使用同步相量数据的印度电网态势感知
Situational Awareness in Indian Power Grid using Synchrophasor Data
AI总结：
本文提出一种基于Prony和FFT的低频振荡分析算法，利用同步相量技术进行振荡检测以提升电网态势感知，并在印度西部区域电网的案例研究中验证了其有效性。
摘要：
广域测量系统（WAMS）可通过扩大监管区域及相邻系统的可观测性，引导系统操作员提高其态势感知能力。电网中的电力系统振荡是一个争议问题。当振荡加剧且未采取纠正措施加以缓解时，可能会使电力系统的稳定运行转变为不稳定系统。本文展示了利用同步相量技术通过振荡检测实现态势感知。提出了一种基于Prony和快速傅里叶变换（FFT）方法的低频振荡相关分析算法。该算法在电网扰动及与电力系统运行相关的其他事件期间提供了更好的洞察力。通过印度西部区域电网的两个案例研究验证了该算法的有效性。
英文摘要：
Wide Area Measurement Systems (WAMS) can guide system operators' to increase their situational awareness by expanding observability of their supervise area and adjoining systems. Power system oscillations in the electrical grid are a matter of contention. It may transform the stable operation of the power system into an unstable system when oscillations grow and if no corrective actions are taken to mitigate them. This paper manifests the utilization of the synchrophasor technology for situational awareness by oscillation detection. An algorithm is proposed for the low-frequency oscillation-related analysis based on Prony and Fast Fourier Transform (FFT) methods. It gives better insight during the grid disturbance and other events related to power system operation. The effectiveness of this algorithm is validated by two case studies in the Western Regional Indian power grid.
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