Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show multi-peaks.Traditional maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output power.In order to solve the above problems,intelligent algorithms are used in MPPT.However,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search oscillation.Therefore,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this paper.Firstly,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that interval.Compared with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking GMPP.Finally,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

Recent advance on the efficiency at maximum power of heat engines

This review reports several key advances on the theoretical investigations of efficiency at maximum power of heat engines in the past five years. The analytical results of efficiency at maximum power for the Curzon-Ahlborn heat engine, the stochastic heat engine constructed from a Brownian particle, and Feynman＇s ratchet as a heat engine are presented. It is found that： the efficiency at maximum power exhibits universal behavior at small relative temperature differences; the lower and the upper bounds might exist under quite general conditions; and the problem of efficiency at maximum power comes down to seeking for the minimum irreversible entropy production in each finite-time isothermal process for a given time.

Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking U+0028 MPPT U+0029 controller. Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then, simulations on FAST U+0028 Fatigue, Aerodynamics, Structures, and Turbulence U+0029 code and experiments on the wind turbine simulator U+0028 WTS U+0029 based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load. © 2017 Chinese Association of Automation.

Comparative assessment of maximum power point tracking procedures for photovoltaic systems

The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.

Maximum Power Point Tracking in Variable Speed Wind Turbine Based on Permanent Magnet Synchronous Generator Using Maximum Torque Sliding Mode Control Strategy

The present study was carried out in order to track the maximum power point in a variable speed turbine by minimizing electromechanical torque changes using a sliding mode control strategy. In this strategy, first, the rotor speed is set at an optimal point for different wind speeds. As a result of which, the tip speed ratio reaches an optimal point, mechanical power coefficient is maximized, and wind turbine produces its maximum power and mechanical torque. Then, the maximum mechanical torque is tracked using electromechanical torque. In this technique, tracking error integral of maximum mechanical torque, the error, and the derivative of error are used as state variables. During changes in wind speed, sliding mode control is designed to absorb the maximum energy from the wind and minimize the response time of maximum power point tracking(MPPT). In this method, the actual control input signal is formed from a second order integral operation of the original sliding mode control input signal. The result of the second order integral in this model includes control signal integrity, full chattering attenuation, and prevention from large fluctuations in the power generator output. The simulation results, calculated by using MATLAB/m-file software, have shown the effectiveness of the proposed control strategy for wind energy systems based on the permanent magnet synchronous generator(PMSG).

Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.

Comprehensive review and performance evaluation of maximum power point tracking algorithms for photovoltaic system

A photovoltaic array is environmentally friendly and a source of unlimited energy generation.However,it is presently a costlier energy generation system than other non-renewable energy sources.The main reasons are seasonal variations and continuously changing weather conditions,which affect the amount of solar energy received by the solar panels.In addition,the non-linear characteristics of the voltage and current outputs along with the operating environment temperature and variation in the solar radiation decrease the energy conversion capability of the photovoltaic arrays.To address this problem,the global maxima of the PV arrays can be tracked using a maximum power point tracking algorithm(MPPT)and the operating point of the photovoltaic system can be forced to its optimum value.This technique increases the efficiency of the photovoltaic array and minimizes the cost of the system by reducing the number of solar modules required to obtain the desired power.However,the tracking algorithms are not equally effective in all areas of application.Therefore,selecting the correct MPPT is very critical.This paper presents a detailed review and comparison of the MPPT techniques for photovoltaic systems,with consideration of the following key parameters:photovoltaic array dependence,type of system(analog or digital),need for periodic tuning,convergence speed,complexity of the system,global maxima,implemented capacity,and sensed parameter(s).In addition,based on real meteorological data(irradiance and temperature at a site located in Addis Ababa,Ethiopia),a simulation is performed to evaluate the performance of tracking algorithms suitable for the application being studied.Finally,the study clearly validates the considerable energy saving achieved by using these algorithms.

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

On maximum power point tracking control strategy for variable speed constant frequency wind power generation

Based on the characteristic of AC-excited variable speed constant frequency(VSCF)wind power generation,the vector control technique was applied in a doubly fed induction generator(DFIG).Maximum wind energy or maximum output power point can be tracked by decoupling control of active power and reactive power.The research result shows that the net power of generation system delivered to grid in maximum wind energy tracking mode is not the most.We presented a novel maximum power point tracking(MPPT)control strategy by analyzing the DFIG mathematic model and power relations which delivered the maximum power to the grid.The maximum power point could be tracked automatically without measuring wind speed in the control strategy and the control was independent of optimal turbine power curve,which had excellent dynamic and static performances and robustness.Simulation and experimental results testify the accuracy and validity of the control strategy.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

Maximum Power Point Tracking With Fractional Order High Pass Filter for Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the performance such as convergence and robustness under disturbance and uncertainty, a fractional order high pass filter (FOHPF) is applied for the MPPT controller design based on the traditional extremum seeking control (ESC). The controller is designed with integerorder integrator (IO-I) and low pass filter (IO-LPF) together with fractional order high pass filter (FOHPF), by substituting the normal HPF in the original ESC system. With this FOHPF ESC, better convergence and smoother performance are achieved while maintaining the robust specifications. First, tracking stability is discussed under the commensurate-order condition. Then, simulation results are included to validate the proposed new FOHPF ESC scheme under disturbance. Finally, comparison results between FOHPF ESC and the traditional ESC method are also provided. © 2014 Chinese Association of Automation.

The Maximum Power Tracking Method and Reactive Compensation Simulation Research Based on DIgSILENT

This paper studies about the mechanical part of wind turbine and wind generator operation stability.1) It makes a comparative study of two control methods for maximum power tracking: curve fitting method and hill climbing algorithm, sets up improved control modules in DIgSLIENT and makes comparison research, thus gets the conclusion that the improved control modules of hill climbing algorithm has good effect on MPPT, and it is more desirable in the condition of steady wind. 2) This paper sets up SVC and STATCOM models and improved control modules in DIgSLIENT, which are connected to wind power system, verifying the validity of SVC and STATCOM models, and verifying its influence on wind power plant and system. The results of the study show that STATCOM is more helpful in voltage recovery when large disturbance of three-phase short-circuit happened in wind power grid, reactive compensation is more effective.

Prediction of a maximum pull-out load of anchor bolts using an optimal combination model

The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.