This research proposes an advanced artificial neural network (ANN) framework optimized for the dynamic, real-time identification of rotor resistance (Rr) in sensorless induction motor (IM) drive systems. The proposed architecture introduces a self-tuning momentum factor within the neural learning update rule, which is adaptively modulated at each sampling interval. This modulation is governed by a Mamdani-based fuzzy inference system to ensure accelerated convergence and enhanced stability of the estimation process. Concurrently, the motor's angular velocity is estimated through a parallel ANN observer. Reliable identification of the time-varying rotor resistance is pivotal for compensating parametric sensitivity in flux observers, thereby optimizing the drive's control fidelity under varying thermal and load conditions. Comprehensive simulation and hardware-in-the-loop experimental results confirm that the proposed estimator tracks the actual Rr with high precision, maintaining steady-state errors within a 5% threshold.
This paper proposes a reliable power optimization strategy that maximizes the harvested power of induction machines driven by wind, taking into account variable wind turbulence and uncertain machine parameters. This work explores the challenging task of designing type-2 fuzzy logic (T2FL) and conventional type-1 fuzzy logic (T1FL) controllers for wind energy conversion systems that exhibit multiple non-linearities. T2FL controllers are proficient in tackling uncertainties and offer quicker and more precise decision-making capabilities. The proposed approach is beneficial as it is independent of accurate wind turbine parameters, wind speed data, or additional sensors. Rather, it utilizes the mechanical rotor speed and the wind turbine power as input, which corresponds to maximum power point tracking (MPPT) through the management of the rotor speed via the machine-side converter. Real data validates the scheme against classical controllers, and via a set of simulations and statistical analyses, performance metrics like steady-state error, overshoot, tracking speed, and efficiency are widely assessed. The results show that the proposed scheme, which is independent of a dedicated wind speed sensor, demonstrates superior tracking performance, lower tracking errors, such as lower RMSE/MAE, and higher energy yield, although the wind speed and the system parameters change rapidly. Overall, this design provides more robust performance to random wind speed variations, increases operational efficiency and wind turbines' service life, and is low in adding mass and cost.
Globally, there is a growing emphasis on switching to green energy, particularly in the transportation sector, due to the effects of global warming, as seen by rising carbon footprints. Fuel cell electric vehicles (FCEVs) are one such technology that has attracted a lot of interest because of their availability, ease of use, high efficiency, and silent operation. Fuel cells are employed along with batteries to drive the vehicle much farther. Motors like permanent magnet synchronous motor (PMSM) provide the driving force for the vehicle, owing to their high torque at variable speeds and compactness. In such systems, it is necessary to have intelligent controllers that can align with the load requirement by means of a consistent and optimized power distribution. The torque ripple phenomenon, which has an impact on dynamic performance and operational stability, is one of the main limitations in the operation of PMSMs. In this work, smart control techniques, which are a combination of adaptive neuro fuzzy inference systems (ANFIS) and proportional-integral (PI) control, are employed to demonstrate the application of PMSM in conjunction with field-oriented control (FOC). Simulation results indicate that the proposed ANFIS-based FOC reduces torque ripple as compared to conventional PI control under varying load conditions.
The high adoption of electric vehicles in transportation has created a demand for compact, efficient, and cost-effective charging solutions for them. Conventional onboard chargers are often bulky, which adds to the overall cost of the drive system, whereas off-board charging infrastructure remains limited. In order to address these issues, this work illustrates the design and modelling of an active power factor corrected integrated onboard charger which gets reconfigured from the electric vehicle drive train components. The proposed circuit setup is designed to work in dual mode, i.e., in the role of a DC-DC converter while charging the vehicle battery and as a three-phase inverter while driving the vehicle. The front-end power factor correction circuit, in addition to the reconfigured DC-DC converter, charges the 24 V, 20 Ah lead acid battery under constant current constant voltage (CC-CV) mode, achieving a power factor close to unity. Modelling and control of the proposed 200 W reconfigurable converter-fed 24 V, 180 W brushless direct current (BLDC) drive is validated using MATLAB/ Simulink Software. Simulation results demonstrate a power factor of 0.996 in grid-connected operation with a total harmonic distortion (THD) of 4.96%. The proposed architecture achieves a compact structure with only 8 switches enabling charging, propulsion and regenerative braking operation. The proposed converter thus contributes to a cost-effective electric vehicle and provides the scope of future extension to vehicle to home (V2H), vehicle to load (V2L), and vehicle to vehicle (V2V) applications as well.
