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Pattern recognition for one shot control in power systems

电力系统中单次控制的模式识别

基本信息

批准号：
1711521
负责人：
Steven Rovnyak
金额：
$ 36.42万
依托单位：
Indiana University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711521&HistoricalAwards=false
关键词：
Pattern recognition shot control power

项目摘要

This work will develop new ways of using pattern recognition for one shot controls in power systems. One shot controls include disconnection of generation and load and fast power changes on HVDC transmission lines. The control actions used in this work generally reduce inter-area power flows which tends to improve transient and steady state stability. The reduction of power flows is also associated with the prevention of relays from tripping on overload. Many blackouts result from cascading outages involving generators and transmission lines tripping off-line due to protective relaying. The proposed controls promise to make these failure modes less likely to occur. In addition to one shot control at the transmission level, the investigators will also develop algorithms to enable consumer appliances such as AC units to detect the loss of a large generator nearby and turn off long enough for the Independent System Operator (ISO) to take corrective action at the transmission level. The proposed methods are inexpensive and easy to incorporate into smart devices because only local measurements are used. No remote communication is required. Utilities could provide rebates or rate discounts to motivate customers to purchase appliances that use the proposed control schemes. All the methods developed in this work from one shot controls at the transmission level to smart appliances that operate autonomously will contribute to a more resilient and self-healing electric grid. Many state variables in turbine governors, voltage regulators and other digital controllers are subject to maximum and minimum limits. The differential algebraic equations used for analytical solution of power system dynamics are only valid if controller variables do not hit their limiting values. This is one reason why accurate prediction of power system behavior requires detailed time domain simulation. Deriving control strategies from a large number of simulations is a big data problem that requires pattern recognition. The proposed controls are response based which means, for example, they do not receive information about the status of particular breakers. Current industry practices focus on event based risk assessment (e.g. Special Protection Scheme) assuming the operators are fully aware of what has happened in the grid. It is challenging to develop response based controls that are actuated quickly enough to stabilize some events. This research will use event detection and pattern recognition prediction to actuate response based control earlier so that the stabilizing effect is greater. Another area of this research will contribute to self healing for a smarter grid. The underlying scientific principle is that local modes of oscillation have higher frequencies than inter-area modes. The derivative engineering contribution of the work will show how a Fourier calculation can help detect nearby loss of generation events using only local frequency measurements. The proposed methods are novel compared to existing proposals in the literature for under frequency load shedding. Utilities are not currently benefitting from autonomous load shedding at the residential level as proposed here.

这项工作将为电力系统的一次性控制提供新的模式识别方法。一次性控制包括在高压直流输电线路上断开发电和负载以及快速功率变化。在这项工作中使用的控制动作通常会减少区域间的功率流动，从而趋于提高暂态和稳态的稳定性。减少功率流还与防止继电器在过载时跳闸有关。许多停电是由于发电机和输电线路因保护继电器而脱机的级联停电造成的。拟议的控制措施有望降低这些失效模式发生的可能性。除了在传输层面的一次控制外，研究人员还将开发算法，使家用电器（如交流设备）能够检测附近大型发电机的故障，并在足够长的时间内关闭，以便独立系统操作员（ISO）在传输层面采取纠正措施。所提出的方法价格低廉且易于集成到智能设备中，因为只使用局部测量。不需要远程通信。公用事业公司可以提供回扣或费率折扣，以激励客户购买使用拟议控制方案的电器。在这项工作中开发的所有方法，从传输级的一次控制到自主运行的智能设备，将有助于建立一个更具弹性和自我修复的电网。涡轮机调速器、电压调节器和其他数字控制器中的许多状态变量都有最大值和最小值限制。用于电力系统动力学解析解的微分代数方程只有在控制器变量不达到其极限值时才有效。这就是为什么准确预测电力系统的行为需要详细的时域仿真的原因之一。从大量的仿真中得出控制策略是一个需要模式识别的大数据问题。拟议的控制是基于响应的，这意味着，例如，它们不接收有关特定断路器状态的信息。目前的行业实践侧重于基于事件的风险评估（例如，特殊保护方案），假设运营商完全了解电网中发生的情况。开发基于响应的控制是一项挑战，这些控制能够足够快地启动以稳定某些事件。本研究将利用事件检测和模式识别预测来更早地启动基于响应的控制，以达到更大的稳定效果。这项研究的另一个领域将有助于智能电网的自我修复。潜在的科学原理是振荡的局部模态比区域间模态具有更高的频率。该工作的衍生工程贡献将展示傅立叶计算如何仅使用局部频率测量来帮助检测附近的发电损失事件。与已有的低频减载方法相比，本文提出的方法是新颖的。目前，公用事业公司并没有像这里提议的那样，从住宅层面的自动减载中受益。