| Title | Machine-Learning-based Advanced Dynamic Security Assessment: Prediction of Loss of Synchronism in Generators |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Vakili, Ramin, Khorsand, Mojdeh |
| Conference Name | 2020 52nd North American Power Symposium (NAPS) |
| Date Published | apr |
| Keywords | Data models, Generators, machine learning, Measurement, Measurement and Metrics Testing, measurement errors, Metrics, online dynamic security assessment, predicting loss of synchronism, Predictive models, pubcrawl, Radio frequency, random forest classifier, stability assessment, Training, Voltage measurement |
| Abstract | This paper proposes a machine-learning-based advanced online dynamic security assessment (DSA) method, which provides a detailed evaluation of the system stability after a disturbance by predicting impending loss of synchronism (LOS) of generators. Voltage angles at generator buses are used as the features of the different random forest (RF) classifiers which are trained to consecutively predict LOS of the generators as a contingency proceeds and updated measurements become available. A wide range of contingencies for various topologies and operating conditions of the IEEE 118-bus system has been studied in offline analysis using the GE positive sequence load flow analysis (PSLF) software to create a comprehensive dataset for training and testing the RF models. The performances of the trained models are evaluated in the presence of measurement errors using various metrics. The results reveal that the trained models are accurate, fast, and robust to measurement errors. |
| DOI | 10.1109/NAPS50074.2021.9449813 |
| Citation Key | vakili_machine-learning-based_2021 |
Machine-Learning-based Advanced Dynamic Security Assessment: Prediction of Loss of Synchronism in Generators