生物启发算法的性能比较,用于优化光伏系统基于 ANN 的 MPPT 预测。

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Biomimetics Pub Date : 2024-10-21 DOI:10.3390/biomimetics9100649
Rafael Rojas-Galván, José R García-Martínez, Edson E Cruz-Miguel, José M Álvarez-Alvarado, Juvenal Rodríguez-Resendiz
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引用次数: 0

摘要

本研究比较了生物启发优化算法,以增强光伏系统部分遮阳条件下基于 ANN 的最大功率点跟踪(MPPT)预测系统。对四种算法--灰狼优化算法(GWO)、粒子群优化算法(PSO)、松鼠搜索算法(SSA)和布谷鸟搜索算法(CS)--进行了评估,并通过扰动增加数据集以模拟遮光。标准 ANN 的表现不佳,第 1 层有 64 个神经元,第 2 层有 32 个神经元(MSE 为 159.9437,MAE 为 8.0781)。在优化方法中,GWO(第 1 层有 66 个神经元,第 2 层有 100 个神经元)的预测准确率最高(MSE 为 11.9487,MAE 为 2.4552),而且计算效率高(执行时间为 1198.99 秒)。PSO 在第 1 层使用 98 个神经元,在第 2 层使用 100 个神经元,MAE(2.1679)最小,但执行时间稍长(1417.80 秒)。采用与 GWO 相同神经元数的 SSA 也表现出色(MSE 12.1500,MAE 2.7003),而且速度最快(987.45 秒)。CS 第一层有 84 个神经元,第二层有 74 个神经元,其可靠性较低(MSE 为 33.7767,MAE 为 3.8547),速度也较慢(1904.01 秒)。事实证明,GWO 是兼顾准确性和速度的最佳方法。该方法未来在现实世界中的应用包括在多变天气条件下提高太阳能发电场的能源效率,以及优化住宅太阳能电池板的性能以降低能源成本。进一步的优化开发可以实时处理更复杂、更大规模的数据集,例如将可再生能源整合到智能电网系统中,以实现更好的能源分配。
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Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
期刊最新文献
Brain-Inspired Architecture for Spiking Neural Networks. Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection. Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm. Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems. Clinical Applications of Micro/Nanobubble Technology in Neurological Diseases.
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