## 1 Introduction  
The power industry is a fundamental sector for the national economy and people's livelihood. Therefore, enhancing the reliability of power supply is one of the crucial tasks of the power industry [1]. In recent years, the widespread use of power electronic technology [2] and the extensive development of new energy generation [3] have led to increasingly serious harmonic pollution in the power system. This pollution has a significant impact on power quality, power system security, and economic operation. At the same time, the power system is not strictly three-phase symmetrical. Even if no faults occur, there will still be harmonics, and the harmonic content will rise sharply during asymmetrical faults. The presence of harmonics can impact power quality, affect the performance of relay protection equipment, increase power grid losses, and affect electronic equipment and precision instruments. To ensure the stable and reliable operation of power systems, high-precision algorithms for detecting power harmonics have become a focus of research. Harmonic management can effectively improve power quality in the power grid [4]. Analyzing the parameters of harmonics to obtain real-time and accurate harmonic data is key to harmonic detection. Currently, power system harmonic analysis primarily relies on time-domain analysis [5], frequency-domain analysis [6], wavelet transform method [7], and Hilbert-Huangtransform method [8]. In classical time-frequency domain analysis methods, spectral overlap occurs when weak power harmonic signals are overwhelmed by a wide spectrum of strong background noise. This makes it difficult to determine the parameters of the filter for extracting weak signals with unknown characteristics. The literature [5] utilizes the Prony algorithm to analyze power system harmonics and interharmonic signals in the time domain. While the Prony algorithm offers high frequency resolution, this method is easily affected by noise signals and can only accurately estimate the characteristic indexes of harmonic and interharmonic components in the absence of noise or with minimal noise. The detection effectiveness is significantly reduced when weak signals are detected in the presence of strong noise. The frequency domain analysis method has issues such as the window function and the complexity and difficulty of designing the correction method. The study [6] utilizes the improved ap-FFT ratio method for spectral correction, which has a simple correction formula and is easy to implement, but when correcting the amplitude, only the strongest spectral line near the actual frequency point is utilized for the interpolation correction, while the information of the second strongest spectral line is not utilized, and the accuracy of the detection results cannot be guaranteed when the number of sample points does not meet the sufficiently large conditions. The wavelet transform is prone to band aliasing in harmonic decomposition and also has issues such as a large computational workload. The study [7] proposed an improved wavelet transform method but did not address the issue of frequency aliasing, and the harmonic suppression effect is inadequate. The study [8] utilized the Hilbert-Huang transform method, which suffers from modal aliasing and noise sensitivity. The stochastic resonance algorithm offers several advantages in detecting weak signals. Unlike traditional algorithms, it is not limited by the length of the data set, allowing for the detection of short data sets. Additionally, it considers noise energy as a source of signal energy, resulting in a higher output signal-to-noise ratio. The algorithm is not restricted to a specific signal modulation mode, making it quite versatile. The discussion of stochastic resonance theory in detecting weak feature signals continues to advance due to its unique signal-enhancement capabilities.