Revolutionary building approach for maximal photovoltaic system results to improve maximum power point tracking in solar inverter

. Due to the inherent frequency ripple in single-phase photovoltaic (PV) grid-connected solar inverters, the Maximum Power Point Tracking (MPPT) will inevitably be affected. To improve the MPPT performances, a passive LC power decoupling circuit with a Robust Sliding-Mode Control (RSMC) is proposed in this article. The frequency pulsation on the DC link is effectively canceled with the passive LC decoupling path. Thus, the MPPT accuracy is significantly enhanced, and the utilization of a small DC-link capacitor becomes possible. The resonance between the LC circuit and the main DC-link capacitor appears, which can be damped through an active damping method. The proposed RSMC offers good steady-state, dynamic performance (voltage fluctuation and settling time), and the robustness of the DC-link voltage, which is also beneficial to MPPT control in terms of high accuracy and fast dynamics. The systematic design of RSMC is presented, and a detailed parameter optimization design of the LC decoupling circuit is discussed. Experimental tests are performed on a 2.5-kW single-phase grid-connected solar inverter


Introduction to photovoltaic system
A vast amount of solar energy is accessible, and converting less than 1% into electricity meets the world's energy needs.Electricity harnessed from the sun employing Concentrating Solar Power (CSP) or photovoltaic (PV) technology [1].PV technology directly transforms sunlight into energy, whereas CSP generates power via a thermodynamic cycle.Solar power is becoming more popular because of the enhanced effectiveness and decreased costs of solar energy cells in the last few years.The price of photovoltaic panels has plummeted by 85.8%, and the median cost of installing utility-scale solar energy systems has decreased by 73.2%.A standard grid-connected PV system consists of many power-conversion steps.The first phase involves using a DC-DC converter to increase the PV voltage, including Maximum Power Point Tracking (MPPT) [2].The generated electricity is reversed using an inverter and then sent to the grid.Multi-stage systems provide benefits like adaptable control execution, with MPPT obtained in the front stage and energy feeding-in control gained in the inverter.The overall system efficiency needs to be improved.The PV system utilizes just one power transformation, known as an inverter, to carry out both tasks: The system optimizes power output from solar PV arrays using an MPPT method and then transfers the generated DC energy to the AC network while ensuring power quality standards are met [3].The singlestage converter outperforms multiple-stage methods in effectiveness, small size, and costeffectiveness.
Because of its single conversion topology, it makes fast and accurate MPPT challenging in a single-stage PV system.From the perspective of MPPT, the DC-link voltage needs to be adjusted rapidly to achieve speedy MPPT characteristics [4].Rapid fluctuations in the DClink voltage provide a stability difficulty for voltage regulation.The resultant power fluctuates at a rate double that of the transmission frequency, resulting in ripples at twofold the frequency, where f represents the fundamental frequency of the grid, and P denotes the power.The pulsation causes the starting point of the PV array to deviate from the planned MPPT condition.This needs to improve the precision of the MPPT and the system's effectiveness.Monitoring precision and the system's overall effectiveness are impacted [5].Solving the precision and stability problem of the MPPT in PV structures is essential.
The following sections are enumerated: Section 2 overviews the literature review on Maximum PowerPoint Tracking (MPPT).The Robust Sliding-Mode Control (RSMC) that is being suggested may be found in section 3. Section 4 examines and presents the results of the RSMC for training, testing, and verification.Section 5 covers the conclusion and potential future directions.

