PV systems control using fuzzy logic controller employing dynamic safety margin under normal and partial shading conditions

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions.Peer ReviewedPostprint (published version

Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system

Photovoltaic (PV) module is a packed solar cell, used for generating electricity from the sun’s energy. The application of PV power generation has gained its popularity with its easy implementation and inexhaustible energy resources. Due to the nonlinear characteristic of PV module, a maximum power point tracking (MPPT) is necessary to adjust the operating point based on the maximum power point (MPP) on the current-voltage (I-V) characteristic curve. However, under partial shaded conditions, the PV system is prone to local maxima problem and the challenge for MPPT increases, due to most of the commonly used MPPT algorithms were unable to track for the MPP effectively. To overcome the challenge, soft computing methods have been adapted in MPPT by researchers, with Particle Swarm Optimization (PSO) being the most prominent. However, the high computational power of PSO becomes a disadvantage in real-time and highly dynamic MPPT application. In addition, with the continuous improvement effort and the possibility of a new-comer algorithm can show superior results on the current problem, a new MFO based MPPT algorithm was proposed. In this study, a four-module 980 W solar PV system together with a DC/DC Boost Converter model was developed in MATLAB-Simulink as the MPPT algorithm test platform. Direct control strategy was adapted as the regulator for the DC/DC converter to replace the conventional proportional-integral (PI) controller to eliminate the need to tune the PI controller. Based on the study from MFO, it is found that the MFO was having the capability to perform effective tracking, despite its limitation of premature convergence problem near the MPP. To lift the limitation off, a new hybrid model named Hybrid MFO (HMFO) was proposed based on the combination of feature from MFO and conventional P&O, together with an additional partial shading detection feature. The performance of MFO and HMFO was compared with two well-established MPPT methods, namely Perturb and Observe (P&O) and PSO. To further evaluate the real-time performance of the MPPT algorithms, hardware-in-the-loop (HIL) was utilized to emulate the behavior of the PV system and power converter while a digital signal processor (DSP) was used to implement the MPPT algorithms in study. All four MPPT methods were simulated and real-time evaluated under 10 constant irradiance test cases, 30 dynamic irradiance test cases and 100 partial shaded irradiance test cases. HMFO has shown fast tracking and achieved the highest average efficiency among the soft computing methods under constant irradiance conditions. Under dynamic irradiance condition, HMFO was able to reach the new MPP faster and more effective than both PSO and MFO. Under partial shaded conditions, the HMFO was able to show the highest average tracking efficiency and the fastest convergence time among the soft computing method. The HMFO was able to track for true MPP for about three times more than the P&O under partial shaded conditions and it was able to achieve the average tracking efficiency up to 99.35 %

Maximum power point tracking and control of grid interfacing PV systems

Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power.Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power

A family of Lyapunov-based control schemes for maximum power point tracking in buck converters

summary:This paper presents a novel family of Lyapunov-based controllers for the maximum power point tracking problem in the buck converter case. The solar power generation system here considered is composed by a stand-alone photovoltaic panel connected to a DC/DC buck converter. Lyapunov function candidates depending on the output are considered to develop conditions which, in some cases, can be expressed as linear matrix inequalities; these conditions guarantee that the output goes asymptotically to zero, thus implying that the MPPT is achieved. Simulation and real-time results are presented, which validate the effectiveness of the proposals

Maximum power point tracking algorithms performance comparison for photovoltaic systems under a wide range of dynamic partial shading condition

In the recent decades, the prolonged and increasing energy demand has caused a significant progress in the renewable energy enhancement and production. Photovoltaic (PV) energy is a fair option each time more considered and developed around the world, becoming more and more a viable option due to the economy of scale and the worldwide rising interest in clean and sustainable energy. PV energy harvest and management has great advantages but some clear inconvenient issues, such as the poor efficiency energy conversion that can be originated as a result of the optical and electrical losses. Within these losses, the first one is usually the most complicated to deal with due to the random component bound to its cause, the partial shading problematic. Therefore, shading mitigation techniques appear as an integrated part of the power converting unit, trying to palliate the negative effect on the extracting power efficiency. Up to now a wide range of diverse methods have been proposed and tested through all the literature, even though all these techniques have their own weaknesses. In this thesis work the study of an efficiency comparison of a set of several maximum power point tracking techniques over a wide range of shading speeds through a MATLAB simulation, where it is designed a PV array configuration in three different setups arrangements under some diverse partial shading scenarios is proposed

