Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network
Abstract
:1. Introduction
2. Variation Mode Decomposition
2.1. The Structure of VMD
- (i)
- Obtain the unilateral frequency spectrum of each mode by computing the associated analytic signal by means of Hilbert transform (in which ):
- (ii)
- Shift the frequency spectrum of each mode to baseband by multiplying an exponential tuned with estimated center frequency:
- (iii)
- Calculate the bandwidth of each mode by means of the squared -norm of the gradient. The constrained variational problem is as follows:
2.2. The Computation of VMD
- (i)
- Initialize the , , , and ;
- (ii)
- Update the and repeatedly according to (6), (7);
- (iii)
- Update dual ascent according to
- (iv)
- Repeat step (2), (3), until convergence: .
2.3. Determination of VMD Parameters
2.4. PQ Disturbances Analysis
2.5. Flicker Separation
3. Power Disturbance Detection and Classification based on VMD and DSCN
3.1. Deep Stochastic Configuration Networks Algorithm
3.2. Disturbance Detection and Classification
4. Results and Discussion
4.1. Synthetic Signal
4.2. Real World Signal
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Synthetic Signal | Results of VMD | ||
---|---|---|---|
Freq. (Hz) | Amp. (pu) | Freq. (Hz) | Amp. (pu) |
50 | 1 | 50.03 | 1.01 |
150 | 0.45 | 150.07 | 0.452 |
250 | 0.3 | 250 | 0.31 |
350 | 0.06 | 350.16 | 0.056 |
367 | 0.12 | 367.19 | 0.124 |
450 | 0.06 | 450.16 | 0.063 |
Parameter | Synthetic Signal | Results of VMD |
---|---|---|
1 | 0.999 | |
(Hz) | 50 | 50.03 |
0.4 | 0.049 + 0.05 = 0.099 | |
41 | 41.02 | |
59 | 59.03 |
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Cai, K.; Alalibo, B.P.; Cao, W.; Liu, Z.; Wang, Z.; Li, G. Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network. Energies 2018, 11, 3040. https://doi.org/10.3390/en11113040
Cai K, Alalibo BP, Cao W, Liu Z, Wang Z, Li G. Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network. Energies. 2018; 11(11):3040. https://doi.org/10.3390/en11113040
Chicago/Turabian StyleCai, Kewei, Belema Prince Alalibo, Wenping Cao, Zheng Liu, Zhiqiang Wang, and Guofeng Li. 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network" Energies 11, no. 11: 3040. https://doi.org/10.3390/en11113040