Single-sensor multi-channel maximum power point tracking controller for photovoltaic solar systems

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.

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.

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.

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.

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.

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.

Photovoltaic (PV) systems have witnessed a rapid increment, the yield mainly relies on the working condition. In most cases, it is hard to obtain the optimal yield. Therefore, Maximum Power Point Tracking (MPPT) controllers witness much attention as an important optimization field of PV systems. These controllers employ different algorithms and they vary in their efficiency, performance, modernity, complexity, and tracking speed. MPPT controllers have witnessed a rapid improvement, they can be generally classified as conventional and advanced methods. Conventional methods are relatively simple but they can't distinguish between the local and global peaks if partial shading occurs, therefore, their efficiency is relatively low. Advanced tracking methods are widely used due to their superior efficiency. Due to the limitation of the singular conventional and advanced methods, hybrid methods find their way to solve these limitations. Selecting the finest MPPT method is still an open issue, this issue can be solved by implementing a survey of the applied methods. This study gives a concise classification and evaluation review of all the applied MPPT methods. This study also provides an accessible reference to undertake mass research works in MPPT in the near future.

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.

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.

Photovoltaic (PV) solar systems with series-connected module integrated converters (MICs) are receiving increased attention because of their ability to create high output voltage while performing local maximum power point tracking (MPPT) control for individual solar panels, which is a solution for partial shading effects in PV systems at panel level. To eliminate the partial shading effects in PV system more effectively, sub-MICs are utilized at the cell level or grouped cell level within a PV solar panel. This study presents the results of a series-output-connection MPPT (SOC-MPPT) controller for sub-MIC architecture using a single sensor at the output and a single digital MPPT controller (sub-MIC SOC-MPPT controller and architecture). The sub-MIC SOC-MPPT controller and architecture are investigated based on boost type sub-MICs. Experimental results under steady-state and transient conditions are presented to verify the performance of the controller and the effectiveness of the architecture.

The limitation of fossil fuels for electricity generation is a very important problem faced today, so that the use of renewable energy is increasing. The photovoltaic system is an electrical energy generator that converts solar energy into electrical energy. The use of PV is increasing because it is pollution-free, free, and low maintenance. However, PV has low conversion efficiency and an increase in PV efficiency can be achieved if it works at the maximum power point (MPP). So it's needed a maximum power point tracking (MPPT) controller. In this article, a modified P&O (MP&O) MPPT controller is designed for a standalone PV system using a boost converter. MP&O finds the MPP by adjusting the duty cycle of the boost converter based on current and voltage measurements. Based on the simulation results, the MP&O MPPT algorithm has a better performance than P&O. MP&O can follow changes in irradiance and produce greater maximum power and faster tracking time than the P&O algorithm. For some irradiance conditions, MP&O can achieve an average efficiency of 92.65% while P&O is 83.45%.

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.

A novel analog maximum power point tracking (MPPT) control IC based on Perturb and Observe (PAO) - arithmetic was proposed in this paper to obtain the maximum power drew from the photovoltaic (PV) array as well as to control small power PV systems more easily and conveniently. Not only is the IC involved in tracking the maximum power point (MPP) of the PV array rapidly, but it also detects the real-time output voltage and current of PV panel. The chip was designed and implemented in 1.5μm BCD (Bipolar-CMOS-DMOS) technology. The simulation results showed that the dynamic response time to impulse of either temperature or solar radiation strength was less than 0.4ms and the static settling time was about 0.8ms with 99.8% tracking precision.