| 基于前馈神经网络的电网基波高精度检测 |
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| 引用本文： | 王勇,付志红,张淮清,王好娜,侯兴哲. 基于前馈神经网络的电网基波高精度检测[J]. 电网技术, 2011, 35(8): 124-128 |
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| 作者姓名： | 王勇 付志红 张淮清 王好娜 侯兴哲 |
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| 作者单位： | 1. 输配电装备及系统安全与新技术国家重点实验室（重庆大学）,重庆市沙坪坝区,400030
2. 国家电网公司电能计量器具性能评估实验室,重庆市渝北区,401123 |
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| 基金项目： | 国家自然科学基金项目(40874094)~~ |
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| 摘 要： | 电网基波是电能计量和电能质量评估的重要指标，提出了基于前馈神经网络的电网基波频率和幅值的高精度检测方法。根据数学推导得出：正弦信号过零点与其两侧对称两点的连线与时间轴交点的时间差，同频率满足单调关系，但并非严格的线性关系，而且与幅值无关，据此用前馈神经网络建立该时间差与频率的映射关系。Matlab仿真表明，提出的算法对频率的检测精度达到10^-4级，幅值的检测精度高达10^-5级，远远高于快速傅里叶变换和Harming窗的插值算法；随机噪声和谐波对前馈神经网络测量精度的影响很小，该算法具有较强的抗干扰能力。
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| 关 键 词： | 电网基波 前馈神经网络 基波频率 基波幅值 |
High Precision Detection of Fundamental of Power Grid Based on Back Propagation Neural Network |
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| WANG Yong ,FU Zhihong ,ZHANG Huaiqing ,WANG Haona ,HOU Xingzhe Shapingba District,Chongqing ,China,.Laboratory For The Performance Assessment of Electrical Metering Devices,State Grid Corporation,Yubei District,Chongqing ,China). High Precision Detection of Fundamental of Power Grid Based on Back Propagation Neural Network[J]. Power System Technology, 2011, 35(8): 124-128 |
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| Authors: | WANG Yong FU Zhihong ZHANG Huaiqing WANG Haona HOU Xingzhe Shapingba District Chongqing China .Laboratory For The Performance Assessment of Electrical Metering Devices State Grid Corporation Yubei District Chongqing China) |
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| Affiliation: | WANG Yong 1,FU Zhihong 1,ZHANG Huaiqing 1,WANG Haona 1,HOU Xingzhe 2 (1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology(Chongqing University) Shapingba District,Chongqing 400030,China,2.Laboratory For The Performance Assessment of Electrical Metering Devices,State Grid Corporation,Yubei District,Chongqing 401123,China) |
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| Abstract: | Fundamental of power grid is an important index for electric energy metering and power quality evaluation.A high-precision detection approach,which is based on back propagation neural network(BPNN),for the frequency and amplitude of power grid fundamental is proposed.It is derived mathematically that the relationship of the time difference,which is between zero-crossing point of sinusoidal signal and the intersection point of time axis and the line connecting two symmetric points on signal curve at both sid... |
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| Keywords: | fundamental back propagation neural network(BPNN) fundamental frequency fundamental amplitude |
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