Recognition method of voltage sag causes based on two‐dimensional transform and deep learning hybrid model

Abstract

The voltage sags' caused recognition is the basis for formulating governance plans and clarifying liabilities for accidents. The diversification of smart grid equipment, the grid-connected power generation of new energy sources and the regional differentiation of power consumption modes pose new challenges to the traditional methods. In this study, a method based on deep learning hybrid model is proposed. The convolutional neural network is used to flexibly receive the voltage after two-dimensional transformation, so as to automatically obtain the time series and spatial characteristics of the voltage sag signals. The deep belief network is used to replace the fully connected layers in convolutional neural network, thereby enhancing the multi-label classification ability of the model. The parameters obtained by the unsupervised training of the stacked sparse denoising auto-encoder are used to initialise the weight of deep belief network, thereby improving the convergence speed and the anti-noise performance of the model. Iterative training and repeated testing of the network using pre-processed simulation data and actual recorded data verify the high recognition accuracy and strong anti-noise performance of the hybrid model. Compared with the traditional methods, the hybrid model also has good generalisation ability and can be effectively applied in practical engineering.

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.…”

Disturbance Evaluation in Power System Based on Machine Learning

“…The above algorithms require feature selection of the signal, which is prone to feature redundancy, and some other features will be lost, resulting in a decrease in recognition rate and reduced anti-noise performance. In order to avoid the process of selecting power quality disturbance features, literature [44,45] converts the power quality disturbance signal into a two-dimensional gray image, and then uses a two-dimensional CNN for image recognition, but the conversion process is complicated, and the grayscale is temporarily reduced and interrupted. The degree of image features is not obvious, resulting in a decline in the recognition rate.…”

Amplitude and phase estimations of power system harmonics using deep learning framework

“…Furthermore, there are many research works using CNN for power quality (PQ) analysis. Several works that use DL in PQ have been reported and they are interested in not detection but usually a classification of parameters or events in PQ [15–27]. In this study, a new method based on CNN is proposed for fast and accurate detection of phase and amplitude information of rapidly time‐varying harmonic components of voltages and currents of the power systems.…”