Enhanced predictive control for nine-level packed U-cell inverter in grid-tied PV systems

A DC-DC converter plays a crucial role in a photovoltaic (PV) system. Power generated by the PV system is a function of solar irradiance and temperature. Power voltage (P-V) characteristic of a PV module exhibits a single power peak at uniform irradiance and temperature. To operate the PV array at its maximum power point, a maximum power (MP) point (MPP) tracking (MPPT) algorithm is required. The DC-DC converter placed in between the PV array and load, works as an impedance matching circuit. Depending on the application, a suitable selection of DC-DC converter is an important decision. In this study, a comparative simulation analysis of different buck-boost derived non-isolated DC-DC converters are discussed in terms of power conversion efficiency, output power ripple, and tracking speed. The converters studied are buck-boost, Cuk, single-ended primary-inductor converter (SEPIC), Zeta. To evaluate the performance of each DC-DC converter, a widely used Perturb and Observe (PO) MPPT algorithm is modeled and simulated in MATLAB Simulink.

This paper presents a comparive analysis between four Maximum Power Point Tracking (MPPT) algorithms in centralized grid-tied Photovoltaic (PV) systems. The algorithms are used to control the duty cycle of a DC-DC converter in order to boost the output voltage of the PV generator and guarantee the operation of the solar panels in the Maximum Power Point (MPP). Three of the analyzed algorithms correspond to the classical Perturb and Observe (P&O) method, the Incremental Conductance (IC) technique and the Constant Voltage (CV) method. The fourth analyzed algorithm is a modification of the classical P&O method and it has been designed to improve the tracking of the MPP under voltage disturbances at the DC-side capacitor (interface between the DC-DC and the DC-AC stage). The performance of the MPPT techniques is compared through simulations in PSIM under two conditions: sudden changes of irradiance and a distortion of second order in the voltage at the DC-side capacitor. The comparative analysis is made according to some criterias such as the number of sensors, the rise time and the efficiency.

In this paper, a high-reliability photovoltaic (PV) system based on a 9-level Packed U-Cells (PUC9) inverter is investigated. The PUC9 inverter is a cost-effective topology, which requires less components to generate nine voltage levels than traditional Multilevel Inverter (MLI) topologies. The main goal of the proposed system is to inject PV power into the grid with high efficiency and high-power quality (unity power factor and low THD). A Finite-set Model Predictive Control (FS-MPC) approach is designed to control the injected grid current while balancing the capacitors' voltages. A current oriented incremental conductance (IncCon) based Maximum Power Point Tracking (MPPT) algorithm is implemented to extract the maximum PV power. The simulation results are presented to prove the high capability of the proposed controller in balancing the capacitors' voltages and injecting grid current with high power quality.

In order to maximize the efficiency of the photovoltaic system, it is inevitable to implement a Maximum Power Point Tracker (MPPT) to drive the system to operate at the maximum power point under different weather conditions. The performance of an MPPT technique is mainly evaluated by its accuracy, convergence speed, ease of implementation and the ability to track the Global Maximum Power Point (GMPP) under partial shading conditions. Many MPPT techniques and algorithms were presented in literature. However, each individual or single MPPT techniques managed to achieve one or two of those desired traits, which paved the way to hybridizing MPPT techniques that combined two individual MPPT techniques into one Hybrid to integrate their individual desired performance traits. These Hybrid techniques add up the advantages of individual techniques and eliminate their drawbacks which result in a better overall performance of the MPPT algorithm. However, none of the individual nor the Hybrid MPPT algorithms was successful in combining all four desired aforementioned performance characteristics. This work presents a new approach of tracking the maximum power point of the PV, by combining two Hybrid MPPT algorithms working sequentially with the first stage selected based on the existence of partial shading conditions, in an attempt to achieve an accurate and fast-tracking algorithm that is also capable of tracking the GMPP under partial shading conditions without any added complexity.

For the optimal operation of photovoltaic system, The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and the P&O algorithm. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB/ Simulink, we used in The proposed ANN two inputs which are irradiation and ambient temperature, and one output is the optimum voltage of the PV system. The proposed ANN was analyzed under different irradiation conditions. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the P&O algorithm. Comparisons of MPPT with P&O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.