Light-emitting diodes (LEDs) are essential to current lighting due to their perfect control, long lifespan, great energy efficiency, and environmental friendliness. However, issues like output ripple and uneven brightness could have an impact on both visual comfort and system performance. This research presents the design and implementation of a non-inverting Cuk (NI-Cuk) converter operating in discontinuous conduction mode (DCM), integrated with a valley-fill circuit (Vfc) that reduces voltage and current (V and I) ripple and improves lifetime. The study begins with an analysis of the classic Cuk (CCuk) converter, highlighting its shortcomings, including inverted output polarity and the high current stress across the switching device. A NI-Cuk is proposed to overcome the shortcomings of CCuk, delivering a positive output with higher efficiency. Vfc offers a faster steady-state response, reduces peak loads on components, and reduces losses. To confirm that the design and hardware prototype were developed, and the results are validated with the simulated outcomes. The approach's viability is confirmed by experimental results, and a comparison of CCuk, NI-Cuk, and NI-Cuk with and without Vfc is conducted using voltage regulation, efficiency, and ripple. The results show that the suggested converter assurances are a reliable, effective, and superior power source for LED lighting applications.
Conventional power plants pose a threat to the environment because of their substantial carbon emissions. Photovoltaic (PV) systems are becoming more and more popular as a sustainable alternative for clean electricity generation. However, because weather and environmental factors vary, partial shadowing affects PV output. The stacked multi-cell converter (SMC) provides a practical way to improve power extraction under these circumstances. This paper suggests a hybrid control approach for a photovoltaic (PV)-based distributed system (DS) using an SMC that is based on the attentive evolutionary generative adversarial network (AEGAN) and prairie dog optimization (PDO) algorithm. The AEGAN forecasts load requirements, while the PDO maximizes converter control to improve reliability, efficiency, and power quality (PQ). Under various load and irradiation circumstances, the system is modelled and verified in MATLAB/Simulink. Results from simulations show that the AEGAN-PDO approach performs better in both dynamic and steady-state situations. Transient disturbances on the load side are rapidly reduced with minimal overshoot. In contrast to traditional particle swarm optimization (PSO), ant lion optimizer (ALO), and archerfish hunting optimizer (AHO) controllers, AEGAN-PDO maintains the lowest THD (1.1%), least power loss (0.24 MW), and best efficiency (98.59%). These results validate the AEGAN-PDO approach as a reliable and effective way to operate renewable-integrated power systems in real-time, promoting improved PQ and grid dependability.
The global integration of renewable energy sources like photovoltaics requires efficient high-step-up DC-DC converters. Conventional boost converters exhibit inherent limitations in achieving high voltage gain efficiently, particularly under high duty cycle operation, where switching losses, device stress, and output voltage ripple become significant. This paper proposes a novel hybrid DC-DC converter that integrates a four-phase interleaved input stage with a five-level switched-capacitor (SC) multiplier network. The proposed topology introduces a modular and structurally decoupled architecture, in which current conditioning and voltage boosting functions are independently realized. This enables scalable voltage gain through modular expansion without requiring extreme duty cycles or additional magnetic components. The interleaved stage reduces input current ripple and improves current sharing, while the multilevel SC network provides a high voltage conversion ratio and balanced voltage stress across components. Comprehensive simulations using PSIM software validate the converter's performance. With a 25 V input, the proposed converter achieves an output voltage of approximately 250 V (gain of 10), a high efficiency of 95.2%, output voltage ripple below 2%, and balanced capacitor voltages. The results confirm that the proposed converter offers an efficient, scalable, and high-performance solution for high step-up applications.
Efficient DC-DC power conversion is essential for sustainable solar photovoltaic systems. Conventional converters often suffer from leakage currents, higher circuit complexity, and limited flexibility in interfacing with grid-connected inverters. This study introduced a novel hybrid DC-to-DC converter based on the C4S (coupled capacitor combined Cuk-SEPIC) converter, proposed precisely for sustainable solar photovoltaic systems. The designed converter offers a dual output in the form of a bipolar direct current (DC) bus, allowing flexible combination with grid-connected inverters that receive either unipolar or bipolar DC inputs. This setup not only enables effective transfer of power to the grid but also efficiently removes the leakage currents without the necessity of lossy DC-link capacitors from the load-side current loop. Moreover, the magnetic cores are integrated by employing the input and output coupled capacitors, which considerably minimize ripple current and ensure the capability of power extraction from the PV unit. A fuzzy logic controller is employed to dynamically adjust the converter’s action under varying load conditions and solar irradiance. The proposed topology minimizes driver circuits, reduces system complexity, eliminates leakage current without requiring lossy DC-link capacitors, and improves reliability. Simulation results demonstrate stable voltage regulation, reduced ripple, improved efficiency, and superior dynamic response compared to conventional control methods. The proposed converter demonstrates its potential as a high-performance, intelligent, and energy-efficient process innovation for modern sustainable solar energy systems.
Renewable energy, particularly hydropower, is a key focus in reducing reliance on fossil fuels and mitigating environmental impacts. Permanent magnet generator (PMG) has emerged as a highly efficient option for converting hydro-energy into electricity, offering advantages such as high efficiency, compact design, and minimal maintenance. This review explores the latest developments in PMG technology, particularly for small and medium-scale hydropower applications. A systematic review method was used to analyse 617 papers and narrow them down to 20 relevant studies. Key findings highlight advancements in PMG design, including modular stators, counter-rotating turbines, and cordless designs that enhance efficiency and adaptability in low-speed environments. However, significant challenges remain, including the high cost of magnetic materials like Neodymium Iron Boron (NdFeB), thermal stability issues, and more robust control systems to manage variable water flow conditions. The review concludes that while PMG holds great potential for hydropower applications, Further research is needed to optimize material usage, improve design, and reduce costs. Future work should focus on developing new magnetic materials and innovative rotor designs to ensure PMG can provide a scalable and sustainable solution for global energy needs.
Enhancing photovoltaic models' performance and dependability requires optimal parameter extraction. This paper presents a practical method for determining these values from experimental current-voltage data: the war strategy optimization algorithm. RTC France, PWP201, and STP6-120/36 are the three PV models to which the war strategy optimization algorithm was successfully applied. According to the findings, the RMSE values for RTC France were 0.0000077298; PWP201 was 0.0020528; and STP6-120/36 was 0.0014253. These results demonstrate the great potential of the warfare strategy optimization (WSO) to improve the accuracy of photovoltaic models and advance photovoltaic technology.
This article provides an in-depth examination of recent advances in high-gain DC-DC converters, emphasizing soft-switching techniques and topological innovations that minimize voltage stress for renewable energy applications. High-gain DC-DC converters are crucial in photovoltaic and fuel-cell systems, where boosting low input voltages to higher levels must be achieved with high efficiency and compact design. Traditional boost converters fall short due to elevated switching stress, discontinuous input currents, and lower efficiency at high-gain levels. To address these limitations, this review categorizes and critically evaluates state-of-the-art converter topologies developed for high-gain operation. The main contributions of this review are as follows: i) A systematic classification of high-gain converter configurations with emphasis on their operational principles; ii) A detailed evaluation of soft-switching techniques, including zero voltage switching (ZVS) and zero current switching (ZCS), focusing on their roles in reducing switching losses and electromagnetic interference; iii) An analytical discussion on voltage stress mitigation methods and improved control strategies; and iv) An assessment of emerging trends in integrating advanced power electronics with renewable energy systems. These contributions collectively provide a comprehensive reference for researchers and engineers, supporting the development of next-generation high-performance DC-DC converters tailored for sustainable energy applications.
