基于人工神经网络和非线性Backstepping控制器的分段太阳能光伏模拟器

IF 1.204 Q3 Energy
Ambe Harrison, Njimboh Henry Alombah
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引用次数: 0

摘要

光伏模拟器(PVE)的现状面临着两个主要挑战:求解光伏非线性方程的复杂性和PVE功率转换级(PCS)的有效控制问题。在本文中,提出了一种新的基于电力电子的PVE来模拟光伏电池/模块的动态和静态特性。使用一种新的分段分割技术来求解光伏电池/模块的非线性方程,包括将电流-电压(I–V)曲线分割为与字母a至m(a–m)相关的十二个线性段。基于输入的环境条件,构造了一个经过训练的人工神经网络(ANN),通过预测这些分段的电流-电压边界坐标来辅助线性化过程。通过使用具有边界坐标的简单线性方程,然后为PVE生成参考电压。设计了一种利用PVE参考电压稳定PCS的非线性反推控制器。用李亚普诺夫定律验证了控制器的稳定性。采用粒子群优化(PSO)方法保证了PCS的最优性能和控制。在MATLAB环境中对整个系统进行了研究,主要测试包括对快速变化的辐照度和温度的响应、EN 50530测试以及对负载变化的响应。所提出的PVE在模拟PV特性方面显示出令人满意的动态性能。此外,发现PVE作为平均绝对百分比误差(MAPE)的函数的准确性即使在环境条件的最坏情况下也低于0.5%。在实际环境条件下对所提出的PVE进行了实验验证,进一步验证了其良好的动态和静态鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A New Piecewise Segmentation Based Solar Photovoltaic Emulator Using Artificial Neural Networks and a Nonlinear Backstepping Controller

A New Piecewise Segmentation Based Solar Photovoltaic Emulator Using Artificial Neural Networks and a Nonlinear Backstepping Controller

The current state of affairs on the Photovoltaic emulator (PVE) is facing two main challenges: complexity in resolving the nonlinear equations of the photovoltaic (PV) and the problem of effective control of the PVE power conversion stage (PCS). In this paper, a new power electronics-based PVE is proposed to emulate the dynamic and static characteristics of the PV cell/module. The nonlinear equations of the PV cell/module are resolved using a new piecewise segmentation technique, involving the splitting of the current-voltage (I–V) curve into twelve linear segments associated with the letters a to m (a–m). Based on input environmental conditions, a trained artificial neural network (ANN) is constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. By the use of simple linear equations with the boundary coordinates, a reference voltage is then generated for the PVE. A nonlinear backstepping controller is designed to exploit the PVE reference voltage and stabilize the PCS. The stability of the controller is verified by Lyapunov laws. Optimal performance and control of the PCS were ensured by resorting to particle swarm optimization (PSO). The overall system has been investigated in the MATLAB environment with major tests including the response to fast-changing irradiance and temperature, the EN 50530 test, and the response to change in the load. The proposed PVE revealed a satisfactory dynamic performances in mimicking the PV characteristics. Furthermore, the accuracy of the PVE as a function of the mean absolute percentage error (MAPE) was found less than 0.5% even for the worst case of environmental conditions. Experimental validation of the proposed PVE under real environmental conditions further validated its good dynamic and static robustness.

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来源期刊
Applied Solar Energy
Applied Solar Energy Energy-Renewable Energy, Sustainability and the Environment
CiteScore
2.50
自引率
0.00%
发文量
0
期刊介绍: Applied Solar Energy  is an international peer reviewed journal covers various topics of research and development studies on solar energy conversion and use: photovoltaics, thermophotovoltaics, water heaters, passive solar heating systems, drying of agricultural production, water desalination, solar radiation condensers, operation of Big Solar Oven, combined use of solar energy and traditional energy sources, new semiconductors for solar cells and thermophotovoltaic system photocells, engines for autonomous solar stations.
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