TY - GEN
T1 - A hybrid machine learning framework for enhancing PMU-based event identification with limited labels
AU - Li, Haoran
AU - Weng, Yang
AU - Farantatos, Evangelos
AU - Patel, Mahendra
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - The energy industry is experiencing rapid and dramatic changes on both the generator side and the load side, necessitating faster, more accurate, and robust event detection methods for situational awareness. Growing installations of PMU devices that provide high resolution synchronized measurements combined with the advancement of artificial intelligence and big data analytics techniques have recently attracted the RD community interest. Some supervised learning techniques have been proposed using PMU measurements, however, they are facing challenges in 1) limited interpretability, 2) biased learning models/results, and 3) insufficient labeled data for learning. To address these issues, we propose a machine learning-based framework for physically-meaningful interpretability, hybrid-learning method with indexes, and a flexible data-preparation approach. Specifically, a thoroughly designed feature selection method is proposed for discovering event signatures. Then, a hybrid machine learning process is constructed to reduce biases of different machine learners due to their diversified working mechanisms. Finally, we propose to utilize unlabeled data via semi-supervised learning and add strategical event data via active learning, e.g., simulations. The goal is to significantly improve the supervised learning results via computational efficient techniques. Extensive simulations are conducted using a commercial power system dynamics simulator and synthetic realistic transmission grid models. Significant improvements are observed via hybrid supervised learning methods, semi-supervised learning, and active learning.
AB - The energy industry is experiencing rapid and dramatic changes on both the generator side and the load side, necessitating faster, more accurate, and robust event detection methods for situational awareness. Growing installations of PMU devices that provide high resolution synchronized measurements combined with the advancement of artificial intelligence and big data analytics techniques have recently attracted the RD community interest. Some supervised learning techniques have been proposed using PMU measurements, however, they are facing challenges in 1) limited interpretability, 2) biased learning models/results, and 3) insufficient labeled data for learning. To address these issues, we propose a machine learning-based framework for physically-meaningful interpretability, hybrid-learning method with indexes, and a flexible data-preparation approach. Specifically, a thoroughly designed feature selection method is proposed for discovering event signatures. Then, a hybrid machine learning process is constructed to reduce biases of different machine learners due to their diversified working mechanisms. Finally, we propose to utilize unlabeled data via semi-supervised learning and add strategical event data via active learning, e.g., simulations. The goal is to significantly improve the supervised learning results via computational efficient techniques. Extensive simulations are conducted using a commercial power system dynamics simulator and synthetic realistic transmission grid models. Significant improvements are observed via hybrid supervised learning methods, semi-supervised learning, and active learning.
KW - Active learning
KW - Event identification
KW - Feature selection
KW - Hybrid learning
KW - Phasor measurement unit
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85071380183&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071380183&partnerID=8YFLogxK
U2 - 10.1109/SGSMA.2019.8784550
DO - 10.1109/SGSMA.2019.8784550
M3 - Conference contribution
AN - SCOPUS:85071380183
T3 - 2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019
BT - 2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Smart Grid Synchronized Measurements and Analytics, SGSMA 2019
Y2 - 20 May 2019 through 23 May 2019
ER -