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Scientific Reports volume 15, Article number: 41846 (2025)
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In this paper, an interconnected Alternating Current (AC) grid architecture powered by solar photovoltaic energy is conceptualized, evaluated, and implemented to promote rural electrification in developing countries. In order to maximize Photovoltaic (PV) system efficiency, a novel Maximum power point tracking (MPPT) technique is presented in this study that can distinguish between abrupt changes in sunlight and disturbances in the reference voltage. MPPT provides maximum energy conversion efficiency by different solar radiation and temperature environments. Enhanced Incremental Conductance Algorithm (EICA) continuously adjusts the step size to trace the maximum power point more quickly and accurately. Using an enhanced incremental conductance algorithm, this technique adjusts the duty cycle of the DC-DC Boost converter to prevent Maximum Power Point (MPP) divergences that can occur when using a conventional incremental conductance approach under rapidly changing brightness levels. Using simulation in the Matlab / Simulink software, the proposed methodology, which includes a boost converter as the interface to feed the load is tested while accounting for variations in temperature and irradiance. Satisfactory computational findings demonstrate that the Enhanced Incremental Conductance (EIC) method can optimally monitor PV maximum power across a wide range of operational conditions. The EIC-based MPPT tracks the maximum power point more accurately by adjusting the step size according to variations in irradiance. This results in 22% fast convergence and low oscillations at the maximum power point, thereby 15% increasing the efficiency of the photovoltaic system. In addition, the Enhanced Incremental Conductance (EIC) controller, the THD values are relatively lower. The simulation results indicate VTHD of 13.15% and ITHD of 12.31%, whereas the hardware implementation registers VTHD of 14.52% and ITHD of 13.55%.
Rural communities require special attention as nations work towards accomplishing Sustainable Development Goal (SDG) 7, which calls for “affordable, reliable, sustainable, and modern energy for all”1. Despite a historic decline in the number of individuals lacking access to electricity from 1 billion in 2016 to a mere 770 million in 2019, progress in rural areas has been considerably more restricted. The global rate of electricity availability in urban regions (about 97%) is higher than the rate in rural areas (82%)2, meaning that 84% of the world’s population lacks access to electricity and lives in rural areas. Off-grid electrification projects are likely to be successfully commissioned once they are designed, but rural and poorly inhabited areas typically wait the longest to be supplied by electrification technology3. In order to achieve SDG7, electrification of rural areas may, therefore, need new, coordinated strategies adapted to the structures and demographics unique to the environment4. Approximately 31 million rural dwellings in India have yet to be electrified5. The majority of remote villages in India that have not yet undergone electrification share certain essential attributes, including6:
Extremely low household count (between 2 and 200) and population (less than 500).
Limited communication and transportation infrastructure.
Low affordability and income level.
Low technical proficiency and literacy rates.
In recent years, academic and commercial research has been devoted to determining which renewable energy sources are the most viable for electrifying rural regions, given their accessibility and capacity to integrate with the power grid. The need to develop carbon-free power grids, reduce global warming, and run out of fossil fuels has sped up the integration of solar energy systems, particularly photovoltaic systems, into power networks. Solar energy has numerous benefits in comparison to energy derived from fossil fuels. It is limitless, cost-free, organic, pure, and free of ecological pollution. Its modular design enables the creation of solar arrays at various power levels. However, it has a significant drawback, namely the inefficient conversion of light into electrical energy7.
Furthermore, a PV generator’s power output is contingent upon several elements, including temperature, brightness, and the load to which it is attached. The photovoltaic array operates at its maximum power at only one specific point for each operating condition. It’s critical to make use of the solar generator’s peak power in the majority of PV applications, including hybrid, stand-alone, and public grid-connected systems. An appropriate MPPT technique is required for a power electronics device in order to accomplish this goal8,9. A large number of these MPPT algorithms are presented in several publications10. A number of factors affect these methods differently, including the popularity or equipment needed for execution, the complexity, cost, efficiency, convergence speed, and the number of sensors needed. A comprehensive analysis of thirty distinct MPPT algorithms is available in11,12. Figure 1 shows the DC-DC Boost converter with MPPT techniques.
DC-DC Boost converter with MPPT techniques.
