Hybrid Maximum Power Point Tracking Method Research Articles

Coordinated PSO-ANFIS-Based 2 MPPT Control of Microgrid with Solar Photovoltaic and Battery Energy Storage System

The microgrid is a group of smaller renewable energy sources (REs), which act in a coordinated manner to provide the required amount of active power and additional services when required. This article proposes coordinated power management for a microgrid with the integration of solar PV plants with maximum power point tracking (MPPT) to enhance power generation and conversion using a hybrid MPPT method based on particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) to acquire rapid and maximum PV power along with battery energy storage control to maintain the stable voltage and frequency (V-f) of an isolated microgrid. In addition, it is proposed to provide active and reactive power (P-Q) regulation for the grid connected. The approach used provides more regulation due to the least root mean square error (RMSE), which improves photovoltaic (PV) potential extraction. The comparison results of the PSO-ANFIS and P&O controllers of the MPPT and the controller of the energy storage devices combined with the V-f (or P-Q) controller of the inverter all show effective coordination between the control systems. This is the most important need for contemporary microgrids, considering the potential of changing irradiance in the grid following mode, the grid forming mode under an island scenario, and back-to-grid synchronization. With the test model, the islanded and grid-islanded-grid connected modes are investigated separately. The results demonstrate conclusively that the proposed strategies are effective. To run the simulations, MATLAB and SimPowerSystems are utilized.

A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

Hybrid SFLA MPPT design for multi-module partial shading photovoltaic energy systems

ABSTRACT Under partial shading conditions, the photovoltaic (PV) array will induce several local maximum power points, such that the traditional maximum power point tracking (MPPT) methods will trap in false maximum power points and reduce the energy conversion efficiency of the solar system. To solve this problem, this paper firstly introduces a multi-module solar energy system, where a master boost converter is used as a voltage regulator for the DC load, while other slave boost converters in parallel perform MPPT to support the load power as high as possible. Furthermore, a hybrid MPPT method is proposed by combining the shuffled frog leaping algorithm (SFLA) and the incremental conductance (In-Cond) method. The hybrid SFLA method can successfully and quickly find the maximum power region, while the exact MPP is achieved in the incremental conductance searching phase. As a result, trapping local maximum power points is avoided, while the computing complexity is reduced. Finally, simulation and experiments are carried out to verify the validity of the proposed MPPT method under partial shading conditions.

A Hybrid Simulated Annealing and Perturb and Observe Maximum Power Point Tracking Method

Time-invariant maximum power point tracking (MPPT) methods, such as particle swarm optimization (PSO) and simulated annealing (SA), have been shown to be effective at locating the region of the global maximum power point (GMPP) in photovoltaic systems where partial shading conditions are observed. These techniques, however, are not effective in providing continuous tracking and identifying when a change in conditions warranting a new global search has occurred. In this article, the SA algorithm is combined with the conventional perturb and observe (P&O) technique in a hybrid MPPT method. This approach leverages the advantages of each technique. The technique is further enhanced by the integration of a novel sentry particle approach to identify when a change in the environmental conditions has occurred leading to a new global search being initiated. The simulation and experimental validations are provided and showed that the proposed technique with the sentry particle reinitialization outperforms both the SA and P&O-based MPPT algorithms by ensuring more consistent tracking to the GMPP.

Hybrid Maximum Power Point Tracking Method Based on Iterative Learning Control and Perturb & Observe Method

Maximum power point tracking (MPPT) is used to utilize intermittent solar power fully in the photovoltaic (PV) systems. Tracking the MPP fast, and accurately with changes in the solar irradiance, and the temperature is the goal of MPPT techniques. In this paper, a hybrid MPPT method based on iterative learning control (ILC), and perturb, and observe (P&O) algorithm is proposed. ILC can deal with the periodic variations to eliminate the steady-state oscillations, and errors, when the operation point is close to the MPP or a small irradiance variation occurs. In the proposed hybrid MPPT technique, a high frequency power P&O method without deadtime is used to improve the dynamic response when the irradiance changes rapidly. This paper presents the theoretical background of the hybrid MPPT algorithm, design, and stability analysis. Simulation, and hardware validation results substantiate the effectiveness of the proposed method.

An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system–particle swarm optimization (ANFIS–PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS–PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS–PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS–PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.

A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems Under Partial Shading Conditions

Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results.

Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions

For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.

