利用小波变换和独立成分分析检测电力系统故障
Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis
AI总结：
本文提出基于小波变换和独立成分分析的新型故障检测方法，通过时间-频率分析和性能指标识别故障起始点，验证了方法在不同工况下的鲁棒性。
摘要：
不间断电源是电力公司的主要动力，促使他们迅速识别和定位不同类型的故障以保护电力系统，防止完全停电。本文提出了一种基于小波变换（WT）和独立成分分析（ICA）的新型故障检测方法。在故障条件下采集电压信号，并通过小波和ICA进行处理以检测故障。小波变换的时间-频率分辨率用于检测信号中的故障起始点，而独立成分分析计算的性能指标用于检测电压信号中的故障扰动。所提出的方法在无噪声、20-dB噪声和频率变化等不同运行场景下均表现出足够的鲁棒性。进一步，通过性能指标、能量内容等，应用现有傅里叶变换（FT）、短时傅里叶变换（STFT）和所提出的小波变换进行故障检测研究。如果每个场景中计算的能量大于相应的阈值，则检测到故障扰动。故障检测研究在MATLAB/Simulink中对典型电力系统进行了模拟。
英文摘要：
Uninterruptible power supply is the main motive of power utility companies that motivate them for identifying and locating the different types of faults as quickly as possible to protect the power system prevent complete power black outs using intelligent techniques. Thus, the present research work presents a novel method for detection of fault disturbances based on Wavelet Transform (WT) and Independent Component Analysis (ICA). The voltage signal is taken offline under fault conditions and is being processed through wavelet and ICA for detection. The time-frequency resolution from WT transform detects the fault initiation instant in the signal. Again, a performance index is calculated from independent component analysis under fault condition which is used to detect the fault disturbance in the voltage signal. The proposed approach is tested to be robust enough under various operating scenarios like without noise, with 20-dB noise and variation in frequency. Further, the detection study is carried out using a performance index, energy content, by applying the existing Fourier transform (FT), short time Fourier transform (STFT) and the proposed wavelet transform. Fault disturbances are detected if the energy calculated in each scenario is greater than the corresponding threshold value. The fault detection study is simulated in MATLAB/Simulink for a typical power system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
