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雷达卡
摘要翻译：
光伏发电系统的性能受到环境条件的影响，当部分遮光时，跟踪最大功率点(MPP)并保持最优性能变得更加困难。本文提出了一种改进的最大功率点跟踪(MPPT)方法，利用序贯Monte Carlo(SMC)滤波进行状态估计，并辅以人工神经网络(ANN)对最大功率点进行预测。在增量电导（I-C）MPPT方法的框架下，提出了一种用于MPP序列估计的状态空间模型，该模型基于观测到的电压、电流或辐照度数据预测全局MPP(GMPP)，以改进SMC的估计。此外，本文还采用了一种快速的不平度变化检测方法，使得基于SMC的MPPT方法仅在检测到部分阴影时才借助于人工神经网络的辅助。仿真结果表明，本文提出的增强MPPT方法具有较高的效率，对不同噪声水平下辐照度的快速变化具有较强的鲁棒性。
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英文标题：
《An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and
Quickest Change Detection》
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作者：
Leian Chen, Xiaodong Wang
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this paper, we propose an enhanced maximum power point tracking (MPPT) method utilizing the state estimation by the sequential Monte Carlo (SMC) filtering which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance (I-C) MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP (GMPP) to refine the estimation by SMC. Moreover, a quick irrandiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change under different noise levels.
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PDF链接：
https://arxiv.org/pdf/1805.04922
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