Photovoltaic maximum power point tracking based on IWD-SVM

This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill climbing, and incremental conductance (Inc-Cond) etc.) are trial-and-error-based, which may result in steady-state oscillations and loss of tracking direction under fast-changing ambient environment. In addition, partial shading is also a challenge due to the difficulty of finding the global maximum power point on a multi-peak characteristic curve. As an attempt to address the aforementioned issues, a novel Rprop-NN MPPT algorithm is developed and elaborated in this work. Multiple case studies are carried out to verify the effectiveness of the proposed algorithm.

The cloud model is a mathematical representation to fuzziness and randomness in linguistic concepts, and integrates the fuzziness and randomness of a linguistic concept in a unified way. This model is a new method for transformation between qualitative and quantitative in the knowledge. In this paper, through analysis of the output of the nonlinear characteristics of PV cells the cloud model will applies to photovoltaic system maximum power point tracking control. The article is designed solar photovoltaic maximum power point tracking (MPPT) method based on cloud model. Simulation result shows that the cloud controller is simple and easy, directly perceived through the senses, and has strong robustness, better control performance, and good application.

This paper proposes a hybrid photovoltaic (PV) Maximum Power Point Tracking (MPPT) strategy to tackle local optima, slow dynamic response, and steady-state oscillations under partial shading conditions (PSC). The method combines an Improved Whale Migration Algorithm (IWMA) with a variable-step Incremental Conductance (VINC) technique. IWMA employs a Tent–Logistic–Cosine chaotic initialization, dynamic weight coefficients, random feedback, and a distance-sensitive term to enhance population diversity, strengthen global exploration, and reduce the risk of convergence to local maxima. The VINC stage adaptively adjusts the step size based on incremental conductance, providing fine local refinement around the global maximum power point (GMPP) and suppressing steady-state power ripple. Extensive MATLAB/Simulink simulations with multiple random trials show that the proposed IWMA-VINC strategy consistently outperforms the Whale Migration Algorithm (WMA), A Simplified Particle Swarm Optimization Algorithm Combining Natural Selection and Conductivity Incremental Approach (NSNPSO-INC), and the Grey Wolf Optimizer and Whale Optimization Algorithm (GWO-WOA) under both static and dynamic PSC, achieving the highest tracking accuracies (99.74% static, 99.44% dynamic), higher average output power, shorter convergence times, and the smallest variance across trials. These results demonstrate that IWMA-VINC offers a robust and high-performance MPPT solution for PV systems operating in complex illumination environments.

In this research study, an automated design is introduced for the Photovoltaic (PV) Maximum Power Point Tracking (MPPT) Technique using a Programmable Logic Controller (PLC) S7-1200 type. The perturb and Observe (P&O) algorithm was employed to track the maximum power point continuously and reduce output fluctuations. This innovative design seeks to address the usual constraints associated with standard P&O-MPPT algorithms by harnessing the power of PLC automation. The system was meticulously modelled and simulated using the Siemens Automation program (TIA Portal) with the use of a ladder and Structured Control Language (SCL) languages, depicted as a function block. Furthermore, an experimental prototype was developed employing a PLC as the control unit to authenticate the accuracy and effectiveness of the P&O algorithm’s outcomes with a DC-DC Boost converter. The system’s performance was compared under varying meteorological conditions. Additionally, a Supervisory Control and Data Acquisition (SCADA) screen was developed for periodic monitoring and displaying results on the PC screen. The system has proven to be highly efficient in tracking maximum power, periodic monitoring, and displaying results on the computer screen.

An efficient fuzzy logic control algorithm for photovoltaic maximum power point tracking under partial shading condition

A new control method of photovoltaic generation system maximum power point tracking (MPPT) is proposed in this paper. A variable step Perturbation and Observation method(P&O) is presented, and then contributes to a new method which combines Constant Voltage Tracking (CVT) and the Variable Step Perturbation and Observation Method together. According to the simulation results, the new method presented can help track the maximum power points of the PV array more rapidly, and more accurately, while solve the shortcoming of CVT, of which is that it cannot track precisely, and reduce the energy loss nearby the maximum power points in P&O at the same time. Thus, the energy conversion efficiency of PV array is improved.

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

Duty cycle perturbation is widely applied in Photovoltaic (PV) Maximum Power Point Tracking (MPPT) control strategy, but its Control Principle hasn't been studied deeply. According to mathematical physics model of PV Cell, the PV Boost converter based on Duty Cycle Perturbation (DCP) control is deduced, and control principle of DCP-based hill climbing method is analyzed and simulated with Power Simulation (PSIM). The simulation results show validity of the theoretical analysis. On this basis, the importance of initial duty cycle value to increasing of start-up speed and photoelectric conversion efficiency is pointed out, and train of thoughts will be provided for improvement of MPPT control strategy, which is of practical significance for engineering design of PV system.

