•A memetic adaptive DE is proposed for parameter estimation of different PV models.•Ranking-based elimination strategy is developed to accelerate convergence speed.•The performance of our method is ...verified through different PV models.
Parameter estimation of photovoltaic (PV) models plays an important role in the simulation, evaluation, and control of PV systems. In the past decade, although many meta-heuristic methods have been devoted to parameter estimation of PV models and achieved satisfactory results, they may suffer from consuming large computational resources to get promising performance. In order to fast and accurately estimate the parameters of PV models, in this paper, a memetic adaptive differential evolution, namely MADE, is developed. The proposed MADE can be featured as: (i) the success-history based adaptive differential evolution is used for the global search; (ii) the Nelder-Mead simplex method is employed for the local search to refine the solution; and (iii) the ranking-based elimination strategy is proposed to maintain the promising solutions in the external archive. To verify the performance of our approach, it is applied to estimate the unknown parameters of different PV models, i.e., the single diode model, the double diode model, and the PV module. Experimental results obtained by MADE are compared with several state-of-the-art methods reported in the literature. Comparison analysis demonstrates that the proposed MADE exhibits remarkable performance on accuracy and reliability. It also consumes less computational resources than other compared methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A new method GOFPANM is proposed for parameters estimation of solar cells/modules.•The GOFPANM is based on the FPA, the Nelder-Mead simplex, and the GOBL mechanism.•The GOFPANM features simple ...structure and good accuracy.•The GOFPANM performs better than most reported algorithms.
Building highly accurate model for solar cells and photovoltaic (PV) modules based on experimental data is vital for the simulation, evaluation, control, and optimization of PV systems. Powerful optimization algorithms are necessary to accomplish this task. In this study, a new optimization algorithm is proposed for efficiently and accurately estimating the parameters of solar cells and PV modules. The proposed algorithm is developed based on the flower pollination algorithm by incorporating it with the Nelder-Mead simplex method and the generalized opposition-based learning mechanism. The proposed algorithm has a simple structure thus is easy to implement. The experimental results tested on three different solar cell models including the single diode model, the double diode model, and a PV module clearly demonstrate the effectiveness of this algorithm. The comparisons with some other published methods demonstrate that the proposed algorithm is superior than most reported algorithms in terms of the accuracy of final solutions, convergence speed, and stability. Furthermore, the tests on three PV modules of different types (Multi-crystalline, Thin-film, and Mono-crystalline) suggest that the proposed algorithm can give superior results at different irradiance and temperature. The proposed algorithm can serve as a new alternative for parameter estimation of solar cells/PV modules.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper introduces the design and implementation of two parallel dual simplex solvers for general large scale sparse linear programming problems. One approach, called PAMI, extends a relatively ...unknown pivoting strategy called suboptimization and exploits parallelism across multiple iterations. The other, called SIP, exploits purely single iteration parallelism by overlapping computational components when possible. Computational results show that the performance of PAMI is superior to that of the leading open-source simplex solver, and that SIP complements PAMI in achieving speedup when PAMI results in slowdown. One of the authors has implemented the techniques underlying PAMI within the FICO Xpress simplex solver and this paper presents computational results demonstrating their value. In developing the first parallel revised simplex solver of general utility, this work represents a significant achievement in computational optimization.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The simplex method is a popular method to solve a linear programming problem. It starts by choosing an initial basis associated with the origin point which is far from the optimal point in some ...problems. In this paper, we introduce a construction of an initial basis for starting the simplex method associated with an extreme point which is close to the optimal solution. This construction involves the angle between the coefficient vector of the objective function and the coefficient vector of each constraint. The small angles will be considered to construct a basis. If the proposed initial basis gives the infeasible solutions, then the nonfeasible basis method is performed to avoid the use of artificial variables. Not only the starting point is close to the optimal point, but also the artificial variable is needless. The results in the computational aspect show that the number of iterations and the running time of our method are less than other methods.
•A developed coupled hydro-thermal model was applied for artificial ground freezing.•The influence of water flow on freezing temperature around freeze pipes was investigated.•A new combining ...optimization method of freeze pipes arrangement was proposed.•Three common distribution functions were suitable to determine the optimum positions of freeze pipes.