The output power of PV array changes with the variation of environmental factors, such as temperature and solar irradiation. Therefore, a maximum power point (MPP) tracking (MPPT) algorithm is essential for the photovoltaic generation system. However, theP-Ucurve changes dynamically with the variation of the environmental factors; here, the misjudgment may occur if a simple perturb-and-observe (P&O) MPPT algorithm is used. In order to solve this problem, this paper takes MPPT as the main research object, and an improved MPPT algorithm for PV generation applications based onP-Ucurve reconstitution is proposed. Firstly, the mathematical model of PV array is presented, and then the output dynamic characteristics are analyzed. Based on this, aP-Ucurve reconstitution strategy is introduced, and the improved MPPT algorithm is proposed. At last, simulation and comparative analysis are conducted. Results show that, with the proposed algorithm, MPP is tracked accurately, and the misjudgment problem is solved effectively.

High penetration of renewable energy resources (RERs) in the existing microgrid is the dire need to fulfill increasing load demand while considering the alarming situation of global warming and higher emissions. Remotely located areas need special attention to fulfill their daily electricity needs according to the requirements during whole day activities. Renewable energy utilization including solar photovoltaic (PV) and the wind is increasing across the globe while the topology of PV-wind-battery is offering a cost-effective solution for standalone microgrid (MG) clusters with remotely connected loads. Intelligent controllers for interlinking inverter (ILI) guarantee power quality and efficiency of load voltage and frequency for critical as well as sensitive loads. Moreover, maximum power point tracking (MPPT) algorithms for PV and wind enable the controllers to extract maximum power from solar and wind generating sources (SWGS), while providing the reliable energy solution with the battery storage (BS). This paper proposes a finite control set model predictive control (FCS-MPC) based ILI control (ILIC) for PV-wind-BS standalone microgrid system for the stable AC load voltage. Control hierarchy comprises of DC-DC boost converters control for maximum power extraction from PV and wind; bidirectional DC-DC buck-boost converter control of BS for stabilizing constant DC bus voltage while DC-AC inverter (VSI) control with FCS-MPC for regulation of load voltage, is proposed and implemented in MATLAB/Simulink® for PV-wind-BS standalone microgrid system (SMGS). FCS-MPC strategy is implemented for primary control, which comprises power droop control, three-phase reference generator, and inner control loop. Fluctuating and intermittent behavior of PV and Wind under variable load conditions is investigated. Simulation results show the effectiveness of MPC strategy over PI control strategy in obtaining voltage efficiency with less than 1% THD.

This study proposes a grid-tied single phase single-stage solar photovoltaic (SPV) system. The standard voltage source converter (VSC) is used as the single-stage power converter. The proposed system consists of an SPV array, the VSC, the single-phase distribution grid and linear/nonlinear loads. The control algorithm includes a maximum power point tracking (MPPT) algorithm to draw the maximum available power from the solar photovoltaic (PV) array. The perturb and observe (P&O) MPPT algorithm is used. Aside from feeding power to the grid, the system mitigates harmonics and also provides power factor correction at the point of common coupling (PCC). The employed VSC control algorithm is the normalized least mean logarithmic square (NLMLS) algorithm. The total harmonic distortion (THD) of the grid supply current and the grid supply voltage at the PCC conform to the IEEE-519 standard. The performance of the system is studied experimentally under a variety of load and insolation conditions, on a prototype developed in the laboratory.

The single-stage grid-connected photovoltaic (PV) topology has recently drawn attention as it can reduce overall losses and installation costs. This paper presents a new control approach for single-stage grid-connected PV systems. The proposed controller is a combination of a finite control set model predictive control (FCS-MPC) and a maximum power point tracking (MPPT) algorithm, which ensures the extraction of maximum power from the PV panels and good transient performance for the output voltage and current. The disadvantages of classical MPPT algorithms in tracking the global maximum power point under fluctuating environmental conditions are avoided by including additional constraints in the cost function of the FCS-MPC. Further, the controller is tested for partial shading in PV. The performance of the proposed controller is compared with the two-stage and single-stage PV configuration with different controls and MPPT algorithms. The simulation results show that the single-stage PV system with the proposed control can effectively extract the maximum power from the PV system and maintain a stable output signal for the transient condition. Finally, experimental results according to a control hardware-in-the-loop (C-HIL) approach are presented to validate the effectiveness of the proposed algorithm.

