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Photovoltaic models parameter estimation via an enhanced rao-1 algorithm

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第2期19卷 1128-1153页

作者： Ku, Junhua Li, Shuijia Gong, Wenyin Qiongtai Normal Univ Sch Sci Haikou 571127 Hainan Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

The accuracy of unknown parameters determines the accuracy of photovoltaic (PV) models that occupy an important position in the PV power generation system. Due to the complexity of the equation equivalent of PV models, estimating the parameters of the PV model is still an arduous task. In order to accurately and reliably estimate the unknown parameters in PV models, in this paper, an enhanced rao-1 algorithm is proposed. The main point of enhancement lies in i) a repaired evolu-tion operator is presented;ii) to prevent the rao-1 algorithm from falling into a local optimum, a new evolution operator is developed;iii) in order to enable population size to change adaptively with the evolutionary process, the population size linear reduction strategy is employed. To verify the validity of Erao-1 algorithm, we embark a study on parameter estimation of three different PV models. Ex-perimental results show that the proposed Erao-1 algorithm performs better than existing parameter estimation algorithms in terms of the accuracy and reliability, especially for the double diode model with RMSE 9.8248E-04, three diode model with RMSE 9.8257E-04 for the R.T.C France silicon cell, and 2.4251E-03 for the three diode model of Photowatt-PWP201 cell. In addition, the fitting curve of the simulated data and the measured data also shows the accuracy of the estimated parameters.

关键词： Parameter estimation photovoltaic model rao-1 algorithm parameter extraction

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Parameter estimation of photovoltaic cell model with rao-1 algorithm

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OPTIK 2020年 210卷

作者： Wang, Long Wang, Zhongju Liang, Huazhao Huang, Chao Guangdong Prov Key Lab New & Renewable Energy Res Guangzhou 510640 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Xihua Univ Key Lab Fluid & Power Machinery Minist Educ Chengdu 610039 Peoples R China

A novel photovoltaic (PV) cell parameter estimation method based on rao-1 algorithm is presented in this paper. rao-1 algorithm is a metaphor-less heuristic search algorithm and only addition and multiplication operations are included. The simple solution updating equation is easy to be implemented on the embedded system of PV panels. The model parameters of a real commercial PV cell are estimated and the performance of the proposed method is compared with other recently published methods. The experimental results demonstrate that the proposed method can be utilized to accurately identify PV cell model parameters.

关键词： Photovoltaics cell Heuristic search Parameter estimation rao-1 algorithm

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Modelling and optimization of fabrication parameters for multi-walled carbon nanotubes-filled GFRP composites using RSM and rao-1 algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Ch, Rajeswari Punna, Eshwaraiah GITAM Dept Mech Engn Hyderabad India

This study presents an optimized approach for fabricating multi-walled carbon nanotubes (MWCNTs) filled glass fibre reinforced polymers (GFRP) composites using hybrid optimization approach. The experimental runs performed as per Box-Behnken design of response surface methodology (RSM) by considering three fabrication parameters: MWCNT loading, sonication time (ST), and oven curing temperature (OCT), and the output response, ultimate tensile strength (UTS) is noted. Analysis of variance (ANOVA) is employed to ascertain the significance of the effects that each factor has on UTS and found fabrication variables, OCT, and combined effects of ST and OCT are most significant. Other variables, direct effects of MWCNT loading, interaction effects of all three combinations have influence on UTS. Mathematical modeling is postulated using RSM from which contour plots are drawn to illustrate both direct and interactive effects and reveal fabrication parameters have detrimental effects on UTS. The mathematical equation of UTS is then solved by rao-1 optimization algorithm and obtained condition is: 1.0% of MWCNT loading, 97.5 min of sonication time, and 76 degrees C of oven curing temperature and corresponding UTS of 624 MPa. SEM analysis has also been performed to verify the distribution of MWCNTs in the GFRP and observed uniform dispersion of MWCNTs in the developed composite. A confirmatory test validates the predicted optimal fabrication condition derived from the RSM combined with rao-1 algorithm, ensuring that the methodology has ability to enhance the UTS of MWCNTs-embedded GFRP composites.

关键词： MWCNT filled GFRP composites fabricating parameters mathematical modelling ultimate tensile strength rao-1 algorithm

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Optimization of material removal rate of Ti-6Al-4V using rao-1 algorithm

Optimization of material removal rate of Ti-6Al-4V using Rao...

