Data Driven Method for Event Classification via Regional Segmentation of Power Systems

Abstract

This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy. INDEX TERMS Synchrophasor, phasor measurement unit (PMU), event classification, clustering, wavelet analysis, characteristic ellipsoid, convolutional neural network (CNN).

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

“…Machine learning has found numerous applications in power systems. It has been used for designing demand response programs [88], consumer behavior modeling [89], fault location detection [90,91] and protection [92], cybersecurity [93], electricity price forecasting [94], demand prediction [95], renewable energy generation forecasting [96,97], transient stability assessment [98], voltage control [79,99], bad data detection [100], energy theft detection [101], grid topology identification [102], outage identification [103], microgrid energy management [104], emergency management [105], power flow estimation [106], optimal power flow prediction [107], unit commitment [108], state estimation [109], reliability management [110], event classification [111], power fluctuation identification [112], energy disaggregation [113], and power quality disturbance classification [114]. However, most of the presented works use a centralized learning framework, and, despite these accomplishments, research on distributed learning architectures in power systems remains very limited.…”

Power Grids as Complex Networks: Resilience and Reliability Analysis

“…To this end, Wide Area Monitoring Systems (WAMP), mainly based on Phasor Measurement Units (PMU), have been extensively studied to increase controllability and stability of the grid [142]. Intermittence and uncertainty of renewable resources have encouraged researchers to apply data-driven approaches, such as those based on machine learning, for segmentation [143,144]. For example, load pattern segmentation can be performed in residential power grids using clustering techniques [145].…”

A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering

“…Such prosperity of the PMU application studies also trig-29 gers the industry's new interest in event signature datasets. 30 Specifically, the Department of Energy in the U.S. launched 31 a new working group, titled ''Grid Signature Library User 32 Group'' in March 2022, inviting both academia (universi-33 ties and research institutes) and the industry (transmission 34 system operator (TSO), regional system operator (RTO), 35 manufacturers, and PMU vendors). The major goal is to 36 establish a solid and reliable grid event signature library 37 based on the real-world grid events measured by PMUs 38 and event logs recorded by TSOs/RTOs (https://darknet-39 01.ornl.gov/apps/siglib).…”