基于混沌理论的电力系统微弱谐波检测方法研究

针对微弱谐波信号时频域分析检测方法在宽谱环境噪声较强场景中无法识别目标信号的缺陷，首先引入混沌系统的随机共振原理，在信号成分完全未知的情况下，利用随机噪声增强微弱目标信号进行频率盲检。进一步引入Duffing混沌系统，利用其对特定频率周期信号幅值的极高敏感性和噪声抵抗力，对检出的各谐波频率分量进行幅值精准检测。在经典随机共振模型的基础上，通过归一化改进了系统参数。结合Duffing混沌系统模型，设计了实时检测控制策略。通过粒子群寻优算法，以系统输入输出信号的互相关系数为适应度函数，寻找最优系统参数。对强随机噪声背景下的多整次谐波与间谐波混叠的谐波信号进行了仿真实验。结果表明，所提方法能在恶劣的噪声环境中，实现各谐波分量的无差频率检测与高精度幅值检测。

A weak harmonic detection method for a power system based on chaos theory

The time-frequency domain analysis and detection method of a weak harmonic signal cannot identify the target signal in a scene which has strong noise in the wide spectrum environment. Thus this paper first introduces a stochastic resonance principle of a chaotic system, and when the signal component is completely unknown, uses random noise to enhance the weak target signal for frequency blind detection. It introduces the Duffing chaotic system further, uses its high sensitivity and noise resistance to the amplitude of specific frequency periodic signals, and accurately detects the amplitude of each harmonic frequency component that has been detected. Using the classical stochastic resonance model, the system parameters are improved by normalization. Combined with the Duffing chaotic system model, a real-time detection control strategy is designed. The particle swarm optimization algorithm is used to find the optimal system parameters by taking the correlation number of input and output signals of the system as the fitness function. Simulation experiments are conducted on harmonic signals with multiple integer harmonics and inter harmonics mixed in a strong random noise background. The results show that the method proposed can achieve accurate frequency detection and high-precision amplitude detection of various harmonic components in harsh noisy environments.