Hardware Implementation of Maximum Power Point Tracking for Thermoelectric Generators

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

By detection of input signal, Maximum Power Point Tracking (MPPT) algorithms are used to maximize the potential output power. Since Perturbation and Observe (P&O) method was first applied in a Photovoltaic (PV) system, a myriad of MPPT algorithms has been proposed. With the pros and cons of various MPPT algorithms, a detailed analysis of several typical direct search and module-integrated MPPT algorithms is presented in this paper. Directions of future work for implementing new MPPT algorithms are also outlined.

This paper presents a sensor-less control strategy based on the Fuzzy Logic Controller (FLC) Maximum Power Point Tracking Algorithm (MPPT) for stand-alone hydrokinetic energy harnessing in the river. The FLC MPPT algorithm was proposed due to the variation of the water velocity and the weakness of the conventional MPPT algorithm, such as the Perturb and Observe (P&O) MPPT algorithm under highly nonlinear water velocity. The hydrokinetic comprises a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC boost converter. In this work, MATLAB/Simulink has been implemented to design the circuit topology for hydrokinetic technology. Then, the modeling of the complete system has been simulated under the different speeds of water velocities. The proposed FLC MPPT has been compared to the conventional P&O MPPT algorithm regarding power efficiency and tracking response time. The results show that the proposed FLC MPPT algorithm achieved an average 93 % efficiency in extracting the output power and a 35.49 % faster response time than the P&O MPPT algorithm.

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

ABSTRACTThis paper presents the modelling, simulation and hardware implementation of the incremental conductance (INC) maximum power point tracking (MPPT) algorithm. The extraction of maximum power from the solar photovoltaic system was made by integrating the PV module with a DC–DC converter and utilising the MPPT algorithm. In this work, the INC MPPT algorithm is practically implemented on an Arduino Uno board to track the maximum power point (MPP) and also simulated in Matlab/Simulink under the same environmental conditions. The performance of the INC algorithm was also compared with the conventional Perturb and Observe (P&O) MPPT algorithm. The experimental and simulation results are provided for both MPPT techniques. Matlab/Simulink was used for simulation studies.

A comparative study between frequently used maximum power point tracking (MPPT) algorithms and a proposed Least Squares Support Vector Machine (LS-SVM) based MPPT algorithm under rapidly changing insolation condition (RCIC) is presented in this paper. The comparison shows that the proposed LS-SVM based MPPT algorithm offers some advantages over existing MPPT algorithms under RCIC, namely elimination of ripples in the output voltage of a PV solar system as well as improved tracking accuracy. The algorithms have been tested through simulation studies carried out on real data recorded by the Australia Bureau of Meteorology. The study compares the performances of LS-SVM, P&O and IncCond.

This paper presents a performance analysis of grid connected SPV system for different maximum power point tracking (MPPT) algorithms. The detailed mathematical model of grid connected three-phase solar photovoltaic (SPV) system with parametric model of SPV cell, thermal modelling and switching loss calculation of switching devices are discussed. The performance evaluation has been carried out for perturb-and-observe (P&O) and incremental conductance (IC)-based MPPT algorithms for various outputs of the SPV array, in terms of energy injected to grid, switching losses, junction temperature and sink temperature, for switching in the DC-DC boost converter. The results validated the effectiveness of the MPPT algorithm in increasing SPV output energy, decrease in the switching losses, junction and sink temperature. The results show that IC method is slightly better to P&O method in energy injected to the grid with lower switching losses, junction and sink temperature.

This paper presents the analysis and operation of a grid connected photovoltaic (PV)energy conversion system with an Adaptive Neuro-Fuzzy Inference System (ANFIS)based maximum power point tracking (MPPT)algorithm. Particle swarm optimization is used to train the membership functions while the least squares algorithm is used to update the consequent parameters of the ANFIS with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck boost converter to change the output voltage of the solar panel and hence achieving the maximum panel output power for a given set of environmental conditions. The ANFIS is trained by using a hybrid algorithm implementing least squares estimator and particle swarm optimization with data obtained by operating the system using the Perturb and Observe (P&O)MPPT algorithm. The performance of the proposed ANFIS based MPPT algorithm is validated in simulation using MATLAB/Simulink at different operating conditions. It is proven that the designed ANFIS based MPPT scheme achieves a very fast response with little oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional P&O based MPPT scheme.

