## 1 Introduction  
With the emergence of the grid connection of new energy sources and the increasing use of nonlinear electrical equipment, harmonic pollution in power systems has become more and more severe, posing a threat to power quality, power supply safety, and economic benefit. Therefore, there has been growing demand to curb harmonic pollution to ensure power quality. In this context, accurate detection of harmonics and interharmonics in signals is a prerequisite for effective harmonic pollution management [1; 2; 3; 4]. At present, the detection of harmonics and interharmonics is mainly divided into frequency domain analysis and time-domain analysis. Frequency domain analysis mainly refers to Fourier transform, which is simple in method and fast in computation, but suffers from spectrum leakage and the fence effect [5; 6; 7]. Time-domain analysis includes Wave Transform (WT) [8; 9; 10], Empirical Mode Decomposition (EMD) [11; 12; 13], Hilbert-Huang Transform (HHT) [14; 15; 16; 17; 18], the Prony algorithm [19; 20; 21; 22], etc. WT is, in essence, Fourier transform with an adjustable window size, which can reflect the entire process of signal changes. However, difficulties arise in selecting an appropriate wave basis function. When using WT, many tests must be carried out to determine the most suitable wave basis, which is not self-adaptive to the signal. EMD is an adaptive decomposition method for nonlinear and non-stationary time series data. However, mode aliasing exists in the decomposition process of EMD, which leads to different time-scale components appearing in the same characteristic mode function [23]. HHT is composed of EMD and Hilbert transform, which can be adaptively decomposed with high precision according to the signal. Nevertheless, it is susceptible to end effects and severe mode aliasing effects. Literature [24] uses a multi-resolution analysis of WT to process the original signal. Then, the EMD algorithm is used to obtain a series of Intrinsic Mode Function (IMF) components from the processed signals, and fundamental and harmonic components are extracted from the IMF components. Finally, the Hilbert transform is used to calculate the frequency and amplitude of the signal. The Prony algorithm uses a series of exponential formal functions to fit the sample signal, which can extract the frequency, amplitude, phase and attenuation factor of the signal. The frequency resolution is high and the calculation is simple. Nevertheless, the Prony algorithm is known to be highly sensitive to noise, and its fitting results often contain many false components and data screening in the fitting results. In literature [25], the Prony algorithm is used to screen the detection results of harmonics and eliminate false components. However, due to the absence of signal denoising, in the presence of strong environmental noise, the fitting accuracy would be reduced and the amplitude of false components in the fitting results can be higher than that of non-false components, which may lead to unsatisfactory screening results. Literature [26] improves the denoising of Variational Mode Decomposition (VMD) and combines the Prony-GSO algorithm to detect harmonics. Yet, a mode aliasing effect exists in the VMD method, and the signal is distorted to some extent after denoising, which affects the detection accuracy. Literature [27] adopts Singular Value Decomposition (SVD) to denoise signals and determine the denoising order by the rate of singular value change to improve SVD's denoising capability and overcome the noise sensitivity of the Prony algorithm. Despite its effectiveness, the adaptability of this method is limited: in the presence of strong environment noise, the singular value does not change obviously and the denoising ability decreases. In view of the advantages and disadvantages of the aforementioned methods, this paper proposes a novel harmonic and interharmonic detection method based on enhanced SVD and the Prony algorithm.