Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning

Accurate and efficient parameter extraction of PV models from I-V characteristic curves is significant for modeling, evaluation and fault diagnosis of PV modules/arrays. Recently, a large number of algorithms are proposed for this problem, but there are still some issues like premature convergence, low accurate and instability. In this paper, a new improved shuffled complex evolution algorithm enhanced by the opposition-based learning strategy (ESCE-OBL) is proposed. The proposed algorithm improves the quality of the candidate solution by the opposition-based learning strategy. Moreover, the basic SCE algorithm evolves with the traditional competition complex evolution (CCE) strategy, but it converges slowly and is prone to be trapped in local optima. In order to improve the exploration capability, the complex in the basic SCE is evolved by a new enhanced CCE. The ESCE-OBL algorithm is compared with some state-of-the-art algorithms on the single diode model (SDM) and double diode model (DDM) using benchmark I-V curves data. The comparison results demonstrate that the proposed ESCE-OBL algorithm can achieve faster convergence, stronger robustness and higher efficiency

Chaos embedded opposition based learning for gravitational search algorithm

Due to its robust search mechanism, Gravitational search algorithm (GSA) has achieved lots of popularity from different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.Comment: 33 pages, 5 Figure

Identification of parameters in photovoltaic models through a runge kutta optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively

Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios

Uma breve revisão sobre métodos Meta-Heurísticos para a extração dos parâmetros Fotovoltaicos

As mudanças climáticas, o aumento da poluição e as crescentes preocupações ambientais colocam a humanidade diante de um problema energético. É nesse contexto que as energias renováveis assumem um papel fundamental para alcançar a neutralidade carbónica. Assim, para reduzir a utilização dos combustíveis fosseis é indispensável que as fontes de energia renovável se afirmem como uma solução vantajosa e viável para a produção de energia elétrica. Este aumento de produção de energia elétrica a partir de fontes renováveis é vital para se cumprirem os vários acordos mundiais e europeus que foram assinados com o propósito de atingir os desígnios assinados. A fonte de energia renovável com o maior potencial no futuro é a energia solar. No entanto, para esta energia se consolidar é necessário que as tecnologias fotovoltaicas sejam mais eficientes. A presente dissertação tem como objetivo analisar uma série de fatores que influenciam a determinação dos parâmetros e que caraterizam os respetivos modelos matemáticos. Concretamente, os fatores determinantes que foram analisados foram: os modelos matemáticos, as tecnologias PV, os métodos/algoritmos de otimização que foram utilizados para simular o comportamento de uma célula ou módulo fotovoltaico e, por último, a técnica aplicada para contornar a natureza implícita das equações que caraterizam o respetivo modelo fotovoltaico.Climate change, the increasing pollution, and growing environmental concerns place humanity in the face of an energetic problem. In this context, renewable energies play a key role in achieving carbon neutrality. Thus, in order to reduce the use of fossil fuels it is essential that renewable energy sources establish themselves as an advantageous and viable solution for the production of electricity. Increasing the production of electrical energy from renewable sources is crucial to meet the various global and European agreements that have been signed aiming the achievement of the proposed objectives. The renewable energy source with the highest potential for the future is solar energy. However, to consolidate this energy, photovoltaic technologies must be more efficient. The present dissertation aims to analyse a series of factors that influence the determination of the parameters that characterize the respective mathematical models. Specifically, the determining factors that have been analysed are: the mathematical models, the PV technologies, the optimization methods/algorithms that were used to simulate the behavior of a photovoltaic cell or module, and the technique applied to avoid the implicit nature of the equations that characterize the respective photovoltaic model

Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems