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Scientific Reports volume 15, Article number: 10369 (2025)
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At present, power transmission systems are moving to renewable sources because their features are more reliable, easy to install, increase public health, and lower development costs when associated with conventional energy systems. Here, solar technology is considered because of its advantages when equated with other renewable sources are free energy production, good energy density, low air pollutants, and less maintenance requirement. In this article, a triple diode cell technology is considered for achieving the accurate voltage of the PV, and its power production is increased by proposing the Adjustable Cuckoo Search Methodology (ACSM). Here, the ACSM is studied by comparing it with the other swarm and conventional Maximum Power Point Tracking (MPPT) methodologies. The proposed MPPT controller tracks the MPP with 0.025 s time duration at 355 K. Finally, the one-switch non-isolated voltage improvement DC-DC circuit is used in the proposed system for giving the wide voltage gain to the consumer loads. The proposed ACSM-fed DC-DC converter system is investigated by utilizing the MATLAB/Simulink tool and the converter is investigated experimentally by applying the programmable power source.
From the literature investigation of energy production systems, it has been seen that conventional energy usage is reduced because of its huge effect on atmospheric pollution, more primary fossil fuel utility, less convenience for the urban location peoples, heavy impact on living human beings, more catchment area utilization, heavy transportation necessity, and huge damage of earth1. In2, the scholars studied the various categories of conventional energy systems, and their merits and applications are defined in detail. The major utilization of non-renewable energy systems are oil energy, nuclear, natural gas, and thermal energy systems. Here, most of the oil energy systems are located in the United Kingdom and these systems produce an energy efficiency rate is 30%. In this energy system, an excessive amount of oil is stored in the tanks3. The storage oil is transferred to the steam-generated boilers to generate the excessive steam thereby producing electricity to the water compressor systems. The tremendous advantages of oil systems are more energy density, good accessibility, more usefulness for lather industries, and a good constant energy source. However, the demerits of these energy systems are the emission of harmful gasses, more water pollutants, heavy toxic substances, and less reliability4.
So, coal energy production methods are utilized for cement production networks, medicine systems, carbon fibers, commercial heating applications, and petroleum applications. In5, the coal-based steam generation energy production system is used for agriculture irrigation applications. Here, the complete coal is converted into small pieces for the efficient burning process of the boiler thereby entire energy production rate is increased. The energy production efficiency rate for the coal system is 33% which is a very small value when equated with oil energy. Also, the demerits of this coal energy are high amounts of sulfur dioxide production, lead, nitrogen oxide production, heart diseases, cancer, and neurological problems that come from the coal mine systems6. The coal energy drawbacks are compensated by applying nuclear energy. In these nuclear systems, each neuron collides with the other atom to generate a huge heat energy. The available heat energy from the fusion process is utilized for the transformation of water energy into steam power. Here, the highly pressurized steam runs the turbine network which is interlinked with the generator system for achieving the peak load demand. The merits of this nuclear energy are low carbon production, zero carbon dioxide, more continuity in the energy supply, highly affordable sources, and better reliability7. However, the disadvantages of nuclear energy are highly radioactive, big effect on human health, has intensive water consumption, has more difficulty in nuclear waste storage, and high risk of catastrophe. The present energy production companies are developing renewable energy technologies instead of conventional energy systems for the future rural peak power demand conditions.
Based on the utility and its application, the accessible renewable energy systems are organized as ocean energy, wind power, geothermal energy, hydropower, bioenergy, fuel stack, and solar energy. In the ocean energy system, the wave energy is captured by applying the power converters. Here, the oscillated wave columns forcibly move the bidirectional turbines for discontinuity energy supply to industrial applications. The main utility of the ocean system is coastal community energy consumption8. Also, wave energy is used for water robots, remote islands, marine research, offshore work, and fishing applications. However, the ocean energy network drawbacks are more electromagnetic disturbances for marine life, only located in ocean areas, and completely dependent on the wavelength. Geothermal technology is considered for directly heating individual buildings, drying fruits, vegetables, timber, and cooking applications. Here, the steam comes from the reservoirs of hot water which are available a few miles below the earth. In this system, the natural steam is converted into more pressurized steam for moving the prime mover of the turbine to obtain constant electricity9. The geothermal merits are good availability, does not require more space for operation, very silent energy, providing a recorded number of jobs, capable of double recycling, long-lasting, and takes very low maitainence10.
