1. Introduction
The network structure of power systems is becoming more and more complex, and a variety of non-linear loads are now widely used. This will bring some transient disturbance signals to the power grid, which seriously affects power quality [
1,
2]. At the same time, the presence of power quality disturbances (PQDs) reduces the lifetime of power semi-conductor and solid-state switching devices. In order to ensure the normal operation of various electrical equipment, it is necessary to take appropriate action to improve the quality of electricity.
The effective detection of PQD types is a precondition for the dynamic compensation of power quality. Research on PQD detection mainly focuses on the identification of transient signal types. Transient signals have the characteristics of being non-stationary and having a short duration. Moreover, due to the combined effect of the diversity of disturbance sources, the complexity of the grid structure and other factors, transient signal disturbances in the power grid are not always a single basic disturbance, and most of them are composite PQDs mixed by a variety of basic disturbances. These single disturbances have different disturbance types, different disturbance energies, and different starting and stopping moments. These bring greater challenges to the accurate identification of transient disturbances.
The problem of transient signal PQDs in power systems has become the focus of research among many scholars [
3]. Traditional transient disturbance analysis methods mainly rely on signal processing techniques to extract the physical characteristics of transient disturbance signals, and then achieve the classification of disturbance types by classifiers. This approach requires an understanding of power systems and a specific analysis of the equipment characteristics and operating conditions. Such signal processing methods include short time Fourier transform (STFT), wavelet transform (WT), S-transform, empirical modal decomposition (EMD), and variational modal decomposition (VMD).
Today, traditional signal processing methods are well studied in the field of extracting transient disturbance features. Carvalho et al. [
4] obtained the time-domain maximum magnitude vector of the disturbance signal by Blackman window STFT, and then input it as a feature vector into the support vector machine to achieve the identification of PQD types. While STFT has a fixed analysis window size, it is not suitable for analyzing the multi-scale characteristics of the signal. WT is suitable for analyzing and identifying non-smooth signals, but it is difficult to choose the wavelet basis function for WT; in addition, it causes some signal distortion when the number of decomposition layers is large in [
5]. S-transform is a time-frequency analysis algorithm derived from the combination of STFT and WT. It has good time-frequency characteristics, but the disturbed signal is highly informative after the decomposition of S-transform, and there is no unified form of disturbed eigenvector in [
6]. EMD has been widely used in the field of identification of transient signals. Due to the lack of rigorous mathematical proofs for EMD, it leads to the need for strong empirical guidance in its practical application in [
7]. VMD is applied as an adaptive and completely non-recursive signal decomposition algorithm for the type detection of transient signals in [
8]. However, an inaccurate number of decompositions can easily lead to high noise levels in the signal components, which affects the effectiveness of signal detection. The above methods can effectively identify and classify a single disturbed signal. Due to the feature coupling between the compound transient disturbed signals, it is difficult for traditional signal processing techniques to extract distinguishable features. Therefore, such methods are difficult to cope with composite disturbances in power systems.
The diagnosis of power quality transient disturbances based on artificial intelligence technology is the focus of many scholars’ research. This approach does not require an in-depth understanding of the physical operating processes of the power system, but relies on data analysis and processing to identify the characteristics and patterns of PQDs. Shoryu et al. [
9] proposed a PQD identification model based on a convolutional network and long short-term memory. The model can efficiently extract salient features from noisy signals and classify them through a SoftMax layer. Hezuo et al. [
10] used convolutional neural networks to adaptively learn disturbance features to classify unknown instances. At first, one-dimensional power quality waves are mapped into two-dimensional gray-scale images and disturbance features in the form of quadratic Dgray scale; then, a CNN architecture is constructed for PQD classification based on a LeNet-5 network under different noise conditions. Compared with traditional signal processing techniques, the above methods have achieved some satisfactory results based on the powerful feature extraction capability of CNN. However, when a CNN performs feature extraction, the receptive field of the CNN is limited by the smaller convolutional kernel, which causes it to focus mainly on local regions in the input data, ignoring the global space of the input data.
Inspired by the significant success of the Transformer architecture [
11] in the field of natural language processing (NLP), the stacked multi-head attention mechanism captures global features among sequential data, which has great potential for intelligent fault diagnosis. Liang et al. [
12] used the STFT to transform a one-dimensional signal into a two-dimensional time-frequency image, and then the time-frequency map was input as a feature map into a ViT network to train for achieving the classification task. Although the Transformer network has better performance in image classification tasks [
13], the Transformer lacks some of the inductive biases inherent in CNNs, such as local connectivity, parameter sharing, etc. [
14], which makes it possibly unable to extract meaningful features from a dataset when the amount of training data is insufficient. On the other hand, stacked attention mechanisms often limit the ability of Transformer networks to efficiently extract local contextual information.
In this paper, a transient signal identification method is proposed for power systems based on a dynamic large convolution kernel and multi-level feature fusion network by integrating the advantages of a CNN to extract local features and the Transformer network to capture global features. First, to solve the problem whereby a CNN small receptive field cannot effectively capture the important local features in the time series data, a dynamic large convolution kernel module is designed, which solves the small receptive field problem caused by the small convolution kernel. Meanwhile, unlike the fixed large convolution kernel, the module can make more efficient use of global context information by dynamically adjusting the size of the large convolution kernel to suit features of different sizes. Then, in order to solve the problem of the Transformer network’s lack of ability in extracting local contextual information, a dynamic large convolution kernel module is used to replace the multi-head attention mechanism in the transformer network. Finally, a dynamic feature fusion module is designed, which can dynamically find important feature information for adaptive fusion of multi-scale features. Our contributions are presented as follows:
1. In order to increase the receptive field of the convolutional neural networks and enhance the feature extraction capability of the network, a dynamic large convolution kernel module is proposed, which can make more efficient use of global context information by dynamically adjusting the size of the large convolution kernel to adapt to different sizes of features.
2. A multilevel feature fusion module is proposed, which improves on the popular fixed-weight feature fusion strategy by adaptively assigning different fusion weights to different input sequence data.
3. To validate the network framework, experiments were conducted on single and composite PQD datasets under different noise conditions.
The rest of the paper is structured as follows. The proposed dynamic large convolution kernel structure and the multilevel feature fusion module are presented in
Section 2. The PQD diagnosis network framework is described in
Section 3.
Section 4 illustrates the validity of the proposed method through extensive experiments. Finally,
Section 5 concludes this paper.