•A novel application of SFO algorithm to extract PV model parameters is presented.•Three-diode PV model is used in this paper.•Parameters of SFO-TDPV model are compared with other optimization based ...models.•The SFO-TDPV model is verified by comparing its results with measured data.•The error among these results records a value less than 0.5%.
This article proposes an accurate and straightforward method for modeling and simulation of photovoltaic (PV) modules. The main target is to find the nine-parameter of a three-diode (TD) model based on the datasheet parameters, which are given by all commercial PV modules. The objective function is formulated based on short circuit, open circuit, power derivative, and maximum power equations. Two parameters (parallel resistance and photo-generated current) are calculated analytically and rest parameters are optimally designed using the sunflower optimization (SFO) algorithm. The presented method is applied to model three types of commercial PV modules (multicrystal KC200GT, poly-crystalline MSX-60, and mono-crystalline CS6K-280M). The optimal nine-parameters obtained in this paper are paralleled with that attained by other approaches. In order to assess the efficiency of the offered approach, I-V and P-V characteristics are validated with measured data under various temperatures and solar irradiations. The error among these results records a value less than 0.5%. Therefore, the simulation results indicate an excellent agreement with the measured data. This proposed approach can be utilized to model any marketable PV module based on given datasheet parameters only.
The accurate electrical modeling of photovoltaic (PV) module is vital due to the extensive installation of photovoltaic power plants. Therefore, the scientists suggested a three-diode photovoltaic ...(TDPV) model for precise modeling of PV losses. However, TDPV is a complex and nonlinear model that contains nine unknown parameters. Hence, this paper presents a new method that is combining the computation and Harris Hawk Optimization (HHO) algorithm to extract the unknown parameters of the TDPV model. Also, this paper exhibits a new objective function based on the datasheet values instead of using extensive experiments for PV modeling for time-saving. The industrialists provided the datasheet values of PV modules at standard test conditions (STC) and normal operating cell temperature (NOCT). Therefore, this paper utilized these data to compute four parameters using equations and identify the remaining five parameters using the HHO algorithm. In this paper, the offered method is employed to find the TDPV model of two commercial PV panels, such as multi-crystal KC200GT and monocrystalline CS6K280M. After that, the I–V and P–V curves of these TDPV models plotted and compared with the curves of the measured data under different temperatures and solar irradiations. Moreover, the absolute current error of the proposed method compared with that obtained by using other methods. Accordingly, the results revealed that the proposed method is efficient and can be easily applied to identify the electrical parameters of any commercial PV panel based on the datasheet values only.
•This paper presents a novel method for PV modeling, combined computation and optimization.•A novel application of Harris Hawk Optimization is presented.•Three-diode PV (TDPV) model with nine electrical parameters is used in this paper.•The effectiveness of the proposed method verified using experimental data.•Two commercial PV modules are used in this paper (CS6K-280M and KC200GT).
This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced ...salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost.
•This paper proposes an enhancement to the salp swarm algorithm (ESSA).•The ESSA is tested with twenty-three benchmark functions.•The ESSA is compared with eight published algorithms.•The ESSA and SSA are applied to the variable speed wind generators.
•A novel optimization method is presented for photovoltaic modeling.•An accurate three-diode photovoltaic model is used in this paper.•Transient search optimization is compared with other ...algorithms.•The simulation results of the Photovoltaic model are verified by the measured data.
This paper presents a novel efficient metaheuristic algorithm called Transient Search Optimization (TSO), which is inspired by the transient process of the inductive and capacitive circuits. Also, this paper presents an objective function based on the datasheet of PV modules at standard test conditions (STC). Then, the TSO algorithm is applied to minimize the objective function to find the optimal nine parameters of the three-diode model (TDM) of the PV module. Also, the results of the proposed TSO algorithm are compared with that obtained by using other metaheuristic algorithms, where in this regard the TSO achieved the best results. The proposed technique is verified by applying it to find the optimal TDM of three commercially common PV modules with different cell types, rated power, and terminal voltage. Then, the simulated I-V and P-V characteristics of these PV modules matched with the measured data under many environmental conditions. Accordingly, the results have proved that the offered technique is useful to find the optimal TDM of all PV modules based on the dataset given by the manufacturers.
This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of ...switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test,
p
value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems.
This paper exhibits a novel application of the coyote optimization algorithm (COA) in order to extract the nine unknown parameters of the three-diode photovoltaic (PV) model of PV modules. The main ...target of this study is to obtain a very highly precise PV model, which can be efficiently applied to represent the PV system in the simulation of dynamic power systems. The optimization problem is formulated to take into consideration the root mean squared current error between the calculated model current and the experimental current of the PV module. The COA is applied to minimize this fitness function. In this study, the COA-PV model is validated by the numerical results which are performed at different environmental conditions such as temperature and irradiation variation conditions. Moreover, its effectiveness is executed by making a comparison between its numerical and experimental results for some commercial PV modules in the market like the KC200GT and MSX-60 modules. With the adoption of the COA, a highly precise three-diode PV model can be established. This represents a novel contribution to the field of PV systems and its modeling.
•This paper presents a novel application of the COA to extract PV model parameters.•Three-diode PV (TDPV) model is used in this paper.•Parameters of COA-TDPV model are compared with other optimization based models.•COA-TDPV model is verified by comparing its results with the experimental results.•Two commercial PV modules are used in the paper (KC200GT and MSX-60).
