SREEDHAR, R.

Kumar R.T., Balaji V., Sakthidhasan K., Gomathi S., Sreedhar R., Janaki N.

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.

Amosedinakaran S., Reddy S.B., Rao P.B., Bhuvanesh A., Prakash S.V., Sreedhar R.

Pasupulati B., Sushmi M., Sudhakiran P., Amosedinakaran S., Sreedhar R., Vinoth John Prakash S.

Sreedhar R., Pradeepkumar Ch S.K., Karunanithi K., Ramesh S., Pasupulati B., Dhinakaran A.

Sreedhar R., Karunanithi K., Ramesh S.

Solar Photovoltaic (PV) system based power generation is going to become the major power source in the following few decades. Because of the advantages such as reliability, cleanliness, feasibility in small scale to medium scale power generation etc., Solar Photovoltaic (PV) based power generation is developing very rapidly. Hybrid MPPT control methods are essential to track and achieve maximum power generation from solar PV systems under normal and Partially Shaded Conditions (PSC). The proposed Hybrid Grey Wolf Optimization (GWO) algorithm with Adaptive Neuro-Fuzzy Inference System (ANFIS) will effectively tracks the Maximum Power Point when compared to the conventional hybrid MPPT methods. Luo converter is employed as DC-DC converter to convert the variable DC output from solar panel into a nominal DC level. The Luo converter output is given to a single phase Voltage Source Inverter (VSI) and the inverter is connected to grid. The Arithmetic Optimization Algorithm (AOA) tuned Fractional order Proportional Integral controller (FOPI) is adopted to maintain the stability of the grid connected inverter. The tracking efficiency of the proposed system is maintained around 98 % even under different irradiance conditions with very less convergence time of 0.016 sec.

Sreedhar R., Karunanithi K., Ramesh S.

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using  VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

Karunanithi K., Ramesh S., Raja S.P., Sreedhar R., Kannan S., Ramudu V.

In this paper, a standalone Photovoltaic (PV) system with Hybrid Energy Storage System (HESS) which consists of two energy storage devices namely Lithium Ion Battery (LIB) bank and Supercapacitor (SC) pack for household applications is proposed. The design of standalone PV system is carried out by considering the average solar radiation of the selected city. During the frequent load variation conditions, the Supercapacitor can discharge its energy since it can discharge high current with less time and during constant load condition LIBs take care of supplying required energy to the load. In this system, P&O algorithm is used for Maximum Power Point Tracking (MPPT) to achieve higher efficiency. MATLAB-Simulink is used to evaluate the performance of the proposed standalone PV system with HESS under various solar irradiance as well as AC loading conditions.

Teja R.B., Baskar S., Rao G.N., Sreedhar R.

Sreedhar R., Chandrasekar P., Karunanithi K., Vijayakumar S.C., Raja S.P.

The key intention of this research article is to design and validate a single-phase buck–boost inverter which can be utilized to modify DC power from solar panel to AC power without the need of a DC-DC converter. The proposed topology is designed to perform Maximum Power Point Tracking (MPPT) directly and the output from the inverter can be used for standalone load or can be integrated with grid. In this paper, a Global Maximum Power Point Tracking (GMPPT) is executed to gain maximum power under the Partial Shading Conditions (PSC) of solar panel. GMPPT is tracked instantaneously using Grey Wolf Optimization (GWO) technique. The proposed work is successfully carried out using MATLAB software and a prototype model is developed to validate the simulation results.

Venkata Ramana D., Chandrasekar P., Sreedhar R., Karunanithi K., Aruna Rani K., Vijayakumar A.

The objective of the paper is to analyse and compare the performance of three-phase Cuk-based inverter with PWM and SPWM techniques in the aspects of power quality using MATLAB/Simulink. In three-phase inverters configuration, a distinct method known as single-stage inverters, which has less control circuit complexity and requires minimum number of switching components, is implemented for this comparative analysis. Three-phase Cuk inverter is one of the capable methods to implement in grid-integrated and stand-alone applications. The normal PWM technique involves modulation of 50 Hz square wave with high frequency switching pulses, which was introduced originally for the inverters. Here, 125 kHz pulses are selected as high frequency switching pulses. In SPWM technique, a 50 Hz sine wave is modulated by high frequency switching pulses. Since sinusoidal signal is selected as modulating signal in SPWM, the harmonics are reduced when compared to other PWM control techniques.