In order to lower the likelihood of tracking direction loss and hence improve PV system energy efficiency, this research suggests a new, enhanced Incremental conductance-based MPPT algorithm. An MPPT controller-enabled controlled switch raises the PV output of a DC-DC boost converter to match the reference voltage. The DC output is subsequently converted to AC via a grid-connected three-phase inverter. In order to eliminate the high-frequency harmonics that are disrupting our grid integration, a low-pass filter was implemented. The PV module and the Grid are interconnected through the use of an isolation transformer. In order to function as intended, substantial adjustment and control are required for each of these circuits and converters. Precisely tuned PI controllers are utilized in this circumstance to achieve optimal outcomes.
The main objective of the proposed work, using an Enhanced Incremental conductance algorithm, this technique adjusts the duty cycle of the DC-DC Boost converter to prevent MPP divergences that can occur when using a conventional incremental conductance approach under rapidly changing brightness levels. Using simulation in the Matlab/Simulink software, the proposed methodology, which includes a boost converter as the interface to feed the load, is tested while accounting for variations in temperature and irradiance. Satisfactory computational findings demonstrate that the EIC method can optimally monitor PV maximum power across a wide range of operational conditions.
The key novelty features of the proposed Enhanced Incremental Conductance algorithm are:
New approach for Robust MPPT – In order to cope with fast changing environmental conditions we propose a new Enhanced Incremental Conductance algorithms made for the distinction between rapidly short moments of solar irradiation and voltage changes to robustly follow environmental change.
Adaptive Step-Size Adjustment – The EIC method has the capacity to adapt modulating irradiance step size for rapid seeking at the full power point and can track the MPP more accurately than the IC technique.
Better Power Conversion Efficiency – The developed MPPT exhibits faster (up to 22%) convergence in locating the MPP, and thus higher ECE of 15% which substantially minimised the oscillations about the MPPs squeezes thereby increased overall performance by 12%.
(i_{L}) : Inductor input current
D : Duty cycle
The organization of the paper is arranged as follows: the introduction comes first, followed by the literature review in Sect. 2. Section 3 delves into the design of the method’s numerous components. In part 4, the experimental verification is conducted and briefly discussed. In Sect. 5, the conclusion and future directions are presented.
Traditional MPPT methods are renowned for their ease of use and inexpensive implementation costs, including fractional short-circuit current, fractional open-circuit voltage, Perturb and Observe (P&O), and hill-climbing tactics13,14,15. The main limitations of fractional open-circuit voltage and fractional short-circuit current methods are their inability to be used in low-power situations and their inaccurate tracking. While approaches such as Hill Climbing (HC), (P&O), and Incremental Conductance (INC) are effective when sunshine is constant, they become ineffective when sunlight is partially obscured or changes often. Furthermore, the main drawbacks of traditional techniques are large steady-state oscillations and poor tracking speed. Soft computing techniques, like fuzzy logic control16,17, sliding mode control, artificial neural networks, and metaheuristic-based algorithms like genetic algorithms and particle swarm optimization, have been developed to overcome these drawbacks18,19. In particular, when partial shading is present, these techniques are effective in overcoming the nonlinearity in the system and attaining the global maximum power point. Nevertheless, there are issues with computational load and complexity when using soft computing MPPT approaches20,21. In order to improve system tracking efficiency, Reference22,23 presented a unique MPPT technique based on the particle swarm optimization strategy. Table 1 shows the Comparison analysis of Traditional MPPT methods.
In recent studies, Incremental conductance-based MPPT gained significant research interest again24,25,26. Safari et al.27 used a Cuk converter to implement the Incremental Conductance (INC) direct duty cycle control in solar arrays for the first time. In this study, the final approach is referred to as basic INC. Utilizing a predetermined step size modifies the duty ratio until the peak power is achieved. Nevertheless, it has issues with low tracking speed and direction loss with abrupt changes in the surrounding environment. Because of this, a lot of researches use INC MPPT with a configurable step size28. Additionally, basic and variable step size INC approaches are unable to react accurately to sudden changes in sun radiation26,27,28,29,30, the author uses the MPPT technique responding to varying temperatures, but the major drawback is that they require temperature sensors. In31, a simplified approach for reducing oscillations during EIC -based MPPT tracking is proposed, but they have slightly slower response times. In32,33,34, a hybrid approach combining EIC with Artificial Neural Networks (ANN) for improved efficiency under partial shading is proposed. The drawback is the increased complexity and computational resources needed for ANN implementation. Several studies test their algorithms using basic irradiance and temperature profiles (constant or step), which presents another issue to take into account. As such, a genuine demonstration of the performance improvements is impossible. This research suggests a new MPPT control based on an enhanced incremental conductance to lessen the impact of these limitations. The proposed tracker bears a resemblance to its predecessor; however, it integrates two additional tests that measure changes in both current and voltage with identical indications. The Table 2 shows the Comparative Analysis of exciting systems.
