Maximum Power Point Tracking Control Strategy Research Articles

An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor

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.

Research on MPPT control strategy based on CCAOA algorithm

ABSTRACT Photovoltaic arrays operating under partial shading conditions (PSCs) may exhibit multiple peaks in the power-voltage curve of the PV system output. The conventional maximum power point tracking (MPPT) algorithm cannot address the multipeak problem and demonstrates slow convergence speed, often falling into a local optimum. Consequently, this study proposes a collaborative and cosine arithmetic optimisation algorithm (CCAOA). First, a cosine factor is introduced into the mathematical optimisation. Circle chaotic mapping and cross-variance strategy were incorporated to boost the diversity and randomness within the algorithm population followed by a cooperative search strategy involving addition and subtraction to enhance the algorithm’s local search capability, thereby accelerating its convergence speed. We assessed the effectiveness of the CCAOA using six IEEE standard test functions. The CCAOA outperforms other algorithms in terms of convergence speed and accuracy. Furthermore, when applied to the MPPT control strategy, the CCAOA’s performance is validated through simulations and experiments. MPPT can be performed within approximately 0.3 to 0.4 s under static conditions, with 99.98% efficiency. Under dynamic conditions, only approximately 0.4 s is required for MPPT, thereby achieving a maximum efficiency of 99.99%. Conversely, the AOA, TSO, and PSO algorithms require over 0.4 s for MPPT and can be trapped in local optima under these two conditions. Experiments were conducted using a PV simulator and DC/DC converter, and the algorithm achieved efficiencies of 99.22%, 99.33%, and 99.54% under uniform irradiation and two PSCs, respectively.

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.

Adaptive integral sliding mode MPPT control for wind turbines with fixed‐time convergence

AbstractIn the context of increasing renewable energy penetration and the imperative for efficient utilization, this paper explores the application of maximum power point tracking (MPPT) control for wind turbines. The objective is to optimize wind power generation by maximizing energy capture efficiency while addressing challenges associated with behind‐the‐meter renewables, load flexibility, and encouraging prosumers' participation. This paper proposes an adaptive integral sliding mode MPPT control strategy for wind turbines in the presence of external disturbance and model uncertainties. A new fixed‐time integral sliding mode manifold is constructed, such that the singularity problem can be avoided in the controller design without using any piecewise continuous functions. Then, an adaptive fixed‐time MPPT controller is developed to ensure that the tracking error can converge to a small neighbourhood around zero in fixed time, where the upper bound of the convergence time can be pre‐set based on actual engineering needs by selecting appropriate parameters. By designing update law, no prior knowledge on the upper bound of the lumped uncertainties is required anymore in advance. Stability analysis of the closed‐loop system is conducted using Lyapunov stability theory, and the effectiveness of the proposed strategy is validated through numerical simulations.

Design and analysis of improved mountain gazelle optimization tuned PID and FOPID controllers for PV MPPT system

The solar photovoltaic (PV)-based Maximum Power Point Tracking (MPPT) systems have gained popularity in recent times. This work proposes the improvement and implementation of a newly introduced optimization technique, the Improved Mountain Gazelle Optimization (IMGO) algorithm, for tuning the Fractional Order Proportional-Integral-Derivative (FOPID) and Proportional-Integral-Derivative (PID) controllers for the MPPT control strategy. The performances of the controllers were evaluated with reference to error criteria and settling time of the response. The performance parameters mentioned above are compared with those of PID and FOPID controllers tuned using Genetic Algorithm (GA) and Grey-Wolf Optimization (GWO) algorithms. The simulation study was carried out in the MATLAB/SIMULINK environment. The analysis found that the FOPID controller tuned using the Improved Mountain Gazelle Optimization algorithm provides better results in terms of settling time and error when compared to the PID controller.

