A novel simplified buck power system control algorithm: Application to the emulation of photovoltaic solar panels

Manna S., Singh D.K., Akella A.K., Kotb H., AboRas K.M., Zawbaa H.M., Kamel S.

This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation and design of the new MRAC for MPPT based on a boost converter are addressed here. Moreover, a mathematical model is formulated and an efficient MRAC is designed for MPPT. To validate the robustness of the controller, MATLAB/Simulink is utilized to compare with the state-of-the-art approach, which is incremental conductance (INC) and perturb & observe (P&O) under various operating conditions based on the convergence time, tracking efficiency, PV current & voltage ripple, overall efficiency, and error rates. The proposed controller’s average tracking efficiency is 99.77% and 99.69% under diverse temperature and radiation conditions, respectively. In addition, it takes only 3.6 msec to capture MPP, which is around ten times faster than INC and twelve times faster than the P&O approach. When compared to INC and P&O, the MPP error rates in the MRAC-MPPT scheme are significantly lower. The simulation outcomes indicate that the presented controller exhibits excellent tracking under varying circumstances like solar radiation and temperature.

Harrison A., Alombah N.H., Kamel S., Ghoneim S.S., El Myasse I., Kotb H.

Harrison A., Dieu Nguimfack-Ndongmo J.D., Alombah N.H., Aloyem Kazé C.V., Kuate-Fochie R., Asoh D.A., Nfah E.M.

A PV system is subject to random variations in environmental conditions, and continuous tracking of the maximum power point is an indispensable step to improve the PV operational efficiency. Numerous techniques of maximum power point tracking have been reported in the literature. However, these techniques suffer from numerous problems such as oscillation around the maximum power point and do not provide satisfactory robustness. Taking into account the nonlinear nature of the PV module and power electronics converters in PV systems, nonlinear control represents a vital control solution to guarantee both an optimal and robust PV system. The nonlinear control strategy proposed in this work forms a closed-loop system between the PV module, boost converter, load, an artificial neural network model for reference prediction, and an integral backstepping controller. The stability of the controller has been verified by Lyapunov theory and the controller has been optimized using the particle swarm optimization (PSO) method. Numerical simulations with rigorous robust tests have proved the superior performance of the proposed controller as compared to perturb and observe, and PSO-terminal sliding mode controller. The proposed controller was further verified under real experimental environmental conditions and found to yield satisfactory performance.

Harrison A., Alombah N.H., de Dieu Nguimfack Ndongmo J.

The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

Ahamad N., Chhetri A., Saklani M., Bajaj M., Kotb H., Khan B., Sikander A.

AbstractThe regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters , and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time.

Harrison A., Alombah N.H.

The current state of affairs on the Photovoltaic emulator (PVE) is facing two main challenges: complexity in resolving the nonlinear equations of the photovoltaic (PV) and the problem of effective control of the PVE power conversion stage (PCS). In this paper, a new power electronics-based PVE is proposed to emulate the dynamic and static characteristics of the PV cell/module. The nonlinear equations of the PV cell/module are resolved using a new piecewise segmentation technique, involving the splitting of the current-voltage (I–V) curve into twelve linear segments associated with the letters a to m (a–m). Based on input environmental conditions, a trained artificial neural network (ANN) is constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. By the use of simple linear equations with the boundary coordinates, a reference voltage is then generated for the PVE. A nonlinear backstepping controller is designed to exploit the PVE reference voltage and stabilize the PCS. The stability of the controller is verified by Lyapunov laws. Optimal performance and control of the PCS were ensured by resorting to particle swarm optimization (PSO). The overall system has been investigated in the MATLAB environment with major tests including the response to fast-changing irradiance and temperature, the EN 50530 test, and the response to change in the load. The proposed PVE revealed a satisfactory dynamic performances in mimicking the PV characteristics. Furthermore, the accuracy of the PVE as a function of the mean absolute percentage error (MAPE) was found less than 0.5% even for the worst case of environmental conditions. Experimental validation of the proposed PVE under real environmental conditions further validated its good dynamic and static robustness.

Harrison A., Alombah N.H.

Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage ( I - V ) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

Manna S., Akella A.K., Singh D.K.

AbstractThe technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

Liu X., Wu X.

