Step by Step FPGA-Based Implementation of MPPT Fuzzy Controller for PV Systems

In this paper, a control scheme for wind energy conversion systems (WECS) with induction generator is presented that is composed of a copper loss minimization (CLM) controller and a maximum power point tracking (MPPT) controller. The CLM is accomplished by controlling the excitation current of the induction generator and the MPPT control is attained by controlling the rotational speed of the wind turbine. The suggested control scheme can be easily implemented because, neither the measurement of the wind speed nor the knowledge of the wind turbine characteristics are required. Moreover, it exhibits fast dynamic response and thus, it can follow the fast dynamics of the wind. The generator is connected to the power grid by means of two vector controlled back-to-back converters. Experimental results are presented to validate the cooperation of the CLM controller with the MPPT controller.

This study presents a maximum power point tracking (MPPT) method and controller for a multi-channel ( N -channel) photovoltaic solar system. The N -channel single-sensor MPPT (SS-MPPT) controller is able to track the maximum power point (MPP) of each solar panel using only one output current sensor in the N -channels. Compared with a conventional N -channel MPPT system which requires N voltage sensors, N current sensors, 2 N analogue-to-digital converters (ADCs) (or an ADC with 2 N channels) and N -MPPT controllers along with the associated conditioning circuitries, the proposed method only requires one sensor, one ADC and one MPPT controller. In the proposed N -channel SS-MPPT controller, the MPPT for each channel is achieved by tracking the maxima of the output (load) current (resulting in the maxima of the system output power) using the Perturb and Observe algorithm and the maxima of each channel power via channel output current difference optimisation. Two algorithms which can be used to realise the SS-MPPT controller are discussed and compared. The concept and operation of the SS-MPPT controller is presented, analysed and verified by results obtained from a proof-of-concept experimental prototype.

This paper aims at to realize the solar cell charged systems of 120W PV module with the dsPIC30F4011 control chip. All of the control schemes are written and accomplished with the C language. In the power converter, a single-ended primary inductance converter (SEPIC) is adopted which has the features including the buck-boost operating mode, no polarity inversion, low input current pulsation, and wide input voltage range, so, it is very suitable for the conversion of solar cell. In order to obtain the high efficient solar cell energy conversion, a fuzzy maximum power point tracking (MPPT) control is activated if the solarization is insufficient. The fuzzy controller is to maintain a charged current so as that the battery voltage can reach to desired value. The effectiveness of the proposed methods is demonstrated with the experimental results.

This paper proposes a T-S fuzzy model based maximum power control to enhance the efficiency of the wind power generation systems. To draw the maximum wind energy, a buck converter is applied to adjust the recified output voltage of the permanent-magnet synchronous generator. Based on the T-S fuzzy representation of the system, the fuzzy maximum power point tracking (MPPT) controller is developed to maintain the maximum power voltage. The MPPT control guarantees asymptotic convergence, while control gains can be systematically designed by solving linear matrix inequality (LMI). Furthermore, the robust MPPT is also discussed to cope with varying wind speed and system uncertainty. Satisfactory performance is shown from the numerical simulations.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

In order to maximize the power generation capacity of solar cells, the power rate transmission mode of power controller generally adopts the maximum power point tracking (MPPT) control mode. Among them, the energy system with centralized topology only contains one MPPT controller, which cannot ensure that each photovoltaic module works at its maximum power point, resulting in a significant decline in the conversion efficiency of the energy system. The architecture of distributed energy system includes parallel and series. Compared with the parallel MPPT controller, the voltage ratio of the series MPPT controller can be smaller, which is helpful to improve the conversion efficiency of the controller. Firstly, a distributed energy system based on series MPPT is proposed, and a start-up strategy without communication is proposed. Then, according to the characteristics of the proposed system, a high-precision and fast MPPT control strategy is proposed. Then, according to the system architecture and control strategy, the parameters of MPPT controller are designed. Finally, an experimental platform is built to verify the effectiveness of the proposed energy system architecture and control strategy.

An efficient maximum power point tracking (MPPT) controller is a crucial part of solar photovoltaic (PV) system, which can handle the non-linear characteristics of a solar PV array. In this study, a proposed solar PV system with boost converter and dc load is modeled and compared according to the operating characteristics (i.e. voltage, current, power and efficiency under varying solar irradiance and cell temperature) using five conventional and modern MPPT techniques. The proposed solar PV system and MPPT controllers are modeled and simulated in MATLAB Simulink platform. Among these five MPPT controllers, artificial neural network (ANN) MPPT controller provides the highest efficiency of 97.55% and produces less voltage and power fluctuations. The novelty of this paper is that it focuses on the key characteristics and simulated results of the five MPPT techniques to make a comparison between them.

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP.

This paper presents a maximum power point tracking (MPPT) controller employing a modified hill top algorithm implemented using embedded microcontroller for solar photovoltaic (PV) module. The proposed MPPT algorithm has high efficiency compared to conventional hill top algorithm in terms of transferring power from source to load. The designed controller regulates the output voltage through control of the DC-DC boost converter under varying environmental conditions. The comparative result for hill top and modified hill top algorithm based on the proposed MPPT controller has been obtained through the simulation studies in PSCAD/EMTDC software. The validity of the proposed modified hill top algorithm has been obtained through experimental implementation of the MPPT controller on a solar PV module driving a load.

