A Simulation-Based Classification Approach for Online Prediction of Generator Dynamic Behavior Under Multiple Large Disturbances
- 10 September 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Power Systems
- Vol. 36 (2), 1217-1228
- https://doi.org/10.1109/tpwrs.2020.3021137
Abstract
This paper proposes a novel method for the machine learning-based online prediction of generator dynamic behavior in large interconnected power systems. Unlike the existing literature in this domain, which assumes faults occur immediately after a steady-state situation, the proposed method takes the possibility of multiple disturbances into account. It is founded on a simulation-based classification approach to indirectly take advantage of phasor measurement unit (PMU) data, which leads to improvements in robustness against load model uncertainties. Relying on offline scenarios, the method developed conducts multiple time-domain simulations (TDSs) in parallel for a set of feasible two-machine dynamic equivalent models (DEMs) for each case. Thereafter, common descriptive statistics are computed for the rotor angles obtained to form the feature space. The values taken via a feature selection process are then applied as inputs to ensemble decision trees, which train models capable of predicting both stability status and generator grouping ahead of time. In online situations, PMU data are used to create DEMs and the predictors are collected by performing parallel TDSs for DEMs. The functionality of the proposed hybrid machine learning and TDS-based approach is verified on several IEEE test systems, followed by a discussion of results.Keywords
Funding Information
- Natural Sciences and Engineering Research Council of Canada
- SaskPower
This publication has 29 references indexed in Scilit:
- LEs based framework for transient instability prediction and mitigation using PMU dataIET Generation, Transmission & Distribution, 2016
- Benchmarking and Validation of Cascading Failure Analysis ToolsIEEE Transactions on Power Systems, 2016
- Online Identification of Power System Dynamic Signature Using PMU Measurements and Data MiningIEEE Transactions on Power Systems, 2015
- Post‐disturbance transient stability assessment of power systems by a self‐adaptive intelligent systemIET Generation, Transmission & Distribution, 2015
- PMU-Based Model-Free Approach for Real-Time Rotor Angle MonitoringIEEE Transactions on Power Systems, 2014
- A Decentralized Control of Partitioned Power Networks for Voltage Regulation and Prevention Against Disturbance PropagationIEEE Transactions on Power Systems, 2012
- Conditional Mutual Information Based Feature Selection for Classification TaskLecture Notes in Computer Science, 2007
- Dynamic Equivalent Modeling of Large Power Systems Using Structure Preservation TechniqueIEEE Transactions on Power Systems, 2006
- A novel method to compute the closest unstable equilibrium point for transient stability region estimate in power systemsIEEE Transactions on Circuits and Systems I: Regular Papers, 1997
- Development of coherency-based time-domain equivalent model using structure constraintsIEE Proceedings C Generation, Transmission and Distribution, 1986