In11, the authors provided the geothermal demerits which are a high abundance of greenhouse gasses, creating high surface instability, more expensive, and useful for particular locations only. These demerits of geothermal systems are limited by considering the wind energy turbines. In this windmill, the axial air kinetic power transformation has been done by applying the wind turbines. In this windmill, the air flows towards the windmill prime mover for building the lift and this lifting force helps to run the wind systems. The windmill consists of a nacelle part in the middle of the turbine shaft and generator for obtaining the smooth voltage from the generator hub. The windmill network uses are grinding grain, extraction of oil from the seeds, pumping water, saw-milling of timber, and processing of tobacco, cocoa, dyes, and paints. However, the above-discussed renewable sources drawbacks are limited locations, high difficulty in auxiliary parts transportation, and more difficulty in managing the system’s operational conditions12. So, solar technology is recommended in this article for obtaining the peak energy for automotive systems. From the recently existing literature investigation, it has been mentioned that the PV cells are developed by considering the three major materials which are illustrated as crystalline silicon, cadmium telluride, copper indium diselenide, and monocrystalline13. In the United States, 82% of PV utilization customers use the back-to-back integration of cadmium telluride and indium gallium.
However, in India, the monocrystalline material is used for the implementation of the PV modules because its properties are ultimate tensile strength, yield stress, and shear modules. Also, it gives high thermal stability, good mechanical strength, and highly efficient lighting absorption. The monocrystalline cell operation completely depends on the P and N material electrons and holes. From the sun, the holes and electrons capture the insolation waves with different wavelengths for activating the overall cell electrons thereby running the entire PV module14. As of now, the sunlight modules are implemented by considering three kinds of PV diodes which are named as single diode model, two diode model, and 3-diodes involved PV system. From the literature study, the approximated one-diode category is utilized more frequently for the study of various solar applications. However, this one-diode model creates approximation issues which leads to less power availability from the sunlight module15. In this work, the 3-diodes category PV technology has been considered for the development of a sunlight array. The features of the three-diode category PV array are efficient utilization of P and N model semiconductor materials, more solar fill factor, good power transformation ratio, and more adaptability with various natural circumstances.
Implemented adjustable CS technology for quick variation of natural conditions.
In this triple diode category system, the cells are organized in a series fashion for enhancing the system potential and also organized in a parallel manner for system current rating improvement. The introduced model of the proposed solar system is illustrated in Fig. 1. From Fig. 1, it is seen that the per unit solar energy production price is higher and it is optimized by focusing on the various advanced semiconductors implementation. Also, due to the huge disturbances in atmospheric conditions, solar energy comes in a non-linear fashion. So, the proposed system operation at peak load conditions is not possible. From the literature review, the peak power extraction has been done by implementing the MPPT technology in the sunlight system to identify the suitable working point of the sunlight system. Here, MPPTs are separated based on the adaption of different algorithms which are swarm intelligence, soft computing, machine learning, differential evolutionary, and artificial intelligence16. The conventionally presented MPPT methodologies are Kalman filter, IC, P&O, fractionally available voltage of the PV, slider technology, and look-up tabular columns method. From the literature investigation, the solar modules provide voltage with continuous distortions. These disturbances are utilized in the Kalman filter technology for generating the duty signal for the voltage transformation circuit17.
This Kalman technology reduces the system current heating losses thereby increasing the sunlight system voltage transformation efficiency. However, this Kalman block improves the size of the system and the cost of the controller. The dual-axis slider tracking technology is referred to in the article18 for the identification of rapid position change of solar modules and it tries to move from one axial state to another axial state concerning the sunlight falling incident angle on the earth. This method provides more flexibility in obtaining the peak voltage of the solar. However, this methodology consumes more time for catching the global required MPP state19. The features of this axial tracing MPPT technology are easy solving of nonlinear problems and better robustness. In the article20, the scholars mentioned that the IV curve slope (dp/dv) of the wind and hybrid solar system is monitored for the effective DC-link current sharing of the smart grid system. Here, the z-source-related bidirectional voltage transformation circuits are interfaced with the central grid busbar network for active power balancing between the local loads and industrial automation systems21. This dp/dv technology may not be suitable for the shaded sunlight modules because of the variable slope value of the IV curve. So, in this article, an ACS algorithm is developed for the quick deviation of the sunlight insolation and PV module heating conditions. The features of this developed MPPT technology are less iteration count necessity, an easy tracking process, a low convergence rate, and more power transformation efficiency (99.982%).
The MPPT moves the solar operational working point position from the origin place to the peak voltage position of the proposed solar module. However, the photovoltaic module supplied voltage may not be equal to the consumer load requirement22. From the literature study, the voltage transformation circuits are two categories which are defined as transformer-oriented converters and non-isolated oriented voltage transformation circuits. Here, the two-leg bridge-oriented converter, flyback converter, and LLC model power transmission technologies are developed in the article23 for solar-based smart lighting applications. Here, the isolating circuit provides electrical power transformation separation and physically removes the intersecting of the supply from the consumer load. The merits of forward-isolated circuit technology are the optimization of load supply ripple content and more energy density with less noise. Additionally, this technology provides lower peak currents on the selected power semiconductors and occupies less space for the implementation process24. However, the additional equipment usage creates a high expenditure cost and needs more technical handling candidates. In this work, a unique switch voltage transmission circuit is used for the system voltage profile increment purpose. From Fig. 1, the selected converter features are one inductor and one capacitor utilized for stabilizing the disturbances of the sunlight energy system.
