## 1 Introduction  
In recent years, with the increasing development of the smart grid, ensuing energy quality disturbance problems have emerged in an endless stream, which has a great impact on the safety, efficiency, and economy of the overall operation of the power grid. Every year, the economic losses caused by power quality problems are as high as hundreds of millions of Chinese yuan worldwide. Therefore, improving power quality problems has far-reaching significance for the overall safety and stability of the power grid as well as economic benefits [1, 2, 3]. It is particularly important to track, monitor, and analyze the power quality of the power grid. At present, the parameter estimation methods for PQDs detection and processing include singular value decomposition (SVD) [4, 5], short-time Fourier transform (STFT) [6, 7], wavelet transform (WT) [8, 9, 10], Kalman filtering (KF) [11, 12, 13], Hilbert-Huang transform (HHT) [14], compressed sensing, (CS) [15], phase space reconstruction (PSR) [16], and so on. Because there are obvious singular values near the mutation point of the power quality transient disturbance, SVD becomes an effective method to deal with this kind of signal. However, the results of this time domain analysis method lack the description of the frequency domain characteristics of the signal, which has certain limitations. STFT can obtain better detection results by selecting the appropriate window function, but due to the fixed window function selection of STFT in time and frequency, it is not suitable for the detection of transient disturbances. On the basis of STFT, WT can adaptively change the window function by introducing scale and translation factors, so that WT is suitable for analyzing the power quality transient disturbances. However, the results of WT are easily affected by the selection of the wavelet basis and the number of decomposition layers. KF is a signal processing method based on state space estimation. By decomposing the PQDs into a constant component and a fluctuation component, the amplitude, phase, and frequency can be accurately estimated. On the basis of KF, a harmonic detection method based on the maximum likelihood of Kalman filter residual analysis is proposed in [11], which can accurately detect the start and end time of harmonic. Hilbert-Huang transform is a new time-frequency analysis method suitable for non-stationary and nonlinear signals in recent years. It is mainly composed of empirical mode decomposition (EMD) and Hilbert transform. Because the intrinsic mode function (IMF) extraction of the EMD method is a time-frequency analysis process based on the local characteristic time scale of the signal, it is easy to have endpoint effects and modal aliasing problems for transient oscillation. Compressed sensing (CS) is a signal processing technique that enables efficient signal reconstruction using a reduced number of samples by exploiting the sparsity of the signal. In power quality disturbance classification, CS can significantly reduce data dimensionality and computational burden, thereby improving detection efficiency. However, CS assumes that the signal is sparse, which may lead to reduced reconstruction accuracy when the disturbance signals are complex or non-sparse, thus affecting classification performance. To address these issues, the literature [15] proposes combining CS with deep learning techniques, such as deep convolutional neural networks (CNNs), to enhance the robustness and accuracy of the disturbance classification, even in the presence of non-sparse signals. This hybrid approach leverages the strengths of both CS and CNNs, resulting in improved classification performance and efficiency. Phase space reconstruction (PSR) is a technique for extracting dynamic features from time series data by constructing a phase space, which helps reveal the nonlinear and time-varying characteristics of power quality disturbance signals. PSR provides a comprehensive view of the system's dynamics, making it well-suited for analyzing complex disturbance signals.