The MPPT (Maximum Power Point Tracking) control topology based on Fuzzy logic, and analysis of the Vienna Rectifier for the small-scale Wind Turbine System (WTS) is proposed in this paper. The PMSG (Permanent Magnet Synchronous Generator) of the WTS generates the output power that is fluctuated due to irregular wind velocity, which has to be controlled for the smooth output power. Many controlstrategies are projected by the researchers for the settled power output, but the conventional control techniques getting more complex. One of the simple and robust methods that enable for the MPPT is fuzzy logic control. The above mechanism regulates the speed of PMSG and the DC power output. Moreover, it is engaged in the parameter optimization and the speed control of the PMSG. A fuzzy logic MPPT controller based Vienna Rectifier is used in this paper for a 1kW WTS with improved efficiency and reduced harmonics, and the results are justified using MATLAB/Simulink.

In the literature, both photovoltaic voltage and PV current need to be sensed to perform maximum power point tracking (MPPT) control. For three-level boost converter, both capacitor voltages need to be sensed and an additional voltage balancing control loop is required to balance the capacitor voltages. In this paper, the MPPT control and the voltage balancing control with sensing only inductor current is proposed. The provided simulation and experimental results demonstrate the proposed method.

This paper has proposed maximum power point tracking (MPPT) control for stand-alone solar power generation systems via the type-2 Takagi-Sugeno (T-S) fuzzy-model-based approach. In order to deal with the uncertainties in the modeling procedure, the type-2 T-S fuzzy technology is employed. To handle reducing the conservatism of LMI-based stability conditions for type-2 T-S fuzzy systems, stability conditions are relaxed by introducing more slack matrix variables. Finally, the control performance is shown from the numerical simulation and experimental results.

Solar Photo-voltaic (PV) arrays have non-linear characteristics with distinctive maximum power point (MPP) which relies on ecological conditions such as solar radiation and ambient temperature. In order to obtain continuous maximum power (MP) from PV arrays under varying ecological conditions, maximum power point tracking (MPPT) control methods are employed. MPPT is utilized to extract MP from the solar-PV array; high-performance soft computing techniques can be used. In this paper, the proposed hybrid MPPT algorithm is used in the solar-PV system with variable climatic conditions. The performance of the proposed hybrid MPPT algorithm with different membership functions is analyzed to optimize the MPP. Simulation results establish that with the application of MPPT controller such as Perturb and Observe, Fuzzy Logic and a proposed hybrid MPPT for the solar-PV system, the proposed hybrid MPPT controller provides more accurate performance and also reduces the fluctuation about the MPP as compared to other MPPT techniques.

This study proposes a novel maximum power point tracking (MPPT) control strategy for the solar photovoltaic power system (SPPS). The proposed system adopts two solar photovoltaic modules of 430 W, which are connected to a boost converter and an MPPT controller, since the traditional MPPT algorithm (such as perturbation and observation [P&O] algorithm) can hardly reach maximum power point (MPP) under low irradiance level and partial shading conditions (PSC), which leads to the low efficiency of the SPPS. The speed of light optical path difference measurement (SLOPDM) MPPT control strategy has been developed in this study to overcome this problem. The estimation of the optical path angle difference is used as the basis for the proposed control strategy. This is done by determining the relationship between the optical path angle difference, solar photovoltaic power impedance Rspv and load Ro, and then calculating the duty cycle corresponding to the MPP, which then drives the boost converter to capture the MPP. The experimental results verify the proposed system, which shows the efficiency comparison between the SLOPDM MPPT algorithm, solar angle and horizon (SAH) algorithm, and P&O algorithm under PSC and uniform irradiance conditions (UIC) at irradiance levels of 700 W/m2 and 65 W/m2. It is evident from the comparison that the efficiency of the SLOPDM MPPT algorithm is 99% under both conditions, which is higher than the SAH and P&O algorithms. The SLOPDM MPPT algorithm can precisely, rapidly, and stably be operated at MPP. The contribution of this study is that the proposed MPPT control strategy can help achieve the high−performance of SPPS without changing the hardware circuit design and requiring any additional solar power meter. This reduces the cost and the complexity of the system significantly.

This paper presents a photovoltaic system with maximum power point tracking facility. Maximum power point trackers are so important in photovoltaic systems to increase their efficiency. The system consists of a photovoltaic solar module connected to a DC–DC buck–boost converter and load. The adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller is used to track the maximum power. DC–DC boost converter and space vector modulation-based inverter are used to provide the required supply to the load. The proposed ANFIS-based MPPT improves the system efficiency even at abnormal weather conditions. Here a lot of reduction in torque and current ripple contents is obtained with the help of ANFIS-based MPPT for an asynchronous motor drive. Also, the better performance of an asynchronous motor drive is analyzed with the comparison of conventional and proposed MPPT controller using MATLAB simulation along with experimental implementation. The experimental implementation of MPPT with hardware setup is done using dSPACE real-time controller. Data acquisition and codes of the successfully simulated model can be linked and loaded directly to the dSPACE DS-1104 controller for real-time hardware operation. The simulation and the practical results represent that the proposed system tracked the maximum power accurately and successfully under different working conditions. Practical validations are also carried out and are represented.