With the development of more-electric aircrafts, bidirectional DC-DC converter has been widely used in this field. In order to improve the voltage regulation range and stability of the bidirectional DC-DC converter system, a two-stage bidirectional DC-DC converter with some operation modes is used and analyzed in this paper. The converter is composed of a four-switch buck-boost circuit and an isolated CLLC resonant circuit, where all MOSFETs can realize soft switching action. The four-switch buck-boost circuit regulates the output voltage in a certain range through the duty cycle control, and the CLLC resonant circuit can control the output voltage by changing its switching frequency, where the two-stage bidirectional DC-DC converter adopts voltage closed-loop control strategy to realize the stable voltage output. Due to the existence of the intermediate part conversion voltage, the converter has three-port network characteristics, which makes it applicable to the working occasions with multiple voltage levels. The feasibility of this two-stage bidirectional DC-DC converter system combined with control strategy and its three-port operation used for more-electric aircraft power system are verified by simulation analysis.

Harrison A., Alombah N.H., de Dieu Nguimfack Ndongmo J.

Maximum power point tracking (MPPT) is becoming more and more important in the optimization of photovoltaic systems. Several MPPT algorithms and nonlinear controllers have been developed for improving the energy yield of PV systems. On the one hand, most of the conventional algorithms such as the incremental conductance (INC) demonstrate a good affinity for the maximum power point (MPP) but often fail to ensure acceptable stability and robustness of the PV system against fast-changing operating conditions. On the other hand, the MPPT nonlinear controllers can palliate the robust limitations of the algorithms. However, most of these controllers rely on expensive solar irradiance measurement systems or complex and relatively less accurate methods to seek the maximum power voltage. In this paper, we propose a new hybrid MPPT based on the incremental conductance algorithm and the integral backstepping controller. The hybrid scheme exploits the benefits of the INC algorithm in seeking the maximum power voltage and feeds a nonlinear integral backstepping controller whose stability was ensured by the Lyapunov theory. Therefore, in terms of characteristics, the overall system is a blend of the MPP-seeking potential of the INC and the nonlinear and robust potentials of the integral backstepping controller (IBSC). It was noted that the hybrid system successfully palliates the conventional limitations of the isolated INC and relieves the PV system from the expensive burden of solar irradiance measurement. The proposed hybrid system increased the operational efficiency of the PV system to 99.94% and was found better than the INC MPPT algorithm and 8 other recently published MPPT methods. An extended validation under experimental environmental conditions showed that the hybrid system is approximately four times faster than the INC in tracking the maximum power with better energy yield than the latter.

Obeidi N., Kermadi M., Belmadani B., Allag A., Achour L., Mesbahi N., Mekhilef S.

The present paper proposes a modified current sensorless approach to reduce the implementation cost of maximum power point tracking (MPPT) controller for photovoltaic (PV) systems under partial shading conditions (PSCs). The proposed scheme relies only on the input voltage signal without the need for a current sensor to track the global maximum power point (GMPP). This is performed using a predefined objective function derived from the mathematical model of the buck-boost converter with an adaptive step-size mechanism for quick convergence. In addition, a formula of lower and upper limit duty cycle values for every local peak is incorporated for fast detection of local power peaks. An extensive experimental study, using a hardware prototype composed of a buck-boost converter driven by a dSPACE DS1104, is carried out to validate the proposed control scheme. Experimental results show that the proposed controller is able to accurately track the GMPP with high speed under various PSCs scenarios. Additionally, the proposed scheme reduces the implementation cost by 27.95% compared to conventional MPPT techniques. • Capability of proposed method to track global maximum power under different patterns. • Ability of the proposed algorithm to operate in full range of power-duty cycle plane. • Fast-tracking by determining lower/upper duty cycle limits for every local peak. • Reducing the steady-state oscillation by incorporating an adaptive step-size gain. • Reducing the implementation cost of photovoltaic systems controller.

Harrison A., Nfah E.M., de Dieu Nguimfack Ndongmo J., Alombah N.H.

This paper presents an enhanced perturb and observe (P&O) method for reconciling the trade-off problem between the dynamic response and steady-state oscillations in maximum power point tracking (MPPT). The constraint of having to sacrifice either the dynamic response or the steady-state oscillations has been solved. The method uses the relationship between the open-circuit voltage and maximum power voltage from the fractional open-circuit voltage (FOCV) MPPT method to establish a valid, reduced, and confined search space within which an enhanced P&O via dynamic adaptive step size terminates the search for the maximum power point. The feasibility of the proposed method has been validated by comparing its performance with the conventional P&O algorithm. It was noted that the proposed method increased the operational efficiency of the PV module to 99.89%, reduced the tracking time to 1.8 ms, and preserved the good steady-state response with a power attenuation of less than 0.10 W or relative 0.16% under MATLAB environment. An experimental setup was used to collect real irradiance and temperature data which was used in real-time simulations. The enhanced P&O method was able to resist abrupt changes in irradiance and temperature as it effectively and efficiently followed the maximum power point (MPP). Finally, to appreciate the supremacy of the proposed method, it was compared to nineteen different MPPT methods from literature. The comparison showed that the enhanced P&O MPPT method is highly efficient and effective for MPPT in photovoltaic (PV) generation systems.