Enhancing the Modeling and Efficiency of Photovoltaic Systems

Solar energy is a strong contender among the sustainable alternatives that offer practical potential for replacing increasingly depleted fossil fuels and supplying the world’s growing energy demands. However, despite its sustainability, the spread of its use has been limited due to the high costs arising from its inadequate efficiency. With this challenge as motivation, the goal of the research presented in this thesis was to contribute to the expansion of the utilization of photovoltaic (PV) systems. To achieve this goal, the work was approached from two perspectives: 1) facilitation of research into PV systems through the enhancement of existing PV models and simulation tools, which are highly complex and necessitate substantial computational effort, and 2) improvement of the efficiency of PV systems through the development of new techniques that mitigate power losses in PV systems. With respect to the first perspective, two innovative modeling approaches are introduced. The first, a new circuit model for PV systems, features accuracy comparable to that of existing models but with a reduced computational requirement. The proposed model mimics the accuracy of existing models without their dependency on a transcendental implicit equation, thus providing a shorter computational time without sacrifying the accuracy. The second modeling approach, which was developed for use in model-based online applications, involved the creation of a fast tool for estimating the power peaks of the power-voltage curves for partially shaded PV systems. Utilizing a PV circuit model for estimating the power peaks in large PV systems through the simulation of their entire power curve consumes extensive computational time, which is unacceptable for online applications even with the use of the proposed circuit model mentioned above. Rather than employing a PV circuit model to find the power peaks, the proposed tool relies on simple rules that govern the formation of power peaks in a partially shaded PV system as a means of establishing the power peaks directly, thus significantly reducing the time required. The second perspective led to the development of three methods for reducing different types of power losses prevalent in PV systems. The first is an MPPT technique for use with partially shaded PV systems that exhibit multiple power peaks in their output power curves. The proposed MPPT is uniquely distinguishable because of its ability to eliminate misleading power losses in PV systems. Rather than searching and scanning heuristically for the GMPP, it employs the fast modeling tool mentioned above to calculate the location of the GMPP deterministically, thus avoiding the need for curve scanning. The irradiance values required by the modeling tool are estimated innovatively using captured images of the PV modules obtained by an optical camera. The objective of the second was to reduce the mismatch power losses common in partially shaded PV systems through the development of an improved PV reconfiguration method. The reconfiguration proposed in this thesis is produced by a simple algorithm that establishes a better configuration and requires only negligible computational time for ensuring the minimization of mismatch power losses. The third is an enhanced maximum power point tracker (MPPT) for reducing tracking power losses in PV systems that operate under rapidly changing irradiance levels. The proposed method combines model-based and heuristic techniques in order to accelerate the tracking speed and thus decrease this type of loss. In the proposed MPPT, the temperature measurements typically necessary in any model-based PV application have been eliminated through reliance on a new set of equations capable of estimating the temperature through the utilization of current and voltage measurements

Model-based Reinforcement Learning of Nonlinear Dynamical Systems

Model-based Reinforcement Learning (MBRL) techniques accelerate the learning task by employing a transition model to make predictions. In this dissertation, we present novel techniques for online learning of unknown dynamics by iteratively computing a feedback controller based on the most recent update of the model. Assuming a structured continuous-time model of the system in terms of a set of bases, we formulate an infinite horizon optimal control problem addressing a given control objective. The structure of the system along with a value function parameterized in the quadratic form provides flexibility in analytically calculating an update rule for the parameters. Hence, a matrix differential equation of the parameters is obtained, where the solution is used to characterize the optimal feedback control in terms of the bases, at any time step. Moreover, the quadratic form of the value function suggests a compact way of updating the parameters that considerably decreases the computational complexity. In the convergence analysis, we demonstrate asymptotic stability and optimality of the obtained learning algorithm around the equilibrium by revealing its connections with the analogous Linear Quadratic Regulator (LQR). Moreover, the results are extended to the trajectory tracking problem. Assuming a structured unknown nonlinear system augmented with the dynamics of a commander system, we obtain a control rule minimizing a given quadratic tracking objective function. Furthermore, in an alternative technique for learning, a piecewise nonlinear affine framework is developed for controlling nonlinear systems with unknown dynamics. Therefore, we extend the results to obtain a general piecewise nonlinear framework where each piece is responsible for locally learning and controlling over some partition of the domain. Then, we consider the Piecewise Affine (PWA) system with a bounded uncertainty as a special case, for which we suggest an optimization-based verification technique. Accordingly, given a discretization of the learned PWA system, we iteratively search for a common piecewise Lyapunov function in a set of positive definite functions, where a non-monotonic convergence is allowed. Then, this Lyapunov candidate is verified for the uncertain system. To demonstrate the applicability of the approaches presented in this dissertation, simulation results on benchmark nonlinear systems are included, such as quadrotor, vehicle, etc. Moreover, as another detailed application, we investigate the Maximum Power Point Tracking (MPPT) problem of solar Photovoltaic (PV) systems. Therefore, we develop an analytical nonlinear optimal control approach that assumes a known model. Then, we apply the obtained nonlinear optimal controller together with the piecewise MBRL technique presented previously

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above