Sreedhar R., Mahato R., Chaturvedi A., Kumar Jaiswal P.

Sreedhar R., Karunanithi K., Chandrasekar P., Teja R.B.

Space-vector methods for two/three level inverters involve computation of dwell times for synthesizing the reference space vector which is practically impossible for high resolution inverters. Moreover, density of space vectors in the space vector diagrams for high resolution inverters is very high and that computation of dwell times may not be necessary. So, nearest space vector approach is used for controlling high resolution inverters. This paper presents a different approach to find the nearest space-vector algorithm for high-resolution multilevel inverters. The method finds coordinates of all the space vectors in the vicinity of a given reference space vector and determines the nearest one and thus controls the inverter. Working of the control strategy is verified by simulations in MATLAB/Simulink.

Chandrasekar P., Karunanithi K., Sreedhar R., Ramana D.V., Bhavani Teja R.

Induction motor drives are the indispensable choice for the constant as well as variable speed applications due to evolution of the power electronics based sophisticated precise control systems. On the other hand, they offer consistent performance in any operating environment with less maintenance cost. However, faults are inevitable to occur and usually arbitrary in nature, so the detection of the faults well in advance is of paramount importance, especially the ones that can cause severe damage to the machine. This paper attempts to simulate the faults utilizing Fast Fourier Transform and Wavelet signal processing techniques for analysing the signals and diagnosing the fault condition. Further it will present that the Wavelets provide information on the localization of fault and stand out from the other techniques, especially for identifying the faults at the start-up of the drive. Another and most important advantage of Wavelets is that it diagnoses the fault at incipient level, which helps in reconfiguration of drive without shut down if load condition permits. Diagnosis of fault at incipient level is most sought in process control industries.

Sreedhar R., Karunanithi K.

Now a days, Electric Vehicles (EVs) are familiar with abundant types of battery chemistries with different specifications. Battery Management System (BMS) is an essential and important device of an EV for smooth operation and long life. In present scenario, a separate BMS has been used for the particular battery. So, there is a need of a common BMS for all type of EVs. This paper proposes a simulation design of common BMS for four different batteries namely Nickel-Metal-Hydride, Nickel-Cadmium, Lithium-Ion and Lead-Acid. A common simulation and mathematical modelling of aforementioned four types of battery is executed here. The proposed system is designed to manipulate and control the instantaneous values of the battery parameters like Battery voltage, Current, Power consumption and State of Charge (SoC). This proposed BMS continuously monitors the battery parameters with high accuracy, especially in SoC measurement. The working of BMS is controlled and monitored by using Extended Kalman Filter (EKF). The entire mathematical modelling and simulation work of BMS is analysed by using MATLAB/Simulink.

Hou G., Guo Z.

Kascak S., Resutik P., Prazenica M., Frivaldsky M.

Abstract This article presents the design and development of a supercapacitor for defined power profiles, focusing on the selection process for an optimal supercapacitor to form a high-performance supercapacitor module. The study outlines the methodology for selecting suitable components based on energy and power density requirements, considering operational efficiency and durability under specific load conditions. A control algorithm for bidirectional power flow is developed to regulate the charging and discharging processes of the supercapacitor module, ensuring efficient energy management. Practical verification of the designed SC module is performed to evaluate the system dynamic response under various operational scenarios. The practical experimentation using the supercapacitor module is tested in laboratory conditions. The experimental results demonstrate the effectiveness of the control strategy in achieving bidirectional operation, confirming the suitability of the designed supercapacitor module for applications requiring fast power delivery and recovery.

Turkay Y., Yüksek A.G.

Firah A., Birane M., Degla A., Hadroug N.