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International Academic Conference on Advances in Materials, Mechanics, Mechatronics and Manufacturing (IC4M)

作者： Agarwal, Neeraj Irshad, Md Singh, Mahendra Raj Singh, Gurjeet IES Coll Technol Dept Mech Engn Bhopal 462044 MP India Shri Ram Inst Technol Dept Mech Engn Jabalpur 482002 MP India

Titanium alloy Ti6Al4V has attractive properties for engineering applications. Electric discharge machining is nowadays widely adapted for titanium alloy machining to produce proper geometric profiles and surface integrity. EDM has a high machining cost and has a lower material removal rate (MRR). It is always desirable to improve the machining efficiency. This research involves, enhancing the MRR of Titanium alloy during EDM. The influence of four EDM input parameters on MRR is investigated in this research. Current, pulse on time, voltage and duty factor is selected as EDM control parameters. Response surface methodology (RSM) is utilized to create a design of experiment (DOE) plan. Thirty experiments were conducted as per the DOE. The experimental results are used to develop a predictable quadratic regression model for MRR using RSM. This MRR regression model is optimized using an advanced optimization technique, the rao-1 algorithm. This is a new algorithm and easier to implement. The study reveals current and voltage are identified as the most influential factors in the MRR. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Electrical discharge machining MRR Advanced optimization rao-1 algorithm RSM

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An improved rao-1 algorithm for parameter estimation of models

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OPTIK 2022年 260卷

作者： Farah, Anouar Benabdallah, Feres Belazi, Akram Almalaq, Abdulaziz Chtourou, Mohamed Abido, M. A. Univ Hail Coll Engn Dept Elect Engn POB 2440 Hail Saudi Arabia Univ Hail Coll Sci Dept Phys POB 2440 Hail Saudi Arabia Tunis El Manar Univ Lab RISC ENIT LR 16 ES07 Tunis 1002 Tunisia Univ Sfax Natl Engn Sch Sfax Sfax Tunisia King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran 31261 Saudi Arabia

The efficient operation of the solar photovoltaic (PV) system is influenced by the accurate determination of its model parameters. The appropriate selection of these parameters is a challenging task due to the multi-modality and non-linearity of the problem. Given less sensitivity to initial solutions and the powerful search capability, many metaheuristic algorithms have been successfully applied to estimate the unknown parameters of PV cell/module models. However, most used techniques are time-consuming, control parameters tuning, etc. This paper proposes an improved rao-1 (Irao-1) algorithm for efficiently and accurately extracting the PV system parameters. Irao-1 is a new variant of the rao-1 algorithm, which improves the global search ability of the rao-1 algorithm by implementing three new search equations. The modification introduced in rao-1 does not affect its structural simplicity. The effectiveness of the proposed algorithm is evaluated using several PV models, including PV cells (single diode and double diode) and three PV modules. Experimental results demonstrate the superiority of Irao-1 in study cases in terms of efficiency and accuracy by comparing it with recent works.

关键词： PV model Parameter estimation Optimization problem rao-1 algorithm Improved rao-1 algorithm

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Self-adaptive classification learning hybrid JAYA and rao-1 algorithm for large-scale numerical and engineering problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 114卷

作者： Zhang, Yu-Jun Wang, Yu-Fei Tao, Liu-Wei Yan, Yu-Xin Zhao, Juan Gao, Zheng-Ming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Foreign Languages Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

In this paper, a self-adaptive classification learning hybrid JAYA and rao-1 algorithm, which is called EHRJAYA, is proposed for solving large-scale numerical problems and real-world complex engineering optimization problems. JAYA algorithm and rao-1 algorithm are two kinds of algorithms with simple structure and superior performance, which have the characteristics of no public parameters. In EHRJAYA the evolution strategies of the two algorithms are selected through a random selection mechanism. Then, a novel self-adaptive classification learning strategy is proposed, which fully utilizes information from different individuals. On this basis, two different adaptive coefficients are introduced to guide the population towards the optimal individual and away from the worst individual. Finally, combining the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed and ability to jump out of local optimum of the algorithm are greatly improved. To verify the performance of the proposed EHRJAYA, 59 complex functions from the CEC2014 and CEC2017 competitions are solved by EHRJAYA. Then, EHRJAYA and more than 20 algorithms with superior performance jointly solve ten challenging real-world engineering optimization problems. Experimental results show that the proposed EHRJAYA can obtain optimal results with the least computational resources in most cases. Therefore, in the face of these problems, effective solutions can be provided by EHRJAYA.

