Identification of Power Quality Disturbances Based on FFT and Attribute Weighted Artificial Immune Evolutionary Classifier

Nowadays, the issue of Electromagnetic Compatibility is of great importance and urgency. In this paper, we propose a novel hybrid automatic identification system for power quality disturbances, which lays foundations for further analyzing the electromagnetic compatibility. Specifically, we firstly extract features by using the FFT and envelope detection method. Then we utilize the attribute weighted artificial immune evolutionary Classifier (AWAIEC) for classification of power quality disturbance events. Experimental results have shown that the proposed method performs better than existing approaches.

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Quality Disturbance Recognition Based on the Generalized-S Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.246-247.251 ◽

2012 ◽

Vol 246-247 ◽

pp. 251-256

Author(s):

Bin Liu ◽

Xi Wang

Keyword(s):

Power Quality ◽

Simple Structure ◽

Recognition Rate ◽

Automatic Identification ◽

S Transform ◽

Power Quality Disturbance ◽

Generalized S Transform ◽

Signal Simulation ◽

Power Quality Disturbances

In order to achieve the power quality disturbance signal feature extraction and automatic classification of power quality disturbances based on the generalized S transform to identify the improved algorithm, the generalized S transform results according to the power quality disturbance signal, extract the characteristics of power quality disturbance signal, to achieve power quality disturbances automatic identification of the signal. Through a standard sinusoidal signal simulation examples prove that the algorithm has high noise immunity, simple structure, and high recognition rate.

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Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

Energies ◽

10.3390/en12244732 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4732

Author(s):

Ruijin Zhu ◽

Xuejiao Gong ◽

Shifeng Hu ◽

Yusen Wang

Keyword(s):

Power Quality ◽

Nearest Neighbor ◽

Small Sample ◽

Small Samples ◽

K Nearest Neighbor ◽

Siamese Network ◽

Power Quality Disturbance ◽

Power Quality Disturbances ◽

Small Sample Sizes

The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability.

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