Sorouri H., Sedighizadeh M., Oshnoei A., Khezri R.

Buck DC–DC converters are broadly used in DC microgrids to provide a constant dc voltage for generation and storage components. Changing of load condition affects the quality of voltage in the buck DC–DC converters. When constant power loads (CPLs) are used, the stability of these power electronic devices is at risk due to negative impedance characteristics of the CPLs. In such condition, an efficient control method is required to ensure the proper operation of the converter. For this purpose, development of an adaptive control methodology is essential to evaluate the accurate values of controller parameters in the shortest time to damp the ripples quickly. This paper develops a backstepping controller with nonlinear disturbance observer to regulate the output voltage of a dc/dc converter feeding a CPL. An artificial neural network (ANN) methodology is used to estimate the backstepping control parameters of the buck converter. The training ability of the ANN technique prevents the existing controller from depending on the working point of the microgrid. The ANN methodology adapts the controller with various changes and reflections of uncertainties in the microgrid. Case studies are conducted on a dc/dc buck converter in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator. • Applying a backstepping controller to regulate the output voltage of a dc/dc converter. • Developing a nonlinear disturbance observer to estimate the variation of uncertainty. • Using ANN to optimally tune the backstepping control parameters. • Verifying the simulation results by the OPAL-RT real-time simulator.

He W., Shang Y., Namazi M.M., Ortega R.

The existing results mainly focus on the full-information control for converters with constant power load (CPL). More precisely, it is assumed that all of the states of the systems should be measured. There are no theoretical results on an adaptive sensorless control scheme for the converters feeding CPL with the guaranteed stability. Note that a sensorless control will facilitate the decrease of the overall cost and fault rate as well as the increase of the reliability of the systems. In this paper, the sensorless control problem for DC–DC buck converter with unknown CPL is addressed. The main contribution of this paper is to design a reduced-order generalized parameter estimation-based observer to simultaneously reconstruct the unmeasurable inductor current and unknown power load of the system. Borrowing the dynamic regressor extension and mixing technique, its main idea is to transform the state observation into the parameter estimation problem. Besides, the finite-time convergence of the observer can be ensured. It is noted that the buck converter is only regarded as an application example. As a matter of fact, this observer can be extended to a large class of converters with CPLs. Then, introducing the observed terms into an existing full-information controller, in a certainty equivalent manner, an adaptive sensorless control scheme is achieved. Finally, the performance of the designed controller is assessed via simulation and experimental results. • A reduced-order state observer is designed for buck converter with unknown CPL. • This observer design can be extended to a large class of power converters with CPLs. • An adaptive sensorless controller is proposed for the system. • Simulation and experimental results of the designed controller are presented.

Izci D., Hekimoğlu B., Ekinci S.

Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter’s output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integral-error-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter’s transient and frequency responses.

Chen J., Harrison A., Alombah N.H., Mbasso W.F., MOLU R.J., Alharbi A.M., Jangir P.

Alombah N.H., Harrison A., Ndongmo J.D., Fotsin H.B., Rehman A.U.

Ouahab S.A., Bakkali F., Amghar A., Sahsah H., Mentaly L., Mahfoud L.

Paliyal P.S., Mondal S., Layek S., Kuchhal P., Pandey J.K.

Abstract An automatic solar tracking system is an approach for optimizing the generation of solar power and modifying the angles and direction of a solar panel by considering changes in the position and path of the sun. The performance status of an automatic solar tracking system depends on various factors, including its design, location, and maintenance or repairs. The solar energy from the sun that the Earth intercepts is approximately 1.8 × 1011 MW, which is thousands of times greater than the intensity at which the Earth now uses all other commercially available energy sources combined. Currently, research into automatic solar trackers is on the rise, as solar energy is abundant in nature, but its use in a highly efficient way is still lacking. This paper provides a detailed literature review and highlights some key advancements and challenges associated with state-of-the-art automatic solar tracking systems. The performance of the dual-axis photovoltaic tracking system outperforms that of the stationary systems by more than 27% based on the overall system efficiency. Under diverse weather conditions, the efficiency of the scheduled-based solar tracking systems was enhanced by 4.2% compared with that of the light-dependent resistor-based solar trackers.