Artificial ground freezing is usually used to improve ground and provide temporary support. However, the freezing process is dramatically influenced by water flow. Evidently, water flow with high velocity brings large heat energy that prevents the freezing of porous media around freeze pipes. In this paper, for safety and energy-saving, the influence of water flow on freezing process is simulated by a developed coupled hydro-thermal model considering water/ice phase transition and the positions of freeze pipes around circular tunnel are optimized through combining this model with Nelder–Mead simplex method based on COMSOL multiphysics platform. According to the evolution law of freezing band under well-distribution of freeze pipes, three kinds of potential frequently-used probability distribution functions are adopted to reduce control parameters and improve the efficiency of optimization program including normal distribution, Poisson distribution and chi-square distribution. The results show that the proposed combining simulation method is suitable for optimization of freeze pipes arrangement. The freezing time is significantly reduced no matter which one of the above distribution functions is employed through careful design.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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•A novel NMSOLMFO approach is proposed to optimize the parameters of the PV model.•OL can enhance the exploration and exploitation abilities of the inferior agents in MFO.•NMS can ...further strengthen the exploitation capacity of MFO.•The new mechanisms reach desirable stability between the exploration and exploitation inclinations.•The performance of the proposed method is more competitive than the conventional MFO.
Defining the optimal parameters of the photovoltaic system (PV) models according to the actual real voltage and current data is a crucial process during designing, emulating, estimating, dominating, and optimizing photovoltaic systems. Therefore, it is necessary to effectively advance the optimal parameters of the models based on the proper optimization methods. For this purpose, this paper proposes an orthogonal moth flame optimization (MFO) with a local search for identifying parameters of photovoltaic cell models, which is named NMSOLMFO. The presented method is organized based on the principal exploratory and exploitative mechanisms of MFO. Also, its exploration and exploitation capability is strengthened by the orthogonal learning (OL) strategy and Nelder-Mead simplex (NMS) method, and this new scheme supports a more stable equilibrium between the central propensities. In the new MFO-based method, OL strategy can construct a healthier candidate location for the inferior agents, and then, it directs them to probe a reasonable prospective zone throughout a few rational trials. Besides, the NMS local search scheme can augment the accurateness of the global optimal solution by searching its neighborhood throughout the searching process, and the global optimum is taken as the initial point. In our study, first, the developed MFO-based approach is employed to tackle IEEE CEC 2014 benchmark cases with 30D to evaluate the effectiveness of the method in solving high dimensional and multimodal problems. Then, it is utilized to deal with parameters identification of single diode model (SDM), double diode model (DDM), and photovoltaic module model (PVM). The results and statistical studies indicate that NMSOLMFO can outperform the majority of other investigated methods concerning accuracy and convergence rapidity. The obtained results imply that the novel approach can provide a new practical tool for parameter definition in PV models, and it can be beneficial to upgrade the PV systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
Abstract
In this paper we development of the complementary method to Solve Fractional Linear Programming Problems (FLPP), Instead of taking the Numerator and denominator elements and set them in in ...tables as D1 and D2, then calculating its value and the value of (Ze), hence we could minimize the tables and calculations in this method quickly and with fewer tables.
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•ISCA is proposed to identify the parameters of PV models.•Multiple learning strategies balance exploration and exploitation.•The exploitative trend is boosted with the aid of NMs ...strategy.•The exploratory behavior is enhanced based on OBL strategy.
Identifying the optimum parameters of photovoltaic models based on the measured current-voltage info is a vital step in monitoring, simulating, and optimizing the photovoltaic systems. We need efficient optimizers to reliably obtain the best model’s parameters. In this study, a novel optimizer is proposed to effectively approximate the unknown parameters of solar cells and PV modules. The proposed ISCA is constructed based on the exploratory and exploitative cores of the sine cosine algorithm (SCA). Also, it is enhanced using the Nelder-Mead simplex (NMs) concept and the opposition-based learning (OBL) scheme. In ISCA, NMs method can guarantee the population's intensification and enhance the exploitation ability. In addition, the opposition-based learning scheme can improve the diversification of the population, which ensures a more steady balance between exploitation and exploration trends. The theory and structure of this algorithm are concise; therefore, it can be implemented easily. The developed ISCA is utilized to realize the unidentified parameters of the single diode, double diode, and photovoltaic module. Inclusive results and statistical analyses imply that the ISCA is superior to most of the reported techniques with regard to the accuracy of concluding solutions and convergence ratio. The results indicate that the proposed method can be treated as an effective, promising tool for parameter detection of the solar cells modules in dealing with practical cases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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