Abstract：Partial discharge (PD) is an early indicator on insulation deterioration that will cause catastrophic failure on the power system, so PD diagnosis is a significant approach to monitor the operating status of the electrical equipment. Recently, deep learning (DL) has gradually reached the mainstream in the field of PD diagnosis and the increasing intelligent terminals near power equipment maybe serve as the carrier for such DL models. However, the existing DL-based PD models tend to occupy higher computing resources, while the current power intelligent terminals usually have small memory space and limited calculation capacity. To address it, a lightweight PD diagnosis method based on depth-width joint pruning is proposed in this paper, which can effectively compress the computational resource consumption of DL model while ensuring the accuracy of PD diagnosis. Firstly, a set of 1D PRPD matrix is constructed based on the discharge amplitude, phase and pulse number, including four types of PD defect such as point discharge, surface discharge, air-gap discharge and suspended discharge. Then, this method selects MobileNetV2 as the basic model, and an iterable importance factor α is inserted in the training process to assess the importance of each convolution module. According to α, several modules with low importance factors (close to 0) are pruned to simplify the basic model in depth direction. Finally, to further compress this model, it adopts a filter-level pruning approach called filter pruning via geometric median (FPGM) to remove redundant convolution filters, in which the pruning ratio of filters in each layer is adaptively calculated by an enhanced simulated annealing search (ESA). Through cyclical search, a highly compressed model can be generated with almost no loss of accuracy, while greatly reduces the computational cost and time. The experimental results show that, with the premise of remaining the diagnosis accuracy, the proposed method can automatically design an efficient PD diagnosis model with lightweight architecture and less diagnosis time, achieving 98.23% accuracy, 9.9 times of parameter compression and 2.3 times of inference acceleration. The comparison with different pruning methods such as pre-training pruning, Slimming, FPGM and AutoML for model compression (AMC) shows that, the proposed method has 0.31%~4.53% better diagnosis accuracy than the others’, and its pruning speed is 34 faster than AMC’s. Therefore, it is more appropriate to apply in the compression of PD diagnosis model. Furthermore, compared with other DL-based diagnosis models such as VGG16, ResNet18, ShuffleNetV2 and GhostNet, the proposed method shows similar diagnosis accuracy under different noise levels, and more significantly, the storage and memory consumption reduce to only 0.90 MB and 12.89 MB respectively, and the maximum speedup of diagnosis speed reaches up to 9.3 times. The following conclusions can be drawn from the above analysis: (1) Driven by the PRPD data,the proposed depth-direction pruning can automatically learn the importance of each convolution module for PD diagnosis task, and then pruning the low importance parts have less impact on the accuracy of PD diagnosis. (2) In the breadth-direction pruning, the pruning ratio of filters in each layer is adaptively determined by ESA search, which achieves the comprehensive optimum among accuracy, parameters number and PD diagnosis time. Therefore, it benefits for avoiding over- or under-compression of DL model. (3) By jointing both depth- and width-directions pruning, the proposed method realizes the automatic design of the architecture for PD diagnosis model, which greatly reduces the storage space, memory consumption and diagnosis time. Compared with traditional deep learning models, it is more suitable for the scenarios of resource-constrained power intelligent terminals.
张翼, 朱永利. 基于深度-广度联合剪枝的电力设备局部放电轻量化诊断方法[J]. 电工技术学报, 2023, 38(7): 1935-1945.
Zhang Yi, Zhu Yongli. A Lightweight Partial Discharge Diagnosis Method of Power Equipment Based on Depth-Width Joint Pruning. Transactions of China Electrotechnical Society, 2023, 38(7): 1935-1945.
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2023, Vol. 38 
