1. Introduction
When a single-phase ground (SPG) fault occurs in a resonant grounding system, it is allowed to continue operation for about 1 to 2 h, according to the regulations. However, with the passage of operation time [
1], phase short circuits and power supply interruption may occur, which seriously affect the power supply reliability of the power system. Therefore, fast and reliable fault detection has become a basic requirement for modern distribution networks [
2]. For the complex fault situation of the resonant grounding system, where the fault current is susceptible to arcing, noise and other factors, fast and accurate fault detection is of great significance for the long-term reliable and stable operation of the distribution network.
Existing SPG fault detection methods for resonant grounding systems are mainly classified into steady-state methods [
3,
4,
5,
6], transient methods [
7,
8,
9], and data-driven methods [
10,
11,
12,
13,
14,
15,
16]. The steady-state method often uses fault characteristics such as phase, amplitude, and the fifth harmonic of the zero-sequence current to achieve fault detection. However, the steady-state method is not obvious in the case of fault conditions or changes in the network structure fault characteristics, which can easily lead to errors in feeder detection. The transient method has received a lot of attention from scholars because of its rich information on fault characteristics, compared with the steady-state method. Transient methods often use digital signal processing methods such as variational mode decomposition (VMD), Hilbert–Huang transform (HHT), wavelet transform (WT), and S-transform to extract single or multiple fault features of TZSC to improve fault detection accuracy. However, fault features extracted based on digital signal processing methods rely heavily on fusion theory and human experience, and lack completeness [
10]. With the development of the digital power grid, the digitalization and intelligence level of the distribution system will be improved, and the data-driven method based on the data is expected to make up for the shorfalls in the transient method.
Machine learning (ML) methods use digital signal processing methods to extract multidimensional fault features of transient zero-sequence currents to compensate for the shortcomings of single fault features to improve the characterization ability of fault features. Ghaderi et al. [
11] extracted the current waveform energy and normalized the joint time-frequency matrix input to a support vector machine to implement fault detection. M. Sahani et al. [
12] used VMD to extract the corresponding amplitude standard deviation, energy, Rényi entropy, and crest factor from the current as feature vectors for fault detection. Deep learning (DL) methods use digital signal methods to convert time-domain signals into time-frequency domain signals to obtain more useful fault information as a way to enhance the characterization of fault features. Guo et al. [
13] used HHT bandpass recording filters for faulty signals to construct the time-frequency energy matrix and used the time-frequency energy matrix as an input to the CNN. Wang et al. [
14] obtained a wavelet coefficient energy matrix image of the fault signal by WT to obtain the fault information in the frequency domain. In terms of classification method improvement, the detection accuracy of deep learning models such as CNN and LSTM was better than that of shallow learning methods such as SVM and extreme learning machine (ELM) [
15,
16]. However, the fault features processed or extracted by signal processing methods may exhibit strong uncertainties and randomness, which are hardly applicable to all fault conditions, especially extreme fault conditions, such as high impedance and strong noise [
17]. In other words, which feature extraction method is the best choice for faulty signals still has no concrete answer [
18]. Therefore, the limitations of the signal processing method can affect the robustness of the detection model.
In summary, the existing data-driven detection methods still have the following problems: (1) the existing methods have insufficient feature characterization capability; (2) the limitations of the digital signal processing methods can lead to poor robustness of the models. To address the above issues, in this study, a fault data stitching and image generation method is proposed, which stitches TZSC of each feeder into a system fault signal and then converts it into a grayscale image. This method has the advantage of small computational effort compared to existing fault detection methods and eliminates the influence of expert experience as much as possible. Then, the proposed improved CNN can adaptively extract the features of these grayscale images. The simulation results show that the proposed method has high accuracy and robustness in fault detection.
The main contributions of this paper are summarized in the following three points. First, a fault data stitching and image generation method is proposed, which enhances the characterization of fault features. Second, an improved CNN model is proposed to speed up the training of convolutional neural networks and reduce the sensitivity to network initialization using the BN method [
19]. Third, the proposed method is compared with existing feeder detection methods in this paper to demonstrate the effectiveness of the proposed method.
The subsequent structure of this paper is as follows:
Section 2 provides a detailed analysis of the existing data-driven fault detection-based methods and their limitations. The fault data stitching and image generation methods are elaborated in
Section 3.
Section 4 provides experiments and a discussion to verify the effectiveness of the proposed method through comparative experiments.
Section 5 summarizes the proposed method in this paper.
3. The Proposed Fault Detection Framework
This paper proposes a fault feeder detection method based on fault data stitching and image generation, which includes three modules: (1) a startup module; (2) an image generation module; and (3) an image recognition module.
To this end, this paper proposes a fault data stitching method that considers the degree of difference between the faulty and healthy feeder TZSC under the same fault condition, which requires only simple preprocessing of the original data and has the advantage of small computational effort. The schematic diagram of the proposed method is shown in
Figure 3. It is worth noting that the input samples of the proposed model in this paper are obtained by preprocessing using fault data stitching and image generation methods, rather than the digital signal processing methods mentioned in Option 1 and Option 2. In comparison, the method proposed in this paper requires only simple processing of the raw data, rather than relying on digital signal processing methods, and is less computationally intensive. The proposed method in this paper considers the differences and correlations between faulty and non-faulty feeders under the same fault conditions, which can enhance the fault characterization capability. The description of the fault characterization capability will be detailed in
Section 5.
The proposed detection method can be divided into two stages: offline training and online application. In the offline training stage, a large number of historical samples are obtained by simulating the occurrence of single-phase grounding in different feeders under different fault conditions through MATLAB/SIMULINK simulation. The TZSC in the first half-cycle after the occurrence of each feeder fault is collected from the historical samples, and the TZSC of each feeder is stitched together sequentially to form the system fault data. Then the system fault data is converted into grayscale images with the signal-image conversion method, and the improved CNN is trained using a large number of fault history samples. In the online application stage, the system zero-sequence voltage and TZSC of each feeder is collected first, and when is not satisfied, it means that no single-phase ground fault occurs. When is satisfied, the collected TZSC of each feeder is stitched together and constitutes the system fault data. Finally, the system fault data are converted into grayscale images with the signal-image conversion method and input to the improved CNN model trained offline for fault feeder detection.
The fault detection method proposed in this paper has the following advantages:
- (1)
It does not rely on the experience of experts and has the advantage of small computational effort.
- (2)
The improved CNN identifies not the TZSC of a single feeder, but the grayscale images formed by stitching the TZSC of each feeder in a specific order, taking into account the differences and correlations between normal and faulty feeders.
- (3)
The proposed fault data stitching and image generation method can enhance the characterization capability of data features.
The specific implementation will be described in detail in the next section.