A variable-weather-parameter optimization strategy to optimize the maximum power point tracking speed of photovoltaic system

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

ABSTRACTIn this paper, we propose and implement a 12‐bit area‐efficient folded all‐digital maximum power point tracking (MPPT) chip based on gain‐adaptive perturb‐and‐observe algorithm for photovoltaic energy conversion system. Alternative to DSP or micro controller, realizing the MPPT algorithm by using ASIC can achieve higher energy conversion efficiency, lower power consumption and smaller chip area. By using gain‐adaptive perturb‐and‐observe MPPT algorithm, overall system power consumption can be reduced by overcoming the periodic perturbation issues that occur in conventional perturb‐and‐observe MPPT algorithm. The utilization of proportional integral controller allows fast and stable tracking of the maximum power point. Under high intensity sun illumination, the gain‐adaptive perturb‐and‐observe algorithm performs three times faster than the conventional perturb‐and‐observe MPPT algorithm. Under low intensity sun illumination, the gain‐adaptive perturb‐and‐observe algorithm can provide the same power conversion efficiency as the conventional perturb‐and‐observe MPPT algorithm. By using folding VLSI architecture, the MPPT algorithm can be realized with 74% chip area saving and 77% power consumption reduction. Finally, our proposed MPPT chip is implemented in TSMC0.18‐µm process, with 0.85 mm*0.79 mm chip area and 97.9% power conversion efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

This paper proposes a cascaded Takagi-Sugeno Model Predictive Controller (TS-MPC) for a Doubly-fed Induction Generator (DFIG) based Wind Power Conversion System (WPCS) to maximize power extraction, maintain zero stator reactive power, and enhance power quality. For this purpose, the Takagi-Sugeno Fuzzy Logic Control (TS-FLC) is arranged in a sequential configuration with the Finite Control-Set Model Predictive Control (FCS-MPC) strategy to enhance the overall performance of the wind power system. The introduced control technique, which is applied to govern the Rotor Side Converter (RSC) of the DFIG, consists of two cascaded control loops for achieving Maximum Power Point Tracking (MPPT). The innermost control loop is implemented to regulate the d-q axis rotor currents using FCS-MPC strategy. Meanwhile the outermost control loop is employed to regulate the DFIG’s rotational speed pursuant to the Tip Speed Ratio MPPT (TSR-MPPT) control framework using the TS-FLC, thus improving the predictive accuracy and control effectiveness. To validate the performance of the devised control scheme, a numerical simulation of a 1.5MW DFIG based WPCS was conducted using MATLAB/Simulink software. The simulation results demonstrate that the proposed cascaded TS-MPC not only outperforms the cascaded PI-MPC in terms of superior adaptability to nonlinearities and varying wind conditions—thanks to the inherent flexibility of TS-FLC—but also in various performance metrics, including response time, steady-state error, and total harmonic distortion (THD).Furthermore, while FCS-MPC approaches are often criticized for computational complexity, the TS-FLC structure enhances real-time feasibility by reducing computational overhead compared to conventional FLC methods. These findings reinforce the practical viability of TS-MPC for large-scale wind energy applications and indicate the effectiveness of the proposed control scheme.

Off-grid solar photovoltaic (PV) power system characteristics are used widely in many far-away areas during theses years. The new control strategy employs Maximum Power Point Tracking (MPPT) algorithm. The maximum power point tracker is a high efficiency boost converter, whose function is like an optimal electrical load for solar PV cells, and converts the power to voltage or current which is appropriate in any situation. MPPT through perturbation and observation control method can trace the maximum power point, then it will set the operating point. The simulations show that in the off-grid solar PV power system can search and trace the maximum power point automatically by using this strategy. Moreover, this control strategy is comparatively easy, and has high practical value.

This paper brings forward a novel hybrid maximum power point tracking(MPPT) algorithm. The new MPPT algorithm is a hybrid between the Improvised Binary Sequence(IBS) MPPT algorithm and the well known Perturb and Observe(P&O) MPPT algorithm. This algorithm helps to give the much-needed flexibility to the IBS algorithm to take care of the minute changes in temperature and solar radiance, which leads to a change in the maximum power point(MPP) of any solar module. The initial operating point is found out by using the IBS algorithm. Thereafter, the control is transferred to the P&O algorithm to take care of gradual and minute changes. In case rapid changes in operating conditions are sensed due to any reason, the entire algorithm restarts from the beginning by re-initializing the parameters and putting the IBS algorithm to use. The proposed algorithm decides when to re-initialize the parameters depending on the limits set by the operator. The proposed algorithm was coded in Matlab and was tested in simulation in Simulink.