Document Type

Journal Article

Publication Title

Sustainable Energy, Grids and Networks

Volume

31

Publisher

Elsevier

School

School of Engineering

RAS ID

44383

Comments

This is an Accepted Manuscript of an article published by Elsevier in SUSTAINABLE ENERGY, GRIDS AND NETWORKS, available online: https://doi.org/10.1016/j.segan.2022.100752.

Vinayagam, A., Veerasamy, V., Tariq, M., & Aziz, A. (2022). Heterogeneous learning method of ensemble classifiers for identification and classification of power quality events and fault transients in wind power integrated microgrid. Sustainable Energy, Grids and Networks, 31, article 100752.

https://doi.org/10.1016/j.segan.2022.100752

Abstract

This paper proposes heterogeneous based ensemble Classifiers (voting and stacking method) to identify and classify different power system disturbances (power quality (PQ), faults, transients, and wind power variation) in wind integrated microgrid network. In the pre-processing stage of classification, a Discrete wavelet transform (DWT) technique is applied to extract the features from power system disturbance signals. The classification process for the proposed ensemble models involves two levels of classification. At the first level, the extracted features from the simulated power system events are used to learn the different individual base classifiers (logistic regression (LR), K-Nearest Neighbor (KNN), and J48 Decision tree (JDT)]. In second stage, a Meta-level classification is carried out based on predictions of base classifiers to get final predictions of class labels. First, the proposed ensemble models are utilized to discriminate the power system disturbances under random varying wind power condition and the predictive results (classification accuracy and performance indices) of ensemble models are compared with individual base classifiers (LR, KNN., and JDT). In addition, a sensitivity analysis is carried out under real time varying wind power condition and noisy environment of event signals to verify the effectiveness of ensemble models in further level. Furthermore, the robustness of proposed stacking ensemble model is verified with classification of single and combined PQ events of synthetic data, generated from the mathematical based PQ model software Predictions results under the different conditions show that stacking ensemble model offers substantial performance and discriminates the power disturbances with higher accuracy of classification than base classifiers and voting ensemble model.

DOI

10.1016/j.segan.2022.100752

Access Rights

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Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Research Themes

Natural and Built Environments

Priority Areas

Engineering, technology and nanotechnology

Available for download on Monday, September 30, 2024

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