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 novel-based tracking controller is developed to ensure maximum power point operation for photovoltaic (PV) water pumping systems (PVWPS). PVWPS consists of a PV array followed by a controlled DC cuk converter which feeds a permanent magnet DC motor coupled with a centrifugal pump. The proposed controller is adaptive-logic based that search, detect and track by using Cuckoo Search (CS) technique. This controller may be specified by three control algorithms, each one has its own constraints for Maximum Power Point tracking achievement. The first two CS control algorithms are based on the sensed old chopping ratio, insolation and temperature as inputs in addition to the PV and motor–pump performance parameters. These controllers are implemented to predict the operating and maximum point voltage and power. The third CS control algorithm is constrained by the outputs of the first two CS control algorithms and motor–pump performance parameters. The third CS controllers are implemented also to predict the optimum DC–DC converter duty ratio based on solar insolation, ambient temperature and motor–pump constants. Each algorithm is provided by its own proposed control function. The new approach has proven accuracy, robustness and effectiveness of efficient energy utilization for stand-alone PVWPS one as it is the most accurate, fastest and easiest.
This article proposes three new maximum power point tracking control schemes for permanent magnet synchronous generators in variable-speed wind energy conversion systems. Unlike previously control methods based on traditional voltage source fed equivalent circuit, a current source fed equivalent circuit is proposed where an efficient maximum power point tracking–based load angle control can simply be achieved. The three new control strategies are based on concurrent load angle control–rotor field–oriented method at desired speeds. Each strategy has its own load angle methodology. The first strategy applies constant flux control technique. The second one is based on keeping constant 90° torque angle (zero d-axis current control) method. Finally, the third strategy presents an optimum maximum power point tracking at unity power factor with achieving the favorite linear relationship between the generator stator current and optimum torque. A unified detailed phasor diagram is provided from which the phasor diagram for any of the aforementioned control techniques is produced. Mathematical analysis and MATLAB Simulink model results are presented for each control pattern. Effective validation for the proposed mathematical models is approved.
Self-excited induction generator (SEIG) has the ability to generate power at varying speed, which facilitates its application for wind energy conversion in remote and windy areas. Estimation of the generator behavior under actual operating conditions is essential, but the SEIG analysis with conventional techniques take long time with fatigued mathematical procedures. This paper presents a new technique for the performance analysis of a three phase SEIG supplying an isolated resistive load using adaptive neuro-fuzzy inference system (ANFIS). Proposed ANFIS model has been implemented to predict the effect of prime mover speed, capacitance and load on generated voltage of SEIG. Experimental data is used for the training of ANFIS. Results obtained from the trained have been compared with the experimental results. The comparison confirms the validity and accuracy of the ANFIS based modeling of induction generator.
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.
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