PMUs data based detection of oscillatory events and identification of their associated variable: Estimation of information measures approach

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-06-27 DOI:10.1016/j.segan.2024.101457
Sanjay Singh Negi , Nand Kishor , A.K. Singh
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Abstract

Information theory can be a useful tool for quantifying the perturbations in the associated state variables at the time of disturbance occurrence. The study introduces a framework for the spectral decomposition of multivariate information measures to detect initiation of low frequency oscillations (LFOs), caused due to physical events in the power grid. A frequency-specific quantification of the information is shared between a target variable and two source variables from their time series data. Initially, the approach is applied on different synthetic test signals having different oscillatory frequency modes and decay time constant. Then, approach is extended on PMUs signals. The combination of cross-spectral and information-theoretic approaches is applied for the multi-variable analysis of PMUs signals from the same bus. The interdependence among the frequency, voltage angle and voltage magnitude, corresponding to specific oscillations, manifested due to cause-effect relationships obtained in terms of statistics is estimated. The dynamics in terms of unique (interaction), redundant and synergetic information is determined with the contribution from two of these three signals as source variables to target variable (frequency/voltage angle). This provides a direct coupling to identify driver-response relationships between source variables and target variable to indicate the onset of LFOs, following physical events in power network. The extension of approach among the variables from different buses aids to identify the responsible area of event occurrence.

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基于 PMU 数据的振荡事件检测及其相关变量的识别:信息量估算方法
信息论是量化干扰发生时相关状态变量扰动的有用工具。本研究介绍了一种多变量信息测量的频谱分解框架,用于检测电网物理事件引起的低频振荡(LFO)。一个目标变量和两个源变量从其时间序列数据中共享特定频率的量化信息。起初,该方法应用于具有不同振荡频率模式和衰减时间常数的不同合成测试信号。然后,将该方法扩展到 PMU 信号上。跨谱法和信息理论法相结合,用于对来自同一总线的 PMU 信号进行多变量分析。对频率、电压角和电压幅值之间的相互依存关系进行了估算,这些相互依存关系与特定的振荡相对应,并通过统计得到的因果关系表现出来。通过将这三个信号中的两个信号作为目标变量(频率/电压角)的源变量,确定了独特(交互)、冗余和协同信息方面的动态。这就提供了一种直接耦合,以确定源变量和目标变量之间的驱动-响应关系,从而在电网发生物理事件后指示 LFO 的发生。在不同总线的变量之间扩展方法有助于确定事件发生的责任区域。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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