The following important observation from exciting systems and Traditional MPPT methods.
Enhanced incremental conductance (E-INC) offers the best balance of performance, adaptability, and implementation complexity for rural electrification.
While fuzzy logic and neural network methods may yield higher efficiencies, they are generally less suitable for low-cost rural deployments due to their high implementation and maintenance complexity.
E-INC is especially advantageous in grid-tied PV systems, where stability and accuracy are crucial for power quality and grid compliance.
Combines simplicity of traditional INC with adaptive intelligence.
Dual tests (ΔI & ΔV with identical indications) improve tracking under dynamic irradiance.
Avoids extra hardware like temperature sensors.
Reduces oscillation and direction loss issues.
Better suited for real-world rural solar PV systems under partial shading and dynamic environments.
The block diagram of the proposed methodology is shown in Fig. 2. The major components include PV array, a boost converter, EIC based MPPT controller, 3-phase Voltage Source Inverter (VSI), Voltage Source Converter (VSC) and filter. To achieve desired PV voltage and power, numerous solar cells are connected in series and parallel. A Inductor Inductor Capacitor (LLC) filter is followed by 3-phase VSI which helps in achieving a unity power factor and maintaining output current in phase with utility voltage by giving appropriate switching signal35.
Block diagram of proposed methodology.
Figure 3 illustrates the circuit model of a single diode type. The fundamental purpose of solar cells, which are integral elements of Photovoltaic (PV) systems, is to directly convert sunlight into energy. The feasibility of solar arrays is facilitated through their interconnection in parallel and series configurations, increasing both output voltage and current. In many publications, it is observed that the researchers used either single diode or double diode model of solar cell36. The Tables 3 and 4 shows the specifications of PV module and electrical specifications of PV module in standard conditions.
Circuit model of a single diode type.
Utilizing the Eq. (1), the PV module’s output current is determined.
Where,
(I_{{PV}}) : PV output current
(I_{{SC}}) : Short circuit current
V : PV array terminal Voltage
(I_{{PH}}) : PV generated current
(R_{{SH}}) : Shunt resistance
(N_{P}) : Parallelly connected solar cell count
(N_{S}) : Series connected solar cell count
A : Ideality factor
G : Solar irradiance W/m2
q : Electric charge
K : Boltzmann constant
(K_{i}) : (I_{{SC}}) temperature coefficient
The temperature-dependent reverse saturation current is expressed as
Where,
(:{text{I}}_{text{SAT}}): Reverse saturation current (A).
(:{text{I}}_{text{SC}}): Short-circuit current at reference temperature (A).
(:{text{K}}_{text{i:}}): Temperature coefficient of short-circuit current (A/K).
T: Cell temperature (K).
(:{text{T}}_{text{i}}): Reference temperature (K).
(:{text{V}}_{text{oc:}}): Open-circuit voltage at reference temperature (V).
(:{text{K}}_{text{v::}}): Temperature coefficient of open-circuit voltage (V/K).
(:{text{V}}_{text{t}}) Thermal voltage, given by (:{text{V}}_{text{t}}) = (:frac{text{kT}}{text{q}}), where.
k = 1.381 × 10⁻²³ J/K (Boltzmann constant).
q = 1.602 × 10⁻¹⁹ C (Electronic charge).
A step-up DC-DC converter, alternatively referred to as a boost converter, is employed for the purpose of linking the grid-side inverter and Photovoltaic (PV) module. The design elements of this converter consist of an input inductor, an output capacitor, an output diode, and a Pulse Width Modulation (PWM) controlled Insulated Gate Bipolar Transistor (IGBT). The main objective of this converter is to monitor the Maximum Power Point (MPP) when the degree of irradiance varies. In addition, it elevates the output voltage of the photovoltaic to match the level of grid integration. One can manipulate the output voltage by manipulating the duty cycle of the IGBT switch, which can be alternated between the ON and OFF states. The variable “switching state” (u) denotes the position of the switch. It will be zero when switch is off and 1 when it is on. The dynamic behaviour of the boost converter can be elucidated by employing Kirchhoff’s laws37.
