On-chip integrated power management MPPT controller utilizing cell-level architecture for PV solar system

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.

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 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.

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.

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.

In this work, a simulation study and comparative analysis of conventional Maximum Power Point Tracking (MPPT) technique, i.e., Incremental Conductance (IC) with nonlinear technique, i.e., Ripple Correlation Control (RCC), has been done. Photovoltaic (PV) array and the DC/DC boost converter along with MPPT control strategy have been implemented and simulations analysis has been presented for both the techniques for various solar irradiance levels at 25 °C temperature. The system has been designed to achieve 450 V and 10 kW output at 1000 W/m2 irradiation, which is attained through a DC/DC boost converter. Simulation results show that the IC technique’s output is very much affected by oscillations under steady-state and varying environmental conditions, which affects the system’s stability; whereas, RCC removes all ripples from voltage, current, and power outputs in steady-state and upto some extent in transient state, due to its inherent ripple removal feature. However, during transitions RCC also exhibits underdamped response, but it is minimum as compared to other MPPT techniques.KeywordsPhotovoltaic (PV)DC/DC Boost converterMaximum Power Point Tracking (MPPT)IrradianceRipplesIncremental Conductance (IC)Ripple Correlation Control (RCC)

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.

This paper presents the application of “ripple correlation control (RCC)” maximum power point tracking (MPPT) algorithm for photovoltaic (PV) system employing dc-dc Cuk converter to maximize the output power of PV module. Soft switching commutation of the main and auxiliary switches at zerovoltage using active clamp technique allows the operation of the Cuk converter at very high switching frequencies with reduced size of reactive elements and electromagnetic interference. The combination of auxiliary switch, clamp capacitor and resonant inductor is used to exploit the advantage of high efficiency with zero switching losses. RCC technique uses the high frequency signal ripple, which makes it to converge asymptotically to the maximum power point independent of module configuration and parameters. The operating modes of converter for different switching time intervals are analyzed and design considerations are presented. The simulated performance of the converter with RCC MPPT over conventional MPPT algorithms is presented. The MPPT methods are compared on the basis of the time taken to track the maximum power point (MPP), steady state oscillations about MPP and dependence on array parameters. To evaluate the viability of ZVS Cuk converter with RCC MPPT, the entire system is simulated using MATLAB/SIMULINK platform for an 87 Wp solar PV system.

With the escalating demand for renewable energy sources, photovoltaic (PV) systems have emerged as a pivotal solution for sustainable power generation. The efficacy of these systems is paramount for their widespread implementation. This research article delves into the efficiency assessment of silicon carbide (SiC) components within a boost converter integrated into a PV system. Notably, the boost converter switch is under the intelligent control of an adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) controller. This innovative approach leverages AI to optimize energy extraction from PV panels, thereby enhancing overall system efficiency. The cooperation of SiC components and AI-driven control presents a novel perspective on robust and efficient PV systems. To substantiate the research, data collected from the Sidi Bel-Abès PV central is utilized to train the ANFIS. The utilization of real-world data enhances the accuracy of the predictive model, thereby increasing its applicability to practical scenarios. Integrating AI technologies with PV systems marks a significant advancement toward intelligent and adaptive energy systems.

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.

Recently, real-time simulation of renewable energy sources are indispensible for evaluating the performance of the maximum power point tracking (MPPT) controller, especially in the photovoltaic (PV) system in order to reduce cost in the testing phase. Nowadays, real time PV simulators are obtained by using analog and/or digital components. In this paper, a real-time simulation of a PV system with a boost converter was proposed using only the digital signal processor (DSP) processor with two DC voltage sources to emulate the temperature and irradiation in the PV system. AMATLAB/ Simulink environment was used to develop the real-time PV system with a boost converter into a C-program and build it into a DSP controller TMS320F28335. Besides, the performance of the real-time DSP-based PV was tested in different temperature and irradiation conditions to observe the P-V and V-I characteristics. Further, the performance of the PV with a boost converter was tested at different temperatures and irradiations using MPPT algorithms. This scheme was tested through simulation and the results were validated with that of standard conditions given in the PV data sheets. Implementation of this project helped to attract more researchers to study renewable energy applications without real sources. This might facilitate the study of PV systems in a real-time scenario and the evaluation of what should be expected for PV modules available in the market.

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.

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.

Abstract: This paper presents a single-phase grid-connected photovoltaic power system with the capability of maximum power point tracking (MPPT) of photovoltaic (PV) array and reactive power compensation. The non linear characteristics of the proposed modified p-q theory have been improved with the help of maximum power point tracking (MPPT) controller. MPPT controller helps to feed the inverter with maximum power from the solar grid, for switching pattern generation hysteresis controller is used. The simulation results developed in MATLAB/Simulink software, verify the performance and advantages of the proposed system used to integrate the single phase grid to photovoltaic for enhancing the power quality. Also the proposed scheme has been verified in laboratory setup for its real time implementation.

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system