NDLI: Transient stability evaluation model based on SSDAE with imbalanced correction

Content Provider	IET Digital Library
Author	Wang, Huaiyuan Ye, Weitao
Abstract	With the rapid development of machine learning technology, a new tool is provided for real-time stability evaluation in power systems. The training of a machine learning-based model is inseparable from a large number of training samples. However, compared with stable samples, unstable samples in power systems are infrequent. The results of the model evaluation are biased due to the imbalance of training samples. Faced with such a problem, a framework based on deep imbalanced learning is proposed. Firstly, for each sample, the samples nearby in the opposite class are applied to calculate its space information. Based on the space information of all samples, the spatial distribution characteristics of the training samples are obtained. And then, in order to obtain balanced training samples, unstable samples are generated according to their spatial distribution characteristics. Finally, stacked sparse denoising auto-encoder (SSDAE) based model, which has the ability of anti-noise, is established as the classifier. Simulation results in IEEE 39-bus system show the high performance of the proposed imbalanced correction scheme and evaluation scheme.
Starting Page	2209
Ending Page	2216
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 11, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1388
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-02-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Balanced Training Samples Computer Vision And Image Processing Technique Data Handling Technique Deep Imbalanced Learning Evaluation Scheme Image Denoising Imbalanced Correction Scheme Knowledge Engineering Technique Learning in AI Machine Learning-based Model Optical, Image And Video Signal Processing Pattern Classification Power System Power System Control Power System Transient Stability Real-time Stability Evaluation Space Information Spatial Distribution Characteristic Stable Sample Stacked Sparse Denoising Auto-encoder Based Model Statistics Transient Stability Evaluation Model Unstable Sample
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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