Abstract
Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event via the high-resolution phasor measurement unit (PMU) data, such that proper actions can be taken before any sporadic fault escalates to cascading blackouts. Under the framework of random matrix theory, the proposed approach maps the raw data into a high-dimensional space with two parts: (1) factors (spikes, mapping faults); (2) residuals (a bulk, mapping white/non-Gaussian noises or normal fluctuations). As for the factors, we employ their number as a spatial indicator to estimate the number of constituent components in a multiple event. Simultaneously, the autoregressive rate of the noises is utilized to measure the variation of the temporal correlation of the residuals for tracking the system movement. Taking the spatial-temporal correlation into account, this approach allows for detection, decomposition and temporal localization of multiple events. Case studies based on simulated data and real 34-PMU data verify the effectiveness of the proposed approach.
Highlights
A multiple event is composed of several constituent components that occur successively within a short time period in the power system, such as load fluctuations, oscillations and faults.For a large-scale power grid, multiple events can hardly be identified properly as it is difficult to distinguish their features via the raw data
Along the well-established research line of random matrix theory (RMT) [11,12,13,14], this paper proposes a novel statistical approach, namely, high-dimensional factor models, for the multiple event detection, decomposition and temporal localization in a modern grid
Given the characteristics of phasor measurement unit (PMU) measurements, we explore the spatial correlation and temporal correlation in PMU data structure
Summary
A multiple event is composed of several constituent components that occur successively within a short time period in the power system, such as load fluctuations, oscillations and faults. Along the well-established research line of random matrix theory (RMT) [11,12,13,14], this paper proposes a novel statistical approach, namely, high-dimensional factor models, for the multiple event detection, decomposition and temporal localization in a modern grid. The factors are related to some certain events (anomaly signals or faults) occurring in a power grid, whereas the residuals are associated with white/non-Gaussian noises or normal fluctuations within the raw PMU measurements. The formulation of both time and space jointly is very demanding analytically.
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