This paper presents the performance analysis of a new maximum power point tracking (MPPT) algorithm for solar photovoltaic (SPV) system using LUO converter. For effective utilization of the SPV panel, MPPT is essential. Hence, it is significant to simplify the tracking control algorithm with faster response, reduced ripple, higher efficiency, and cost-effective system. The proposed technique performs MPPT using a simple control technique by fine-tuning the duty cycle of the converter so as to make the input resistance of converter equal to the load resistance of the solar panel. Hence, the need for proportional–integral control loop is eliminated. The performance analysis of the proposed MPPT algorithm is compared with an existing perturb-and-observe algorithm in MATLAB simulation and experiment results of the proposed MPPT is implemented with the field programmable gate array controller using LUO converter with 40 W solar panel. From the results, it is proved that the response of the proposed method is faster than the existing perturb-and-observe method under varying solar irradiation conditions for the same step size. The input and output side ripples are considerably low and also the proposed algorithm has higher efficiency and low cost.

A software tool for testing maximum power point tracking algorithms using real working conditions

Due to the nature of unpredicted wind speed, determining the optimal generator speed to extract the maximum available wind power at any wind speed is essential. Therefore, it is significant to include an intelligent controller that can track the maximum peak regardless of wind speed. This paper describes the design and development of particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to variable-speed fixed-pitch wind turbines. Other than the electrical power subjected to maximization, the proposed algorithm does not need any additional sensor. In addition, the MPPT algorithm does not require any prior knowledge of the wind energy system. Unlike the conventional search optimization method, PSO-based MPPT algorithm produces almost negligible oscillations at the maximum power once the true peak is located. In short, the proposed MPPT is simple, flexible, accurate and efficient in maximum wind power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm.

This paper presents an event-triggered approach to optimally implement a Maximum Power Point Tracking (MPPT) algorithm into a Digital Signal Processor (DSP). The proposed method allows improving the amount and distribution of time required for executing control tasks. The used nested loop control architecture has an outer loop of MPPT generating the conductance reference used by an inner loop which regulates the input conductance of a DC-DC converter. This last loop enforces a sliding-mode loss-free-resistor behavior for the power converter by means of a simple hysteresis comparator. Computations required by the MPPT algorithm are synchronously executed by the two possible commutation events produced by the inner loop during a switching period. Then, the acquisition of signals must be activated only at an instant before each one of the switching events, releasing the most of the time to implement other tasks. This last characteristic and the use of a nested loop control architecture facilitate the integration of the other essential control functions for photovoltaic (PV) generators in microgrids. Simulation and experimental results confirm the high potetialities of this implementation approach.

In this study, a review on several maximum power point tracking (MPPT) algorithms is given. MPPT algorithms have three major categories: Conventional methods, Advanced Soft Computing methods and Hybrid algorithms. MPPT algorithms are significant to operate the photovoltaic energy conversion systems as close as possible to the MPP resulting in photovoltaic (PV) arrays with high efficiency. In this review, the MPPT algorithms implementation complexity, the complete procedure and its effects in the PV output were given with special attention to the sensors used in the system in the first portion. Further, different MPPT algorithms for PV systems are highlighted with examples, it's parameters like complexity, and sensors used are described. In the end, the MPPT algorithms for PV systems were compared, reviewed and reported. The ultimatum of this work is to provide a survey reference for users of PV based power generation and valued information for researchers of this particular field.

This paper proposed an adaptive Delta (AdaDelta) based maximum power point tracking (MPPT) algorithm for a PV system. Unlike existing gradient descent (GD) MPPTs in which the learning rate (or step) size is fixed, the learning rate in the proposed method is automatically changed with the past accumulated gradient and distance from the maximum power point (MPP), e.g., the closer to MPP, the smaller step size becomes. Compared to the conventional perturb and observe (P&O) MPPT algorithm, the proposed AdaDelta-MPPT is more general, i.e., P &O is a special case of the AdaDelta- MPPT. The simulation and experimental results validated its superiority over the P&O in both transient and steady-state conditions, regardless of the operating points. Particularly, the approximate zero-step size around MPP with AdaDelta-MPPT eliminates the steady-state power oscillation.

Maximum Power Point Tracking (MPPT) algorithms are essential in solar photovoltaic (PV) generation. In the last years, a major research effort has been made in order to get more efficient MPPT algorithms. However, in the literature, there is not a unique methodology for testing and comparing these kind of algorithms when facing dynamic irradiance conditions. This paper proposes a systematic way of evaluating and comparing MPPT algorithms for PV systems, at the simulation stage, unifying the following aspects: the PV system configuration, the type of simulation to be performed, and the dynamic irradiance tests to take into account. The MPPT algorithm is implemented into the dc-dc converter of the system for simplification purposes, simulations are carried out through state-space equations in order to reduce the total simulation time, and three irradiance dynamic tests are used systematically to provide full comparison under different environmental conditions. To test the flexibility of the proposed technique, a comparison of three commonly known Perturb and Observe (P&O) algorithms has been performed, giving special attention to the MPPT drift phenomenon. All interested researchers can request a computer application that implements the proposed methodology.