Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network
Abstract
:1. Introduction
2. Methodologies
2.1. The Principles of Phase Space Reconstruction
2.2. The Theory of the Convolutional Neural Network
3. The Proposed Algorithm of PQDs Detection and Classification Based on PSR and CNN
3.1. PQ Disturbance Model
3.2. The Proposed Method
3.3. The Training of the Model
4. Results and Discussion
4.1. Synthetic Signals
4.2. Real-World Signals
4.3. Discussion
- (1)
- The PQD classification can transform a complicated 1D signal processing problem into a simpler 2D space image classification problem. A CNN model-based method was established to achieve this. This idea may be applied to similar research fields.
- (2)
- The 2D images transformed from 1D voltage disturbance signals are in grayscale which has only one color channel. That is, the input data used in this paper is much simpler than traditional image classification methods, which use color graphs with three color channels.
- (3)
- A high classification rate can be obtained with a more succinct CNN model (three-level) by the proposed method.
- (4)
- An average pooling layer is used in the CNN model instead of the traditional max pooling layer because the 2D PQD images as the inputs of the model are sparse. In the grayscale, high values in the image matrix are presented in white and low values are in black. By doing so, key information in the 2D image can be preserved in this method.
- (5)
- From real-world signals, the classification rate can be improved by adding a small amount of real-world data into the training process to fine-tune the parameters of the model. It proves that the proposed method has an excellent capability of learning and adaptation.
- (6)
- In addition, sag, swell, and interruption were considered in this paper. These kinds of disturbances have similar shapes, but with different amplitudes. For accurate classification, the coordinate information is reserved in their 2D images. Through this operation, the three types of voltage disturbance can be effectively distinguished.
- (7)
- The proposed method can be implemented very quickly for classification after the training process. It is convenient for end users without requiring specialist knowledge. Whilst this work is based on the offline tests, it can be applied in online tests. This will be implemented in future work.
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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PQ Disturbance | Label | Numerical Model | Parameters |
---|---|---|---|
Sag | Sag | ||
Swell | Swell | ||
Interruption | Inter | ||
Flicker | Flicker | ||
Harmonic | Har | , | |
Oscillatory Transient | Osc | ||
Spike | Spike | ||
Sag & Harmonic | Sag & Har | , | |
Interruption & Harmonic | Inter & Har | , | |
Swell & Harmonic | Swell & Har | , |
Method | Feature Extraction (Handcrafted/Automatically) | No. PQDs | Accuracy. (%) | Ref. |
---|---|---|---|---|
EMD+Balanced Neural Tree | Handcrafted | 8 | 97.9 | [41] |
ST+NN+DT | Handcrafted | 13 | 99.9 | [25] |
Hybrid ST+DT | Handcrafted | 11 | 94.36 | [42] |
ADALINE+FNN | Handcrafted | 12 | 90.58 | [43] |
VMD+DeepSCN | Handcrafted | 7 | 99.4 | [22] |
PSR+CNN | Automatically | 10 | 99.8 | Proposed method |
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Share and Cite
Cai, K.; Hu, T.; Cao, W.; Li, G. Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network. Appl. Sci. 2019, 9, 3681. https://doi.org/10.3390/app9183681
Cai K, Hu T, Cao W, Li G. Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network. Applied Sciences. 2019; 9(18):3681. https://doi.org/10.3390/app9183681
Chicago/Turabian StyleCai, Kewei, Taoping Hu, Wenping Cao, and Guofeng Li. 2019. "Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network" Applied Sciences 9, no. 18: 3681. https://doi.org/10.3390/app9183681