Maximum Power Point Tracking Control Strategy Research Articles

A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC–DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions.

A Novel MPPT Heating Control Strategy Applied to the Induction Heating System

This study proposes an induction heating system with maximum power-point tracking (MPPT) control strategy. The system architecture adopts a 1.3 kW full-bridge series resonant circuit with a step-down transformer and adjusts the operating frequency by a microcontroller unit to improve the heating efficiency. Secondly, the proposed MPPT control strategy based on induction heating uses the relationship between the operating frequency and the system heating temperature to find the operating frequency corresponding to the maximum power point (MPP) quickly. Then, an additional hill-climbing algorithm adjusting the duty cycle is applied to reach the duty cycle corresponding to the MPP. Under the simulation and actual experimental measurement, the traditional control strategy has 76% and 68% at 500 and 750 degrees, respectively, and it takes 320 s for the system to reach 750 degrees. By contrast, the proposed MPPT control strategy achieves 96% efficiency when the system heating temperature is 500 and 750 degrees, and it only takes 120 s to reach the system heating temperature to 750 degrees. The contribution of this study is that the traditional full-bridge series resonant converter is implemented for the proposed induction heating system, where the proposed MPPT control strategy applied to the proposed induction heating system significantly has high efficiency, high stability, and high heating speed advantages, which can be accurately controlled.

A New Analytical MPPT-Based Induction Motor Drive for Solar PV Water Pumping System With Battery Backup

In this article, an analytical method is presented to achieve maximum power point tracking (MPPT) for a solar photovoltaic (PV) array-based water pumping system comprising of an induction motor drive (IMD). The gating signals are generated using the space vector pulsewidth modulation (SVPWM) method for three-phase inverter. This analytical MPPT control (AMPPTC) scheme exhibits fast dynamic performance and minimum overshoot with the IMD-based water pumping system. Additionally, the AMPPTC scheme provides absolute steady-state robustness under variations in solar irradiance/parameters that improves the stability of the system under a wide speed range of IMD and makes the system insensitive toward parameter variations. The dc-link voltage regulation is assured by three-phase inverter, and MPPT regulation is achieved by the boost converter. This two-stage PV system, with MPPT controlled boost converter and SVPWM-based three-phase inverter operating the pump, is modeled and simulated in Simulink environment of MATLAB. The performance and robustness of the AMPPTC-based PV water pumping system are corroborated through the laboratory experimentation.

An Improved Mechanical Sensorless Maximum Power Point Tracking Method for Permanent-Magnet Synchronous Generator-Based Small Wind Turbines Systems

This article proposes an improved maximum power point tracking (MPPT) method without mechanical and voltage sensors for small wind turbines (SWT) systems. The technique is suitable for SWT systems that employ a permanent-magnet synchronous generator (PMSG), a three-phase diode bridge rectifier, and a dc–dc converter, which is one of the most popular configuration for this kind of system. The proposed MPPT extracts the maximum available power from the PMSG operating with variable speed, and it is based on the output power characteristic in relation to the output current of the rectifier. The article proves that a constant output current in the rectifier ensures extracting the maximum electrical power available in the SWT system, independent of the rotational speed. Additionally, this constant current value depends solely on the PMSG characteristics. Therefore, the proposed MPPT control strategy is a simple current mesh with a constant reference that is implemented in a dc–dc converter connected in series to the rectifier, which is responsible to guarantee a constant current in the rectifier output. The proposed MPPT method was corroborated through simulation and experimental results in a 1.5-kW SWT. The tracking efficiency of the proposed MPPT was close to 97.64%.

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Maximum power point tracking (MPPT) is an effective method to improve the power generation efficiency and power supply quality of a proton exchange membrane fuel cell (PEMFC). Due to the inherent nonlinear characteristics of PEMFC, conventional MPPT methods are often difficult to achieve a satisfactory control effect. Considering this, artificial bee colony algorithm combining fuzzy control (ABC-fuzzy) was proposed to construct a MPPT control scheme for PEMFC. The global optimization ability of ABC algorithm was used to approach the maximum power point of PEMFC and solve the problem of falling into local optimization, and fuzzy control was used to eliminate the problems of large overshoot and slow convergence speed of ABC algorithm. The testing results show that compared with perturb & observe algorithm, conductance increment and ABC methods, ABC-fuzzy method can make PEMFC obtain greater output power, faster regulation speed, smaller steady-state error, less oscillation and stronger anti-interference ability. The MPPT scheme based on ABC-fuzzy can effectively realize the maximum power output of PEMFC, and plays an important role in improving the service life and power supply efficiency of PEMFC.