A stochastic variance reduction gradient-based GSO-ANFIS optimized method for maximum power extraction of proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cells (PEMFCs) play a pivotal role in the clean energy landscape, yet their efficiency is contingent upon effective power optimization. This paper presents Maximum PowerPoint Tracking (MPPT) control schemes for PEMFCs, focusing on a ground-breaking methodology. Traditional MPPT controllers are instrumental in maintaining optimal performance; however, they often struggle with dynamic operating conditions. In response to this challenge, this research work presents a pioneering MPPT control scheme employing a stochastic variance reduction gradient system. The novelty of this approach lies in its fusion with the Glow Swarm Optimization (GSO) and the Adaptive Neuro Fuzzy Inference System (ANFIS), resulting in a robust hybrid controller. In the pursuit of optimizing the PEMFC system, the proposed GSO-ANFIS controller is subjected to rigorous testing under dynamic variations in both PEMFC temperature and load. Notably, PEMFCs, due to fluctuations in pressure and temperature, exhibit stochastic behaviour, forming a Gaussian surface. In this research, the popular Perturb and Observe (P&O) and Incremental Conductance methods are evaluated alongside the newly introduced GSO-ANFIS model. The proposed GSO-ANFIS controller outperforms its counterparts, showcasing an impressive accuracy level of 89.97%. In contrast, the Artificial Neural Network (ANN) achieves 80.33% accuracy, and the standalone ANFIS controller attains 86.5% accuracy. This disparity underscores the efficacy and potential of the novel hybrid approach, which not only adeptly handles the stochastic nature of PEMFCs but also significantly enhances accuracy in power optimization. This research not only contributes a valuable addition to the field of MPPT control but also offers a promising trajectory for the future development of efficient and reliable PEMFC systems.

Laboratory Tests Assessment of a Mechanical Sensor-less MPPT Control Strategy for Tidal Turbines

The purpose of this study is to demonstrate through towing tank experiments the effectiveness of a novel sensor-less Maximum Power Point Tracking (MPPT) control for Tidal Stream Turbines (TST) under fluctuations of the onset flow. Ocean energies will play a crucial role in the renewable energy sector over the next decades. Instream turbines for tidal currents are a rapidly maturing technology to exploit a highly predictable energy source. However, short-term fluctuations on the inflow velocity caused by waves or turbulence determine fatigue loads that affect system reliability. Power control strategies to maximize the energy yield can be also used to mitigate the effects of transient loading on drivetrain components. Aim of this paper is to present a straightforward and robust MPPT control method based on the linear relationship between the current and voltage squared of the generator's DC outputs. The method requires pre-determined turbine characteristics to establish the control reference that is effective across operating conditions. The proposed MPPT model was derived mathematically through linearization and simplifications of the turbine power conversion system and validated by model tests carried out in the wave-towing tank facility of CNR-INM in Rome, Italy, using the 1.5 m diameter Tidal Turbine Testing (TTT) device developed at the Queen’s University Belfast (QUB). In the study, a conventional TSR control method was also considered in order to perform a comparative analysis of system response to inflow speed fluctuations with time scales comparable to turbine revolution periods. TSR control was tested using two control references: TSR = 5 (design point) and TSR = 6 (over-speed zone) to verify the operation of the turbine under different loading conditions. The tests were conducted in two scenarios: calm water (steady state) and unsteady inflow with a regular (sinusoidal or monochromatic) waveform, with amplitude chosen to simulate an extreme wave case. The power output was measured from the turbine during regular wave conditions and compared to results from steady flow to assess the impact of wave-induced velocity on turbine performance (Fig. 1). Test results showed that by using the proposed MPPT control strategy, the algorithms converged to the maximum power coefficient (Fig. 2), which validates the proposed methodology. Results also demonstrated the capability of the proposed MPPT to significantly reduce mechanical loads fluctuations as compared to the TSR control. In the full-length paper, the proposed MPPT control strategy is outlined, the test methodology, set-up and conditions are described, and main results are presented and discussed.

Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

Design and Modeling of Hybrid Electric Vehicle Powered by Solar and Fuel Cell Energy with Quadratic Buck/Boost Converter

Considering technological advancements and international standards mandating reduced greenhouse gas emissions, automobile manufacturers have shifted their focus toward innovative technologies related to fuel-cell electric vehicles. Despite having an all-electric powertrain, fuel cell electric vehicles (FCEVs) utilize a fuel cell stack as their energy source, which runs on hydrogen and results in the emission of only water and heat. Due to the absence of tailpipe pollutants, fuel-cell electric vehicles are considered zero-emission vehicles. Among fuel cell types, low-temperature and low-pressure fuel cells, such as Proton Exchange Membrane Fuel Cells (PEMFCs), are well-suited for vehicular applications as they exhibit high power density, operate at lower temperatures (60-80°C), and are less prone to corrosion than other types of fuel cells. The main objective of this paper is to investigate solar-assisted electric fuel cell vehicles that efficiently integrate the fuel cell system with an electrolyzer and solar power to fulfill the fluctuating power demands of the electric motor and auxiliary systems. A novel EV configuration with a fuel cell, electrolyzer and onboard PV cell is proposed. An onboard PV cell can assist the fuel cell when the irradiation is enough to generate the power. During the idle conditions of vehicles, PV-generated power can be converted into chemical energy using an electrolyzer and generated hydrogen can be stored in a hydrogen tank. To match the voltage required by the motor and sources a quadratic bidirectional buck-boost converter is employed. The proposed configuration is examined by considering variable irradiance and variable speed values. To obtain the maximum power output from the photovoltaic (PV) panel, a Maximum Power Point Tracking (MPPT) algorithm is employed to regulate the PV system. To enhance the efficiency and cost-effectiveness of PV systems, an enhanced version of the incremental conductance algorithm is utilized as the MPPT control strategy. Outer voltage and inner current control are adopted to regulate the DC output voltage of the QBBC converter. An indirect vector-controlled induction motor is used as vehicle drive. Simulations are performed to investigate the proposed EV configuration in MATLAB/SIMULINK.

Quad-Input Single-Resonant Tank LLC Converter for PV Applications

A quad-input LLC converter for photovoltaic (PV) application is proposed in this paper. In this topology, a single LLC resonant tank is utilized to transfer the energy from four PV panels to the output by using two Y configurations of switches. The PWM generation of all these switches depends on switching frequency and three phase shifts. An MPPT control strategy based on Perturb and Observe (P&O) method has been proposed in this paper to ensure the independent maximum power point tracking (MPPT) for all PV panels. The zero-voltage switching (ZVS) across all the switches for a wide input range and load variations is achievable with this topology. A detailed mode-by-mode analysis followed by the switching waveforms has been illustrated, which led to the gain equation derivation. In that context, a state plane trajectory has also been drawn according to the various modes of LLC topology. Finally, a 500W prototype has been built and tested to verify the operation of the converter. The achieved peak efficiency of the converter is 94.3% when all four PV panels track their maximum power point (MPP) individually.

A Distributed Multimode Control Strategy for the Cascaded DC–DC Converter Applied to MVAC Grid-Tied PV System

The cascaded dc–dc converter is a popular topology for large-scale medium-voltage photovoltaic (PV) system with the advantages of high voltage conversion ratio, high efficiency, great flexibility, and independent control capability. However, the absence of optimal string maximum power point tracking (MPPT) operation under power mismatch is a big issue in common centralized control. Furthermore, the cost and time delay are unacceptable resulting from the distance among PV strings in the large-scale PV station and the cascaded dc–dc converter needs to adopt the distributed control method without real-time communication. To this end, by introducing other necessary constraints including voltage constraints, power constraints, and gain constraints on the basis of the distributed MPPT control strategy, a distributed multimode control scheme is proposed in this article. Each submodule (SM) can be switched flexibly between multiple operation modes to maximize the extracted solar energy. Individual string MPPT and autonomous distribution of output voltage are achieved without communication under the proposed distributed control strategy. In this way, optimal operation range is wider, mode switching is simpler, and applicability and robustness are improved. A downscaled prototype and a 35-kV/6-MW simulation system are constructed to verify the effectiveness of the proposed control strategy.

