Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Abstract

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

Recognition of Voltage Sag Sources Based on Phase Space Reconstruction and Improved VGG Transfer Learning

“…The methods of reference [ 4 , 5 , 15 ], and [ 17 ] (hereinafter referred to as method 1, method 2, method 3, and method 4) are compared with the experimental results of voltage sag source identification method based on phase space reconstruction and improved VGG migration learning proposed in this paper, as shown in Table 3 and Table 4 .…”

“…At present, the research on the recognition of voltage sag sources falls into two categories: direct methods [ 2 , 3 , 4 , 5 , 6 , 7 ] and indirect methods [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. The direct methods include the RMS method [ 2 , 3 ] and the deep learning method [ 4 , 5 , 6 , 7 ]. Indirect methods include two parts: feature extraction and pattern recognition.…”

“…The main methods of pattern recognition include neural network [ 15 , 16 ], support vector machine [ 17 ], principal component analysis [ 18 ], fuzzy comprehensive evaluation [ 19 ], and so on. Among them, the RMS method [ 2 , 3 ] is simple and easy to implement, but it is easy to produce misjudgment for complex sag situations; the deep learning method [ 4 , 5 , 6 , 7 ] does not rely on manual extraction of features, but the model training efficiency is low. In the feature extraction process of indirect methods, the mathematical models are mature, the features are clear.…”

Transient stability evaluation model based on SSDAE with imbalanced correction

“…Therefore, deep learning-based model is introduced into transient stability assessment. The application of deep learning in stability evaluation has developed rapidly, such as deep belief network [15], convolutional neural networks (CNNs), and stacked denoising auto-encoder (SDAE) [16,17]. There are always measurement errors and transmission interference in the acquisition of real-time information.…”

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

“…A relevant summary and interpretation of these standards are given in References [3][4][5]. Furthermore, other research has been focused on how to identify the different type of disturbances from occurring events; for instance, in References [6][7][8][9][10][11]. Once PQ disturbances are detected, classifying them and compressing them into separate events is a challenging task and requires advanced tools; see References [12][13][14][15].…”