谐波
谐波
电力系统
总谐波失真
计算机科学
波形
有源滤波器
控制理论(社会学)
谐波分析
电子工程
非线性系统
功率(物理)
拓扑(电路)
工程类
电气工程
电压
声学
物理
人工智能
控制(管理)
量子力学
作者
Ahmadreza Eslami,M Negnevitsky,Evan Franklin,Sarah Lyden
标识
DOI:10.1109/ichqp53011.2022.9808722
摘要
Harmonics and waveform distortion are substantial power quality concerns for power systems with high penetration of renewable energy generation and non-linear loads. Harmonics and interharmonics should be mitigated for efficient and proper operation of power grids, with high harmonic injection from electric loads and devices. In this paper, an intelligent Active Power Filter (APF) based on Adaptive Linear Neuron (Adaline) is proposed which can provide accurate harmonic estimation and proper mitigation in real-time without any prior knowledge about harmonic/interharmonic orders. A novel formulation is derived, and adaptive learning with momentum is used for training. The proposed APF not only inherits the high adaptability of Adaline APFs but also compensates for their drawbacks by updating weights in the presence of unknown frequency orders. Additionally, an electric topology is proposed where one APF is able to mitigate harmonics of nonlinear loads connected to two adjacent Points of Common Coupling (PCCs). For a radial structure, harmonic currents from the grid are completely mitigated while for a ring structure, only harmonics from one side of the grid are mitigated. Highly varying and distorted load scenarios are studied. Comparison of the proposed APF with the state-of-the- art APFs in the literature proves the effectiveness and suitability of the proposed intelligent APF.
科研通智能强力驱动
Strongly Powered by AbleSci AI