Analisis Kinerja Metode Fuzzy Teroptimasi PSO untuk Strategi Kendali MPPT pada Sistem Solar Photovoltaic

A fuzzy control system has been widely used in various problem solving. Its performance can be optimized using particle swarm optimization (PSO). This performance can be proven by applying it to the maximum power point tracking (MPPT) control strategy on solar photovoltaic systems. Solar photovoltaic power generation systems are increasingly popular because they are clean and renewable energy sources. The power generated by solar photovoltaic is strongly influenced by solar irradiation and the load carried. In order to obtain maximum power output, an MPPT control strategy is needed. An MPPT control strategy based on fuzzy and PSO hybrid control systems is proposed in this research. The fuzzy-PSO method selects and produces the optimal duty cycle for the boost dc-dc converter in a solar photovoltaic system. Variable duty cycle due to solar irradiation and load changes can be conditioned by the fuzzy-PSO-based MPPT method to extract maximum power. The research results show that the fuzzy-PSO method can control the solar photovoltaic output voltage through a dc-dc converter to produce maximum power at various solar irradiations. Test result by applying a resistive load produces output power at the maximum point. The best result is obtained in the 100 Ohm load test: the response time of 0.0818 seconds and excellent robustness.

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

AbstractSolar energy is the most techno‐economically viable renewable source of energy and can be effectively converted into electrical power by Photovoltaic (PV) systems. As partial shading (PS) may reduce the harvested energy of PV systems, this paper proposes a novel tunicate swarm algorithm (TSA) based MPPT (maximum power point tracking) strategy to tackle the PS issue. More specifically, the simple and effective modeling of TSA with a search and skipping (SAS) scheme is utilized to minimize the tracking time and search area. The SAS scheme can discard the voltage range lacking global maximum power point (GMPP) and significantly reduce computation time. Consequently, power tracking, tracking time, and robustness can be greatly enhanced. The performance of the proposed TSA strategy is comprehensively analyzed against state‐of‐the‐art techniques, including incremental conductance (InC), improved particle swarm optimization (IPSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA), through detailed case studies, which include standard array configurations, PS conditions, varying irradiance patterns, fast‐changing temperature, and the field atmospheric data. TSA is further validated on a low‐cost hardware setup, confirming its superior performance. The results provide insightful validation of the practicality of the proposed TSA strategy in the real‐world applications.

Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter

An improved Maximum Power Point Tracking (MPPT) method based on a purely mechanical wave energy converter (WEC) of gyroscope precession is proposed. The method adopts dynamic perturbation step adjustment, which improves the stability of power output and reduces steady-state oscillation. The paper introduces the principle of the device, establishes the mathematical model, and obtains the complete expression of power. The effect of wave frequency, pitch amplitude, power take-off (PTO) damping coefficient, and flywheel rotating speed on power output is analyzed. The output regression equation is established, and the extraction conditions of the maximum power are summarized and predicted. Aiming at the time-varying nature of actual ocean waves, a variable step size modified maximum power point (MPP) tracking control algorithm based on perturbation and observation (P&O) method is proposed. The algorithm has a unique technology to dynamically change the perturbation size, which not only improves the dynamic response but also reduces the oscillation. Besides, the boundary conditions ensure that the algorithm will not deviate from the motion trajectory, and the average filtering method and steady-state judgment can further reduce steady-state oscillation. In various ocean conditions, the proposed method has better output stability compared with other variable step size algorithms. Finally, different wave working conditions are given in the experiment, and the results verify the effectiveness of the proposed MPPT control strategy in experimental equipment. The device will be suitable for distributed power sources in small islands and ports.

Research on MPPT control strategy of photovoltaic cells under multi-peak

Under the condition of partial shadow occlusion, the P-U curve of the photovoltaic array exhibits the characteristics of multi-peak output. The traditional maximum power point tracking (MPPT) algorithm is easy to fail, and the particle swarm algorithm, as multi-objective optimization algorithm, is characterized by its low volatility and fast convergence speed. It is applied to the optimization process of multi-peak photovoltaic cells. Aiming at the multi-peak output of the photovoltaic array, this paper builds a simulation model based on the standard particle swarm algorithm and the perturbation and observation method through MATLAB/Simulink. The simulation results show that compared with the traditional perturbation and observation method, the standard particle swarm algorithm can reduce fluctuations when the power is stable and can quickly track the output multi-peak maximum power point when the light intensity changes suddenly.

