用于太阳能光伏模型参数预测的混合麻雀搜索-指数分布优化与差分进化算法

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2024-01-09 DOI:10.3390/a17010026
Amr A. Abd El-Mageed, A. Al-Hamadi, Samy Bakheet, Asmaa H. Abd El-Rahiem
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引用次数: 0

摘要

由于电流-电压(I-V)特性曲线的非线性,很难确定未知的太阳能电池和光伏(PV)模块参数。尽管如此,精确的参数估计仍是必要的,因为参数会对光伏系统的电流和能量效果产生重大影响。该问题的特点使得算法处理容易出现局部最优和资源密集型处理。为了有效提取光伏模型参数值,本文介绍了一种基于差分进化(DE)技术和约束条件修改程序的改进型混合麻雀搜索算法(SSA)和指数分布优化(EDO),称为 ISSAEDO。该混合策略利用 EDO 改善全局探索,利用 SSA 有效探索解空间,而 DE 则促进局部搜索以改善参数估计。利用太阳能光伏系统数据,将提出的方法与标准优化方法进行了比较,以证明其在获取单二极管模型(SDM)和双二极管模型(DDM)等光伏模型参数方面的有效性和速度。结果表明,混合技术可以准确预测光伏模型参数,是提高太阳能光伏系统设计和性能分析的可行工具。
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Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models
It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem’s characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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