Proposed Maximum Power Point Tracking Method Research Articles

MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions

The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.

Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System

Polymer electrolyte membrane (PEM) fuel cells demonstrate potential as a comprehensive and general alternative to fossil fuel. They are also considered to be the energy source of the twenty-first century. However, fuel cell systems have non-linear output characteristics because of their input variations, which causes a significant loss in the overall system output. Thus, aiming to optimize their outputs, fuel cells are usually coupled with a controlled electronic actuator (DC-DC boost converter) that offers highly regulated output voltage. High-order sliding mode (HOSM) control has been effectively used for power electronic converters due to its high tracking accuracy, design simplicity, and robustness. Therefore, this paper proposes a novel maximum power point tracking (MPPT) method based on a combination of reference current estimator (RCE) and high-order prescribed convergence law (HO-PCL) for a PEM fuel cell power system. The proposed MPPT method is implemented practically on a hardware 360W FC-42/HLC evaluation kit. The obtained experimental results demonstrate the success of the proposed method in extracting the maximum power from the fuel cell with high tracking performance.

Random inertia weight PSO based MPPT for Solar PV under Partial Shaded Condition

Since the power-voltage characteristic curve of a photovoltaic (PV) arrays has multiple peaks under partially shading conditions (PSC), the conventional maximum power point tracking (MPPT) control methods fail to work. In this paper, a PSO algorithm based on random inertia weights is proposed to achieve global maximum power tracking. By improving the inertia weight coefficient of the traditional PSO algorithm and optimizing the search order of the particles, the population size and the number of iterations are decreased, thus finding the MPP (maximum power point) in a shorter time to ensure accurate tracking of the maximum power. By using the same parameters, its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method, and the existed PSO algorithm is compared with the improved PSO to verify the correctness of the algorithm. The concordance of simulation results prove the advantage of the proposed MPPT method to ensure rapidity and stability of the output PV power.

Design and Implementation of a Dual-Input Single-Output Photovoltaic Converter

In many solar inverters, a dc/dc converter is mainly located between the solar arrays and the inverter. This study presents an enhanced maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems that drives solar array voltages to track a reference value and decreases fluctuations and oscillations in PV voltage. Different from the previously presented methods, a novel MPPT method is proposed that ensures tracking accuracy by considering output voltage in addition to input voltage and currents. The proposed method detects d I / d V variations, compares the output voltage with the desired reference to shift operation mode and refreshes step size. The digital filtering, enhanced PI, and perturb-and-observe (P&O) tracking features of the proposed MPPT method make it robust to mitigate source fluctuations and sensitivity to partial shading based oscillations. In order to validate the success of the proposed method, a test rig has been installed with dual boost converters. The performance improvements have been verified by both simulation and experimental results that are compared to InCon and P&O MPPT methods. It is also confirmed by experimental results that the proposed MPPT method provides robust control capability in terms of tracking the reference voltage and rejecting the effects of various shading situations on solar arrays.

Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system

The integration of artificial intelligence (AI) control techniques for efficient energy extraction will provide the solar energy systems with increased efficiency. Therefore, this manuscript proposes a solar water pumping system topology using a radial basis function neural network (RBFNN) to effectively track the maximum power point (MPP) in a photovoltaic (PV) array fed brushless DC (BLDC) motor drive. The RBFNN maximum power point tracking (MPPT) predicts the duty ratio of a single-ended primary inductor converter (SEPIC) to reach the MPP. The performance of the system under study is compared to trivial MPPT techniques with varying irradiance, temperature and partial shading condition (PSC). The performance in terms of voltage ripple, current ripple, average power loss, MPP settling time, efficiency, torque ripple and stator current total harmonic distortion (THD) is evaluated to show the effectiveness of the proposed MPPT method.

Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems

Maximum power point tracking (MPPT) is essential for photovoltaic systems to ensure a maximum power extraction from PV panels. However, some issues such as oscillations, power loss and other technical aspects still unsolved. This paper presents and discusses a new MPPT algorithm with zero-oscillations and unity efficiency in transient and steady-states. This algorithm leads to track the maximum power point under extreme operating conditions. The proposed MPPT method is based on the simple adaptive linear neuron. In addition, its implementation is achieved without any additional control loop, which resulted in a simple control. In order to validate the proposal effectiveness, both simulation and experiment tests are carried out under variable irradiance and load. Comparison between the developed MPPT and the conventional perturb and observe algorithm is also performed. Obtained results show that with the proposed method, unity efficiency is reached and oscillations are fully removed in the transient and steady-states. The originality of this work is the design of a simple and efficient MPPT algorithm based on the ADALINE with unity efficiency and zero-oscillations. Moreover, the proposal is verified using a real PV system under irradiance and load changes.

A novel MPPT method for autonomous wireless sensor networks node with thermal energy harvesting

Energy harvesting is a promising alternative to batteries for autonomous WSN nodes. Although there are various energy harvesting methods using ambient energy sources, this research is focused on thermal energy harvesting because there are abundant thermal sources in industrial sites. As only several mW can be obtained by a thermal energy harvester based on a thermoelectric generator (TEG), improvement of the energy conversion efficiency of the thermal energy harvester is essential. This paper proposes a novel maximum power point tracking (MPPT) method combining coarse adjustment and fine adjustment to enhance the energy conversion efficiency of the harvester. The Direct Adjustment (DA) method for MPPT is adopted in the coarse adjustment stage to avoid disconnecting TEG modules from conversion circuits, while an observation and self-optimization (O&SO) method for MPPT is used in the fine adjustment stage to avoid introducing perturbations and to maintain a stable output power at the maximum power point (MPP). After building up the system simulation model, a series of experiments are conducted to evaluate the proposed MPPT methods. The results show that the DA + O&SO method is an effective MPPT method for a WSN node powered by a TEG. It is able to enhance the regulation speed of the MPPT, improve the continuity of Pout, and avoid the power oscillations around the MPP.

A Novel Piecewise Velocity Control Method Using Passivity-Based Controller for Wave Energy Conversion

By studying the working principle of a maximum power point tracking (MPPT) method called active phase control (APC), we found that not only was the phase difference between the wave excitation force and the velocity reduced, but also the magnitude of the velocity increased. For the direct-drive wave energy converter with a permanent magnet linear generator (PMLG), the velocity of the PMLG in the no-load state is larger than that in any power generation states. According to this characteristic, a novel piecewise velocity control (PVC) method and its trajectory planning scheme for a sealed-buoy wave energy converter (SBWEC) are proposed to obtain the maximum power extraction condition. Considering the irregularity of ocean waves and that the proposed method needs to frequently change the operating state of the PMLG, a passivity-based controller (PBC) is designed. With energy modification and damping injection, rapid convergence and asymptotic stability of the system are guaranteed, so that the proposed MPPT method can be perfectly implemented. The comparative simulation results for regular and irregular waves demonstrate that it is an effective and promising power control method.

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.

Optimal Command for Photovoltaic Systems in Real Outdoor Weather Conditions

The photovoltaic panel is characterized by a unique point called the maximum power point (MPP) where the panel produces its maximum power. However, this point is highly influenced by the weather conditions and the fluctuation of load which drop the efficiency of the photovoltaic system. Therefore, the insertion of the maximum power point tracking (MPPT) is compulsory to track the maximum power of the panel. The approach adopted in this paper is based on combining the strengths of two maximum power point tracking techniques. As a result, an efficient maximum power point tracking method is obtained. It leads to an accurate determination of the MPP during different situations of climatic conditions and load. To validate the effectiveness of the proposed MPPT method, it has been simulated in matlab/simulink under different conditions.

A Novel Spline Model Guided Maximum Power Point Tracking Method for Photovoltaic Systems

This paper develops a novel data-driven maximum power point tracking (MPPT) method, which is of two-fold, to benefit the power generation of photovoltaics (PV) systems facing variable partial shading conditions (PSCs). Under each PSC, the proposed MPPT utilizes a compact data-driven modeling process to develop the power-voltage (P-V) curve model via the natural cubic spline. Next, the proposed MPPT method develops a novel natural cubic spline guided iterative search process to update the P-V curve model having multiple peaks and to promptly obtain the global maximum power point (GMPP) under the considered PSC. This is a pioneer study which discusses a GMPPT algorithm using a natural cubic spline-based P-V curve model. The convergence of the MPP tracked by the proposed algorithm to the GMPP is theoretically ensured by the property of the natural cubic spline. The effectiveness and robustness of the proposed algorithm have been comprehensively evaluated via extensive simulation studies and experiments. Computational results demonstrate that the proposed algorithm is more efficient and effective to attain GMPPs under variable PSCs by comparing with recent MPPT methods using heuristic techniques, which are easily trapped into local MPP under variable PSCs.

Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions

The changes in temperature and radiation cause visible fluctuations in the output power produced by the photovoltaic (PV) panels. It is essential to keep the output voltage of the PV panel at the maximum power point (MPP) under varying temperature and radiation conditions. In this study, a maximum power point tracking (MPPT) method has been developed which is based on mainly two parts: the first part is adapting calculation block for the reference voltage point of MPPT and the second one is Fuzzy Logic Controller (FLC) block to adjust the duty cycle of PWM applied switch (Mosfet) of the DC-DC converter. In order to evaluate the robustness of the proposed method, Matlab/Simulink program has been used to compare with the traditional methods which are Perturb & Observe (P&O), Incremental Conductance (Inc. Cond.) and FLC methods under variable atmospheric conditions. When the test results are observed, it is clearly obtained that the proposed MPPT method provides an increase in the tracking capability of MPP and at the same time reduced steady state oscillations. The accuracy of the proposed method is between 99.5% and 99.9%. In addition, the time to capture MPP is 0.021 sec. It is about four times faster than P&O and five times faster than for Inc. Cond. and, furthermore, the proposed method has been compared with the conventional FLC method and it has been observed that the proposed method is faster about 28% and also its efficiency is about 1% better than FLC method.

An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems

In this brief, an analog, bipolar junction transistor (BJT)-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic modules is proposed. The conventional fixed voltage reference method is the simplest method for tracking, but it does not obtain good MPPT efficiency because the maximum power point (MPP) voltage changes at different insolation levels. In reality, an approximately linear slope is formed when connecting the MPPs measured from the highest insolation level to the lowest. Utilizing this characteristic, a BJT, which has a similar electrical property, is used to implement a variable voltage reference that improves the accuracy of the MPP voltage when the insolation changes. The proposed circuit is simple and easy to implement, and it can track the MPP very quickly without the need for a digital controller or PID controller. Hence, the circuits cost and complexity are reduced. Experimental results are given to verify the feasibility of the proposed MPPT method.

Extremum Seeking-Based Model Predictive MPPT for Grid-Tied Z-Source Inverter for Photovoltaic Systems

This paper proposes a maximum power point tracking (MPPT) method for a grid-tied Z-source inverter (ZSI) that interfaces photovoltaic (PV) sources to the grid. The proposed MPPT method maximizes the energy harvest by guaranteed convergence to maximum power point (MPP) under dynamic weather condition from the PV panels by the ZSI as a power electronics interface. The proposed method uses the concept of model predictive control (MPC) in conjunction with extremum seeking optimization to track the true MPP and can operate without a priori knowledge of the PV panel parameters or ambient condition. At the grid side, the system injects to the grid maximum harvested energy from the PV panel obtained using the proposed MPPT algorithm. In addition, the ratio of active/reactive power injected to grid is controlled (power factor control). The optimal switching signal is determined and applied to the ZSI by minimizing the developed MPC cost function, thus eliminating the need for a modulator that simplifies the controller schematic for ZSI and can be implemented with reasonable processing effort on an inexpensive digital controller. The proposed method in this paper features: fast dynamic response to change in ambient PV panel condition, true and guaranteed convergence to MPP, negligible oscillation around MPP, and simple control structure without requirement of many cascaded control loops.

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

Maximum power point tracking of PV system under partial shading conditions through flower pollination algorithm

For maximum utilization of solar energy, photovoltaic (PV) power systems should be operated at the maximum power point (MPP) which can be achieved using maximum power point tracking (MPPT) methods. However, the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process. The global MPP cannot always be achieved by the conventional MPPT methods. Therefore a novel MPPT method for PV systems using flower pollination (FP) algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm. MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method, and the proposed method is compared with two well established MPPT methods. The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition.