Robust Control and Kalman MPPT for Grid-Assimilated Wind Energy Conversion System

A new robust control and Kalman maximum power point tracking (MPPT) is proposed for a grid-connected wind energy conversion system (WECS) to regulate the active and reactive power. During the operation, the wind speed and parameters of the WECS may vary; thus, the power control becomes a difficult task. The proposed Kalman-filter-based MPPT extracts the maximum available power effectively from the WECS during wind speed variations. The proposed robust control with a mixed $H_{2}/H_{\infty }$ optimal+state feedback recurrent neural network (MOSRNN) control scheme regulates the amount of power with an improved quality and stability even during the system uncertainties. The recurrent neural network controller is trained with an optimal variable step-size normalized least-mean-square algorithm. The main objectives of this work are as follows: developing the Kalman MPPT for a high-gain boost converter to extract maximum power from the WECS; designing the MOSRNN control scheme for an LCL -filtered voltage source inverter of the WECS; analyzing the outcomes of the WECS with the proposed Kalman MPPT and MOSRNN control scheme during the speed variation, load change, and parameter uncertainty; and testing the practical feasibility of the proposed distributed generation system in dSPACE environment.

Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application

An artificial neural network (ANN) based maximum power point tracking (MPPT) technique for proton exchange membrane fuel cell (PEMFC) is analysed and proposed in this paper. The proposed ANN technique employs Radial basis function network (RBFN) based MPPT strategy to extract the maximum available power from fuel cell in different operating condition. In order to achieve high voltage rating, a novel high step up DC/DC converter is incorporated in the proposed configuration. To validate the performance of the proposed configuration, the result is compared with different DC/DC converter and MPPT control strategy. The proposed system is simulated in MATLAB/Simulink platform to analyse the performance of the system.

The global peak forecasting method for PV array based on the conservation of energy at uniform and partial shading

In order to extract as much power as possible from the costly PV power system, the maximum power point tracking (MPPT) is necessary. It is well known that the output characteristics of PV array are nonlinear, the MPPT control scheme at same irradiation is easy because of there is only one maximum power point (MPP). However, the P–V output curves of the PV array display the multiple peaks with several local peaks (LPs) and one global peak (GP) at partial shading. Rapid fluctuation of the shading and improper physical constraint make the MPPT method difficult. Generally, there will exist several peaks due to the passing cloud, shading of buildings, dust, and excreta of passing birds. In this paper, a novel forecasting technology using the law of conservation of energy is proposed to roughly track the GP of a PV array which located in the adjacent area of accurate GP. The proposed strategy is easy to combine with the conventional MPPT method and rapidly gain the actual GP. Study results and experiment tests demonstrate that the proposed algorithm can perform on a real PV power system under uniform or complex irradiation conditions.

Backstepping Based Super-Twisting Sliding Mode MPPT Control with Differential Flatness Oriented Observer Design for Photovoltaic System

The formulation of a maximum power point tracking (MPPT) control strategy plays a vital role in enhancing the inherent low conversion efficiency of a photovoltaic (PV) module. Keeping in view the nonlinear electrical characteristics of the PV module as well as the power electronic interface, in this paper, a hybrid nonlinear sensorless observer based robust backstepping super-twisting sliding mode control (BSTSMC) MPPT strategy is formulated to optimize the electric power extraction from a standalone PV array, connected to a resistive load through a non-inverting DC–DC buck-boost power converter. The reference peak power voltage is generated via the Gaussian process regression (GPR) based probabilistic machine learning approach that is adequately tracked by the proposed MPPT scheme. A generalized super-twisting algorithm (GSTA) based differential flatness approach (DFA) is used to retrieve all the missing system states. The Lyapunov stability theory is used for guaranteeing the stability of the proposed closed-loop MPPT technique. The Matlab/Simulink platform is used for simulation, testing and performance validation of the proposed MPPT strategy under different weather conditions. Its MPPT performance is further compared with the recently proposed benchmark backstepping based MPPT control strategy and the conventional MPPT strategies, namely, sliding mode control (SMC), proportional integral derivative (PID) control and the perturb-and-observe (P&O) algorithm. The proposed technique is found to have a superior tracking performance in terms of offering a fast dynamic response, finite-time convergence, minute chattering, higher tracking accuracy and having more robustness against plant parametric uncertainties, load disturbances and certain time-varying sinusoidal faults occurring in the system.

Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid.

Renewable energy resources connected to a single utility grid system require highly nonlinear control algorithms to maintain efficient operation concerning power output and stability under varying operating conditions. This research work presents a comparative analysis of different adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) control schemes for maximum power point tracking (MPPT) of PV subsystem tied to a smart microgrid hybrid power system (SMG-HPS). The proposed schemes are differentiated based on structure and mathematical functions used in FRANF embedded in the FBL model. The comparative analysis is carried out based on efficiency and performance indexes obtained using the power error between the reference and the tracked power for three cases; a) step change in solar irradiation and temperature, b) partial shading condition (PSC), and c) daily field data. The proposed schemes offer enhanced convergence compared to existing techniques in terms of complexity and stability. The overall performance of all the proposed schemes is evaluated by a spider chart of multivariate comparable parameters. Adaptive PID is used for the comparison of results produced by proposed control schemes. The performance of Mexican hat wavelet-based FRANF embedded FBL is superior to the other proposed schemes as well as to aPID based MPPT scheme. However, all proposed schemes produce better results as compared to conventional MPPT control in all cases. Matlab/Simulink is used to carry out the simulations.

Nonlinear robust integral backstepping based MPPT control for stand-alone photovoltaic system.

PV (Photovoltaic) cells have nonlinear current-voltage (I − V) and power-voltage (P − V) characteristics with a distinct maximum power point (MPP) that entirely depends on the ambient meteorological conditions (i.e. solar irradiance and temperature). Hence, to continuously extract and deliver the maximum possible power from the PV system, under given meteorological conditions, the maximum power point tracking (MPPT) control strategy needs to be formulated that continuously operates the PV system at its MPP. To achieve this goal, a hybrid nonlinear, very fast and efficient MPPT control strategy, based on the robust integral backstepping (RIB) control, is formulated in this research article. The simulation testbed comprises a standalone PV array, a non-inverting buck-boost (NIBB) DC-DC power converter, a purely resistive and a dynamic load (sound system). The proposed MPPT control scheme consists of two loops, where the first loop generates the real-time offline reference peak power voltage through an adaptive neuro-fuzzy inference system (ANFIS) network, which is then utilized in the second loop as a set-point value for generating a control signal and then forcing the PV system to be operated at this set-point by continuously adjusting the duty ratio of the power converter. This control strategy exhibits no overshoot, fast convergence, good transient response, fast rising and settling times and minimum output tracking error. The MATLAB/Simulink platform is used to test the performance of the proposed MPPT strategy against varying meteorological conditions, plant current and voltage faults and plant parametric uncertainties. To validate the superiority of the proposed control strategy, a comparative analysis of the proposed control strategy is presented with the nonlinear backstepping (B), integral backstepping controller (IB) and conventional PID and P&O based MPPT controllers.

An improved MPPT control strategy based on incremental conductance method

Photovoltaic cells efficiency can be effectively improved by maximum power point tracking (MPPT) technology. An improved MPPT control strategy is proposed to solve the current problems of poor convergence speed and accuracy of incremental conductance method. In this method, the P–U characteristic curve is divided into three sections: non-MPP sections, MPP-like section and MPP sections. In the non-MPP section, the constant voltage method is adopted to reduce the tracking time. In the MPP-like section, the incremental conductance method is adopted and its step size is improved, which effectively reduces the tracking time. In MPP section, particle swarm algorithm is adopted to improve tracking accuracy. Taking light intensity and temperature variation as examples, the proposed method and the traditional method are simulated respectively. Simulation results show that compared with the constant voltage method, the accuracy can be improved by more than 4% when the temperature or light intensity is changed, while maintaining the tracking speed. Compared with the traditional incremental conductivity method, the method can reduce the tracking time by 33% and improve the tracking accuracy by 1% when the light intensity or temperature changes.

