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

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

Simulation-based coyote optimization algorithm to determine gains of PI controller for enhancing the performance of solar PV water-pumping system

In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on theCanis latransspecies, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler-Nichols and trial-error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities

Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

Fuel cell as an effective energy storage in reverse osmosis desalination plant powered by photovoltaic system

A hybrid renewable energy systems (HRESs) comprises of photovoltaic (PV), and self-charging fuel cells (SCFC) is designed for securing electrical energy required to operate brackish water pumping (BWP) and reverse osmosis desalination (RO) plant of 150 m3 d-1 for irrigation purposes in remote areas. An optimal configuration of the proposed design is determined based on minimum cost of energy (COE) and the minimum total net present cost (NPC). Moreover, a comparison with a stand-alone diesel generation (DG) or grid extension is carried out against the optimal configuration of PV/SCFC HRES. The modeling, simulation, and techno-economic evaluation of the different proposed systems, including the PV/SCFC system are done using HOMER software. Results show that PV array (66 kW), FC (9 kW), converter (25 KW) –Electrolyzer (15 kW), Hydrogen cylinder (70 kg) are the viable economic option with a total NPC of $115,649 and$0.062 unit cost of electricity. The COE for the stand-alone DG system is 0.206 $/kWh, which is 69.90% higher than that of the PV/SCFC system. The PV/SCFC system is cheaper than grid extension. This study opens the way for using a fuel cell as an effective method for solving the energy intermittence/storage problems of renewable energy sources

Fuzzy fractional-order PID control for heat exchanger

Fractions have recently become widespread in technology and science. The current research looked at a fractional-order fuzzy PID controller. Furthermore, the tubular heat exchanger procedure aids in the investigation of controller behavior. The advantages of the recommended control strategy are highlighted to examine controller reactions under variations in variation and loads within the reference environment. There are additional comparisons with the conventional PID control scheme. The findings showed that the control approach performs better than a traditional PID control framework

Twin Support Vector Machine Method for Identification of Wiener Models

Twin support vector regression is applied to identify nonlinear Wiener system, consisting of a linear dynamic block in series with static nonlinearity. The linear block is expanded in terms of basis functions, such as Laguerre or Kautz filters, and the static nonlinear block is determined using twin support vector machine regression. Simulation of a control valve model and pH neutralization process have been presented to show the features of the proposed algorithm over support vector machine based algorithm

Nonlinear systems identification using support vector machines

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Design and Sensitivity Analysis of Hybrid Photovoltaic-Fuel-Cell-Battery System to Supply a Small Community at Saudi NEOM City

This research paper aimed to design and present a sensitivity analysis of a hybrid photovoltaic-fuel-cell-battery (PV/FC/B) system to supply a small community for the recently planned grand city NEOM in Saudi Arabia. The location of the city of NEOM is characterized by a high average level of solar irradiance. The average daily horizontal solar radiation is around 5.85 kWh/m2. A detailed feasibility and techno-economic evaluation of a PV/FC/B hybrid energy system were done to supply a daily load demand of 500 kWh (peak-35 kW). The PV array was the main source to meet the load demand. During the surplus periods, the battery was charged using extra energy and powered the electrolyzer for hydrogen production. The produced hydrogen was stored for later use. During the deficit periods, the FC and/or battery supported the PV array to meet the load demand. Two benchmarks, the cost of energy (COE) and net present cost (NPC), were used to identify the best size of the PV/FC/B system. Variation of the tilt angle of the PV array and the derating factor were considered to determine the effect of the performance of the PV/FC/B system’s COE and NPC. The main findings confirmed that a 200 kW PV array, 40 kW FC, 96 batteries, 50 kW converter, 110 kW electrolyzer, and 50 kg hydrogen tank was the best option to supply the load demand. The values of total NPC and COE were $500,823 and$0.126/kWh. The annual excess energy was very sensitive to the declination angle of the PV array. The minimum annual excess energy was achieved at an angle of 30 degrees. It decreased by 75.7% and by 60.6% compared to a horizontal surface and 50 degrees of declination, respectively. To prove the viability of the proposed system, a comparison with grid extension along with a diesel generation system was carried out

Modified tunicate swarm algorithm-based methodology for enhancing the operation of partially shaded photovoltaic system

The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the global power as they fall in local optima. Therefore, this paper proposes a new metaheuristic approach named modified tunicate swarm algorithm to track the global power of the photovoltaic array in the event of partial shade operation. In the proposed approach, two distinct random numbers are incorporated in the tunicate's updating process to enhance the search ability and avoid falling in local optima. The proposed approach is validated using two applications, CEC’17 test suite and practical one of designing the tracker installed with the photovoltaic array operated at partial shade. The proposed approach controls the dc-dc boost converter at the array terminals via obtaining the suitable duty cycle at which the global power is extracted. Five shade patterns with distinct locations of global power are analyzed in addition to normal operation, the proposed tracker is compared to teaching–learning-based optimizer, particle swarm optimizer, Coot optimization algorithm, capuchin search algorithm, and perturb and observe. Moreover, statistical tests of ANOVA table, Wilcoxon rank test, and Friedman test are conducted to assess the proposed approach. The fetched results clarified that, the best power error obtained via the proposed tracker is 0.000042 W at the third shade pattern while the worst one is 0.0652 W at the fifth pattern. Moreover, the p-value obtained via ANOVA table is 1.85399e-19 which confirms significant difference in the column means. The results proved the superiority and competence of the proposed tracker in solving the CEC’17 test suite and extracting the best global power from the photovoltaic array in all studied operating conditions