Alaas Z.

The effective implementation of Photovoltaic (PV) systems relies heavily on controlled testing using Photovoltaic Emulator (PVE). This paper presents a novel PVE for rapid and robust testing of PV systems. A fifteen segments linearization (FSL) methodology is put-forward to resolve the nonlinear current-voltage (I-V) equations of PV modules. The I-V curve is precisely segmented into 15 linear elements, whose coordinates are used to construct a suitable reference voltage for the PVE. To ensure a robust and fast emulation of the solar panel, a nonlinear power stage controller (NPSC) is designed to maximize the Buck converter-power stage of the PVE. Several experiments have been performed under diverse environmental and load testing profiles, revealing a mean error and maximum error of less than 0.2 %, and less than 0.6 % respectively for three classes of commercial PV modules (multicrystal KC200GT, poly-crystalline MSX-60, and mono-crystalline CS6K-280 M). The high computation accuracy of the FSL position the proposed PVE as a simple but powerful emulation tool for PV systems. By evaluating its performance and comparing it to the state-of-the-art emulators, this paper aims to demonstrate the effectiveness and superiority of the FSL-NPSC-PVE in achieving rapid and robust emulation of different commercial solar panels.

Jamna A., Velmurugan P., Prabhu V.V., Palanisamy K.

The increasing complexity of power distribution systems, coupled with the growing stress on power production and the proliferation of nonlinear loads, poses significant challenges to enhancing smart grid power quality for photovoltaic (PV) systems and plug-in electric vehicles (PEVs). This paper proposes a hybrid method for optimal power quality enhancement of PV and PEV systems, combining the honey badger algorithm (HBA) and golden jackal optimization (GJO), often known as the HBA-GJO methodology. The proposed technique’s goal is to compensate for harmonic and reactive currents, regulate the main grid frequency, and smooth peak demands under full loads. The HBA method is used to identify nonlinear load currents and determine compensating currents for PV and PEV converters, while the GJO method controls phase frequency and manages power exchange to ensure grid frequency stability. On the MATLAB platform, the performance of the HBA-GJO approach is evaluated and contrasted with other strategies that are currently in use. The HBA-GJO method achieves a total harmonic distortion value of 28%, which is significantly lesser, compared to other methods such as the enhanced artificial gorilla troops optimizer, particle swarm optimization-based artificial neural network (PSO-ANN), and generalized predictive control technique techniques. These results exhibit the performance of the HBA-GJO technique in improving power quality in smart grid applications.

Sreedhar R., Karunanithi K., Ramesh S., Raja S.P., Pasham N.K.

The deployment of grid connected photovoltaic (PV) systems has become increasingly vital in the pursuit of sustainable and renewable energy sources. As the global demand for electricity rises, the efficient and reliable incorporation of PV power into electrical grid is of paramount importance. An elementary Luo converter is employed here to enhance the resultant voltage of PV array. To further improve the system’s performance, a Grey Wolf optimized radial basis function neural network (GWO-RBFNN) is employed for maximum power point tracking (MPPT). The GWO algorithm is employed to fine-tune output of RBFNN, making it capable of adaptively extract maximum power. According to the obtained MPP, the input signals to the pulse width modulation generator is tuned using the proposed hybrid MPPT controller. These pulses regulates the operation of elementary Luo converter and guarantees maximum energy conversion efficiency. The converter’s DC link voltage is subsequently subject into grid through a single-phase voltage source inverter which is synchronized with the grid. To facilitate seamless grid integration and synchronization, a conventional proportional integral (PI) controller is deployed. The simulation outputs attained using Matlab results in a robust and efficient system, capable of contributing reliable renewable energy to the grid. The tracking efficiency of the proposed hybrid MPPT controller reaches up to 98.1% and the THD value is reduced to 2.95% which indicates the power quality of the proposed system.

Belghiti H., Kandoussi K., Harrison A., Benbba R., El Otmani R., Chellakhi A., Sadek E.M.