A model predictive control-based hybrid MPPT method for boost converters

ABSTRACTIn this study, Perturb and Observe Maximum Power Point Tracking algorithm and Model Predictive Control (MPC) algorithm are combined to improve the dynamic performance of the control structure of boost converters used in renewable energy systems. For this purpose, a digital control method that does not need any comparator is developed. The maximum power point current value is determined from the Maximum Power Point Tracking (MPPT) algorithm and it is used as the reference for current control operation in the MPC algorithm. Dynamic capability and tracking performance of the proposed control method is validated by simulation and experimentally. The dynamic behaviour of the control algorithm is analysed by carrying out step change tests. Results show that the control method has excellent dynamic performance thanks to its feature of reference tracking in only one switching period. Furthermore, overshoots and undershoots are successfully eliminated.

A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions

The modest changes in operating current and voltage of photovoltaic (PV) panel due to the temperature and radiation fluctuation constitute visible variations in the output power. In this paper, a hybrid method to optimize the performance of the maximum power point tracking (MPPT) controller for mitigating these variations and forcing the system to operate on maximum power point (MPP) is developed. The presented Hybrid MPPT method consists of two loops: (i) artificial neural network (ANN) based reference point setting loop and (ii) perturbation and observation (P&O) based fine tuning loop. To assess robustness of the proposed method, a comparison is performed using the conventional P&O, incremental conductance (INC) and ANN based MPPT methods under both rapidly changing radiation and partially shaded conditions by using PSCAD/EMTDC program. The results obtained from the test cases explicitly demonstrate that the presented MPPT method not only achieves an increase in speed of MPP tracking, but also reduces the steady state oscillations and prevents the possibility of the algorithm from confusing its perturbation direction. The system efficiency more than 98.26%, 120ms improvement in convergence speed and 1.16V decrease in the rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly changing environmental conditions.

A Single-Stage Stand-Alone Photovoltaic Energy System With High Tracking Efficiency

A single-stage flyback PV inverter with maximum power point tracking (MPPT) high-speed capability is presented in this paper. The proposed stand-alone photovoltaic energy system (SAPES) incorporates the use of hybrid MPPT to ensure peak energy harvesting under all weather conditions. The proposed hybrid MPPT method combines the conventional short current pulse (SCP) MPPT method and the perturb and observe (P&O) methods. Measurement of the offline parameter [short-circuit current of PV module ( ${I}_{\rm sc}$ )] for SCP is made on the basis of the difference between the offline parameter and the instantaneous current of PV module ( ${I}_{\rm pv}$ ). Use of a single power conversion stage is achieved by employing a modified flyback inverter operating in a discontinuous-conduction mode. The control structure of the conventional flyback inverter is also modified to incorporate the hybrid MPPT algorithm. The proposed SAPES method is tested using computer-aided simulations and real-time hardware in the loop experimentation using the dSPACE DS1104 board. The results obtained using the proposed systems are better than those for the conventional algorithms under the environmental conditions tested.

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

The output–voltage–power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective.

On application of a new hybrid maximum power point tracking (MPPT) based photovoltaic system to the closed plant factory

Photovoltaic (PV) generation systems have been shown to have a promising role for use in high electric-load buildings, such as the closed plant factory which is dependent upon artificial lighting. The power generated by the PV systems can be either directly supplied to the buildings or fed back into the electrical grid to reduce the high economic costs and environmental impact associated with the traditional energy sources such as nuclear power and fossil fuels. However, PV systems usually suffer from low energy-conversion efficiency, and it is therefore necessary to improve their performance by tackling the energy loss issues. The maximum power point tracking (MPPT) control technique is essential to the PV-assisted generation systems in order to achieve the maximum power output in real time. In this study, we integrate the previously proposed direct-prediction MPP method with a perturbation and observation (P&O) method to develop a new hybrid MPPT method. The proposed MPPT method is further utilized in the PV inverters in a PV system installed on the roof of a closed plant factory at National Taiwan University. The tested PV system is constructed as a two-stage grid-connected photovoltaic power conditioning (PVPC) system with a boost-buck full bridge design configuration. A control scheme based on the hybrid MPPT method is also developed and implemented in the PV inverters of the PVPC system to achieve tracking of the maximum power output of the PV system in real time. Based on experimental results obtained in an outdoor environment over a period of one year, the hybrid MPPT method not only decreases energy loss but also increases power utilization. These results demonstrate the applicability of the PV system to a closed plant factory for saving energy consumption and reducing CO2 emissions.