Literature analysis
Several academics have studied PV and CSP systems' viability and economic efficiency.Tafazoli et al. assessed a 10-MW photovoltaic facility [6].This plant's energy output is 16868 MWh annually, with a Capacity Utilization Factor (CUF) of 12.63%.Another study investigated the leveled energy cost for five PV plants of varying capacities: 1 MW, 10 MW, 20 MW, 50 MW, and 100 MW.The Levelized Cost of Energy (LCoE) for a 1 MW system was double that of a 100 MW plant.Different research evaluated several locations in China for the extensive use of PV systems.The study examined large-scale photovoltaic systems, investigating potential improvements and advancements in current installations [7].Sens et al. reviewed the electrical production and LCoE of a 10 MW PV system [8].Ali et al. assessed the economic feasibility of Oman's 1 MW grid-connected PV system [9].The photovoltaic plant at the suggested site shows an LCoE of 21.47 $/kWh, a yield factor 1749, and a CUF of 23.4%.Barrett [12].The authors stated that a particular technology should not be dismissed solely on a single performance metric since it excels in a different application.PV technologies can reach high temperatures, although devices are often used in utility-scale power stations.Ma et al. suggested combining PV technology with a parabolic trough collector power plant, resulting in a 1.7% increase in the plant's performance [13].The fresh system's LCoE was 3% lower than the facility's.Guccione et al. compared two designs of the PV plants: direct steam production and melted salt-based architecture [14].The straightforward steam production setup had an annual energy production of 20% more than the boiling salt-based arrangement.Zhang et al. suggested that using a combination of tetroxide from dinitrogen and nitrogen oxides in the power cycle of a solar thermal power plant enhances the solar to-electric effectiveness by 1% in comparison to conventional steam cycle solar plants running at 550C [15].At a working temperature of 700°C, the Solar Energy Efficiency (SEE) achieves a level of 21.4%, surpassing the performance of the supercharged carbon dioxide process.Wang et al. introduced a strategy for optimization by employing the machine learning algorithm to optimize the photovoltaic field of a central PV system to maximize yearly weighted effectiveness [16].
The literature study on PV and CSP methods indicates that several authors have examined these methods' various technical and financial aspects.Most current research focuses on analyzing the efficacy of each technology separately.Inter-technology comparison has received less attention.

Proposed robust sliding-mode control
The proposed RSMC is designed, and its mathematical relationship is derived in this work.

PV model
Mathematical representations depict how PV panel's function while calculating the current voltage characteristics.It describes the relationship between current and voltage in a photovoltaic cell as explicit and non-linear.A two-diode PV system is used to achieve accurate PV cell simulation, requiring the recognition of more variables and resulting in a longer calculation time.This system is sometimes referred to as a seven-parameter system.Experiments use the double-diode approach to estimate more effectively than models like the single-diode approach.The PV panel is discussed in Fig. 1. approach.P and O provide the reference value   after the panel's power measurements.The reference voltage is adjusted proportionally based on whether the detected energy is higher or lower than the prior power.

Sliding mode control
Sliding mode control is suggested in research to guarantee proper performance without requiring linearization.This study hinges on using the sliding mode approach to control the solar power voltage and reduce vibrations.The strategy described in this study is seen in Fig. 2.

Fig. 2. The model design of the RSMC
The PV array's voltage and current are input into a P and O method, which sets the reference for the RSMC regulator to achieve the peak power value.The suggested non-linear regulator then regulates this.The control system was developed based on the computational model of a non-inverting boosted conversion.It produces a final signal u that governs the duty-cycle proportion of the signal sent to the conversion switching.

Design of the RSMC for the voltage loop control
Grid-dependent PV converter management typically involves two cycles: an external voltage and internal current circuits.A proportionate multi-resonant regulator is used in the current looping to ensure zero steady-state error monitoring of the current.The paper does not cover the evaluation and layout of the internal current loop because of space constraints.MPPT is done in single-stage devices by adjusting the power.The power loop's control goals are as follows: Minimal voltage fluctuations and quick settling time, No steady-state mistake, and High resilience.Once the 2 0 -ripple is removed by the L-C system, these goals are accomplished with the RSMC.

Control law design
The super twisting method has been created for systems with relative accuracy.It offers the benefit of achieving finite-time convergence to the specified point and effectively rejecting smooth perturbations of any form.The voltage across the loop in the dual-loop control system provides a benchmark for the current magnitude.The magnitude of the grid current standard  ̂ used as a control input.The control rule  ̂ for regulating the voltage consists of two  ̂ =  ̂− +  ̂− (1) The suggested RSMC utilizes the analogous management term to manage known system processes and the super-twisting management term to handle uncertainty arising from variable errors and unmodeled mechanics.

Model uncertainty
Accounting for the tolerance of capacitance   , the electrical losses of the converter, and any additional outside influences.The overall disruption is provided in Equation (2).
The input energy is   , the normalized power is  ̅ , the capacitance deviation is ∆  , and the normalized capacitance is  ̅  .The constant is denoted k.Considering the structure's physical limitations and operational characteristics, it is presumed that the disruption is limited as shown in Equation (3).

