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Analysis of a utility-interactive wind-photovoltaic hybrid system with battery storage using neural network.

机译:基于神经网络的带电池储能-交互式风光互补系统分析。

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摘要

This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage.; The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount.; First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power.; Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input.; In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter.; Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as the sole sources of power, whose variations influence the system variables. Since both subsystems have different dynamics, their respective responses are expected to impact differently the whole system behavior. The dispatchability of the battery-supported system as well as its stability and reliability during gusts and/or cloud passage is also discussed.; In the fifth step, the goal is to determine to what extent the overall power quality of the grid would be affected by a proliferation of Utility-interactive hybrid system and whether recourse to bulky or individual filtering and voltage controller is necessary.; The final stage of the research includes a steady-state analysis of two-year operation (May 96–Apr 98) of the system, with a discussion on system reliability, on any loss of supply probability, and on the effects of the randomness in the wind and solar radiation upon the system design optimization.
机译:本文研究了神经网络理论在带电池存储的4kW互动式风光光伏系统(WPS)分析中的应用。混合动力系统包括一个2.5千瓦的光伏发电机和一个1.5千瓦的风力涡轮机。风力发电机以可变速度和可变频率(VSVF)发电。风能通过受控的树相位全波桥式整流器转换为直流电。光伏功率通过最大功率点跟踪器(MPPT),DC-DC斩波器以45 kHz的频率切换来最大化。两个子系统的全部直流电源都存储在电池组中,或由单相自换相逆变器调节,然后以预定量出售给公用事业公司。首先,使用人工神经网络(ANN)对PV进行建模。为了减少模型不确定性,可将传感器的开路电压 V OC 和短路电流 I SC 设置为选择PV作为ANN的模型输入变量。这些输入变量的优点是可以合并影响PV功率的可量化和不可量化的环境变量的影响。然后,研究了使用递归神经网络(RNN)精确预测并网WPS对阵风的动态响应的简化方法。 RNN是具有外部反馈的单输出前馈反向传播网络,它允许将过去的响应反馈到网络输入。第三步,使用径向基函数(RBF)网络来分析云对实用程序交互WPS的影响。网络使用辐照度作为输入信号,对随机云运动对输出电流,输出电压,光伏系统的输出功率以及并网逆变器的电输出变量的影响进行建模。第四,使用RNN,分析了随机云和阵风对系统的综合影响。对于较短的时间间隔,风速和太阳辐射被视为唯一的动力来源,其变化会影响系统变量。由于两个子系统具有不同的动力学特性,因此它们各自的响应预计会影响整个系统的行为。还讨论了电池支持系统的可调度性,以及阵风和/或云层通过期间的稳定性和可靠性。第五步,目标是确定公用事业-交互式混合动力系统的泛滥将在多大程度上影响电网的整体电能质量,以及是否有必要采用笨重的或单独的滤波和电压控制器。研究的最后阶段包括对系统两年运行(5月96日至4​​月98日)的稳态分析,并讨论系统可靠性,供应损失的可能性以及随机性的影响。风和太阳辐射对系统设计的优化。

著录项

  • 作者

    Giraud, Francois.;

  • 作者单位

    University of Massachusetts Lowell.;

  • 授予单位 University of Massachusetts Lowell.;
  • 学科 Engineering Electronics and Electrical.; Energy.
  • 学位 D.Eng.
  • 年度 1999
  • 页码 143 p.
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;能源与动力工程;
  • 关键词

  • 入库时间 2022-08-17 11:48:04

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