Here, the application of artificial intelligence technology is studied for sunlight energy systems. From the initial investigation, the hill climb, and sliding technologies suffer from the oscillations of IV characteristics of renewable power systems. As a result, the researchers introduced systems that may not provide sufficient voltage to the consumers. The scholars developed ripple correlation technology for the uniformity of wind, sunlight, and lithium-ion battery energy storage systems. In this hybrid system, the sunlight insolation and wind technology kinetic vary based on the natural conditions, and these systems’ operational points fluctuate concerning the microgrid dc-link voltages. So, the DC-link voltages and atmospheric irradiation values are feedback to the ripple control block for operating the quasi-source bidirectional voltage transmission circuit. Also, the ripple correlation technology optimizes the system disturbances at any environmental conditions. However, this method takes a very long time to optimize the disturbances of MPP position.
The extremum-seeking methodology is developed in the organized fuel stack, battery, and solar array system for effective line voltage distribution30,31. Here, the source voltage is penetrated dynamically to obtain the proper steady-state operation thereby improving the source power utilization factor. The features of extremum-seeking technology are less time consumption for understanding, better convergence rate, and low cost for maitainence. The demerit of this technology is less applicable for dynamic shaded solar systems32.
Detailed classification of various categories of MPPT methods.
The state space methodology-dependent controller is developed in the water compressor-based solar system for enhancing the energy consumption level of the renewable source. In this model, the solar, fuel module, and wind technology is hybridized for endless voltage maintenance, easy to analyze the dynamic voltage stability by employing the state variables of the system, and quick power supply to the industries based on the load necessity33. However, the state model MPPT collects a greater number of converter energy storage elements for state estimations and generates the duty signals for the multi-quadrant voltage transmission circuit. The soft computing methodologies are developed for the mitigation of the disadvantages of state space and P&O controllers. The fractional voltage concept is referred to in the solar and cadmium telluride battery charging circuit for the traffic signal display system. In this fractional voltage technology, the complete battery circuit is isolated from the PV to identify the proportionality factor value of the controller. The fractional source voltage technology is one of the assumptions based MPPT methodology and it is less efficient for tree-shaded solar modules34. The outcomes of recently available power point identifying methodologies are defined in Table 1 and its classification is represented in Fig. 2.
Solar cell complex structure is discussed by forming its equivalent electrical circuit and this equivalent cell electric structure helps to analyze the functioning of the PV array. This model is more advanced when compared to the other sunlight models. Here, there are multiple categories of charge recombination are utilized for implementing the solar system which are bulk recombination of semiconductor models, surface semiconductor cells integration, and perfect leakage current optimization. Also, this is a more acceptable and accurate cell for understanding all cell categories in uniform as well as cloudy sunlight conditions35. From Fig. 3, the photocurrent (IPa) of the solar system is derived from the sunlight incident angle and P-N junction charges transformation. Here, the leakage currents of three diodes (Db, Dc, and Dn) are defined as I0b, I0c, and I0n respectively. This reverse current flow level in the solar module is directly proportional to the material’s property, doping levels, and material temperature withstandability.
Solar model by considering the reverse diode current.
From Fig. 3, it is seen that the overall junction reverse current drop is indicated by selecting the diode (Dn). The parameters (:{{uptau:}}_{text{b}}), (:{{uptau:}}_{text{c}}), and (:{{uptau:}}_{text{n}}) are expressed as the ideality constraints for all three diodes. Based on the diode ideality values, the V-I characteristics of the cell accuracy are improved and the existing ideality value for the practical diode is in the range of 0.9 to 1.2. The internal cell resistance loss (RSs) comes from the material contact of the semiconductor diodes and the variable (RShs) existed due to the junction isolation defects. The photocurrent from Fig. 3 is obtained by utilizing Eq. (1). Similarly, the available current of solar cell by focusing on Fig. 3 is given in Eq. (4). The design parameters of the proposed PV module are mentioned in Table 2. The drawn characteristics of PV modules are given in Fig. 4(a), Fig. 4(b), and Fig. 4(c).
(a) Solar modules IV curves, (b). PV curves of sunlight systems, and (c). Solar system PI curves.
Finally, the solar array VI characteristics are drawn at different sunlight intensity values and these curves vary with different environmental parameters. Also, from Fig. 4(a), it has been identified that the operational point of the PV module is changing concerning the load resistance value changes. Here, the load line-1 indicates the moderate load resistance value of the consumer and line-2 represents the reduced consumer load resistive value. Finally, the resistance of the load increases then the operating point of the sunlight falls towards the origin position. So, maintaining the operating point stable on the VI curve at various consumer resistance values, the MPPT technology is referred to for the cloudy conditions of the solar modules. Here, the adjustable cuckoo search methodology is developed for the shaded solar modules’ peak power extraction thereby consecutively running the entire system at a high fill factor value. The proposed technology is studied along with the P&O, RBFN, IC, FOC voltage, and PSO.