Where,
(V_{{dc}}) : Boost converter output voltage
(i_{L}) : Inductor input current
D: Duty cycle
The Grid receives a three-phase supply from a three-phase VSI. At the design stage, voltage, current, and VA ratings are estimated38.
Where voltage safety factor = 1.4.
Where current safety factor = 1.3.
In conclusion, it is anticipated that the required VSI VA rating will be,
A single-phase VSC controls the bidirectional power flow’s direction. In a single-phase VSC, the blocking voltage of the switching devices is determined by the DC link voltage39. Since that is what it is, the switches must block the 270 V DC link voltage. To accommodate for voltage transients caused by high switching frequency, a safety factor of 1.4 is applied. As a result, the expected voltage rating for the IGBT devices is as follows:
The maximum current that the VSC is able to supply to or take out of the Grid. Based on calculations, the stated current is
When the utility grid voltage’s root mean square (Vs) is 180 V.
The IGBT can handle up to maximum current of 11.8 A.
Consequently, it is anticipated that VSC requires a VA rating of.
The Traditional INC method employs a comparison between the zero value and the sum of the instantaneous conductance and incremental conductance of a photovoltaic panel in order to monitor the MPP. The underlying assumption is made by considering the fact that PV curve of the solar panel exhibits positive slope at left of curve, negative slope to the right of curve and zero slope at MPP. If the slope is positive, it is necessary to increase the module voltage in order to shift the operating point to the right. If the module voltage is negative, reducing it will result in a leftward movement of the operating point. The algorithm ceases voltage adjustment when the slope reaches zero, it shows that that the MPP is attained. The velocity and precision of the algorithm in tracking the MPP is based on magnitude of the duty cycle or reference voltage increment. This technique exhibits two primary limitations that are effectively mitigated40. Figure 4 shows the Flowchart of proposed Enhanced incremental conductance algorithm. Firstly, the operating point exhibits oscillations around the MPP in a stable condition.
Flowchart of proposed enhanced incremental conductance algorithm.
Secondly, in the event of rapid variations in solar radiation, the algorithm may experience a loss of track with the MPP. The MPP tracking method demonstrates efficacy in capturing step-by-step and instantaneous variations in radiation. However, if the irradiation fluctuates with a slope, the tracking performance will be suboptimal. The algorithm cannot differentiate between a power fluctuation resulting from a voltage disorder and a variation in solar radiation. A flowchart illustrating the enhanced incremental conductance algorithm, elucidating the underlying concept. Therefore, to assess the efficiency of the proposed methodology for an irradiation profile encompassing diverse types41.
The observed relationship between current and voltage provides unambiguous evidence that alterations in voltage result in corresponding changes in current, albeit with a distinct sign, when the system remains stable under constant conditions of irradiance level and temperature. If variation in voltage results in a corresponding current variation with the same sign, the photovoltaic (PV) array is subject to sudden fluctuations in atmospheric conditions. The enhanced INC method, in contrast to the conventional technique, is capable of distinguishing between the two operating states and preventing divergence in the event of a second occurrence by modifying the perturbation direction. When functioning in dynamic environments, the new algorithm must exhibit distinct behaviour compared to the previous algorithm. The newly introduced component, in contrast to the old algorithm, is shown by the colour green. The test comprises two assessments that quantify alterations in voltage and current using identical indicators. It demonstrates the rapid fluctuations in sunlight.
The model depicted in Fig. 5 illustrates the simulation setup for proposed system, encompassing the PV module, DC-DC boost converter, Enhanced IC MPTT algorithm, voltage source inverter, and the control schemes employed for MPPT control. These control schemes involve the utilization of the three-phase inverter, step up DC-DC converter, and transformer to achieve synchronization between the three-phase inverter and the Gird.
Simulation model of the proposed methodology.
MATLAB simulation is conducted to examine the accuracy of the suggested methodology and algorithm, specifically for the 100 W solar PV systems. Figure 6 displays the MATLAB Simulink model of the planned panel, while Table 5 provides a summary of its parameters.
Simulink model of proposed solar photovoltaic.
The characteristic curve of designed solar PV at constant irradiance and temperature is shown in Figs. 7 and 8, respectively.
