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首页> 外文期刊>Electric power systems research >Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm
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Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

机译：基于粒子群优化算法的模糊系统对单一和组合电能质量扰动的检测与分类。

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摘要

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQcombined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.
机译：本文提出了一种利用模糊逻辑和粒子群算法（PSO）的单次和组合电能质量（PQ）干扰检测和分类的新方法。在提出的方法中，首先提取PQ干扰波形的合适特征。这些特征是从信号的傅立叶变换和小波变换得出的参数中提取的。然后，所提出的模糊系统基于这些特征对PQ干扰的类型进行分类。 PSO算法用于准确确定模糊系统的隶属函数参数。为了测试所提出的方法，假设PQ干扰的波形为采样形式。脉冲，中断，膨胀，下垂，陷波，瞬变，谐波和闪烁被认为是电压信号的单一干扰。另外，单个干扰的八种可能组合被视为PQ组合类型。当将具有各种信噪比（SNR）值的高斯白噪声添加到波形中时，还将研究提出的方法识别这些PQ干扰的能力。仿真结果表明，在嘈杂条件下，对于不同的单个和组合PQ干扰，正确识别的平均识别率约为96％。

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