Maximum Power Point Tracking in Photovoltaic Systems Based on Global Sliding Mode Control with Adaptive Gain Scheduling

Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. For smaller photovoltaic (PV) systems, any improvement will not really be worth mentioning as an achievement. However, for large solar farms, even a fractional improvement will eventually create a significant impact. This paper presents an MPPT control scheme using global sliding mode control (GSMC) with adaptive gain scheduling. In the two-loop controller, the first loop determines the maximum power point (MPP) reference using online calculations, while the GSMC with adaptive gain scheduling in the second loop adjusts the boost converter’s pulse width modulation (PWM) to force the PV system to operate at the MPP with improved performance. The adaptive gain scheduling regulates the gain of the switching control to maintain the controller performance over a wide range of operating conditions, while GSMC guarantees the system robustness throughout the control process by eliminating the reaching phase and improving MPPT performance. The overall PV system also has Lyapunov stability. Furthermore, the robustness analysis of the proposed controller is also performed under load variations and parametric uncertainties at various temperatures and irradiances. In the simulations, the proposed MPPT control scheme has shown faster response than other controllers, reaching the set point with rise time 0.03 s as compared to 0.07 s and 0.13 s for quasi sliding mode control (QSMC) and conventional sliding mode control (CSMC), respectively. The proposed controller showed an overshoot of 1.2 V around a steady state value of 21.9 V as compared to 1.51 V and 1.45 V, respectively, for QSMC and CSMC for a certain parametric variation. Furthermore, the proposed controller and the QSMC-based scheme showed a steady-state error of 0.3 V, while the CSMC-based approach has a more significant error. In conclusion, the proposed MPPT control scheme has a faster response and low tracking error with minimal oscillations.

Improved torque compensation control based-maximum power point tracking strategy for large scale floating offshore wind turbines

The main problems of Floating Offshore Wind Turbines (FOWT) are the slow dynamic response of Maximum Power Point Tracking (MPPT) control and the low wind energy conversion efficiency. This paper proposes an Improved Torque Compensation (ITC) control strategy, which includes three parts. Firstly, the Torque Compensation (TC) strategy is optimized to mitigate the large fluctuation in output power caused by the increased response speed. Secondly, the control gain for ITC is reasonably adjusted by the Fuzzy Inference System (FIS) based on the rotor speed acceleration and the platform pitch motion acceleration. Thirdly, the power coefficient curve of the Wind Turbine (WT) is optimized, so that the WT performs blade pitch operation below the rated wind speed. Not only further improve the wind energy conversion efficiency of the WT, but also solve the problem of increasing the platform pitch angle caused by ITC. The above three parts of the ITC strategy finally are applied to the MPPT control for the FOWT under the action of wind and waves. The effectiveness of the ITC control strategy is verified by comparing with the three existing MPPT control strategies.

Maximum power point tracking algorithms for wind power generation system: Review, comparison and analysis

AbstractWind energy is one of the most important clean energies and the variable speed constant frequency technology is widely used in wind energy conversion systems. Maximum power point tracking (MPPT) is essential for a variable speed constant frequency wind power generation system. Concerning the current research on the MPPT algorithm, this paper studies the principle, characteristics, and reported improvement strategies of principal algorithms. Through the comparison of simulation results for selected control algorithms, the improved optimal torque control algorithm has been found to be the best MPPT algorithm for wind power generation systems because of its simplicity and efficiency. On this basis, further corresponding simulation runs are carried out to analyze the effect of the wind speed fluctuation characteristics on the systematic dynamic performance and power generation efficiency. The results show that the average wind speed, wind fluctuation frequency, and wind fluctuation amplitude can affect the performance of system operation and the efficiency of wind energy capture in different degrees, which has a great practical significance for the research of MPPT control strategy.