The peak voltage transformation of solar modules has been done by integrating the maximum power point identifying block with the DC-DC voltage circuit. In this methodology, the solar module power is adjusted by changing the PV voltage to evaluate the slope of the P-V curve. The solar P-V curve slope has a positive integer then the slope value is increased thereby operational duty signal of the DC-DC circuit is enhanced for boosting the solar supply voltage36. Otherwise, the DC-DC circuit duty value is optimized for moving the MPP from the left-hand side of the solar P-V curve to the right-hand side. The functioning of P&O technology is illustrated in Fig. 5. From Fig. 5, at point “1” the variation of sunlight voltage is negative then the duty value of the system is improved and at point “2”, the converter operational duty signal value is reduced. Here, the step parameter ѯ is selected for moving the MPP position from an identical position to the actual peak voltage position of the PV37.
Where the terminologies D(m), ѯ, D(m-1), and m are the initial utilized duty signal of the DC-DC circuit and its step length value.
Working nature of Perturb & Observe on the Solar P-V curve.
In the present MPPT methodologies, incremental conductance is the high-priority-based conventional controller that is used to remove the oscillation value of the PV current. Here, the P-V curve conductance value is determined by varying the sunlight PV current38. The change of PV curve conductance provides the operating point position of the overall solar grid-connected and standalone system. The incremental conductance change value depends on the PV source equivalent impedance and it should be equated with the consumer load impedance39. Here, Eq. (12) is selected for updating the MPP position from the origin place to the global peak voltage position. Suppose, the MPP is placed at the left side of the VI curve of the solar system then the duty change has been made by utilizing Eq. (13). However, at peak voltage position, the changes involved in the solar system incremental conductance are zero. The change of conductance-related duty signal and its starting duty signals are defined as D(r-1), and D(r) respectively. The operational workflow chart of the incremental conductance technology is mentioned in Fig. 640.
Study of incremental conductance method for PV modules.
The equalization of the source impedance of the sunlight system with load impedance has been made with the help of the converter duty value. Here, the converter duty value is selected by considering the available fractional voltage. This is completely an assumption-based technology and its open-circuited PV voltage is determined by selecting the individual power semiconductor switch. In this technique, the power switch separates the PV supply and consumer power absorption41. As a result, there is an oscillation in the distribution of power grid corporations. Also, it needs high-voltage rating insulators to handle the sudden rise of voltage swells.
Where suitable Koc value is identified from the solar module VI characteristics. This nonlinear solar cell characteristics operational point is varied concerning the proportionality parameter. From the investigation of the solar system, the Koc value varies in the range of 0.7 to 0.75 for rapid changes in sunlight temperature conditions42. The features of this approximated method are very flexible in development, less complex in understanding, and very few sensing devices are applied to sense the solar module voltage. The operational point change of the PV module is explained by utilizing Eq. (15).
From the recently published data on artificial intelligence technology, there are different categories of neural networks are exist in the literature. However, the neural networks are developed from the working nature of the human brain and its internally connected neurons form the nodes43. Here, all the nodes interchange their functioning information by adjusting their weight signal to obtain the specific action. The available neurons in the source layers collect the signals from the sunlight module and the middle layer neurons process the source signals by modifying their strength. In the coevolutionary neural system, there are more than two hidden layers included in the network with different neuron weights for running the entire system data thereby the modified signals available from the middle layer of the neural system are fed to the load output layer44. The neural network output layers deliver the controllable error signal to the switching modulator for obtaining the sequential switching pulses to the voltage transmission circuit. The merits of this conventional neural system are simple structure, ease of understanding, less data sets training necessity, and it operates with high flexibility.
(a) PV module operational point identification by using RBFN model, (b) Simulink model of the radial basis network.
However, the demerits of this general multilayer conventional neural network are high training input data sets utilized for achieving the highly accurate system response, heavy computational complexity, overfitting datasets, lack of generalization, very limited spatial understanding, and more sensitivity with excess source variables. So, the RBFN technology is presented in the article45 for optimizing the computational difficulty of the overall neural network as given in Fig. 7. From Fig. 7(a), there are 3 number of layers, and the middle layer has the radial activation function for running the three-layer networks for making the nonlinear VI curve of the PV in to linear. From Fig. 7(b), the input solar module net voltage ((:text{n}text{e}{text{t}}_{text{a}}^{text{r}}={text{r}}_{text{a}}^{1})), and its related net currents ((:text{n}text{e}{text{t}}_{text{a}}^{text{r}}={text{r}}_{text{a}}^{2})) are utilized to determine the overall 1st layer net value as mentioned in Eq. (16). From Eq. (16), the terms a, r, R, and e are represented as the input layer variable, total input node number, source layer generated signal, and individual neural node number46. Finally, the load layer net is determined as (:text{n}text{e}{text{t}}_{text{c}}^{text{r}}) and the matrix formation of source signals are given in Eq. (19), and (20).
