Synchrophasor-based data mining for power system fault analysis

Karim, Miftah Al; Chenine, Moustafa; Zhu, Kaigui; Nordström, Lars

doi:10.1109/isgteurope.2012.6465843

Cited by 16 publications

(6 citation statements)

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“…A synchrophasor network was designed and used to monitor the PMU data for both on-line and off-line data analysis in [15]. The authors in [16] uses multiple data classification algorithms (k-means and Naïve Bayes) for classification and fault detections in synchrophasor data. Synchrophasor data quality problem has been addressed in [17]for data conditioning.…”

Section: Literature Reviewmentioning

confidence: 99%

Using phasor data for visualization and data mining in smart-grid applications

Mukherjee

Vallakati

Lachenaud

et al. 2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

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This paper presents a density based clustering (DBSCAN) technique to visualize and analyze the smart-grid data. The technique will aid in detecting bad-data, various fault types, deviation on frequency, voltage or current values for better situational awareness. Synchrophasors (or a PMU) is a sensor placed on a transmission line that tracks voltage, current, phase and frequency of the line. To improve situational awareness of the smart grid monitoring in real-time, the utility must monitor the phasor data measurement delivered by the sensors. Time-stamped synchronized measurements offer tremendous benefit for pre and post-event analysis. The paper uses data from openPDC framework to aid system operators in carrying various predictive analytics, decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Using phasor data for visualization and data mining in smart-grid applications

Mukherjee

Vallakati

Lachenaud

et al. 2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

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“…Current research on applying data mining to synchrophasor data for power-system fault and disturbance classification can be found in [12] and [13]. The K-nearest neighbor algorithm was used to classify three phase faults (3LG), voltage oscillation, and voltage sag scenarios in [11].…”

Section: Related Workmentioning

confidence: 99%

“…The K-nearest neighbor algorithm was used to classify three phase faults (3LG), voltage oscillation, and voltage sag scenarios in [11]. The algorithm accuracy is not provided in [12]. Hoeffding Tree-based stream data mining is used in [13].…”

Section: Related Workmentioning

confidence: 99%

“…This approach was able to classify 3LG and 1LG faults grouped for binary classification with greater than 90% accuracy. Both [12] and [13] used simulated powersystem data. Both [12] and [13] propose methods to mine synchrophasor data.…”

Section: Related Workmentioning

confidence: 99%

“…Both [12] and [13] used simulated powersystem data. Both [12] and [13] propose methods to mine synchrophasor data. However, both are designed for powersystem measurement data only and do not incorporate any other types of system information.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

Pan

Morris

Adhikari

2015

IEEE Trans. Ind. Inf.

128

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Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.Index Terms-Common paths, cyber-attack detection, disturbances, symmetric and unsymmetrical faults, synchrophasor data and device log mining. 1551-3203

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