Copper loss minimization in combination with MPPT control in a wind energy conversion system with induction generator

In this paper, a new Maximum Power Point Tracking (MPPT) control scheme for wind generation systems is proposed. A new procedure based on an adaptive neuro-fuzzy training technique is proposed for the self-tuning of the MPPT controller parameters in order to compensate for the unmodeled nonlinearities and degradation due to mechanical aging of various parts 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 has fast dynamic response and thus it can follow the fast dynamics of the wind. The effectiveness and fast dynamic performance of the proposed control scheme has been verified experimentally.

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.

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.

This paper presents an approach for implementing a fuzzy maximum power point tracking (MPPT) controller based on field programmable gate array (FPGA). In the presented approach, at first, the Matlab code of the fuzzy MPPT controller is converted to VHDL code using Matlab HDL coder. Then, the generated VHDL code of the controller is used for programming the Altera FPGA device using QUARTUS 17.1 software. To examine the efficiency of the proposed approach, the control output surface results of the MPPT controller are evaluated experimentally and theoretically. The experimental results are evaluated by testing the FPGA whereas the theoretical results are evaluated by using the Matlab simulation software. It is demonstrated from the comparison of experimental and simulation results, the proposed approach is successfully used to design on the FPGA device.

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.

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.

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.

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 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.

In this paper, a prototype fuzzy logic based maximum power point tracking (MPPT) controller for photovoltaic (PV) systems is presented. The prototype is a boost converter controlled by dSpace® using DS 1104 boar to control current will be extracted from PV. The current is related to maximum power at the moment. That is the implementation MPPT. This paper show comparison result MPPT using FLC and conventional method (Perturbation and observation) in Matlab simulation. Simulation is done to verify and justify the controller before hardware implementation. A prototype hardware implementation is carried out for 5 KW case to manifest in real system.

The purpose of this paper is to discuss a modeling and control of stand- alone photovoltaic system using voltage mode control maximum power point tracking (MPPT) method. The PV module was modeled based on the parameters obtained from a commercial PV data sheet while voltage mode control was modeled using simulink block model. A DC-DC boost converter was chosen to step-up and regulates the input DC voltage of the PV module. The voltage mode control (VMC) maximum power point tracking model was simulated and compared with perturb and observe (P&O) maximum power point tracker in order to validate the performance of output results. Results showed that the voltage mode control maximum power point tracking model yields the similar performance as produced by the photovoltaic system controlled by perturb and observe maximum power point tracking algorithm. The simulation is made to analyze the voltage, current and power generated under the changing irradiant and temperature condition. As a conclusion, the voltage mode control technique is possible to implement which yields the similar performance as the results from conventional MPPT method.

Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.