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arXiv 2210.09414eess.SYcs.SY
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一种用于检测配电系统电压越限的数据驱动传感器布置方法

A Data-Driven Sensor Placement Approach for Detecting Voltage Violations in Distribution Systems

Paprapee Buason, Sidhant Misra, Samuel Talkington, Daniel K. Molzahn

AI总结:

针对配电系统电压越限监测,本文提出双层优化的传感器布置方法,结合保守线性潮流近似、问题重构与阈值梯度调整,实现少传感器、无误报且全覆盖检测。

摘要:

分布式能源资源(DERs)功率注入的随机波动与负荷可变性相结合,可能导致配电系统中的约束越限(例如电压限值被超过)。为了监测电网运行,需要布置传感器以测量电压幅值等重要物理量。本文考虑一个传感器布置问题,旨在确定安装传感器的位置,使其能够捕获所有可能的电压幅值限值越限。我们构建了一个双层优化问题:上层最小化传感器数量并避免传感器误报警,下层则确保检测到任何电压越限。由于潮流方程的非线性以及二元变量的存在,该问题具有挑战性。为此,我们采用近期提出的潮流方程保守线性近似,该近似会高估或低估电压幅值。通过将非线性潮流方程替换为保守线性近似,我们可以确保所得传感器位置和阈值足以识别任何约束越限。此外,我们应用多种问题重构,在显著提升计算可处理性的同时确保传感器得到合理布置。最后,我们通过一种近似梯度下降方法调整传感器阈值,从而提高结果质量。我们在多个测试案例上验证了所提方法的有效性,其中包括一个具有多种开关配置的系统。

英文摘要:

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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