Design and Analysis of Rate Predictive Fractional-Order Sliding Mode Controller (RP-FOSMC) for MPPT and Power Regulation of DFIG-based Wind Energy Conversion System (WECS)

Now a days, maximum power point tracking (MPPT), power regulation and transient load reduction are the major issues in the WECSs. To overcome these challenges and to cope with WECS nonlinearity and MIMO nature, nonlinear predictive control algorithms based on Tip-Speed Ratio MPPT (TSR-MPPT) and Rate Predictive Control (RPC) strategies are proposed for the variable speed DFIG-based WECS. The designed control system consists of a FOSMC that is based on fractional-order PID sliding surface model and super-twisting control algorithm (STA) for MPPT through Optimal Torque Control (OTC) algorithm and a Nonlinear-RPC (N-RPC) that is designed based on STA and RPC strategies for power regulation via pitch angle control. FOSMC based on STA improve the dynamics of the controlled system by increasing the degrees of freedom of the sliding surface, minimizing the effects caused by imperfect modelling of nonlinear systems and provide chattering free response of the system and the stability of the designed STA based FOSMC law was proven by Lyapunov criterion. N-RPC is a novel adaptive model-less feedback control algorithm that addresses the limitations of MPC and PID controllers. It limits the power production above rated wind speed and ensure the transient load reduction. The proposed cascaded control algorithms are implemented in a MATLAB/Simulink environment. In this work, the system parameters of Sany SE7715 wind turbine that is installed in Adama-II wind farm, Ethiopia with generating capacity of 1.5 MW is used. The simulation result confirms that the proposed control algorithms achieve optimum power extraction as compared to second-order SMC (2-SMC) and excellent stress reduction in the drive train under variable wind speed. Moreover, they are effective and robust regarding to the application of different external disturbance and sensor noise (i.e., Gaussian & non-Gaussian) and perfectly achieved the required objectives of our investigation.

A Novel SLOPDM Solar Maximum Power Point Tracking Control Strategy for the Solar Photovoltaic Power System

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.

Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine

In recent years, wind power has become one of the most popular sources of renewable electricity generation. However, the wind is, by its nature, a highly intermittent source of energy. To capture the maximum power, wind turbines are generally equipped with a Maximum Power Point Tracking (MPPT) Controller. This paper proposes effective and robust MPPT control strategies based on Fuzzy Logic controller PI (FPI) and Fuzzy logic Fractional Order controller PI (FFOPI). Particle Swarm Optimization (PSO) is used to optimize the membership functions of FPI and FFOPI. The proposed MPPT strategies are validated on a Permanent Magnet Synchronous Generator (PMSG)-variable-speed wind energy conversion system. The overall model of the wind turbine-PMSG and control scheme is developed in MATLAB/Simulink and SimPower Systems toolbox. The results show that the MPPT based on FFOPI control optimized by PSO leads to the best transient response performance and robustness.

A High Voltage Gain Isolated PV Micro-Converter With a Single-Voltage Maximum Power Point Tracking Control Loop for DC Micro-Grid Systems

A high voltage gain, isolated high frequency Photo-voltaic (PV) microconverter with natural continuous conduction mode (CCM) input current and a wide range of zero voltage switching operation is proposed for use in dc microgrid systems. The proposed converter consists of an integrated CCM boost converter and a capacitor inductor inductor (CLL) step-up resonant converter. Further, a new single voltage loop based maximum power point tracking (MPPT) control technique that utilizes the converter's resonant capacitor voltage is also proposed. Theoretical analysis and operating principles of the proposed converter, as well as the descriptions of the proposed MPPT control scheme are provided. Results on a 220 W, 230 kHz and 380 V-output hardware prototype are presented to demonstrate the performance of the proposed step-up PV converter.

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.