Dynamic Modeling and Analysis of Buck Converter Based Solar PV Charge Controller for Improved MPPT Performance

Maximum power point tracking (MPPT) control is a key functionality in solar photovoltaic (PV)-based power conversion systems. A variety of perturbative MPPT control schemes are available in the literature, many of which are voltage-based techniques wherein the PV bus voltage is perturbed and set to the required level by an appropriate converter control that achieves the MPP tracking. However, a comprehensive plant model of the PV-fed converter system and a systematic control design of the PV bus voltage loop that facilitates the design of the MPPT control is not available in the literature. In this article, a detailed small-signal model is proposed for a single-stage PV-fed buck converter that acts as a battery charge-controller. The effects of parasitic storage elements present in the PV source as well as the interconnecting cable from rooftop are discussed in detail, along with the impact of series–parallel connection of the PV modules for power scaling. A full-order as well as a reduced-order model of the system is proposed and the various relevant transfer-functions are analytically derived. Based on this model and the control to converter input bus voltage transfer-function $\widetilde{v}_{\text{in}}/\widetilde{d}$ , a systematic design procedure for the PV bus voltage controller is proposed. It is shown that such a design facilitates the selection of perturbation period of a typical MPPT control algorithm for an improved tracking performance. The steps involved in the system model development and the control design are generalized and can be extended to other converter topologies as well. Active transfer-function measurements and experiments conducted on a 1-kW charge-controller hardware prototype validate the accuracy of the proposed model and the tracking performance of the MPPT control.

Modeling and Analysis of Maximum Power Tracking of a 600 kW Hydraulic Energy Storage Wind Turbine Test Rig

An innovative wind turbine with a particular hydraulic transmission and energy storage system is proposed in this paper. The purpose of applying the hydraulic transmission is to remove the gearbox and power converter of traditional wind turbine and cooperate on wind resource storing with the energy storage system. To overcome the volatility and intermittence shortcomings of wind and improve the output power quality, hydraulic accumulators are used as the energy storage device for wind energy regulation. The original gearbox and generator in the nacelle of a Micon 600 wind turbine were removed and replaced with a hydraulic pump to make a test rig for the investigation into maximum power point tracking (MPPT) of this hydraulic wind turbine concept. The mathematical model of the entire test system is established according to the four function modules. The MPPT control strategy based on the tip speed ratio (TSR) is adopted and a control system containing three closed-loop controls is designed to achieve maximum wind power extracting and produce constant frequency power generation. Ultimately, the dynamic response of rotor speed control is revealed under step change of wind speed and the maximum power tracking performance of the 600 kW hydraulic energy storage wind turbine test bench is simulated and analysed by subjecting to turbulent speed condition. The simulation results demonstrate that the rotor of the wind turbine can run at the expected optimal speed depending on wind speed, and the wind power utilization coefficient of the unit is stabilized at about the maximum value.

A New Adaptive Maximum Power Point Controller for a Photovoltaic System

Controlling extraction of maximum power from photovoltaic (PV) panels is very important for an efficient PV power system. This maximum power control can be performed by using a maximum power point tracking (MPPT) algorithm. We propose anew self-tuning based adaptive MPPT algorithm that is designed with an incremental-proportional-integral-derivative (IPID) controller. Power extraction from a PV panel is greatly influenced by external weather conditions. The IPID-controller monitors the rate of change in control signal during rapid variation in input weather conditions for maintaining stability of a PV system. The proposed adaptive MPPT controller is comprised of two components, namely, parameter estimation of the MPPT converter system and control design strategy for tuning the IPID-controller. In this paper, a recursive least square algorithm and incremental generalized minimum variance (IGMV) control law have been used for parameter estimation and tuning the gains of the IPID-controller, respectively. The efficacy of the proposed MPPT algorithm is verified on comparing its performance with that of the Perturb and observe (P&O) algorithm. From the simulation and experimental results, it is envisaged that the efficacy of the proposed adaptive MPPT control scheme exhibits superior performance as compared to P&O MPPT algorithm.

A variable-weather-parameter MPPT control strategy based on MPPT constraint conditions of PV system with inverter

The maximum power point (MPP) may be not tracked successfully because of the constraint conditions among some circuit parameters. To solve this problem, in this paper, the maximum power point tracking (MPPT) constraint conditions of photovoltaic (PV) system with inverter are found by analyzing its integrated mathematical model. Based on them, a variable-weather-parameter (VWP) MPPT control strategy is proposed. By the use of it, the complex control for the combination of the DC/DC converter and inverter can be avoided, which ensures the lower hardware cost, shorter design period and better MPPT performance. Finally, some simulation experiments show that the proposed MPPT constraint conditions are accurate and reasonable regardless of the varying irradiance, temperature or load, verify that the proposed MPPT control strategy is feasible and available to track the MPP successfully, and illustrate that there is a better MPPT performance than the conventional perturb and observe (P&O) method and fuzzy control method.