The aim of this research undertaking is to investigate an integrated approach that extends the battery life of a single-stage off-grid photovoltaic system while simultaneously increasing the availability of solar photovoltaic power. This paper takes a synergistic approach to addressing these issues, in contrast to the numerous prior papers that have investigated them individually. In regard to the initial issue, this article proposes a hybrid MPPT solution which combines a backstepping controller with a distinct single-sensor MPPT approach. The aforementioned integrated system guarantees the exact attainment of the maximum power point (MPP) of the photovoltaic solar array, regardless of weather fluctuations. With regard to the second issue at hand, the effective management of charge for lead-acid batteries is accomplished by employing a three-stage charging controller (TSCC). The simulation results generated in MATLAB/Simulink indicate that MPPT tracking and battery charge control exhibit markedly improved performance across a range of weather conditions when the climatic conditions of El Jadida city, Morocco are taken into account. The hybrid method, as proposed, indisputable surpasses alternative MPPT approaches such as Inc-PI, FLC, PSO, RegP, and IMP-PO, with respect to convergence time (

Chen T., Harrison A., Alombah N.H., Aurangzeb M., Telba A.A., Mahmoud H.A.

Chen T., Harrison A., Alombah N.H., Aurangzeb M., Iqbal S., Mahmoud H.A.

This article presents a novel two-stage simplified control algorithm designed to enhance the Maximum Power Point Tracking (MPPT) operation of PV systems while minimizing the complexity associated with it. A voltage-based hybrid beta-incremental conductance (hybrid-beta-INC) is developed in the first stage to determine the MPP voltage. The methodology behind this scheme is to first approach the neighborhood of the MPP through an intermediate variable β, which has a simple monotonically decreasing relationship with the PV voltage. Subsequently, the INC is employed to target the actual MPP voltage. The steady-state response of the PV system is thus maintained throughout this stage by the hybrid-beta-INC. In the second stage, a simplified parameter-less (SP) controller is proposed to regulate the underdamped dynamics of the PV. The system's stability as a whole is enhanced through the decoupling of these two phases. In contrast to currently available MPPT controllers, the proposed SP makes a significant contribution to the structural simplicity of the control framework by eliminating the need for adjustable parameters. Simplicity, enhanced control immunity and resilience to load disturbances and parametric uncertainties are the main attributes of the proposed controller. Its superior attributes over exiting controllers such as the P&O, INC, search space, and hybrid INC-integral backstepping nonlinear controllers, is affirmed on the basis of quantitative metrics such as the tracking time, ripples and efficiency. Finally, the performance of the proposed system is monitored under real-time environmental settings for three consecutive days, which confirms its resilience and dependability in tracking the MPP of the PV system under experimental conditions. The results showed that the controller is able to cope with climatic and load changes maintaining a robust response. It is unveiled that it maintains a minimum efficiency of 99.72% despite fluctuations in temperature. In the presence of continually changing irradiance conditions, the EN 50,530 test demonstrates an efficacy in excess of 99.95%.

Harrison A., Ullah S., Alombah N.H., Bajaj M., Mbasso W.F., Iqbal S., Tuka M.B.

AbstractThis article investigates an inventive methodology for precisely and efficiently controlling photovoltaic emulating (PVE) prototypes, which are employed in the assessment of solar systems. A modification to the Shift controller (SC), which is regarded as a leading PVE controller, is proposed. In addition to efficiency and accuracy, the novel controller places a high emphasis on improving transient performance. The novel piecewise linear-logarithmic adaptation utilized by the Modified-Shift controller (M-SC) enables the controller to linearly adapt to the load burden within a specified operating range. At reduced load resistances, the transient sped of the PVE can be increased through the implementation of this scheme. An exceedingly short settling time of the PVE is ensured by a logarithmic modification of the control action beyond the critical point. In order to analyze the M-SC in the context of PVE control, numerical investigations implemented in MATLAB/Simulink (Version: Simulink 10.4, URL: https://in.mathworks.com/products/simulink.html) were utilized. To assess the effectiveness of the suggested PVE, three benchmarking profiles are presented: eight scenarios involving irradiance/PVE load, continuously varying irradiance/temperature, and rapidly changing loads. These profiles include metrics such as settling time, efficiency, Integral of Absolute Error (IAE), and percentage error (epve). As suggested, the M-SC attains an approximate twofold increase in speed over the conventional SC, according to the findings. This is substantiated by an efficiency increase of 2.2%, an expeditiousness enhancement of 5.65%, and an IAE rise of 5.65%. Based on the results of this research, the new M-SC enables the PVE to experience perpetual dynamic operation enhancement, making it highly suitable for evaluating solar systems in ever-changing environments.

Amine H.M., Abdallah L., Fadila T., Aissa B., Harrouz A., Colak I.