Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification

Kaduvettykunnal Sajan, Mohini Bariya, Sanchita Basak, Anurag K. Srivastava, Abhishek Dubey, Alexandra von Meier, Gautam Biswas

8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES@CPSIoTWeek 2020

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Why This Matters

Power grid operators increasingly rely on machine learning and data analytics for monitoring and decision support, but validation of these systems requires access to realistic data that includes various anomaly and event scenarios. Access to real operational data is limited due to security and confidentiality constraints. This work is innovative because it enables development of realistic synthetic data that captures the complexity of real power grid measurements while maintaining data privacy, supporting the advancement of anomaly detection and classification capabilities.

What We Did

This paper presents methodology for realistic synthetic phasor measurement unit (PMU) data generation to support anomaly detection and event classification in power systems. The work develops techniques for adding realistic noise and bad data effects to simulated measurements, enabling development and testing of anomaly detection algorithms without requiring access to real operational data. The methodology includes procedures for detecting and classifying five categories of events relevant to power grid operations.

Key Results

The methodology successfully generates realistic synthetic PMU data with configurable noise characteristics and event signatures. Results demonstrate that anomaly detection and event classification algorithms can be validated on synthetic data and achieve performance consistent with expectations. The work shows that the approach enables development and testing of algorithms for detecting anomalies including bad data, missing data, and various power system events, supporting more robust power grid monitoring systems.

Full Abstract

Cite This Paper

@inproceedings{basak2020mscpes,
  author = {Sajan, Kaduvettykunnal and Bariya, Mohini and Basak, Sanchita and Srivastava, Anurag K. and Dubey, Abhishek and von Meier, Alexandra and Biswas, Gautam},
  booktitle = {8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES@CPSIoTWeek},
  title = {Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification},
  year = {2020},
  abstract = {The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of highresolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers.},
  category = {workshop},
  contribution = {lead},
  keywords = {phasor measurement units, anomaly detection, synthetic data, event classification, power systems, data generation},
  project = {cps-reliability},
  tag = {platform,power}
}
Quick Info
Year 2020
Keywords
phasor measurement units anomaly detection synthetic data event classification power systems data generation
Research Areas
energy CPS ML for CPS
Search Tags

Realistic, Synchrophasor, Data, Generation, Anomaly, Detection, Event, Classification, phasor measurement units, anomaly detection, synthetic data, event classification, power systems, data generation, energy, CPS, ML for CPS, 2020, Sajan, Bariya, Basak, Srivastava, Dubey, von Meier, Biswas