包络提取是电压闪变检测的关键步骤，也是评价和治理电能质量问题的重要前提，已成为国内外研究的重点。但受噪声、谐波、间谐波和多频率调制等影响，现有包络提取及闪变检测方法在抗噪性、准确性和实时性之间不能兼顾。本项目研究面向包络提取的加窗泰勒-傅里叶变换（WTFT）及其在电压闪变检测中的应用，包括：①提出基于时域混合卷积的新型软时域窗，建立基于新型软时域窗的WTFT算法，减少包络提取中的频谱泄漏与混叠；②构建基于WTFT的信号包络提取模型及其快速变换矩阵，提高包络提取的实时性；③建立噪声稳健的基于WTFT的电压闪变检测方法，阐明其准确度与稳定性的内在关系；④构建基于DSP的电压闪变检测平台，验证理论成果的正确性。本项目成果将提升包络提取及电压闪变检测的噪声稳健性、准确性和实时性，为电能质量监测提供新的实现途径；所建立的WTFT算法也可用于载波通信和故障诊断等领域，将成为现有时频分析方法的重要补充。

Envelope extraction is one of the key step of the voltage flicker detection and also an important prerequisite for evaluation and improvement of the power quality, which has become the focus of current research. However, due to the influence of the noise, harmonics, inter-harmonics and multi-frequency modulation, the existing methods of envelope extraction and voltage flicker detection cannot satisfy the requirements of accuracy, anti-noise, and real-time simultaneously. This project studies the windowed Taylor-Fourier transform (WTFT) for envelope extraction and its application in flicker detection, which includes: ①The new adjustable time-domain window will be presented by using the cross convolution, and the WTFT based on the new window will be proposed to suppress the spectral leakage and combined interference; ②The envelope extraction model based on the WTFT with its rapid computation matrix will be presented; ③The noise robustness detection of voltage flicker will be proposed based on the WTFT, and the relationship between the accuracy and stability of the propsed method will be illustrated; ④The test platform of voltage flicker detection will be designed and built based on the DSP, and the correctness of the theoretical method will be verified by the practical test. The proposed envelope extraction model and voltage flicker detection method will feature several significant improvements over the presence method including noise robustness, accuracy, and real-time implementation, which will provide a new approach for the power quality monitoring. Moreover, the proposed WTFT can be used in several fields, e.g., carrier communication and fault diagnose, which will be an important supplement of the available time-frequency analysis tools.