Solar energy is the base for both photovoltaic (PV) power generation and plant growth. Inspired by this biological phenomenon, a novel crowded plant height optimisation (CPHO) algorithm was developed for solar PV maximum power point tracking (MPPT). This CPHO‐tuned MPPT algorithm was developed with the aim of obtaining the optimal duty cycle (d) for DC‐DC boost converter for maximum solar power extraction from PV panels with the help of a proportional‐integral controller. Crowded plants regulate the growth of their stem height in relation to neighbouring plants, also known as height convergence. Using this CPHO‐algorithm, the stable height of the plant found in a numerical value is taken as the optimal height of the plant. This optimal numerical value was converted into (d) for the converter. Under dynamic weather conditions, the (d) was optimally adjusted by the proposed algorithm to regulate the DC output of the converter. On the utility side, d–q vector control‐based voltage source inverter was used for PV power integration into the grid. The performance of the converter control strategy of the proposed CPHO algorithm was compared with perturb and observe algorithm‐based MPPT control, which was analysed on MATLAB/Simulink platform.

Chapter 2 - Comparative study of swarm intelligence based maximum power point tracking algorithms for photovoltaic systems under partial shading conditions

The photovoltaic cells’ output characteristics and maximum power point tracking (MPPT) technology are studied to design a two-stage cascaded photovoltaic off-grid inverter, including the front-end Boost converter and the rear-end three-phase inverter. The state-space averaging method is adopted in front-end Boost converter modeling, and space vector pulse width modulation (SVPWM) technology is adopted in the rear-end three-phase inverter. The simulation drive model is obtained through research principles and algorithm realization. Based on the theoretical analysis and simulation, front-end Boost converter’s and the back-end inverter’s hardware are designed. The main circuit includes the front-end Direct Current (DC)-DC topology and the back-end DC-AC topology. The TMS320F28027 digital signal processor is the core of the front-end DC-DC topology’s control system, and LabView is the core of the back-end DC-AC topology’s control system. The front-end Boost converter’s analog output voltage is 32 V, stable at the maximum operating point in the test. The maximum power point can be effectively tracked in the proposed inverter. The rear-end three-phase inverter’s total harmonic distortion (THD) of phase voltage is less than 3%. Less harmonic wave meets the circuit harmonic distortion rate’s design requirements.

Under partial shading condition or imbalanced temperature distribution, the power-voltage characteristic curve of photovoltaic (PV) array exhibits complicated multiple peaks. Traditional maximum power point tracking (MPPT) method is usually easy to trap into local extreme power point. To solve this issue, this paper proposes a novel MPPT method using quantum-behaved particle swarm optimization (QPSO) algorithm. This algorithm adopts detect-search strategy based on voltage closed-loop control, making voltage particles search photovoltaic characteristic curves in quantum-behaved paths. By setting a modulation strategy of reunion and separation factor and stopping criterion, the proposed QPSO algorithm improves the precision of stable state, and eliminates power oscillation under stability condition. Experimental results show that QPSO algorithm has good performance with less parameters, superior global optimization ability, favorable stability and better adaptability compared with traditional particle swarm optimization (PSO) algorithm in PV MPPT.

In this paper, an analog voltage based maximum power point tracking (MPPT) algorithm for individual photovoltaic (PV) panel is proposed. The fixed voltage reference method is the simplest method for tracking, but it does not give good MPPT efficiency because the MPP voltage point changes at different solar insolation levels. A roughly linear slope is formed when connecting the MPP points measured from the highest isolation level to the lowest. Utilizing this characteristic, a bipolar junction transistor BJT is used to implement a variable voltage reference that improves the accuracy of the maximum power point voltage when the insolation changes. The proposed circuit is simple and easy to implement and it can easily track the maximum power point without the need of a digital controller or PID controller, so the cost and circuit complexity is reduced.

A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach

A novel design of a photovoltaic grid connected inverter is presented. The inverter is realized so as to attain current source characteristics for simple connectivity to the grid. The advantage of this realization is a simplified system due to the fact that no voltage amplitude matching or modulating is required and the wave shape of the grids voltage has no influence. The inverter's current polarity must be taken care of in order to match the voltage polarity of the grid. The simple realization yields simplicity in the switching circuitry and control. The design includes an H-bridge topology that is switched by the AC grid itself; therefore no special synchronization scheme is required. Consequently, this simple switching scheme inherently suits all wave shapes and all frequency changes of the power grid. The system is combined with a maximum power point tracker (MPPT) with only output current control.