Parameter selection
The three variables in RSMC must be carefully set to guarantee the dynamics and equilibrium of the system.The symbol λ represents the pole of sliding-mode mechanics.Therefore, it is advantageous for the controller to choose a big value for λ, even if it comes at the expense of tracking efficiency.An excessively high value of λ results in significant overshoots, whereas a control system with a low λ value leads to extended monitoring time and delayed error resolution.In the PV system, the voltage at the DC-link reference is determined via the method known as MPPT.Thus, to maintain the efficiency of the MPPT and the overall system security, it is essential to consider the following two restrictions when choosing the value λ.
1) The smallest setup time   for the outside loop ought to be 10 times greater than that of the loop itself.2) Ensure that the ultimate setting period   of the outermost loop is less than the updating duration for the MPPT method.The discrepancy must be fulfilled for the sliding factor to get close to zero within a specified time frame concerning variables  1 and  2 .When accounting for a single sample calculation delay in the digital control structure,  1 and  2 must be chosen more conservatively to guarantee reliability.The article does not provide a complete stability study of the framework with time lags due to space constraints.

PSO-based design for controller
The variables selected for particular optimization are capacitor  1 and inductor  1 , denoted as   = { 1 ,  1 }.The depth dimension D is 2. The fitness coefficient related to the optimization goal is expressed as () =   *   . − =  −1 ,  −2 represents the best location encountered by the ith particulate, whereas  − =  −1 ,  −2 denotes the ideal spot experienced by all atoms.Once the optimization process satisfies the terminal requirements, the location of   corresponds to the required values of  1 and  1 , and its suitable value () represents the smallest volume of the LC resonating circuit.3 displays the flowchart outlining the best design process for inductors and capacitors using PSO.The values of   and   are 0.2 mH and 5 mH, respectively, representing the lowest and highest inductance levels.The optimal step size, denoted as   , is equal to 0.1 mH.The operating concept of the PSO method is explained before detailing the layout of inductance  1 and capacitance  1 .
Step 1: Establish a set of components that adhere to the restrictions.Next, determine the original components' fitness.
Step 2: Revise the location X and velocity V of particles.
Step 3: Determine the revised fitness of the modified particles.
Step 4: Verify whether the revised particles adhere to the limitations; if not, go to Step 6.
Step 6: Verify whether the terminal requirement has been satisfied.The terminal conditions are met when the permitted number of iteration steps and fitness value are reached.If not, go to step two.

Simulation analysis and findings
Experimental testing is conducted on a 2.5-kW PV grid-dependent model to confirm the suggested method's MPPT speed and precision improvement.The control methods are executed on a digital signal processor, and the transistor driver outputs are produced by Field-Programmed Gate Arrays (FPGA).A Chromatic PV simulation and customizable AC source are used to replicate the photovoltaic array and grid accordingly.This article utilizes an MPPT method with an update interval of 200 milliseconds.A configurable step size is used for the MPPT controller to balance tracking velocity and steady-state fluctuations.The most extraordinary increment is 6 volts, while the smallest increment is 1 volt.The MPPT approach shows varying levels of accuracy throughout training (91.14% to 97.01%), testing (94.51% to 99.68%), and verification (94.75% to 99.16%) during 24 hours in Fig. 4. Accuracy is calculated by determining the proportion of accurately monitored MPP.The approach consistently achieves noteworthy accuracy, demonstrating flexibility to different sun circumstances.The results highlight the strategy's effectiveness in improving MPPT accuracy, confirming its ability to optimize solar inverter performance in practical situations.The Settling Time in the study varies during the 24 hours for training (5.24 ms to 11.89 ms), testing (8.84 ms to 14.12 ms), and verification (7.76 ms to 13.84 ms) in Fig. 6.Settling time is when a system's output stabilizes within a specific error range after being disturbed.The average settling time ranges from 5.24 ms to 14.12 ms, indicating different reaction durations.The results show that the suggested RSMC adjusts to various situations since shorter settling periods demonstrate a faster reaction to disturbances.The data support the effectiveness of the proposed RSMC in producing prompt and consistent responses in the solar inverter system.

