Online Identification of Transient Stability and Unstable Generators Based on Deep Learning

Zhou, Yanzhen; Liu, Qiang; Guo, Qinglai; Sun, Hongbin; Zha, Xianyu; Feng, Xue; Zhao, Wenhua; Li, Chen; Zhang, Zhijian; Lv, Hao

doi:10.1109/ei2.2018.8582287

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author could not conclude which method was more suitable for transient stability assessment (TSA). In [11], a binary classification CNN model was generated for transient stability prediction. And a multi-class classification CNN model was built for unstable generator identification.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning based dynamic security assessment

Lam

Zhang

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

With the increasing deployment of wide-area monitoring systems (WAMS) and phasor measurement units (PMUs), along with artificial neural network (ANN) and highperformance distributed computation technique for smart grid and smart metering environment, online dynamic security assessment (DSA) plays a key role for early unstable event detection on power system security. It is especially important at a post-fault operation that the timing by DSA to detect an unstable event is critical to emergency remedial control action. However, excessive update training is one of the constraints for ANN to be effectively performed at pre-fault and post-fault operations on online DSA. This paper describes how transfer learning is successfully employed to shorten the training time for online DSA. It also helps to improve the validation accuracy if the training dataset of scratch ANN model is insufficient. Besides, a new approach of using the densely connected convolutional network with kernel principal component analysis (KPCA) is proposed to eliminate the traditional step of dimensionality reduction.

show abstract

Section: Introductionmentioning

confidence: 99%