Where the error value is supplied to the pulse modulator for obtaining the accurate pulses to the power transmission circuit. Here, the applied data samples for the radial function are 629. Due to this less training data set, this neural controller’s working efficiency is more than the P&O.
In the presented evolutionary and metaheuristics algorithms, swarm optimization is the most used methodology for highly complex nonlinear issues solving47. In this computational metaheuristic algorithm, thousands of particles or agents are combined to obtain the actual solution for the solar systems. Also, it is inspired by the ants, fish schooling, and birds flying behavior. Here, the swarm intelligence is not dependent on the gradient of the objective parameter and it provides a more suitable solution by exchanging the searching information. Also, the swarm intelligence analysis process is very easy. However, swarm intelligence faces the problem at the time of multidimensional search space, and there is a possibility of settling a local objective position instead of a global objective place. The limitations of this controller are more computational time and high search space. As a result, the handling of the system is very difficult. In the article48, the scholars stated the adjusted step swarm intelligence technology for rapid identification of any renewable system working point under many atmospheric conditions.
The major features included in this algorithm are less iteration value consideration, optimized convergence rate, easy to capture the required global objective, fine-tuning of the fitness function, and working continuously on both discrete and analog variables. Also, this algorithm takes less memory to complete the computational operation, and it applies the local search operation as well for the better exploitation of the particular problem. Also, the movement of swarm particles and their distance updating has been done by applying the equations (22), and (23).
Where the constant variables “T”, and “Y” are defined as the accelerated and time-varying coefficients which are utilized for the particle’s influence. Here, personal influence makes the particles come to the original position which has a better objective when compared to the present position. Also, the U, and D variables are illustrated as the running movement of the swam and its exact location of the particle. Finally, the variables P, G, u, and q are the swarm’s best position and overall swarm global positions. The PSO Pseudo code and its functioning flow are represented in Fig. 8(a and b).
(a) Pseudo code for the swarm intelligence-based MPPT controller. (b) Swarm algorithm of MPPT technology for PV system.
Cuckoo search is one of the recently implemented metaheuristic technologies for identifying the particular object of renewable and automotive system applications. This evaluation strategy is developed from the brood parasitism of some of the available cuckoos49. Here, the cuckoo search technology follows three categories of rules which are single cuckoo should lay on only a single egg in an instant. Later the good qualified cuckoo eggs go to the next level of host nest operation and finally, the utilized overall host cuckoo nest is made as constant for optimizing the algorithm complexity50. However, the problems of the cuckoo search methodology are levy flight operation and the generation of very new solutions. The features of this technology are a simple structure and less parameter utilization for tuning the cuckoo’s direction and position. The working quality of the cuckoo search methodology depends on the levy flight value of cuckoos, and the identification of fitness value for all cuckoos. The applications of this algorithm are engineering design, multiobjective optimization, data mining process, and machine learning applications.
However, the cuckoo suffers from a premature convergence rate, and it tries to converge at the local objective position without proper searching. Also, it is a more sensitive algorithm for the selection of PV parameters, high discovery rate, more disturbances in step value selection, and high population size creating a less accurate solution selection. In this article, the differentiated step-dependent cuckoo search concept is developed for the renewable solar energy production system to enhance the power quality of the system by making the consumer voltage constant. The introduced structure of the cuckoo search concept is defined in Fig. 9. From Fig. 9, the cuckoo agents are defined with various solar voltages, converter duty values, and PV currents. In the first iteration, all of the cuckoos cross-check the local peak power point value of the solar system,
Where z, t, and u are the distance of cuckoos and their corresponding number and searching iterations. Here, after a cuckoo search, the P&O methodology is applied parallel to the solar system to reduce the final oscillations of the PV system.
Peak power point identification of the PV module by utilizing the cuckoo search technology.
All of the solar energy networks produced voltage is low and also it takes more energy production cost. So, the power transformation from the solar system has been done by choosing the DC-DC boost circuits. The isolated technology is not focused because of its demerits more complex design, and cost. Also, it takes more space because of the additional types of equipment of the transformers, rectifiers, and switching drivers. In this work, the converter selected by focusing on the parameters is optimal utilization space, better-functioning efficiency, good thermal withstand ability, less leakage inductance effect, lower parasitic capacitive effects, more reliability, and better maintenance. The operational solar power included converter circuit is given in Fig. 10a-c.
Proposed converter, (a) normal structure, (b) switching state, and (c) reverse biased condition.
