Real-Time Monitoring of Area Angles with Synchrophasor Measurements

Abstract

This paper develops a comprehensive framework of Area Angle Monitoring (AAM) to monitor the stress of bulk power transfer across an area of a power transmission system in real-time. Area angle is calculated from synchrophasor measurements to provide alert to system operators if the area angle exceeds pre-defined thresholds. This paper proposes general methods to identify these warning and emergency thresholds, and tests a mitigation strategy to relieve the area stress when the area angle exceeds the threshold. In order to handle the limited coverage of synchrophasor measurements, this paper proposes methods to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment to test the framework for monitoring area angle with live-stream and recorded synchrophasor data is described.

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

“…The dataset spans a time period of around two consecutive years (2016-2017). The total size of the dataset is more than 20 TB (in Parquet form) 2 . These data 1 It is worth noting that a few remarkable works, e.g., [3] and [10], developed the learning models using real PMU data.…”

“…From the perspective of machine learning, such data amount might not be sufficient for developing a stable classifier in practice. 2 The research team has processed the raw dataset to 20 TB in Parquet form so as to save memory while facilitating the learning algorithm design and test. files were read in Python and MATLAB environments.…”

“…Large-scale blackouts, such as the Northeast blackout of 2003 in the U.S., which started with a local fault but eventually affected 50 million customers, continuously remind us of the need for better and faster event detection and identification to enhance the wide-area situational awareness of power system operation [1]. Recent years have seen a rapid growth in the deployment of phasor measurement units (PMUs), providing a unique opportunity for preventing cascading failures and blackouts [2]. Unlike the supervisory control and data acquisition (SCADA) system that only offers power system monitoring at steady state, PMU collects the precisely synchronized voltage and current phasor, frequency, and frequency variation at sub-second intervals (e.g., 30 or 60 samples per second in U.S.), which enables capturing the fast dynamics of power systems.…”

Data analytics for real-time event identification using real phasor measurement unit data

“…Moreover, the 2-second analysis window can avoid the curse of dimensionality for model development and ensure the real-time performance of the event identification. Noted that the previous method also utilizes a similar analysis window for PMU-based event identification Ju et al (2020). According to the sampling rate of PMUs, each analysis window should include 120 data points.…”