Hybrid photovoltaic-thermoelectric generator powered synchronous reluctance motor for pumping applications

The interest in photovoltaic (PV) pumping systems has increased, particularly in rural areas where there is no grid supply available. However, both the performance and the cost of the whole system are still an obstacle for a wide spread of this technology. In this article, a hybrid photovoltaic (PV)-thermoelectric generator (TEG) is investigated for pumping applications. The electric drivetrain comprises a synchronous reluctance motor and an inverter. A control strategy for the drivetrain is employed to execute two main tasks: 1) driving the motor properly to achieve a maximum torque per Ampere condition and 2) maximizing the output power of the PV system at different weather conditions. This means that the conventional DC-DC converter is not used in the proposed system. Moreover, batteries, which are characterized by short life expectancy and high replacement cost, are also not used. It is found that the motor output power and the pump flow rate are increased by about 9.5% and 12% respectively when the hybrid PV-TEG array is used compared to only using PV array. Accordingly, the performance, cost and complexity of the system are improved. Measurements on an experimental laboratory setup are constructed to validate the theoretical results of this work

Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions

An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms

The performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

Energy management control strategy for renewable energy system based on spotted hyena optimizer

Hydrocarbons, carbon monoxide and other pollutants from the transportation sector harm human health in many ways. Fuel cell (FC) has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy. The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand. Therefore, adding energy storage systems is necessary. However, to manage and distribute the power-sharing among the hybrid proton exchange membrane (PEM) fuel cell (FC), battery storage (BS), and supercapacitor (SC), an energy management strategy (EMS) is essential. In this research work, an optimal EMS based on a spotted hyena optimizer (SHO) for hybrid PEM fuel cell/BS/SC is proposed. The main goal of an EMS is to improve the performance of hybrid FC/BS/SC and to reduce the amount of hydrogen consumption. To prove the superiority of the SHO method, the obtained results are compared with the chimp optimizer (CO), the artificial ecosystem-based optimizer (AEO), the seagull optimization algorithm (SOA), the sooty tern optimization algorithm (STOA), and the coyote optimization algorithm (COA). Two main metrics are used as a benchmark for the comparison: the minimum consumed hydrogen and the efficiency of the system. The main findings confirm that the minimum amount of hydrogen consumption and maximum efficiency are achieved by the proposed SHO based EMS

Finite element based overall optimization of switched reluctance motor using multi-objective genetic algorithm (NSGA-II)

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM

An enhanced DC-link voltage response for wind-driven doubly fed induction generator using adaptive fuzzy extended state observer and sliding mode control

This paper presents an enhancement method to improve the performance of the DC-link voltage loop regulation in a Doubly-Fed Induction Generator (DFIG)- based wind energy converter. An intelligent, combined control approach based on a metaheuristics-tuned Second-Order Sliding Mode (SOSM) controller and an adaptive fuzzy-scheduled Extended State Observer (ESO) is proposed and successfully applied. The proposed fuzzy gains-scheduling mechanism is performed to adaptively tune and update the bandwidth of the ESO while disturbances occur. Besides common time-domain performance indexes, bounded limitations on the effective parameters of the designed Super Twisting (STA)-based SOSM controllers are set thanks to the Lyapunov theory and used as nonlinear constraints for the formulated hard optimization control problem. A set of advanced metaheuristics, such as Thermal Exchange Optimization (TEO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search Algorithm (HSA), Water Cycle Algorithm (WCA), and Grasshopper Optimization Algorithm (GOA), is considered to solve the constrained optimization problem. Demonstrative simulation results are carried out to show the superiority and effectiveness of the proposed control scheme in terms of grid disturbances rejection, closed-loop tracking performance, and robustness against the chattering phenomenon. Several comparisons to our related works, i.e., approaches based on TEO-tuned PI controller, TEO-tuned STA-SOSM controller, and STA-SOSM controller-based linear observer, are presented and discussed

Modelling and design methodology of an improved performance photovoltaic pumping system employing ferrite magnet synchronous reluctance motors

This paper proposes a novel photovoltaic water pumping system (PVWPS) with an improved performance and cost. This system doesn’t contain a DC-DC converter, batteries nor rare-earth motors. Removing the aforementioned components will reduce the whole cost and increase the reliability of the system. For enhancing the performance of the PVWPS, a ferrite magnet synchronous reluctance motor (FMSynRM) is employed. Besides, the motor inverter is utilized to drive the motor properly and to extract the maximum available power of the PV system. This is performed using a suggested control strategy that controls the motor inverter. Furthermore, to show the effectiveness of the proposed PVWPS, the performance of the proposed system is benchmarked with a PVWPS that is employing a pure SynRM. Moreover, the complete mathematical model of the system components and the control is reported. It is proved that the flow rate employing the proposed system is increased by about 29.5% at a low irradiation level (0.25 kW/m2) and 15% at a high irradiation level (1 kW/m2) compared to the conventional solar system using a pure synchronous reluctance motor (SynRM). An experimental laboratory test bench is built to validate the theoretical results presented in this research work. Good agreement between the theoretical and the experimental results is prove

Multi-objective optimization of switched reluctance machine design using Jaya algorithm (MO-Jaya)

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions

Optimal rotor design of synchronous reluctance machines considering the effect of current angle

The torque density and efficiency of synchronous reluctance machines (SynRMs) are greatly affected by the geometry of the rotor. Hence, an optimal design of the SynRM rotor geometry is highly recommended to achieve optimal performance (i.e., torque density, efficiency, and power factor). This paper studies the impact of considering the current angle as a variable during the optimization process on the resulting optimal geometry of the SynRM rotor. Various cases are analyzed and compared for different ranges of current angles during the optimization process. The analysis is carried out using finite element magnetic simulation. The obtained optimal geometry is prototyped for validation purposes. It is observed that when considering the effect of the current angle during the optimization process, the output power of the optimal geometry is about 3.32% higher than that of a fixed current angle case. In addition, during the optimization process, the case which considers the current angle as a variable has reached the optimal rotor geometry faster than that of a fixed current angle case. Moreover, it is observed that for a fixed current angle case, the torque ripple is affected by the selected value of the current angle. The torque ripple is greatly decreased by about 34.20% with a current angle of 45 degrees compared to a current angle of 56.50 degrees, which was introduced in previous literature