In this working power DC-DC converter, the applied inductors Lc, and Lv values are more for uniform energy production from the solar system as mentioned in Fig. 10(a). Here, these inductors filter the all-unwanted solar network-generated voltage disturbances, and the circuit elements Lc, Dc, Dv, Cc, Cv, and Ca (LD2C2) function as the filter and voltage multiplier circuit. This additional circuit is designed particularly to enhance the solar system’s operational efficiency at quick changes in sunlight insolation conditions. Here, at the starting time duration, the source inductive element Lc completely stores the sunlight energy as mentioned in Fig. 11(a). From Fig. 11(a) and Fig. 10(b), the overall system works in uniform power system condition. After that, the inductor discharges the energy, and the source capacitor captures the voltage until the switch moves in a blocking state. The volt-sec concept is utilized in this circuit operation for evaluating the total voltage consumed by the inductors and the converter circuit voltage gain is derived as,
From Fig. 10(c), the utilized inductor values for this working condition are very low. So, the entire system moves in nonuniform voltage supply conditions. Here, the supply inductor tries to deliver the overall source energy to the resistor through the load diode. In this converter operational state, the parallel connected load capacitor supplies the load voltages. The volt-sec methodology is used to derive the available voltage of the converter. From Eq. (33), the peak inductive current is obtained and it is indicated that the inductor discharges the current in a triangular manner with the time limit of (1-D) times supply time. Finally, from Fig. 10(c), and Fig. 11(b), the voltage conversion value for the solar system is derived in Eq. (38). From the above explanation, the designed inductors and capacitors are obtained as,
Proposed converter working under (a) CCM, and (b) DCM.
From the converter network analysis, the conventional power DC-DC circuits deliver less amount of sunlight voltage because of their low power transformation efficiency and also work at a high number of ripples which is less applicable for the industrial sunlight system applications. Also, due to the switching strategies difficulties, there is noise in the entire power network. From the comprehensive study mentioned in Table 3, the quadrated converter voltage transformation value at 0.3 duty is 4.285 and it is improved stage by stage by improving the duty parameter from 0.4 to 0.9. However, this circuit technology makes 300 times the source voltage value by the operational duty is 0.99. Similarly, the resonant circuit voltage conversion rate at 0.7 is 13.33. However, the structural development takes a high amount of energy storage elements. As a result, the integration and implementational prices are improved. From the analysis of the various converter circuit models, the proposed converter provides better voltage transmission capability with a low amount of passive elements. The introduced converter boundary and its voltage supply differentiation are mentioned in Fig. 12(a and b).
Working converter network, (a) boundary conditions, and (b) its voltage transformation.
The solar electric vehicle systems given power is nonlinear and it may not be uniform because of the quick variation of the available natural conditions. In this work, there are three diode cells are developed for the implementation of the 275 W solar module because this triple diode produces more accurate IV characteristics thereby capturing the sunlight intensity. Also, the triple diode PV circuit transfers the sunlight insolation energy into useful energy. However, the triple diode PV circuit nonlinear voltage is linearized by integrating the cuckoo search technology. This cuckoo search traces the functioning point of the shaded PV module on P-I characteristics with a good convergence rate value. The selected Metal Oxide Semiconductor Field Effect Transistor (MOSFET) voltage rating is more than the solar module voltage value to function the power semiconductor switch at high thermal runaway conditions. The features of high thermal runway-dependent MOSFET are more transient resistivity, optimal size when equated to the junction field effect transistor, negligible static energy absorption, and more energy transformation efficiency. Also, the MOSFETs are developed by utilizing the silicon carbide material for improving the energy convergence rate.
The values of Lc, and Lv are equal to 100mH which limits the distortions of the supply current, and Cc is selected as 40µF which stabilizes the PV produced voltage. Finally, the parameters Cv, and Cn are equal to 60µF for completely unfirming the load power. The tested load resistor R0p is utilized as 40Ω respectively. In this section, the projected cuckoo search developed technology is tested at sunlight insolation 995 W/m2. At this static sunlight insolation value, the adjustable P&O step traces the required functioning point of the sunlight system with a higher oscillation value and IC delivers the peak voltage with the lowest distortions of the system power. However, the P&O takes less development cost and the obtained sunlight PV module produced voltage is low when associated with the PSO technology. Here, the PV and converter circuit delivered power, current, and their relative voltages, and the settled converter voltage times by incorporating the P&O with VS, IC-adjusted step, FOV Controller, RBFN Controller, Step Variation PSO, and Adjustable CSA are 230.997 W, 5.66008 A, 40.80653 V, 225.6332 W, 2.56880 A, 87.8360 V, 0.425 s, 233.431 W, 5.71034 A, 40.87876 V, 229.2946 W, 2.58005 A, 88.8721 V, 0.424 s, 234.8826 W, 5.69980 A, 41.20893 V, 231.3828 W, 2.59991 A, 89.0276 V, 0.422 s, 234.3707 W, 5.68541 A, 41.2232 V, 231.5395 W, 2.6034 A, 88.9373 V, 0.418 s, 236.1011 W, 5.70622 A, 41.37611 V, 235.7516 W, 2.61897 A, 90.0169 V, 0.3542 s, 237.2768 W, 5.70871 A, 41.56409 V, 237.234 W, 2.6327 A, 90.1101 V, and 0.355 s respectively.
