This paper discusses a modified V-I relationship for the solar photovoltaic (PV) single diode based equivalent model. The model is derived from an equivalent circuit of the PV cell. A PV cell is used to convert the solar incident light to electrical energy. The PV module is derived from the group of series connected PV cells and PV array, or PV string is formed by connecting the group of series and parallel connected PV panels. The model proposed in this paper is applicable for both series and parallel connected PV string/array systems. Initially, the V-I characteristics are derived for a single PV cell, and finally, it is extended to the PV panel and, to string/array. The solar PV cell model is derived based on five parameters model which requires the data’s from the manufacturer’s data sheet. The derived PV model is precisely forecasting the P-V characteristics, V-I characteristics, open circuit voltage, short circuit current and maximum power point (MPP) for the various temperature and solar irradiation conditions. The model in this paper forecasts the required data for both polycrystalline silicon and monocrystalline silicon panels. This PV model is suitable for the PV system of any capacity. The proposed model is simulated using Matlab/Simulink for various PV array configurations, and finally, the derived model is examined in partial shading condition under the various environmental conditions to find the optimal configuration. The PV model proposed in this paper can achieve 99.5% accuracy in producing maximum output power as similar to manufacturers datasheet.©2020. CBIORE-IJRED. All rights reserved

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

This article presents the design of an advanced smart grid model with an improved optimal variation strategy by implementing renewable energy sources. The novelty involved in this article is designing a renewable energy-based smart grid system incorporating with Artificial Neural Network for maintaining an improved voltage profile with balanced reactive power level across the grid network. The objective is attained by implementing renewable energy sources, namely solar and wind energy modules, with artificial intelligence techniques for effective reactive power control using DSTATCOM. The power requirement across the power grid network is satisfied by providing solar and wind energy sources managing the smart-grid system’s var level. The optimized power is trained on the Feed Forward Neural Network-based for calculating the amount of voltage profile and power loss based on system load. The DSTATCOM is an optimal var compensating device for maintaining the reactive power within the permissible limit across the smart grid system. The proposed system improves the voltage profile, reduces the power loss, and satisfies the grid by managing the system’s reactive power balance. The proposed approach is executed in MATLAB/Simulink work site, and the performance is analyzed with three different cases. The smart grid system powered with hybrid energy sources strives to attain the objective functions effectively among the three different cases. Integration of hybrid renewable energy source with Artificial Neural Network achieves an improved voltage profile to about 98.45%, which states that the profile rating has improved from 1.056 to 1.083 p.u minimizations of real power loss has done from to 3.23 MW from 3.80 MW.