关键词： JAYA algorithm rao-1 algorithm EHRJAYA CEC2014 and CEC2017 competitions Engineering optimization problems

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A novel maximum power point tracking technique based on rao-1 algorithm for solar PV system under partial shading conditions

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第9期31卷

作者： Bhukya, Laxman Annamraju, Anil Nandiraju, Srikanth Natl Inst Technol Dept Elect Engn Warangal Telangana India

The performance of the Photovoltaic (PV) system is highly affected by fluctuating climatic conditions. Due to these changing climatic conditions, the irradiance diminishes, and it results in the non-linearity in voltage-power (P-V) curves of the PV system. Therefore, it is one of the reasons for the occurrence of the multiple peaks on P-V curves instead of a single peak. Therefore, it is challenging to track the global maximum power point tracking (MPPT) under these circumstances with traditional MPPT techniques viz. perturb and observe (P&O) and fuzzy logic control (FLC). Concerning global MPP tracking, this paper proposes a novel MPPT method based on a rao-1 optimization algorithm. The proposed algorithm is used to overcome the drawbacks of the classical P&O, FLC, and PSO based MPPT techniques under partical shading conditions (PSCs) and rapidly changing atmospheric conditions viz. slow convergence speed and oscillation around the MPP. The proposed MPPT tracks the global MPP under any abnormal conditions like PSCs. To show the efficiency of the proposed MPPT a comparative assessment is performed with well-established MPPT methods in the literature. The simulation results witnessed that the proposed MPPT is more superior in improving the PV system performance under normal as well as under PSCs. The proposed MPPT over the PSO technique decreases the tracking time by 72.7% for pattern 1, 29.8% for pattern 2, 66.7% for pattern 3, 61.9% for pattern 5, 27.8% for pattern 6, and improves the tracking efficiency in all shading patterns.

关键词： global maximum maximum power point tracking partial shading conditions photovoltaic system rao-1 algorithm

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Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第15期30卷 44536-44552页

作者： Benghanem, Mohamed Lekouaghet, Badis Haddad, Sofiane Soukkou, Ammar Islamic Univ Madinah Fac Sci Phys Dept Madinah Saudi Arabia MSB Jijel Univ Elect Dept RE Lab Jijel Algeria

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value 7.73006208 x 10(-4), and in the double diode model, an RMSE of value 7.445111655 x 10(-4) has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic rao-1 algorithm and its other variants. Moreover, the modified QO rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.

关键词： Optimization algorithm Photovoltaic modules rao-1 algorithm Chaotic PV parameters

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An enhanced adaptive comprehensive learning hybrid algorithm of rao-1 and JAYA algorithm for parameter extraction of photovoltaic models

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 5610-5637页

作者： Zhang, Yujun Wang, Yufei Li, Shuijia Yao, Fengjuan Tao, Liuwei Yan, Yuxin Zhao, Juan Gao, Zhengming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Foreign Languages Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid JAYA and rao-1 algorithm, called EHRJAYA, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRJAYA has superior performance and rank in the leading position among the famous algorithms.

关键词： parameter extraction photovoltaic models JAYA algorithm rao-1 algorithm EHRJAYA algorithm

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Parameters identification of photovoltaic models using modified rao-1 optimization algorithm

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OPTIK 2021年 231卷

作者： Jian, Xianzhong Zhu, Yizhuang Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Accurate and robust parameter identification method is helpful to the imitation, control and optimization of photovoltaic (PV) system. Therefore, it is necessary to create some more accurate and robust algorithms. A new metaheuristic algorithm modified rao-1(Mrao-1) is proposed by combining rao-1 with two-way updating strategy based on random individuals. In this strategy, the current or random individuals are chosen as updated starting point and the difference between random individuals is taken as updated direction. Mrao-1 inherits the advantages of the original rao-1 algorithm without additional special parameters and improves the global search ability of rao-1 significantly without increasing the time complexity of rao-1. Mrao-1 is evaluated on benchmark functions and applied to parameter identification of PV models. The results show that the accuracy and robustness of Mrao-1 algorithm are superior to the original algorithm and other recent excellent algorithms. Therefore, Mrao-1 is a promising parameter identification algorithm.

关键词： Photovoltaic model Parameter identification rao-1 algorithm Optimization algorithm

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