Also, at 995 W/m2, the peak working point of the PV efficiency with adjusted step CSA is 99.982% which is the highest value when associated with the P&O, and RBFN controllers. From Fig. 13(a), the PSO methodology provides more current value and optimal voltage value as given in Fig. 13(b). From Fig. 13(c), the obtained power value of the RBFN technology for quick change irradiation of the PV is less when compared to the cuckoo search. So, at the static sunlight insolation value, the swarm intelligence provides better performance as mentioned in Fig. 13(d), and Fig. 13(e), and it is applicable for any sunlight irradiation values as discussed in Fig. 13(f).
Generated sunlight-based PV module with converter currents, voltages, and their existing powers.
Similar to the static status, the proposed cuckoo search technology is applied to the dynamic three-step variation sunlight insolation condition of the PV module. Here, From Fig. 14(a), at dynamic sunlight insolation value, the incremental conductance technology suffers with the fluctuation of PV module functioning temperature. Also, from Fig. 14(b), the PV voltage delivered with highly accurate value by incorporating the swarm intelligence controller and its obtained current values from the application of IC, and FOCV controllers are given in Table 4. From Table 4; Fig. 14(c), and Fig. 14(d), the available PV module current, and its optimized converter currents, load voltage, consumer power, and efficiences by utilizing the P&O with VS, IC-adjusted step, FOV Controller, RBFN Controller, Step Variation PSO, and Adjustable CSA techniques at 495 W/m2 are 3.8561 A, 24.00 V, 92.562 W, 1.30232 A, 68.745 V, 89.369 W, 96.558%, 3.8642 A, 25.44 V, 98.32 W, 1.389854 A, 68.656 V, 95.3644 W, 96.992%, 3.884 A, 25.98 V, 100.94 W, 1.438892 A, 68.4765 V, 98.5247 W, 97.599%,3.9836 A, 26.29 V, 104.75 W, 1.448793 A, 71.1824 V, 103.122 W, 98.437%, 4.1275 A, 26.48 V, 109.33 W, 1.458970 A, 69.082 V, 100.785 W, 99.218%, 4.1082 A, 28.85 V, 118.54 W, 1.467511 A, 80.3220 V, 117.8726 W, and 99.432% respectively. From Fig. 14(e), and Fig. 14(f), at 305 W/m2, the obtained converter voltage settling times of the various MPPT methodologies for sunlight systems are 0.779 s, 0.742 s, 0.616 s, 0.63 s, 0.571 s, and 0.432 s respectively. Finally, the disturbances occurred due to the swarm intelligence, and the iterations utilized for catching the global peak power are very low. So, at three-step sunlight insolation disturbance conditions, the ACSM provides sufficient power to the rural consumers at the time.
(a) Sunlight system delivered current, (b) PV voltage, (c) solar power, (d) converter current, (e) load voltage of the converter, and (f) consumer utilized power at 995 W/m2, 495 W/m2, and 305 W/m2.
Similar analysis of the previous two sunlight insolation conditions, in this section, there are four irradiation steps are selected to find the global peak voltage of the proposed system. Here, the assumed sunlight intensity values for testing the single switch wide voltage conversion ratio are 800 W/m2, 500 W/m2, 700 W/m2, and 980 W/m2. From Fig. 15(a), it has been seen that the incremental conductance methodology provides a huge amount of current to the DC-DC converter. Due to this excess flow current in the converter network, the electrostatic capacitive, and electromagnetic inductive losses are increased beyond the rate value. Also, the extracted PV cell voltage is less by incorporating the P&O which is equal to 37 V as mentioned in Fig. 15(b). At 800 W/m2, from Fig. 15(c), and Fig. 15(d), the triple diode involved PV module power delivery and the associated current of the converter by feeding the P&O with VS, IC-adjusted step, FOV Controller, RBFN Controller, Step Variation PSO, and Adjustable CSA techniques are 178.84 W, 1.8 A, 180.27 W, 1.72 A, 180.5 W, 1.75 A, 181.55 W, 1.8 A, 181.07 W, 1.72 A, 182.61 W, and 1.71 A respectively.
