Feature Extraction of <i>V–I</i> Trajectory Using 2-D Fourier Series for Electrical Load Classification

Mulinari, Bruna Machado; Nolasco, Lucas da Silva; Oroski, Elder; Lazzaretti, André E.; Linhares, Robson R.; Renaux, Douglas P. B.

doi:10.1109/jsen.2022.3194999

Cited by 8 publications

(1 citation statement)

References 38 publications

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“…Power fingerprint identification is a hot issue in the field of Non-intrusive load monitoring (NILM), which relies on power fingerprint features and classifiers to identify different types of devices. Common power fingerprint characteristics typically include voltage, current, harmonics, power, V-I trajectory, etc [7][8][9]. The V-I trajectory, as the most common power fingerprint feature, represents the voltage and current waveforms of appliances in the image and has been successfully applied to load identification with good results.…”

Section: Introductionmentioning

confidence: 99%

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

et al. 2023

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In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.

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Section: Introductionmentioning

confidence: 99%

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

et al. 2023

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Improving multi-state appliance classification by SE-DenseNet based on color encoding in non-intrusive load monitoring

Han,

Dou,

Zhao

et al. 2024

Journal of Renewable and Sustainable Energy

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Non-intrusive load monitoring (NILM) is a technique that efficiently monitors appliances' operational status and energy consumption by utilizing voltage and current data, without intrusive measurements. In NILM, designing efficient classification models and building distinctive load features are crucial. However, due to its continuously variable load characteristics, multi-state load identification remains the most challenging problem in NILM. In this paper, we improve the encoding of the color V–I trajectory by incorporating instantaneous power, thereby enhancing the uniqueness of V–I trajectory features. Furthermore, we investigate a NILM method based on deep learning methods and propose a densely connected convolutional network with squeeze-and-excitation network (SE-DenseNet) architecture to solve the multi-state load identification problem. Initially, the architecture leverages DenseNet's dense connectivity property to generate a multitude of feature maps from the V–I trajectory. Then, SENet's channel attention mechanism is employed to enhance the utilization of effective features, which is more effective for multi-state load identification. Experimental results on the NILM public datasets PLAID and WHITED show that the recognition accuracy of the proposed method reaches 98.60% and 98.88%, respectively, which outperforms most existing methods.

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A Lightweight Convolutional Network Combined with Channel Attention for Non-intrusive Load Monitoring

Zhan

Zhou

Feng

et al. 2023

2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE)

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Feature Extraction of V–I Trajectory Using 2-D Fourier Series for Electrical Load Classification

Cited by 8 publications

References 38 publications

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

Improving multi-state appliance classification by SE-DenseNet based on color encoding in non-intrusive load monitoring

A Lightweight Convolutional Network Combined with Channel Attention for Non-intrusive Load Monitoring

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