The conventional artificial neural network (ANN) model is an effective way to detect harmonic signals. However, the solution of the model cannot converge when the initial value of the excitation function (IVEF) is not suitable, causing a large error in harmonic detection, especially in the case of power system noise. We improve the back propagation (BP) neural network (BNN) model by using an adjustable excitation function and the basic inertial algorithm in this paper. Based on this model and combined with the glowworm swarm optimization (GSO) algorithm, a harmonic/inter‐harmonic detection method is proposed. The network first uses an improved GSO algorithm with an adaptive step to optimize the IVEF of BNN. Then, BNN is trained at this initial value, so that amplitude, phase, and frequency of harmonics/inter‐harmonics can be obtained. The simulation data analyses show that that the method has good stability, convergence, strong anti‐noise ability, and ultrahigh detection accuracy. It can accurately separate the integer and non‐integer harmonics. Compared with the traditional ANN algorithm and Hanning‐FFT, the detection accuracy of this algorithm can be improved by 2–4 orders of magnitude. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于改进的萤火虫群​​优化和神经网络的谐波/谐波间谐波检测

常规的人工神经网络（ANN）模型是检测谐波信号的有效方法。但是，当激励函数（IVEF）的初始值不合适时，模型的解不能收敛，这会导致谐波检测中的较大误差，特别是在电力系统噪声的情况下。通过使用可调激励函数和基本惯性算法，我们改进了反向传播（BP）神经网络（BNN）模型。在此模型的基础上，结合萤火虫群优化算法，提出了一种谐波/间谐波检测方法。网络首先使用带有自适应步骤的改进GSO算法来优化BNN的IVEF。然后，以该初始值对BNN进行训练，从而可以获得谐波/间谐波的幅度，相位和频率。仿真数据分析表明，该方法具有良好的稳定性，收敛性，抗噪能力强，检测精度超高。它可以准确地分离整数和非整数谐波。与传统的ANN算法和Hanning‐FFT相比，该算法的检测精度可提高2-4个数量级。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。