Dawood Z.A., Tackie S.N., Dimililer K.

In this work, the study gives attention for improvement of the Maximum Power Point Tracking (MPPT) using the Perturb and Observe (P&O) algorithm based MPPT applied to solar power generation system (SPGS). The algorithm components are refined, outcomes of the resultant optimized values are compared to address challenges in optimizing power, voltage and efficiency with the research. Further to improve this performance, Fractional Order Proportional Integral Derivative (FOPID) criteria are tuned using optimization techniques such as Black Hole (BH) Optimization, Jaya Optimization Algorithm (JOA), and Sunflower Optimization (SFO). Selection of optimal power and voltage values was aided by these methods. Limitations of the study include the exclusion of dynamic environmental changes in the analysis, and the potential non specificity of optimization algorithms for all SPGS scenarios considered. SPGS offers an opportunity to explore more dynamic models, as well as alternative algorithms, to improve the power and voltage management done in SPGS.

Khazaeefar S., Valizadeh M., Sarvenoee A.K.

In recent decades, grid-connected photovoltaic (PV) systems have been increasingly utilized worldwide for their role in renewable energy generation and sustainability. Among power electronic configurations, the multi-level inverter (MLI) is famous for its efficiency in reducing total harmonic distortion (THD) and distributing power across several switches, enhancing power quality. However, using many switches increases energy losses and system complexity, making optimization crucial. Additionally, the power output from PV arrays is heavily dependent on environmental factors like temperature and solar irradiation, necessitating maximum powerpoint tracking (MPPT) techniques through advanced switching algorithms. These algorithms ensure that the system operates at its optimal point, extracting maximum energy from the PV array under varying conditions. Consequently, THD can be affected by these variations, complicating performance further. This paper proposes a Z-source-based MLI, providing additional benefits in power conversion efficiency and flexibility. This design employs a lower-frequency switching method for MPPT, executed in the Z-source part of the inverter during the shoot-through (ST) state, ensuring that THD remains unaffected by the MPPT process and improving system stability. This model is simulated in MATLAB to verify its performance under different conditions. Results illustrate an efficiency of over 96%, while maintaining a steady-state THD of 4.58%, confirming the effectiveness of the proposed approach.

Rezoug A., Iqbal J., Nemra A.

Song M.

Tharwin Kumar R., Christober Asir Rajan C.

Ibrahim A., Fang Z., Li R., Zhang W., Xu J., Zahir V., Dagal I., Kotb H., AboRas K.M., ELrashidi A.

Murugan O.G., Pandarinathan S., Arunagiri L.S., Dhanamjayulu C., Muyeen S.M.

Lalaymia H., Djellad A., Rekik B., Farou M.

Vijaya Sambhavi Y., Vijayapriya R.

Naima B., Belkacem B., Ahmed T.

Ogunniyi E., Richards B.S.

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%.

Zhu W., Harrison A., Nguimfack-Ndongmo J.D., Iqbal S., Alombah N.H., Mbasso W.F., Mahmoud H.A., Alhasnawi B.N.

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT.

Nemouchi B., Rezgui S.E., Babqi A., Harrison A., Benalla H., Ali E., Ghoneim S.S.

AbstractThe article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results.

Henry Alombah N., Harrison A., Kamel S., Bertrand Fotsin H., Aurangzeb M.

This paper presents a novel simplified and flexible Photovoltaic emulator (PVE) that effectively emulates the static and dynamic characteristics of a solar panel. By meticulously generating a benchmark for the entire system, the proposed PVE utilizes an original explicit PV model (EPVM). As an approximation of the implicit equation of the PV, the EPVM employs a Taylor series resolution. This methodology eliminates the necessity for numerous iterative computations, in contrast to previous frameworks, and as a result possesses formidable computational capabilities. Furthermore, it is clearly illustrated that the EPVM can be effortlessly incorporated into the PVE architecture, resulting in a straightforward and simplified procedure that contributes to its overall flexibility and simplicity. Using MATLAB/Simulink and a 200 W solar system, the developed PVE is investigated numerically. Through the application of nonlinear processes such as (maximum power point tracking) MPPT, the PVE's viability is confirmed. This study repeatedly emphasizes the superior qualities of the proposed PVE in simulating a solar panel in a straightforward, precise, and efficient manner, by conducting a comparative analysis with contemporary PVE designs. It is consistently determined through quantitative evaluations that the developed PVE achieves an efficiency exceeding 99 % and a maximum error rate of less than 0.7 %.