Fig. 7. Damping efficiency analysis
The Damping Efficiency fluctuates during the 24 hours in the proposed RSMC for training (88.05% to 95.04%), testing (83.88% to 89.68%), and verification (82.21% to 90.48%) in Fig. 7. Damping efficiency indicates how well the active damping approach reduces resonance.The average damping effectiveness ranges from 82.21% to 95.04%, showing different levels of resonance suppression.The results indicate that the suggested RSMC technique demonstrates strong performance in managing resonance, with increased damping efficiency values leading to enhanced stability in the solar inverter system.The results confirm that the proposed RSMC effectively reduces resonance problems, emphasizing its importance in improving the overall performance and dependability of the solar inverter.

Conclusion and discussions
This research introduces a control and design strategy to improve the precision and speed of MPPT in single-stage PV grid-dependent converters.The suggested solution comprises a passive LC decoupled technology and a practical control approach.The active LC decoupled circuit suppresses the 2 0 -voltage ripple, allowing for a tiny DC-link capacitance.The MPPT precision and system-wide dynamics are enhanced.An optimum parameter layout for the LC decoupled circuit was achieved using a PSO method.A virtual impedance attenuates the sine wave resonance between the passive L-C branches and primary capacitance.Therefore, the system's reliability is enhanced without incurring extra losses.A strong RSMC was suggested to enhance the stability and responsiveness of the DC-link voltages, thus improving the precision and quickness of the MPPT.The study also analyzed how uncertainty in system parameters affects the controller, and the system's stability was shown using the Lyapunov technique.The experiments and analyses show that the suggested technique performs well in MPPT in both a stable state and dynamic circumstances across multiple scenarios.Therefore, it is appropriate for single-phase grid-linked PV systems with a single level.

Fig. 1 .
Fig. 1.PV panel model To achieve the PV panel's Maximum Power Point (MPP) operation, a DC-DC converter regulated by the MPPT system is placed between the PV panel and the load.DC-DC conversions are often used to control energy efficiently in solar power plants.The boost conversion has been selected for this project.The panel's maximum electrical output is extracted using the Perturb and Observe (P and O) technique, a standard MPP monitoring

,Fig. 3 .
Fig. 3. PSO-based controller designFig.3displaysthe flowchart outlining the best design process for inductors and capacitors using PSO.The values of   and   are 0.2 mH and 5 mH, respectively, representing the lowest and highest inductance levels.The optimal step size, denoted as   , is equal to 0.1 mH.The operating concept of the PSO method is explained before detailing the layout of inductance  1 and capacitance  1 .Step 1: Establish a set of components that adhere to the restrictions.Next, determine the original components' fitness.Step 2: Revise the location X and velocity V of particles.Step 3: Determine the revised fitness of the modified particles.Step 4: Verify whether the revised particles adhere to the limitations; if not, go to Step 6.Step 5: Revise  − and  − .Step 6: Verify whether the terminal requirement has been satisfied.The terminal conditions are met when the permitted number of iteration steps and fitness value are reached.If not, go to step two.

Fig. 5 .
Fig. 5. DC-link volage analysisThe DC-Link voltage fluctuates throughout the 24-hour cycle in the study, with values ranging from 401.59 V to 414.30V during training, 399.10 V to 411.29 V during testing, and 402.69 V to 413.76 V during verification in Fig.5.The DC-Link voltage signifies the voltage differential across the capacitor in the solar inverter.The DC-Link voltage typically ranges from 399.10 V to 414.30V, indicating a stable and functional range.The results show that the suggested RSMC successfully keeps the DC-Link voltage within acceptable boundaries, guaranteeing steady performance and dependability of the solar inverter.The results highlight its capacity to control and maintain the DC-Link voltage to enhance solar inverter performance.

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01146 (2024) MATEC Web of Conferences https://doi.org/10.1051/matecconf/202439201146392 ICMED 2024 et al. assessed the feasibility of implementing extensive solar installations in the United Kingdom to meet climate environmental objectives [10].PV technology has superior thermal effectiveness to other commercialized CSP methods, such as linear Fresnel reflectors [11].Asselineau et al. conducted a techno-economic assessment of all primary CSP systems