Passivity-Based Control of Switched Reluctance-Based Wind System Supplying Constant Power Load

This paper presents a passivity-based control (PBC) scheme for the switched reluctance generator (SRG) in small-scale wind energy conversion systems for dc microgrid applications. The main objective is to stabilize the output voltage in case the system supplies constant power loads (CPLs) and operates with maximum power point tracking (MPPT). Stability improvement and dc-link ripple reduction in the presence of CPLs is achieved using system-level modeling of SRG-based dc microgrid through the Euler-Lagrange system (ELS) from the viewpoint of the machine physical structure. Compared with other control methods, the proposed MPPT method based on passivity-based speed controller employs the back electromotive force (EMF) in the generation process as a position-dependent voltage source to overcome the major challenge of SRG complicated uncertain dynamic model. To deal with the time-varying inductance and back EMF of SRG, an adaptation mechanism is incorporated in the proposed adaptive PBC and the control design is constructed by using the Lyapunov theorem where the closed-loop stability is ensured. The effectiveness of the proposed method in avoiding instability effects of SRG and CPL with voltage ripple reduction and precise wind turbine speed tracking is investigated with simulation results and validated with experimental by using a four-phase, 8/6 SRG drive system.

Global maximum power point tracking method based on sorting particle swarm optimizer

ABSTRACT This paper presents a novel global maximum power point tracking (MPPT) method based on the sorting particle swarm optimizer (PSO). At first, the output characteristics of photovoltaic (PV) array under the partial shading conditions are analyzed. As an artificial intelligence algorithm, the sorting PSO is proposed for global MPPT of PV array under partial shading conditions. The principle of the proposed algorithm is illustrated in detail. Second, a stand-alone PV system based on Z-source inverter (ZSI) is simulated to validate the proposed global MPPT method. Then, the experiments are implemented based on the established ZSI. Compared with the conventional PSO for MPPT, simulation and experimental results both reveal that the proposed global MPPT method based on the sorting PSO can reduce the fluctuation of voltage of PV array during MPPT. Simulation results reveal that 61.53% and 51.81% energy consumption during MPPT is reduced under two different typical partial shading conditions. The proposed MPPT method can also track the global maximum power point more rapidly. Approximately 21.21–39.96% time consumptions during MPPT are saved.

An Efficient and Cost-Effective Hybrid MPPT Method for a Photovoltaic Flyback Microinverter

This paper presents a grid-connected photovoltaic (PV) flyback inverter operating in discontinuous conduction mode (DCM) and controlled through an efficient, reliable, and cost-effective hybrid maximum power point tracking (MPPT) method. Although the proposed method can be applied to any grid-connected inverter topology, in this paper, it is applied on a modified flyback inverter circuit. Moreover, the proposed MPPT method is a two-stage scheme and hence, a hybrid solution, as it consists of a combination of fractional short-circuit current and hill climbing perturb and observe (P $\&$ O) method reformulated. The change in weather condition is accurately detected using the current limit technique such that the difference in short-circuit current and photovoltaic current is monitored continuously. It is demonstrated that the use of current limit enables the system to swiftly track the maximum power point (MPP) in abruptly changing environmental conditions. The proposed MPPT method needs only two sensors for current and voltage measurements and is, therefore, cost effective and reliable. The system is tested through simulations and real-time setup using dSPACE DS1104. The results prove the effectiveness of the proposed hybrid MPPT compared with the P $\&$ O method under uniform and dynamic weather conditions.

A Novel Auto-Scaling MPPT Algorithm based on Perturb and Observe Method for Photovoltaic Modules under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) controller is required in a solar photovoltaic (PV) system to deliver the maximum power to load from PV module. This paper proposes a novel stepped MPPT method to realize a simple MPPT controller, which can track the real maximum power point (RMPP) even under partial shading conditions. The proposed algorithm is started by scanning the characteristic curve of the PV modules to detect the global maximum power point and then the algorithm will be switched to the conventional P&O algorithm to track the true maximum power point. The obtained simulation results, using Power electronic simulation software (PSIM), are compared with those found using the P&O method to confirm the performance of our proposed MPPT method even under non-uniform solar irradiation.