Artificial Neural Network Control Applied to a Photovoltaic-Battery Microgrid System

AI, Computer Science and Robotics Technology Pub Date : 2024-05-17 DOI:10.5772/acrt.34

Chabakata Mahamat, Jessica Bechet, Laurent Linguet

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Abstract

This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.

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人工神经网络控制应用于光伏电池微电网系统

本文论述了应用人工神经网络（ANN）控制法属圭亚那独立微电网的问题。人工神经网络是一种用于控制非线性复杂系统的人工智能技术。与 Levenberg-Marquardt 算法（LM）相关联的人工神经网络有许多优点，如快速决策和改善系统瞬态。因此，该技术应适用于法属圭亚那热带气候下辐照度变化较大的光伏（PV）系统的控制。微电网由一个光伏源和一个蓄电池组成，为一栋孤立的建筑供电，该建筑以直流负载为模型。光伏光源由基于 ANN 的 MPPT（最大功率点跟踪）控制器控制。为了验证 ANN-MPPT 的有效性，我们将其与一种非常流行的 MPPT 算法（即 P&O-MPPT 算法）进行了比较。比较结果表明，我们的 ANN-MPPT 运行良好，因为它能快速找到最大功率点。在电池控制方面，我们测试了与 Levenberg-Marquardt 算法（LM）相关的两种前馈反向传播神经网络（FFBNN）配置，分别称为方法 1 和方法 2。我们改变了这两种 FFBNN 配置中每种配置的隐藏层数，以获得每种配置的最佳隐藏层数，从而优化电池控制。之所以选择方法 1，是因为它比方法 2 更好，因为它尊重了我们应用中电池电流的最大振幅，并改善了该电流的瞬态。然后，将此最佳配置（方法 1）与其他两种学习算法进行比较测试：贝叶斯正则化（BR）和缩放共轭梯度（SCG）方法。采用 LM 算法的系统性能优于 SCG 和 BR 算法。LM 算法提高了系统在瞬态状态下的性能，而 SGG 算法和 BR 算法的结果相似。然后，我们重点讨论了与传统的比例积分控制（PI 控制）相比，使用 ANN 控制的优势。比较结果表明，与传统的 PI 控制相比，采用 LM 算法（ANN-LM）的 ANN 控制可将瞬态下的电池电流峰值降低 26%。最后，我们介绍并讨论了使用 MATLAB Simulink 软件获得的仿真结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AI, Computer Science and Robotics Technology

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