PV array performance at 25 degree C and varied irradiance condition.
PV array performance at 1000 W/m2 and varied temperature conditions.
The primary objective of the methodologies proposed in this study is to demonstrate the efficacy of the proposed EIC. The model was simulated to analyze its performance under varying temperature and irradiance conditions while also considering the presence of a series RLC load. The simulation of the model lasted for 6 s. The duty cycle changes for Δd1, Δd2 and Δd3 in the suggested technique are chosen as 0.0002, 0.0004, and 0.0006, respectively.
The duty cycle generated by EIC MPPT controller is fed to step up DC-DC converter to enhance the PV output voltage and synchronize with the reference voltage. Figure 9 displays the Simulink model of the boost converter that has been designed and Fig. 10 illustrates the simulink model of the 600 W utility grid.
Simulink model of the boost converter.
The synchronization of voltage and current in the utility grid is achieved by employing a Phase Locked Loop (PLL). A sinusoidal unit vector of supply voltage, denoted as sin ε, is produced, exhibiting a fundamental frequency. Conversely, the crucial supply current, Is, can be regulated through the manipulation of the DC bus voltage, Vdc. The voltage is regulated via a Proportional-Integral (PI) controller. In order to mitigate the presence of ripple contents, a first-order low pass filter is employed for the detection and processing of Vdc. The Vdcthat has been filtered is subsequently compared to a constant value, Vdc*. The present Controller efficiently manages errors and compares the measured supply current, denoted as is, with is* in order to create the gating signal for VSC. Figure 11 shows the Simulink model of the Grid side Controller.
Simulink model of the 600 W utility grid.
Simulink model of the grid side controller.
The altering the current direction, the power flow can be adjusted to accommodate the requirements. The suggested methodology also helps in maintaining nearly unity power factor and enhance the power quality of grid by minimising Total Harmonic Distortion (THD). If the Grid is not accessible, it is not possible to regulate the DC bus voltage. Although the PV panel is contingent on weather conditions, it is still capable of supplying power to the Grid. The results of simulation: (a) irradiance, (b) temperature, (c) PV current, (d) PV voltage, (e) Duty cycle is shown in Fig. 12.
Results of simulation: (a) irradiance, (b) temperature, (c) PV current, (d) PV voltage, (e) Duty cycle.
The outcomes of the Enhanced Incremental Conductance (EIC) algorithm, which encompass variations in temperature, irradiance, PV current, voltage, and duty cycle. The graph illustrates that irradiance change have a minor impact on the PV voltage, whereas changes in temperature have a substantial influence. Likewise, the alteration in irradiance exerts a substantial effect on the PV current, resulting in a pronounced response.
Figure 13 displays the output voltage of the boost converter along with its corresponding modulation index. The graphic demonstrates that the Vdc produced by the boost converter aligns with the reference voltage, indicating the superior performance of the EIC algorithm. Figure 14 depicts the sinusoidal waveform of the output voltage and current in the phase Grid, and Fig. 15 illustrates the grid power associated with the B phase. The findings indicate that the photovoltaic (PV) system has been effectively integrated with the Grid, which has a power rating of 600 W.
Results of simulation: (a) Boost converter output voltage, (b) Modulation index.
Results of simulation: (a) B phase grid voltage, (b) B phase grid cCurrent.
Results of simulation -B phase grid power.
Figure 16 shows the comparative analysis of modulation index for proposed work. In the traditional approach, the modulation index has a higher fluctuation with less control accuracy, which contributes to the high harmonic content and poor power quality.
Comparative analysis of modulation index for proposed work.
However, the suggested intelligent controller produces a stable and optimized modulation index, which contributes to good voltage regulation of the output, low THD, and higher system efficiency. The comparative analysis of grid power for proposed work is shown in Fig. 17. Table 6 shows the comparison between MPPT algorithms.
Comparative analysis of grid power for proposed work.
A real-time hardware configuration is used to acquire the experimental performance of the Enhanced Incremental Conductance MPPT for grid tied solar photovoltaic arrangement make up the configuration. Figure 18 shows the displays the PV panel and real-time hardware configuration.
Table 7 presents a comparison of the Voltage Total Harmonic Distortion (VTHD) and Current Total Harmonic Distortion (ITHD) values achieved through simulation and hardware experiments based on various control strategies. The simulation for traditional methods reveals a VTHD of 16.12% and ITHD of 15.36%, while experimental results reveal slightly higher readings with VTHD at 18.23% and ITHD at 17.5%.
