Perturbation Observation Method Research Articles

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point.

Research on Hybrid Approach for Maximum Power Point Tracking of Photovoltaic Systems under Various Operating Conditions

Based on the characteristics of the whale optimization algorithm (WOA) and perturbation observation (P&O) method, this paper proposes a novel hybrid approach called the improved chaotic whale optimization combined with perturb and observe (ICWOA-P&O) method for maximum power point tracking (MPPT) control to solve the challenge of low efficiency in photovoltaic (PV) power generation under local shadows. First, the ICWOA is used for a global search to quickly locate the position of the maximum power point (MPP). Then, the P&O method is used for a fine-grained local search to quickly track the position of the global maximum power point (GMPP) with low oscillation. To ensure accuracy, the tracking performance of the ICWOA-P&O method is comprehensively compared with the WOA-P&O, WOA, and PSO models under four conditions: uniform irradiance, static local shading, dynamic shading, and sudden changes in irradiance and temperature. The simulation results verify that under the above four conditions, the ICWOA-P&O method can track the MPP continuously and stably and greatly improves the convergence time and accuracy. Compared with the other three methods, the ICWOA-P&O method can effectively obtain the fastest tracking speed (less than 0.1 s), the highest tracking accuracy (more than 99.97%), the smallest relative error (less than 0.03%), and the smallest oscillation fluctuation. Finally, this study integrated the ICWOA-P&O algorithm into the designed MPPT controller hardware and established a practical PV experimental platform based on the ICWOA-P&O control algorithm for practical tests.

Maximum power point tracking controller for photovoltaic system based on chaos quantum particle swarm optimization – moth-flame optimization hybrid model

To convert photovoltaic arrays to solar energy in a more efficient way, this paper has proposed a maximum power point tracking controller model based on the chaotic quantum particle swarm-mothballing hybrid algorithm. First, the optimization of the particle swarm algorithm is designed to solve defects, such as premature maturity by using the quantum and chaotic strategies. The mothballing algorithm is introduced to help the model find global optimization-seeking more quickly. After that, further optimization was made to operate the tracking model in both offline and real-time parameters. The conductivity increment method and the perturbation observation method were adopted to effectively track the model under different temperatures and light intensities. Finally, the simulation and analysis experiments were carried out on the Simulink platform. The study’s proposed maximum power point tracking controller achieved a steady-state accuracy η2 of 99.84%. In summary, the study has proposed a hybrid intelligent algorithm with extraction of internal parameters. The maximum power point tracker based on the proposed method is proved to be both effective and accurate.

Integration of MPPT algorithms with spacecraft applications: Review, classification and future development outlook

The electric power system is one of the key subsystems of a spacecraft and is responsible for the acquisition, conversion, storage and control of energy during the spacecraft mission. Therefore, ensuring the adequacy and stability of energy sources has become an important part of the spacecraft design process. Currently, most spacecraft acquire energy through the solar cell arrays they carry, but the actual output power of the photovoltaic system is difficult to reach the peak power due to the influence of the on-orbit space environment such as alternating temperature changes and radiation. In order to improve the output efficiency of PV systems, researchers have proposed the maximum power point tracking method based on the operating characteristics of the PV cells, and the classical methods include the incremental conductance method, hill-climbing method, constant voltage method, and perturbation observation method. On its basis, in order to further improve the control accuracy and adaptability of MPPT under complex environment, related scholars have proposed several new MPPT algorithms, such as fuzzy algorithm and neural network algorithm based on the principle of intelligence, particle swarm optimization algorithm based on the principle of optimization, gray wolf algorithm and hybrid optimization algorithm based on multiple algorithms.This paper provides a comprehensive review of the topology, hardware architecture, mathematical models, and multiple DC-DC circuits of maximum power tracking PV systems; and reviews and summarizes the classical, intelligent, optimization, and hybrid types of maximum power point tracking methods, describing the principles, advantages, disadvantages, and applications of the different methods, respectively. Finally, an outlook on the future development and novel applications of maximum power point tracking methods for photovoltaic systems in aerospace is presented.

Detuning analysis and power tracking of dual‐ended resonant circuit based on improved variable‐step perturbation observation for wireless power transfer system

AbstractA power tracking method based on improved variable‐step perturbation observation approach has been proposed in this paper. This method is aimed at addressing the detuning issues caused by capacitor parameter drift in MCR‐WPT systems based on S‐S compensation circuits. Compared with traditional tuning methods, the proposed method has fast response, high accuracy, low complexity, and less prone to over‐tracking. Firstly, a mathematical model of the system based on the detuning factor has been established. Secondly, the impact of different detuning conditions on the system at the initial resonant frequency has been studied. Thirdly, the response characteristics of the system to different frequencies under different detuning conditions have been studied. Fourthly, based on the above researches, an improved variable‐step perturbation observation method based on the single‐step power drop factor has been proposed. Finally, an experimental platform was constructed, and relevant experiments were conducted. Experimental results validate the effectiveness of power tracking under different detuning conditions, with the lowest transmission efficiency being 81.51%.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

