No abstract

No abstract

Photovoltaic (PV) energy is one of the most important renewable energies because it is clean, requires very little maintenance. However, the relatively high costs and low conversion efficiency are still the major challenge to a larger and faster spread of PV systems. Therefore, the maximum power point tracker (MPPT) is essential in a PV system for maximizing the conversion efficiency of the solar array. Because of the nonlinear behavior of the PV systems, various techniques of the MPPT are employed in order to continuously operate the solar array at their MPP, despite the unavoidable changes in solar irradiance and temperature. This paper presents an assessment of five widely used MPPT techniques. These techniques are simulated in Matlab/Simulink environment in order to provide a comparison in terms of sensors required, ease of implementation, efficiency, the dynamic response of the PV system to variations in temperature and irradiance, and their appropriateness for the different applications of PV system. This can be used as a reference for future research related to the PV power generation.

In this paper, a flexible switching table (FST) for direct torque control (DTC) of permanent magnet synchronous motors (PMSMs) was proposed to enhance the steady-state and dynamic performances of the drive system. First, the influence of each converter output voltage vectors on the torque and stator flux deviation rates was analyzed to assess the voltage selection strategies of the conventional STs and their impact on the DTC system’s performance. Then, a new flexible ST was proposed which uses a simple algorithm to adaptively select the appropriate voltage vector for two of its states according to the system operating condition. The effectiveness and feasibility of the proposed FST were verified through a comparative evaluation with the conventional STs using experimental results obtained from a 0.75 kW PMSM drive system.

The interest in model predictive direct torque control (MP-DTC) for high-performance dynamic control of electric drives has been growing. Unlike the conventional direct torque control (DTC), MP-DTC can achieve optimal voltage selection by predicting the control variables evaluations using a machine model. However, the model-machine mismatch can degrade the control performance significantly, especially for the interior permanent magnet synchronous machine (IPMSM) because of its highly nonlinear characteristics. Therefore, this paper describes an investigation of the MP-DTC algorithm that exploits the advantages of a finite element analysis (FEA) based model in representing the behavior of the IPMSM precisely, including the magnetic saturation and spatial harmonics effects, to suppress the torque pulsations and eliminate the steady-state torque error. Moreover, this approach optimizes the duty ratio simultaneously with the voltage vector selection to guarantee further torque ripple reduction under steady-state operation, especially at low speeds. Simulation results of an 80-kW IPMSM drive are presented to validate the model and the proposed control method.

This paper investigates an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) technique applied to a reconfigurable photovoltaic (PV)-based battery charger. The proposed method uses training data collected from a dynamic model of the PV module to train the ANFIS to locate the maximum power point (MPP) under different environmental conditions. Based on the estimated MPP, the proposed method can select the optimal configuration of a multimodules PV system and the corresponding global MPP under the non-uniform distribution of the temperature and irradiance. In this way, the proposed method can guarantee the highest possible power harvesting to charge a lithium-ion battery under either partial shading conditions or characteristics mismatch, achieving a high system efficiency. The proposed method is compared with the conventional MPPT scheme to verify its feasibility and effectiveness. The verification results show that the proposed method provides higher accuracy, faster response and better tracking efficiency.INDEX TERMS Adaptive Neuro-Fuzzy Inference System (ANFIS), battery charging, maximum power point tracking (MPPT), non-uniform irradiance, photovoltaic system (PV), partial shading, reconfigurable PV system.

PV energy is one of the most important renewable energies because it is clean, requires very little maintenance. However, the relatively high costs and low conversion efficiency are still the major challenge to a larger and faster spread of PV systems. Therefore, the maximum power point tracker (MPPT) is essential in a PV system for maximizing the conversion efficiency of the solar array. Because of the nonlinear behavior of PV systems, various techniques of MPPT are employed in order to continuously operate the PV array at their MPP despite the unavoidable changes in solar irradiance and temperature. This paper presents an assessment of five widely used MPPT techniques. These techniques are simulated in Matlab/Simulink environment in order to provide a comparison in terms of sensors required, ease of implementation, efficiency and the dynamic response of the PV system to variations in temperature and irradiance to determine appropriateness for the different applications of PV system. This can be used as a reference for future research related to PV power generation.

The objectives of this study were to identify the allelic and genotypic polymorphisms of fatty acid binding protein 4 (FABP4) gene and its association with growth traits (weaning weight, post-weaning daily gain and marketing weight), feed efficiency traits (feed intake and feed efficiency), body indices (body mass index, skeletal muscle index, body index and relative body index) and carcass traits [hot carcass weight, dressing (%), neck (%), for-legs (%), ribs (%), loin (%), hind-legs (%), abdominal fat (%) and tail (%)] in thirty males of the Egyptian Ossimi lambs. The polymerase chain reaction-single strand conformational polymorphism (PCR-SSCP) tool was used to identify the allelic and genotypic polymorphisms in FABP4 gene. The SSCP analysis detected two alleles (A and B with frequency of 0.73 and 0.27, respectively), and three genotypes (AA, AB and BB with frequency of 0.67, 0.13 and 0.20, respectively). The FABP4 genotype showed significant effect (P ˂ 0.05) on feed efficiency and body mass index; and high significant effect (P ˂ 0.01) on post-weaning daily gain, marketing weight, feed intake, skeletal muscle index, hot carcass weight and tail (%). However, the FABP4 genotype did not show significant effect (P > 0.05) on the values of the other studied traits [weaning weight, body index, relative body index, dressing (%), neck (%), for-legs (%), ribs (%), loin (%), hind-legs (%) and abdominal fat (%)]. The presence of A allele was significantly associated (P ˂ 0.05) with body mass index and hot carcass weight; and high significantly associated (P ˂ 0.01) with post-weaning daily gain, marketing weight, feed intake, feed efficiency, skeletal muscle index and neck (%), however, the presence of B allele in the genotype high significantly (P ˂ 0.01) affected post-weaning daily gain, marketing weight, feed intake, feed efficiency, body mass index and hot carcass weight. Obtained results showed an association between the FABP4 gene (B allele) and post-weaning daily gain, marketing weight, feed intake, feed efficiency, body mass index and hot carcass weight of the Egyptian Ossimi lambs. Consequently, applying the marker assisted selection using the FABP4 gene is warranted to increase these traits and will be of considerable economic value in sheep production.