From Fig. 15(e), and Fig. 15(f), the converter load delivered voltage from the radial basis function technology is more when equated with the incremental conductance, and FOCV methodologies which are determined as 82.78 V, 80.01 V, and 78.56 V respectively. Also, it has been pointed out that the overall power delivered from the introduced system to the consumer by selecting incremental conductance is low which means that the conventionally available methodologies are not appropriate for any renewable sources. However, the swarm cuckoo search concept helps the proposed system achieve the 260.78 kW power of the converter circuit with the highest efficiency. Similarly, at 700 W/m2, the tracing MPP values of the Step Variation PSO and Adjustable CSA techniques are 0.021 s, and 0.028 s respectively. Also, the iteration values of these two swan-dependent controllers for catching the MPP of the introduced system are 225 and 198. So, the overall analysis of this work indicated that the cuckoo technology provides a more suitable MPP position for global peak power extraction.
(a) Sunlight system delivered current, (b) PV voltage, (c) solar power, (d) converter current, (e) load voltage of the converter, and (f) consumer utilized power at 800 W/m2, 500 W/m2, 700 W/m2, and 980 W/m2.
The implementation of a fundamental converter network is less complex and needs a low amount of electrical elements for the study of solar systems. However, these converter circuits have the issues of bad voltage variation, very slow transient response, too much ripple content value in the load signal, and less satisfied efficiency value. Similarly, the flyback network structures utilize excessive semiconductor values thereby the installation cost value and size are increased. Here, a new converter testing is performed for analyzing the solar voltage conversion value as mentioned in Fig. 16. From Fig. 16, the non-isolated system structure merits are small size, less quantity devices usage, potentially gives highest working performance, better safety when associated to the isolated structures and more reliable system for all renewable energy systems. Here, the driver-activated voltage is 5.2 V which is obtained from the 0 V to 12 V transformer. The transformer protects the driver circuit from the central grid supply voltages.
Experimental development of the proposed circuit.
Here, the switching signals are obtained from the TLP-350 opto-isolator which protects the IRF-840 device from the regulated supply voltage and it receives the modulated signal from the digilent waveform device. From Fig. 17(a), the second diode produced voltage from the source is 48.73 V and the current produced from the diode is 0.49 A. Similarly, the fourth diode captures the voltage value from the regulated power supply as 41.83 V as given in Fig. 17(b). The current value of the fourth diode is 1.21 A. Finally, the generated system voltage is 108.3 V as mentioned in Fig. 18.
(a) Second diodes generated waveforms for 0.4 duty value. (b) 4th diode generated voltage waveform and its associated current.
Evaluated converter-generated voltage waveform.
The revised cuckoo search involved nonisolated converters are investigated by applying MATLAB/Simulink nature. In this work, the 1st aim of this triple-diode PV module is to provide more sunlight power with good accuracy. Also, the sunlight system delivered the nonlinear VI curve. So, the high voltage extraction from the renewable system is more problematic. To solve this problem, the adjustable CSM captures the PV voltage and solar current for linearizing the sunlight-delivered power thereby optimizing duty signal passes to the pulse modulator for supplying the switching signals to the MOSFET. This proposed MPPT provides load-consumed power with high efficiency. Also, it takes very few iteration values to catch the global MPP region. Finally, from the comprehensive investigation of the various MPPT methodologies, it has been found that the swarm controllers are perfectly applicable to most of the nonconventional power-delivering systems in the world. Here, from the experimental investigation, the one-switch DC-DC circuit reduces the PV power per unit generation cost by enhancing its supply voltage.
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
ON line technique + OFF line technique
State-based technique
Maximum power point tracking controller
Particle swarm optimization
Perturb and observe
Adjustable cuckoo search methodology
Forced oscillation technique
On-line search technique
Linear reoriented coordinate technique
Ripple correlation correction technique
Load parameters methods
Curve fitting technique
Optimization methods
FUZZY logic controllers
Neural networks
Biological swarm chasing
Current sweep technique
Sliding mode control
Variable inductor technique
Incremental resistance technique
Extremum seeking control technique
DC-link capacitor droop control technique
Lookup table technique
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The Centre for Emerging Energy Technologies (CEET), Department of Electrical & Electronics Engineering, SR University, Warangal, 506371, Telangana, India
M. Ashwini, CH Hussaian Basha & Mohammed Mujahid Irfan
Electrical Engineering Department, Yanbu industrial college, Saudi Arabia, Saudi Arabia
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Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
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Mangilipally Ashwini: Conceptualization, validation, formal analysis, investigation, resources, writing—original draft preparation, CH Hussaian Basha: Writing—review, editing, visualization, supervision, project administration, validation, formal analysis, investigation.Musfer Alraddadi: Experimnetal development, results analysis, editing, visualization Faisal Alsaif: validation, formal analysis, project administration, validation Mohammed Mujahid Irfan: Supervision, project administration, formal analysis, investigation.
Correspondence to Mohammed Mujahid Irfan.
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Ashwini, M., Basha, C.H., Alraddadi, M. et al. Design and comprehensive analysis of adjustable step MPPT controllers for solar PV systems under stochastic atmospheric conditions. Sci Rep 15, 10369 (2025). https://doi.org/10.1038/s41598-025-95136-1
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