Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network

doi:10.11930/j.issn.1004-9649.202310020

Abstract

Abstract:

In the smart grid, precise monitoring of the operational status of critical equipment for transmission, distribution and power supply is essential for effective online maintenance. Faced with the inefficiencies of manual recording and inspection, as well as the challenges associated with the complex installation, high cost and lengthy periods required for digital upgrades of monitoring devices, a novel approach that integrates image capture devices with image processing technology has been developed. This approach, leveraging the allocation of computational resources for task distribution, introduces a Light-Resnet-based numerical recognition algorithm, which enhances network training through the optimization of the D-Add loss function, enabling remote reading of electrical equipment monitoring data. Experiments have demonstrated that Light-Resnet achieves a rigorous accuracy rate of 98.8% on the MNIST dataset with only 6090 parameters. When combined with edge computing collaboration mechanisms, it resulted in a 20.73% reduction in power consumption on the terminal side. The proposed algorithm not only proves its adaptability and efficiency in resource-constrained environments but also significantly improves the network's accuracy with design of the D-Add loss function.

Key words: light-resnet, D-add, edge-end collaboration mechanism, numerical recognition, smart grid

Zhiheng KONG, Chong TAN, Peiyao TANG, Chengbo HU, Min ZHENG. Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network[J]. Electric Power, 2024, 57(8): 206-213.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202310020

https://www.electricpower.com.cn/EN/Y2024/V57/I8/206

Figures/Tables 9

Fig.1 System architecture illustration

Fig.2 Light-Resnet network architecture

Fig.3 Projection shortcut & identity shortcut

Fig.4 The calculation procedure of the loss function D-Add

Table 1 Performance comparison of different network architectures

网络架构	$\overline {{N_{{\text{ACC}}}}} $/%	$\overline {{N_{{\text{SACC}}}}} $/%	权重大小
CapsNet	99.99	99.23	6804384
Light-Resnet	100.00	98.79	6090
MOCNN	99.94	96.38	6538

Fig.5 Strict accuracy comparison of different network architectures

Table 2 Performance comparison of multiple loss functions

损失函数	$\overline {{N_{{\text{ACC}}}}} $/%	$\overline {{N_{{\text{SACC}}}}} $/%
CEL	99.97	98.45
HRC	99.99	97.82
D-ADD	100.00	98.79
AVG	99.99	97.95

Fig.6 Strict accuracy comparison of multiple loss functions

Table 3 Terminal-side power consumption comparison 单位：J

位置	E_dr	E_dd	E_send	E_sensor	E_other	E
边	0	0	49.624	1.462	6.819	57.905
端	2.218	2.878	2.709	1.517	6.427	15.749
边端协同	2.211	0	2.721	1.514	6.599	13.045

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