In the case of the Enhanced Incremental Conductance (EINC) controller, the THD values are relatively lower. The simulation results indicate VTHD of 13.15% and ITHD of 12.31%, whereas the hardware implementation registers VTHD of 14.52% and ITHD of 13.55%.
Hardware PV panels with MPPT techniques.
In general, the EINC-based proposed converter shows superior suppression of harmonics over traditional methods in both simulation and experimental configurations. Despite slightly higher experimental THD values compared to those obtained through simulation as a result of practical non-idealises, the proposed converter successfully reduces voltage and current distortions, improving power quality.
Figure 19(a) shows the THD for Conventional Converter, the THD value is quite high, reflecting high harmonic components of the output voltage and current. The efficiency reduces because of higher distortion, which negatively impacts the overall power quality. The Fig. 19(b) shows the THD of Proposed Converter. The suggested smart controller considerably minimizes the THD, achieving approximately 15.52% THD as indicated in the display meter. The reduced harmonic content enhances power quality and system efficiency. Overall, the proposed converter performs better than the conventional design by efficiently reducing the harmonic distortions, thus increasing the system stability and performance.
(a) THD for Conventional converter (b) THD for proposed converter.
This study suggests the utilization of a power electronics converter to enhance the output power of a PV by modelling, simulating, and controlling it. The effectiveness of a grid-connected photovoltaic system was assessed by conducting MATLAB simulations, which yielded sinusoidal waveforms representing the grid current. The EIC-based MPPT algorithm was employed to control the system at the MPP. The control methods govern the boost converter, PLL, and three-phase inverter. The control strategy of the MPPT is implemented by adjusting the duty cycle of the boost converter. The utilization of phase-locked loop synchronization is employed to maintain synchronization between the three-phase inverter and the Grid. A control strategy utilizing an Enhanced Incremental Conductance (INC) approach was devised to enhance the PV output power including PV generation and a boost DC-DC converter capable of increasing the load voltage. Future studies will further investigate the theoretical foundations of the MPPT technique, as proposed by alternative converter topologies that have demonstrated more effectiveness. Additionally, it will incorporate the shading phenomenon while evaluating enhanced MPPT systems in the presence of uneven illumination circumstances.
The datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.
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Department of Electronics Communication Engineering, Nandha College of Technology, Erode, Tamil Nadu, India
Parthiban Shanmugam
Department of Electrical and Electronics Engineering, Government College of Engineering, Erode, Tamil Nadu, India
M. Mohammadha Hussaini
Department of Electrical and Electronics Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
Vanchinathan Kumarasamy
Department of Electrical and Electronics Engineering, Nandha Engineering College, Erode, Tamil Nadu, India
Jayakumar Thangavel
Department of Electrical and Electronics Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
Suresh Muthusamy
Department of Electronics and Communication Engineering, Raghu Engineering College (Autonomous), Visakhapatnam, Andhra Pradesh, India
Surya Kavitha Tirugatla
Department of Electronics and Communication Engineering, National Institute of Technology Nagaland, Dimapur, Nagaland, India
Chinnamuthu Paulsamy
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
Ayodeji Olalekan Salau
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
Ayodeji Olalekan Salau
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Parthiban Shanmugam: Conceptualization, Writing – original draft.Mohammadha Hussaini M: Writing – original draft, Formal analysis. Vanchinathan Kumarasamy: Supervision, Project administration. Jayakumar Thangavel: Writing – review & editing, ResourcesSuresh Muthusamy: Conceptualization, Methodology.Surya Kavitha Tirugatla: Software, Validation. Chinnamuthu Paulsamy: Visualization, Data curation. Ayodeji Olalekan Salau: Visualization, Data curation, Writing – review & editing, Investigation.
Correspondence to Ayodeji Olalekan Salau.
The authors declare no competing interests.
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Shanmugam, P., Hussaini, M.M., Kumarasamy, V. et al. Novel resilient solar photovoltaic power extraction strategy for rural AC micro grids with enhanced incremental conductance based MPPT. Sci Rep 15, 41846 (2025). https://doi.org/10.1038/s41598-025-25915-3
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DOI: https://doi.org/10.1038/s41598-025-25915-3
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