•We present a new improvement to the grey wolf algorithm.•The new improvement is tested with twenty-three benchmark functions.•The new improvement is compared with four published algorithms.•The new ...improvement is applied to grid-connected wind power plants.•The new improvement is verified by simulation results.
The grey wolf optimizer (GWO) is a new meta-heuristic algorithm inspired from the leadership and prey searching, encircling, and hunting of the grey wolves’ community. The GWO algorithm has the advantages of simplicity (less control parameters), flexibility, and globalism. In this paper, a simple and efficient augmentation for the GWO (AGWO) algorithm is proposed for better hunting performance. The AGWO algorithm focuses on increasing the possibility of the exploration process over the exploitation process by modifying the behavior of the control parameter (a) and position updating. The AGWO is suitable to the low number of search agents such as the electric power system application. The proposed AGWO algorithm is verified using twenty-three benchmark test functions and is applied to the grid-connected permanent magnet synchronous generator driven by variable speed wind turbine (PMSG-VSWT). The obtained results of the AGWO algorithm are compared with the results of the original GWO and other algorithms. The comparisons verified that the proposed AGWO is significantly augmented the performance of the original GWO algorithm without affecting its simplicity and easy implementation.
Due to the extensive penetration of wind power plants (WPPs) into the grid, grid codes have been imposed such that the WPPs stay linked to the grid during faults for a period to maintain the grid ...stability. This paper designs optimal Sugeno fuzzy logic controllers (FLCs) to improve the fault ride-through (FRT) ability of grid-connected WPPs. The meta-heuristic algorithm, whale optimization algorithm (WOA), is utilized to design the control rules and the Gaussian memberships of eight Sugeno FLCs, simultaneously, by minimizing the high dimensional multi-objective fitness function. The WOA-FLCs and the grid-connected gearless permanent magnet synchronous generator driven by a variable-speed wind turbine (VSWT-PMSG) are modeled using PSCAD/EMTDC environment. The effectiveness of the FRT ability of grid-connected VSWT-PMSG is investigated during balanced and unbalanced grid fault conditions. The simulation results of using WOA-FLCs revealed fast time response, less overshoot, and small steady-state error compared with those achieved by using a genetic algorithm (GA) and grey wolf optimizer (GWO).
•This paper designs optimal Sugeno FLCs using whale optimization algorithm (WOA).•WOA-FLC is used to improve the fault ride-through (FRT) ability of wind power plants.•The FRT of grid-tied PMSG is investigated during balanced and unbalanced faults.•The results of WOA-FLC are compared with the results of GA-FLC and GWO-FLC.
•This paper proposes self-tuned PI controlled SMES for wind power smoothing.•Continuous mixed p-norm (CMPN) algorithm is used to adapt PI controller gains.•Realistic wind speed data and practical ...SMES units are used in this study.•The results of CMPN-PI are compared with the results of GA-PI.•Power quality can be further enhanced by using proposed controlled SMES devices.
This paper exhibits a new application of the continuous mixed p-norm (CMPN) algorithm to self-tune all controlled superconducting magnetic energy storage (SMES) units to smooth wind power plants output power. The proposed algorithm is applied to self-tune all PI controllers of these storage units. In the present article, two wind farms are connected with the power grid. Each wind farm is equipped with a self-tuned (ST) controlled SMES unit. The control strategy of this unit is based on a voltage source converter (VSC) and a DC chopper. The VSC is implemented to control the reactive power and the DC chopper is utilized to control the real power exchange with the power grid. These aforementioned power electronic circuits are fully controlled by the proposed CMPN-ST-PI controllers. To achieve more realistic studies, measured values of wind speed that captured from Hokkaido wind power plants are implemented in system investigation. In addition, the turbine model depends on a two-mass structure, which highly affects system dynamics. Moreover, a practical SMES device of a rated capacity of 10 MVA, which is established in Kameyama, is joined the PCC of wind power plants. The proposed self-tuned controller is validated by using the simulation results, which are extensively performed on PSCAD program. The effectiveness of proposed adaptive controlled SMES devices is compared with that obtained by using optimal genetic algorithm-based PI controlled SMES devices under wind speed uncertainty conditions. With these CMPN-ST-PI controllers, the output power of wind power plants can be further smoothed and its fluctuations can be depressed.
This paper presents a novel application of a grey wolf optimizer (GWO) to improve the low voltage ride through (LVRT) capability and the maximum power point tracking (MPPT) of a grid-connected ...permanent-magnet synchronous generator driven directly by a variable-speed wind turbine (DD-PMSG-VSWT). The LVRT capability and MPPT enhancements are achieved by the optimal tuning of eight proportional-integral (PI) controllers in the cascaded control of the machine-side converter and the grid-side inverter, simultaneously. An online optimization is used and achieved by minimizing the integral-squared error of the error inputs of the PI controllers that are controlling dc link voltage, generated real power, and terminal voltages of the PMSG and the grid. The symmetrical and asymmetrical faults for testing the optimum gain parameters are simulated and examined using PSCAD/EMTDC. The obtained results of the optimum values of the GWO algorithm are compared with those attained using the optimum values of the genetic algorithm and the simplex method.
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