1. Introduction
In emerging power systems dominated by new energy sources, it is common for the voltage and current to exhibit complex and unpredictable behaviors [
1]. The power system exhibits the characteristics of “dual highs” and “dual randomness”, which pose a significant threat to the secure and stable operation of the power system. The effective identification of power quality issues is crucial for implementing corresponding measures to comprehensively manage them [
2]. Research in the field of AC power quality has reached a level of maturity, while the corresponding aspect related to DC power quality remains an area that requires further investigation [
3]. A DC microgrid does not involve the synchronization, harmonic, reactive power control, and frequency control of a traditional AC power grid, and it has higher power quality [
4]. However, the relevant system has not been established, and the definition of DC energy quality is not uniform [
5]. Existing studies only focus on the formation mechanisms and suppression methods of various disturbances. A few scholars have analyzed the occurrence mechanism and treatment methods of typical DC energy quality phenomena, such as voltage ripple and voltage fluctuation. They have also established DC energy quality evaluation standards based on AC system standards [
6]. However, there are few studies on specific identification methods [
7] for DC energy mass disturbances.
The accurate classification of power quality disturbance signals depends on the proficient use of signal detection and pattern recognition. Short-time Fourier transform (STFT) [
8], wavelet transform (WT) [
9], Hilbert yellow transform (HHT) [
10], etc., are commonly used power signal detection methods. Currently, there is no unified technical standard for detecting the quality disturbance signals of direct current (DC) power, especially ripple detection. Furthermore, the existing detection methods mainly focus on time-domain analysis, which cannot capture the instantaneous frequency and other relevant frequency domain characteristics of the signals. Scholars have borrowed and improved upon the detection methods for AC power quality issues, as seen in reference [
10], which utilizes a windowed Fourier transform to mitigate spectral leakage and enhance detection accuracy. LUO [
11] applied the variational mode decomposition method to calculate the pre-decomposition scale of the signal using the orthogonal decomposition method in order to detect the instantaneous frequency, instantaneous amplitude, and start and end time of the ripple signal. However, this approach is prone to mode mixing. The characteristics of several commonly used signal analysis methods are summarized in
Table 1. On the whole, STFT is not suitable for the analysis of transient signals due to the presence of spectrum aliasing and leakage phenomena. WT is highly susceptible to noise interference, making it challenging to differentiate signals such as temporary voltage rise and drop. HHT is suitable for analyzing mutation signals, although its overall performance is subpar. The S transform combines the benefits of WT and STFT and replaces the wavelet basis function with a Gaussian window, addressing the limitation of fixed window width in the STFT. The S transform exhibits superior time–frequency characteristics and is less susceptible to noise. As a result, it is well-suited for detecting power-quality interference signals.
After successfully detecting the interference, it is necessary to utilize a pattern recognition scheme to accurately classify it. This step is typically based on machine learning theory and seamlessly integrated with intelligent optimization algorithms to construct classifiers. Common classification algorithms are KNN [
12], SVM [
13], DT [
14], and BP [
15] among others. Several common classification algorithms and their characteristics are shown in
Table 2. Mahbooba [
16] used the chaotic search principle to obtain the random training set of the basis classification, and then constructed the decision tree. Patil [
17] used SVM to classify signals, and a good classification effect was obtained, but the optimization of kernel function should be considered. Mohamed E [
18] proposed an intelligent random vector function link network (RVFLN) optimized by the beluga whale optimizer (WWO) algorithm to predict the performance of new solar photovoltaic thermal air conditioners [
19], which greatly improved the prediction speed. In ref. [
20], a back propagation neural network (BPNN) was used to establish a classification model. A single BP has fast computation speed and strong generalization ability, which is suitable for classification problems, but it is easy to fall into local optimality. Tang [
21] used particle swarm optimization (PSO) to update the positions of individual and group extreme values, allocate optimal initial weights and thresholds to the BP neural network, and introduce variation factors to enhance the model’s capacity for seeking optimal solutions. Building upon the foundation of the artificial bee colony (ABC) algorithm, Chen [
22] combined three adaptive evolutionary strategies—dynamic self-adaptive factors, probability selection, and gradient initialization—to create the adaptive evolutionary artificial bee colony (AEABC) algorithm. Then, it was used to construct an AEABC-BPNN classification prediction model. Gnyawali [
10] used a genetic algorithm (GA) to optimize the BP neural network, effectively addressing the drawbacks associated with prolonged BP training times and the risk of the neural network’s weight and bias converging to local optima. However, due to the slow BP convergence, it is easy to fall into the local minimum, and it is difficult to approach the target with high precision. How to select suitable optimization algorithm for different research objects to build a BP neural network prediction model is still the focus of current research.
At present, there are few studies on the disturbance signal of DC energy quality, and the detection and recognition are not sufficiently accurate. In order to solve this problem, a novel identification method for DC energy mass disturbance is proposed in this paper. In order to solve this problem, a new identification method for DC energy mass disturbance is proposed in this paper. First, the S transform, a time–frequency analysis method, is used to extract the features of the signal. From the aspects of graphic features, time–frequency domain analysis, and statistical principles, five kinds of disturbance characteristic indexes are extracted from each disturbance signal as the basis of signal classification. The SABO algorithm is used to optimize the weights and thresholds of the BP neural network, improve the searching ability of the algorithm, build a classification model of the SABO-BP neural network, and realize effective classification of signals. The simulation results show that the proposed scheme can detect and identify each disturbance signal effectively. Compared with other classification models, the SABO-BP model has higher accuracy and a shorter running time.