Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Haoran Niu,Olufemi A Omitaomu,Qing C Cao

doi:10.3390/smartcities4010001

Haoran Niu, Olufemi A Omitaomu + Show 1 more

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https://doi.org/10.3390/smartcities4010001

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Abstract

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

Highlights

IntroductionPower grid disturbances are caused by various events, including line trips, generator trips, and load disconnections, among others [1]
Received: November 2020Accepted: December 2020Published: 22 December 2020Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.license
Compared with the individual results of the various detection methods, we found that the Event Detection Machine Committee (EDMC)

Summary

Introduction

Power grid disturbances are caused by various events, including line trips, generator trips, and load disconnections, among others [1]. The timely detection of these events are significant to avoid severe consequences including large-scale blackout, which can cost up to $10 billion in economic losses [2]. A series of time series data can be obtained through real-time monitoring and recording of the power grid frequency using phasor measurement units (PMUs). The objectives of deploying the PMUs are to [3]: (i) capture slow spontaneous or anomalous oscillatory swings that are poorly damped; (ii) capture frequency transients from sudden losses of generation or load; (iii) capture power system disturbance data to support analyses of the events; and (iv) develop experience in recognizing disturbances as a precursor to the development of emergent states and unconventional transient state control.

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