Design and hardware verification of photovoltaic converter based on adaptive P&O with snubber and BJT circuits

The efficient utilization of renewable energy is a key technology area of high domestic and international concern, and the research and development of DC–DC photovoltaic converters with high conversion efficiency is of great significance for improving performance and reducing the cost of solar power generation systems. In order to improve the conversion efficiency of solar energy, this paper proposes the design of a high-efficiency photovoltaic DC–DC converter with a non-isolated DC–DC converter as the object of the study. Solar power conversion is accomplished by designing a simple and reliable snubber circuit and a triode auxiliary circuit and tracking the maximum power point by combining the perturbation observation method with variable step size. The snubber circuit can effectively suppress the problems of pulse spikes and oscillations, and the triode auxiliary circuit prevents the reverse current and improves the switching speed, which reduces the switching loss and combines with the perturbation observation method with variable step size for fast and stable tracking of the maximum power point. In order to verify the feasibility of the converter, a 600 W prototype is designed. The experimental results show that using the snubber circuit reduces the pulse spike by 4.2 V and the overshoot is reduced by 4.3%. The maximum conversion efficiency is increased by 0.88% with the use of the transistor-assisted circuit, and the tracking efficiency of the MPPT is still stable under cloudy conditions. The maximum conversion efficiency of the prototype is finally measured to be up to 98.12%.

MPPT study of PV cells in complex environments

In operation, the solar power generating system’s output power from the photovoltaic array is sensitive to external conditions including light intensity and temperature, and displays typical non-linear characteristics. Hence, monitoring the maximum output power of PV cells in real time is necessary to guarantee that the maximum power output of PV cells is constantly maintained, enhancing the efficiency with which PV cells are used. Therefore, this paper is based on the immune algorithm, simulates that the biological immune system will be affected by the antigen infestation and produce resistance to carry out antigen recognition, memory, and other immune response reactions through MATLAB / Simulink simulation, and adopts the traditional perturbation observation method. The results of the simulation show that the Composite algorithm has the benefits of high tracking accuracy, fast tracking speed, stable tracking, and low false positives.

Perturbation Observation Method-Based Optimization Seeking Control of Soft-Switching and No Backflow Power for LCL Resonant-Type Dual Active Bridge DC–DC Converters

LCL-type resonant dual active bridge (LCL-DAB) DC-DC converters feature high gain, high power density, and low reactive current. To further improve the efficiency, based on the dual-phase shift (DPS) modulation method, the operating characteristics are firstly derived and the design regions of soft-switching and no backflow power are established, and then a perturbation observation method based optimization seeking control of soft-switching and no backflow power is proposed in this paper. In the starting process, the system firstly tracks the reference voltage according to the relationship between the inner and outer phase shift ratio obtained by the first harmonic approximation. Then, the outer phase shift ratio is perturbed based on the characteristics of soft-switching and backflow power, approaching the target phase shift ratio stepwise, while the inner phase shift ratio is calibrated to hold the output voltage. Both H bridges can operate in soft-switching mode, and on this basis, the backflow power was eliminated to the maximum extent. Finally, the simulation and experimental results verified the feasibility and effectiveness of the proposed method.

Analysis and Method Overview of Photovoltaic Cell MPPT Technology

This chapter mainly analyzes the principle of photovoltaic cell MPPT technology and various tracking algorithms, and compares and analyzes the three traditional classical tracking algorithms, perturbation observation method, incremental conductance method, constant voltage method, etc., as well as various improved algorithms evolved on their basis. At the same time, it also analyzes the intelligent control methods based on soft computing that have been studied more in recent years. For example, various intelligent control methods based on neural network control and fuzzy control. The advantages and disadvantages of the above methods are compared and analyzed. According to the shortcomings of these methods, the author puts forward his own improvement strategy, and verifies the superiority of the improved method by comparing and analyzing the improved method with the traditional method.

An Improved Perturbation Observation Method with Variable

Aiming at the problems of oscillation and misjudgment in traditional MPPT technology, the influence of voltage disturbance step size on the tracking of the maximum power point is studied. On this basis, an improved method based on the slope and space division of output characteristic curve is proposed. The voltage disturbance is carried out by using a fixed step length outside the set search interval, so that the photovoltaic cells can quickly work near the maximum power point. Reduce the early search time; When the photovoltaic cell is working near the maximum power point, the voltage disturbance is carried out with small steps to avoid the oscillation caused by repeated pulsation of the operating point around the maximum power point. Meanwhile, the search space is further divided to reduce the search interval. Static and dynamic lighting modes were simulated by Simulink.