Nguimfack-Ndongmo J.D., Harrison A., Alombah N.H., Kuate-Fochie R., Ajesam Asoh D., Kenné G.

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers’ attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions.

El Ouanjli N., Mahfoud S., Al-Sumaiti A.S., El Daoudi S., Derouich A., El Mahfoud M., Mossa M.A.

The Direct Torque Control (DTC) was previously designed to achieve high performance of induction motor (IM) by accurately and independently controlling its flux and torque. However, this classical DTC generates several disadvantages such as strong fluctuations in the torque and stator flux as well as quite significant switching losses. For these reasons, many modern techniques have been proposed in the literature to improve it. On the other hand, as the inadequacy of the parameters seriously affects the efficiency and stability of the induction motor, it is paramount to accurately estimate the stator resistance, for this, robust observers are necessary suggested to be employed. This paper proposes developed structure of DTC strategy using twelve sectors combined with Backstepping speed regulator for the external loop and robust model reference adaptive system based on active power principle to estimate the stator resistance. In addition, several simulations under Matlab/Simulink and experiment results using the dSPACE DS1104 board are discussed to confirm improvements in control and speed estimation.

Ismaeel A.A., Houssein E.H., Khafaga D.S., Abdullah Aldakheel E., AbdElrazek A.S., Said M.

The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD has two types. The first type takes into consideration the minimization of the cost of fuel consumption, this type is called ELD. The second type takes into consideration the cost of fuel consumption and the cost of emission, this type is called combined emission and economic dispatch (CEED). The performance of the OOA is compared against several techniques to evaluate its reliability. These methods include elephant herding optimization (EHO), the rime-ice algorithm (RIME), the tunicate swarm algorithm (TSA), and the slime mould algorithm (SMA) for the same case study. Also, the OOA is compared with other techniques in the literature, such as an artificial bee colony (ABO), the sine cosine algorithm (SCA), the moth search algorithm (MSA), the chimp optimization algorithm (ChOA), and monarch butterfly optimization (MBO). Power mismatch is the main item used in the evaluation of the OOA with all of these methods. There are six cases used in this work: 6 units for the ELD problem at three different loads, and 6 units for the CEED problem at three different loads. Evaluation of the techniques was performed for 30 various runs based on measuring the standard deviation, minimum fitness function, and maximum mean values. The superiority of the OOA is achieved according to the obtained results for the ELD and CEED compared to all competitor algorithms.

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.

Hichem L., Amar O., Leila M.

Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

Silveira R.D., da Silva S.A., Sampaio L.P., Afonso J.A.

This paper presents a hybrid maximum power point tracking (MPPT), which combines a metaheuristic algorithm and a traditional MPPT method applied in a photovoltaic system operating under partial shading conditions. The MPPTs based on traditional methods are not able to track the global maximum power point (GMPP) when partial shadings occur. Thus, MPPT algorithms based on metaheuristic algorithms, which are used for global optimization, have presented efficiency to extract the maximum power from photovoltaic arrays. However, these methods are random, resulting in large power oscillations in transients of small variations in solar irradiance. Therefore, this paper proposes the metaheuristic algorithm called Differential Evolution (DE) to seek and track the GMPP. After the DE convergence, the MPPT algorithm is switched to Incremental Conductance (IC) in order to refine the tracking. The effectiveness of the algorithm is proved through simulation results. Furthermore, comparative analyses are provided for each algorithm (DE and IC) to evaluate their performances in the PV system.

Mahafzah K.A., Al-Shetwi A.Q., Hannan M.A., Babu T.S., Nwulu N.

