## 1 Introduction  
As an essential component of the power system, daily inspection of electrical equipment is imperative to ensure the secure and stable operation of the power system [1]. Image classification is a crucial prerequisite for monitoring the condition of electrical equipment based on image information. In recent years, machine learning has made significant progress in the field of image classification for electrical equipment. [PERSON] presented a machine learning method for determining the state of each switch by analyzing images of the switches in power distribution substations [2]. [PERSON] implemented FINet based on improved YOLOv5 to inspect the insulators and their defects in order to ensure the safety and stability of the power system [3]. To address the few fault cases and deficient monitoring information in transformer diagnostic tasks, [PERSON] provides an improved few-shot learning method based on approximation space and belief functions [4]. [PERSON] proposed a label distribution CNNs classifier to estimate the aging time of the conductor morphology of the high-voltage transmission line [5]. It is noteworthy that the majority of these investigations have concentrated on a restricted range of electrical apparatus. These models necessitate a substantial quantity of training data to guarantee optimal performance. Nevertheless, acquiring adequate electrical equipment images in a practical setting may prove challenging, and the proportion of labeled samples is minimal. To a certain extent, the classification of electrical equipment images is not really a big data problem; rather, it belongs to the few-shot learning (FSL) domains. Few-shot classification (FSC) is the task of classifying samples with a limited number of instances while showcasing rapid generalization capabilities. Recently, a large number of few-shot image classification algorithms have been proposed. Depending on the learning paradigm used, these methods can be broadly divided into two categories: (1) meta-learning-based methods and (2) transfer-based learning methods. Meta-learning is a promising approach that leverages episodic training to simulate the real test environment by randomly selecting several subtasks. Optimization-based meta-learning methods employ a two-tier optimization process to learn the optimizer for quickly processing new tasks. Model-Agnostic Meta-Learning (MAML) [6] obtains the optimal initialization parameters of the model through meta-training, enabling the model to adapt to new tasks after a few gradient updates. In addition, the learning rate and gradient direction are also important factors for the optimizer [7, 8]. However, these methods require the storage and computation of higher-order derivatives, resulting in high memory and computational costs. On the other hand, metric-based methods use nonparametric classifiers as the basic learners, avoiding the aforementioned issues. The key factors of these methods are feature extraction and similarity measurement, which offer ample room for improvement. PARN [9] proposed a feature extractor that learned an offset for each cell in the convolution kernel in order to extract more efficient features, building deformable convolutional layers. CC+rot [10] improved the transfer ability of feature extractors by adopting auxiliary self-supervised tasks. [PERSON] et al. [11] used the pre-trained visual saliency detection model to segment the foreground and background of the image and then extract the foreground and background features, respectively. With the proven effectiveness of attention mechanisms in extracting discriminating features, several few-shot classification (FSC) tasks have adopted this method, including CAN [12], AWGIM [13], and CTM [14]. Additionally, in metric-based meta-learning methods, the measurement of similarity is also crucial. SEN [15] combines Euclidean distance and norm distance to improve the effectiveness of Euclidean distance measurement in high-dimensional spaces.