Energy coordinated control of DC microgrid integrated incorporating PV, energy storage and EV charging

The power of photovoltaic (PV) and electric vehicles (EV) charging in integrated standalone DC microgrids is uncertain. If no suitable control strategy is adopted, the power variation will significantly fluctuate in DC bus voltage and reduce the system’s stability. This paper investigates the energy coordination control strategy for the standalone DC microgrid integrated with PV, energy storage, and EV charging. The specific research contents include the following: The maximum power point tracking (MPPT) control of the variable-step perturbation observation method is proposed to shorten the regulation time of the PV unit to reach the maximum power point; an improved droop control is designed based on the state of charge (SOC) of the batteries, and the power equalization and bus voltage recovery secondary controls are introduced to enhance the regulation effect of the DC bus voltage; a coordination control strategy based on the power difference is formulated to improve further the transient and steady-state performance of the DC bus voltage. The proposed coordination control strategy is applied to the integrated standalone DC microgrid model built by MATLAB/Simulink. The simulation results show that the proposed coordination control strategy can not only effectively improve the stability of the DC microgrid system but also reduce the capacity redundancy of the energy storage device.

Research on photovoltaic MPPT algorithm based on improved perturbation observation method

The connection of new energy, mainly solar energy, is closely related to the output of photovoltaic power supply. When the PV curve of photovoltaic output is optimally solved, it is easy to take the local optimal solution as the global optimal solution. Aiming at the shortcoming that the traditional perturbation observation method is only suitable for single peak tracking in maximum power tracking, the characteristics of photovoltaic output power are studied and analyzed, and the perturbation method is used to track the maximum power according to the distribution characteristics of each extreme point. Through the simulation analysis in MATLAB/Simulink environment, the results show that the improved algorithm has high accuracy and is easy to realize, and it has a good effect on solving the global optimal solution of photovoltaic output power.

Research on MPPT control strategy based on the Perturbation observation method

About traditional disturbance observation of larger changes in the external environment, the need for successive change step length, thus slow convergence speed of the shortcomings, based on traditional disturbance observation method, will be the main factors influencing the photovoltaic battery power curve (temperature, light intensity) is introduced into the control strategy of, when external conditions change dramatically, the method can directly reach the optimal duty ratio near, in this way, the slow convergence speed of the traditional perturbation observation method is effectively improved, and the dynamic tracking performance is optimized. In Matlab/Simulink, the improved control strategy model is built to verify that the improved control strategy can significantly improve the convergence speed and efficiency of the photovoltaic power generation system under the working condition of large changes in temperature and external illumination intensity.

Perturbation Observation Method Based on Fractional Order PID and Extended State Observer

Perturbation Observation Method Based on Fractional Order PID and Extended State Observer

MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

Application in photovoltaic MPPT based on improved hysteresis loop comparison method

In order to overcome the misjudgement and oscillation of the traditional perturbation observation method when tracking the maximum power point of the PV system, this paper proposes a fixed-zone variable-step hysteresis comparison algorithm. The bi-directional perturbation of the hysteresis loop can effectively avoid the occurrence of misjudgement; after setting the partition voltage threshold, different step lengths can be used to track specific regions, which can ensure the tracking speed and maintain the system stability. Finally, the proposed algorithm is validated in Matlab/simulink and the results show that the method is effective in tracking the maximum power point of the PV system in all aspects compared to conventional algorithms.

Global maximum power tracking algorithm for solar photovoltaic array under uneven illumination

Based on the analysis of the shortcomings of the existing maximum power point tracking (MPPT) method, a multi-peak MPPT tracking strategy is proposed based on the geometric characteristics of the output curve of photovoltaic array. This method takes advantage of the fact that the pv array U-I output curve has the characteristics of "the left slope changes slowly and the right slope changes quickly" at the local extrema point, quickly locates the voltage interval including the global maximum power point, and converts it into a perturbation observation method (perturb and observe, P&O) with higher tracking accuracy within this interval. Thus, the global maximum power point can be tracked. By comparing this algorithm with improved disturbance observation (Perturb, observe and check, POC), its application range was determined and applied flexibly. The algorithm is fast and can avoid missing peak value effectively.

DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

There are some problems in the photovoltaic microgrid system due to the solar irradiance-change environment, such as power fluctuation, which leads to larger power imbalance and affects the stable operation of the microgrid. Aiming at the problems of power mismatch loss under partial shading in photovoltaic microgrid systems, this paper proposed a distributed maximum power point tracking (DMPPT) approach based on an improved sparrow search algorithm (ISSA). First, used the center of gravity reverse learning mechanism to initialize the population, so that the population has a better spatial solution distribution; Secondly, the learning coefficient was introduced in the location update part of the discoverer to improve the global search ability of the algorithm; Simultaneously used the mutation operator to improve the position update of the joiner and avoid the algorithm falling into the local extreme value. The results of the model in Matlab showed that the ISSA can track the maximum power point(MPP) more accurately and quickly than the perturbation observation method (P&O) and the particle swarm optimization (PSO) algorithm, and had good steady-state performance.