DC-DC converters play a crucial role in recent and advanced applications, enabling efficient power conversion and management for renewable energy systems, electric vehicles, portable devices, and advanced communication systems. In line with this, the objective of this paper is to introduce a new DC-DC configuration based on the Cuk converter named as Mahafzah converter, which utilizes a coupling capacitor with a lower rated voltage. The paper aims to demonstrate the effectiveness of the proposed converter in terms of improved efficiency, reduced size, and reduced semiconductor device currents compared to the conventional Cuk converter. The proposed configuration comprises the same components as the Cuk converter, but in a different arrangement, without any additional elements. The main advantage of the proposed converter is using a coupling capacitor with a much lower rated voltage than the Cuk converter, resulting in a smaller capacitor size, reduced printed circuit board (PCB) size, and manufacturing cost. Additionally, the proposed converter reduces the currents of the semiconductor devices compared to those in the Cuk converter. To demonstrate its effectiveness, the converter is operated under continuous current mode (CCM) with a constant duty cycle and switching frequency. The study provides an in-depth discussion of the various operating modes by making use of equations relating to currents, voltages, duty cycles, and voltage gains. It also provides detailed illustrations of the limits between CCM and discontinuous current mode (DCM). The effectiveness of the proposed converter is demonstrated through a design example with operating parameters of 1 kW, 200 V/−300 V, and 20 kHz. Additionally, a low voltage–low power prototype (12/−18 V, 3.24 W, 20 kHz) is established to verify the operation of the proposed converter. Simulation and experimental verification of the proposed configuration achieved the desired results to improve efficiency and reduce the rate. The results clearly indicate that the efficiency of the proposed converter surpasses that of the conventional Cuk converter under identical operating conditions, reaching approximately 88% at rated load conditions.

Kouser S., Dheep G.R., Bansal R.C.

The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscured by clouds or structures, several local maximum power peaks (LMPPs) appear in the solar cell characteristics. Traditional MPPT algorithms, rather than following the global peak power point (GPPP), are preferable to following the local peak power point. If partial shading causes numerous LPPPs, it is necessary to look into how the MPPT technique can keep track of GPPP. Employing soft computing approaches such as the hybrid neural network/fuzzy method with variable step size perturb and observing MPPT, it is possible to trace the GPPP and also augment solar energy extraction. The present research paper focuses on hybrid fuzzy/neural network MPPT integrated with a high-step-up DC-DC converter to harvest the utmost power from the solar PV array. The voltage transients are reduced by controlling the DC link voltage along with solar radiation and temperature variations. The proposed MPPT technique is shown to be effective under both uniform and partial shade conditions in a series of simulations. From the test results, the efficiency of the overall system has increased from 91 to 98% for partial shading and uniform operating conditions.

Ragul R., Shanmugasundaram N., Paramasivam M., Seetharaman S., Mary Immaculate S.L.

The goal of this article is to use MPPTs (maximum power point trackers) to extort maximum power from best configuration or combine renewable resources and energy storage systems that all work together in off-grid for electric vehicle charging. The grey wolf algorithm (GWO) searches the MPP at partial shading condition (PSC) with following two consideration one is high oscillations around GMPPs, and other is that they are unable to track the new GMPPs after it has changed positions because the seeking agents will be busy around the previous GMPPs captured. Hence, in this paper, the proposed research objective is to find solutions to these two difficulties. The issue of oscillations around GMPPs was handled by combining GWO with ANFISs (adaptive Neuro-Fuzzy inference system) to gently tune output produced power at GMPPs. ANFISs are distinguished by their near-zero oscillations and precise GMPPs capturing. The second issue called they are unable to track the new GMPPs after it has changed positions is addressed in this work by using novel initialization by GWOs (Grey wolf Optimizations). In the MATLAB-Simulink and experiments demonstrate the effectiveness of the suggested GWO-ANFIS MPPTs based off-grid station for EVs (Electrical Vehicle) battery charging.

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.

Ibrahim M.H., Ang S.P., Dani M.N., Rahman M.I., Petra R., Sulthan S.M.