Ensemble-Based Algorithm for Synchrophasor Data Anomaly Detection
| Autor: | Mengze Zhou, Anurag K. Srivastava, Pratyush Banerjee, Yuhui Wang, Yinghui Wu |
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| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Ensemble forecasting Data stream mining Computer science 020209 energy 020208 electrical & electronic engineering Phasor 02 engineering and technology Ensemble learning Data modeling 0202 electrical engineering electronic engineering information engineering Anomaly detection Online algorithm Real-time operating system Algorithm |
| Zdroj: | IEEE Transactions on Smart Grid. 10:2979-2988 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2018.2816027 |
| Popis: | With the advent of phasor measurement units (PMUs), high resolution synchronized phasor measurements enables real time system monitoring and control. PMUs transmit data to local controllers in substations and phasor data concentrators for system wide monitoring and control application in the control center. They provide real-time phasor data for critical power system applications such as remedial action schemes, oscillation detection, and state estimation. The quality of phasor data from PMUs is critical for smart grid applications. Several methods are developed to detect anomalies in time series data, tailored for PMU data analysis. Nevertheless, applying stand alone methods (with fixed parameters) takes great tuning effort, and does not always achieve high accuracy. In this paper, we adopt an unsupervised ensemble learning approach to develop fast, scalable bad data/event detection for PMU data. The ensemble method invokes a set of base detectors to generate anomaly scores of the PMU data, and makes decisions by aggregating the scores from each detector. We develop two algorithms: 1) a learning algorithm that trains the ensemble model and 2) an online algorithm that infers the anomaly scores with the ensemble model over PMU data streams. The proposed method provides flag for data anomalies and triggers further analysis to differentiate between events and bad data. Using both simulated and real-world PMU data, we show that our ensemble model can be efficiently trained, can achieve high accuracy in detecting diversified errors/events, and outperforms its counterparts that use single standalone detection method. |
| Databáze: | OpenAIRE |
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