Photovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive Reference Voltage (ARV) method, is proposed to determine the optimal operating point of the PV panel. The ANN-based ARV method is a voltage-controlled approach that can adapt to changing atmospheric conditions. The performance of the proposed method is evaluated using both a normal Proportional-Integral (PI) controller and an anti-windup PI controller. Comparative analysis is conducted with the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) methods in the MATLAB/Simulink environment, considering three different atmospheric scenarios with varying radiation levels according to EN50530 standards. The proposed method demonstrates superior efficiency with overall results of 99.4%, 95.9%, and 96% in scenario 1, scenario 2, and scenario 3, respectively. Particularly, the proposed method exhibits notable superiority in rapidly changing atmospheric conditions.

Maximum Power Point Tracking (MPPT) algorithms are crucial for maximizing power extraction from photovoltaic (PV) systems. Traditional MPPT methods often exhibit suboptimal performance under partial shading conditions. Hence, advanced MPPT algorithms have been developed to enhance efficiency in such scenarios. The voltage scanning-based MPPT algorithm is notable for its superior performance under partial shading, characterized by high tracking speed and efficiency. This study introduces a novel enhancement to this method—namely, a voltage skipping algorithm designed to further improve tracking speed. Unlike conventional scanning techniques, this approach dynamically calculates skipping voltages during operation, eliminating the need for prior knowledge of PV panel characteristics. Performance evaluation was conducted using a MATLAB/Simulink model of a PV system comprising 4 series panels and a boost converter, subjected to simulations under 5 distinct partial shading scenarios. Comparative analyses with established optimization algorithms such as particle swarm optimization, cuckoo search algorithm, and grey wolf optimization highlight the proposed method's effectiveness in terms of tracking speed and maximum power output. The proposed algorithm has worked with high efficiency of 99.28 %, 99.61 %, 99.13 %, 99.16 % and 99.58 % in 5 different scenarios, respectively. By using the proposed method, a significant superiority has been achieved over other methods, with tracking speeds of 0.26s, 0.22s, 0.2s, 0.22s and 0.26s, respectively.

Different tracking algorithms have been developed to obtain maximum efficiency from the PV power systems under changing atmospheric conditions. Some of these algorithms are effective under uniform irradiance, while the others are effective under partial shading conditions. In this study, a new MPPT method designed with System Identification-SI and named SI-based polynomial ARV is proposed for a PV system under uniform irradiance. The recommended method is also an adaptive reference voltage-ARV-based method. In this method, the model in which the reference voltage is obtained has a simple polynomial structure. This polynomial model has been obtained by assuming that the PV system is a nonlinear black-box type system. The input-output data, which are required for modeling, were created in MATLAB/Simulink environment. Then, by using these data with SI-Toolbox, the mathematical model of the PV system in polynomial structure giving the input-output relationship was obtained. T temperature information was used as model input, which was the generated reference voltage. Simulation studies were carried out under two different changing atmospheric scenarios; and the performance of the proposed method was analyzed. The obtained results were compared with perturb & observe-PO, incremental conductance-IC, constant voltage reference-CVR, artificial neural network-ANN-based ARV, and SI-based nonlinear ARV methods. All simulation results showed that the recommended method is simple structured, applicable, and has high performance.

In this study, the hysteresis based direct torque control (DTC) of a three-phase induction motor was carried out experimentally. The DTC algorithm was also modelled in the hardware environment by using FPGA’s in-the-loop feature. The dSPACE DS1103 controller board was used in the experimental study and Altera DE2-115 model development board was used in the hardware modelling. Both applications had the same sample time and all of the DTC algorithm was tested within the FPGA. The hardware simulation study conducted in FPGA environment was carried out in MATLAB/Simulink environment. The experimental results were compared with the hardware simulation results obtained from FPGA. As a result of the comparison, it was shown that DTC algorithm could be realized easily in FPGA environment without experimental installation, and the obtained current, voltage and velocity graphs were similar.

The Maximum Power Point Tracking (MPPT) algorithms are applied to obtain maximum efficiency under different atmospheric conditions in photovoltaic (PV) systems. Perturb&Observe (PO) and Incremental Conductance (INC) methods are the oldest algorithms used among MPPT methods. Field Programmable Gate Arrays (FPGA) are used especially in applications requiring high speed. FPGA in-the-loop feature is used to test algorithms designed in MATLAB/Simulink environment. In this study, PO and INC methods have been designed to work in FPGA environment. Both algorithms have been tested under different radiation conditions using FPGA-in-the-loop feature. The FPGA in-the-loop simulation result of PO and INC methods has been graphically shown. In this study, Altera DE2-115 development board was used to test PO and INC MPPT algorithms. On the other hand, PO and INC methods were synthesized using the Quartus-II program. Comparisons of simplicity of algorithms were made according to synthesis results. Thus, by using the FGPA in-the-loop feature and performing the synthesis process, both the algorithms were tested and the areas covered by the algorithms in the FPGA were compared.