该研究计划将统计机器学习与电力系统中的领域知识相结合，并使其适用于实际电力系统中的稳定性分析和控制。该研究计划的长期目标是为电力系统稳定性分析以及有效的在线模型和自我优化控制策略开发新方法。为了实现这一最终目标，短期目标是（i）开发用于预测，控制和优化电力系统的新方法，以解决常规电力系统模拟中使用的离线模型中的偏见问题； （ii）将本程序中提出的一般方法应用于与电力系统稳定性相关的各种问题，并为此开发新的在线控制策略。预计这项研究的结果不仅构成电力系统稳定性分析和控制中的里程碑，而且还有助于发展更可靠和稳定的电力系统。*** **