# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 1 Introduction  
With the continuous development of smart grid construction and the large-scale application of power electronic equipment and nonlinear loads, the harmonic problem of the power grid has become particularly serious. Harmonic pollution not only causes significant power loss and reduces the efficiency and reliability of the grid, but also introduces other challenges such as equipment overheating, sensitive equipment failure, and resonance [1; 2; 3]. Therefore, the primary task for implementing targeted harmonic pollution control is accurately detecting power harmonic signals to provide a basis for subsequent harmonic control [4]. At present, harmonic signal detection methods encompass Fourier transform, neural networks, empirical mode decomposition (EMD), and other algorithms [5; 6; 7]. While these traditional algorithms are straightforward and fast in computation, their adaptability to non-static harmonic signals in noisy backgrounds is limited, leading to poor denoising effects and subsequent loss of crucial characteristic information from the harmonic signal, resulting in reduced detection accuracy. With the emergence of new noise reduction methods, the filtering capabilities of EMD have garnered widespread recognition and application. Researchers in [8] combined empirical mode decomposition with wavelet thresholding to effectively mitigate noise in bridge detection signals. Similarly, [9] proposed a denoising method that integrates empirical mode decomposition with an improvedwavelet threshold, successfully eliminating background noise influence. Additionally, [10] introduced a harmonic detection method augmented by empirical mode decomposition and wavelet threshold denoising, which enhances harmonic signal detection accuracy and exhibits commendable anti-noise performance. While EMD and its improved version coupled with wavelet denoising excel in noise removal, the mode aliasing issue persists, potentially resulting in information loss contained within the mode. Variational mode decomposition (VMD) [11] has garnered significant attention as a signal processing technology. It exhibits remarkable bandwidth division capability in signal frequency decomposition and effectively addresses spectrum aliasing issues encountered in EMD. Moreover, VMD demonstrates superior robustness in signal sampling and noisy environments, making it highly adaptive. However, when employing VMD for signal noise reduction processing, setting parameters in advance is necessary. The decomposition layer \(K\) and penalty factor \(\alpha\) significantly influence decomposition results and performance. Reliance on experience or prior criteria for parameter selection can lead to inaccurate decomposition results and diminish the efficiency of VMD. The methods used to determine the value of VMD parameter \(K\) are mainly divided into two categories. In the first category [12; 13; 14], the minimization residual energy value method, frequency characteristic distribution method, and kurtosis principle are used to select the optimal value of parameter \(K\), all of which can effectively decompose signals, but the applicability of different signals is poor, which can easily cause under-decomposition. The second type [15; 16] uses the optimization algorithm to optimize parameter \(K\), but the computation is too large and the real-time performance is poor. However, the above research content only focuses on the optimization of one of the decomposition numbers \(K\) and penalty factor \(\alpha\), the calculation steps are more complicated, and the comprehensive influence of the synergistic effect between the two parameters on the decomposition effect is not fully considered. In order to comprehensively consider the selection of VMD parameters, particle swarm optimization (PSO) is used in [17] to optimize the decomposition parameter combination of the VMD algorithm, which significantly enhances the decomposition energy efficiency compared with the central frequency observation method.