In a single-phase inverter system, parallel operation of inverters is a strategy to increase capacity, improve reliability, and increase the flexibility of the inverter system. This work discusses the basic operation of a novel parallel H-bridge current source inverter (H-BCSI) and H-bridge voltage source inverter (H-BVSI) operated in a grid-connected operation with isolated direct current (DC) sources equipped with power transformers. Each inverter circuit employed an independent current controller to regulate its alternating current (AC) output current. The proposed inverter system was tested for different operation conditions, and its characteristics were analyzed, especially for its harmonic profile. The test results showed that if the magnitude of the H-BCSI current was varied, while the H-BVSI current was kept constant, the total harmonic distortion (THD) value of load current was much lower than the THD values of H-BVSI current, H-BCSI current, and grid current, i.e., THD Iload ≤ 1%. This condition also occurred when the output current of the H-BVSI was increased gradually while the output current of H-BCSI was maintained constant. Moreover, a similar result was also obtained when both inverters’ output currents were varied simultaneously with the same value. The test results confirmed that the injected AC current of both inverters during parallel grid-connected operation worked well at unity power factor, and met the standards IEEE 1547 and IEC 61727, of which current THDs were ≤ 5%. The proposed grid-connected parallel inverter system worked, supplying a sinusoidal AC load current with high power quality.
Multiphase DC-DC converters are widely adopted in high-power applications such as electric vehicles (EVs) and renewable energy systems due to their ability to reduce current ripple, improve efficiency, and distribute thermal stress across multiple phases. However, under dynamic load variations, mismatches in passive components, device parameters, parasitic elements, and thermal effects can result in phase current imbalance. This imbalance degrades transient performance, increases circulating currents, and reduces overall system reliability. Therefore, selecting an appropriate current control strategy is essential to ensure accurate current sharing and stable output voltage regulation under varying operating conditions. This paper presents a comparative study and selection methodology for current control techniques for MCU-based interleaved DC-DC converters. Various current control strategies are evaluated in terms of dynamic response, steady-state current sharing accuracy, implementation complexity, and embedded feasibility. A 1 kW, 36 V-12 V three-phase interleaved buck converter using Gallium Nitride devices is modeled in MATLAB/Simulink and validated through hardware experimentation. The comparative results highlight the trade-offs among transient performance, current balancing accuracy, scalability, and embedded implementation complexity, providing a structured basis for selecting an appropriate current control technique as per application requirements.
This work focuses on studying and analyzing the photovoltaic power plant of Oued Nechou located in the South of Algeria, in order to create its simulation model. This later can estimate its power production. To achieve this, all system parameters were introduced in the model according to the real data. Then, the characteristics of the photovoltaic panels were tested and plotted under different temperature and irradiation values to understand their influences on the electrical performances. In order to ensure the maximum energy production, photovoltaic panels were associated with converters controlled by a maximum power point tracking (MPPT) algorithm. Two different thin- film technologies of the PV panels (Amorphous silicon (a-Si) and cadmium telluride (CdTe) technologies) were simulated and tested under standard test conditions (STC) and compared with the real characteristics. The results show good accuracy. Subsequently, the real data of four seasons of the same year were introduced in the created model of Oued Nechou station. The obtained results of the simulation show that the performance of the produced energy is affected by the desert climatic conditions, especially the temperature and the solar radiation. However, the positive solar effect is higher than the negative thermal effect, which encourages investment by installing other photovoltaic stations in these areas known by the high and long duration of irradiance.
The rising inclusion of renewable energy sources into distribution networks has accelerated the adoption of distributed generation (DG) technologies such as solar photovoltaic (PV) and wind turbines. This paper explores the effect of solar and wind DG integration on voltage profiles, power losses, and economic performance in a practical 41-bus radial distribution system. Using the Power World Simulator (PWS) software, the load flow analysis is performed to evaluate different DG placement strategies and penetration levels using the loss sensitivity factor (LSF) method. The results indicate that optimal placement of solar and wind DGs notably improves voltage stability and effectively reduces both real and reactive power losses in the distribution system. Furthermore, the economic analysis demonstrates annual savings of ₹29.08 lakhs for solar DG and ₹33.40 lakhs for wind DG, with payback periods of approximately 11 years, indicating strong technical and financial feasibility. The findings highlight that strategic DG planning can simultaneously enhance system reliability, efficiency, and economic viability in modern distribution systems.
Solar-powered electric vehicle (EV) charging stations are essential in advancing low-carbon transportation. However, determining optimal locations remains challenging due to spatial, technical, and environmental constraints. This systematic review, conducted under the PRISMA 2020 framework, synthesizes optimization techniques for siting solar-powered EV charging stations from 15 peer-reviewed studies published between 2016 and 2024. The reviewed methods are classified into five major categories: geographic information systems (GIS)-based spatial models, multi-criteria decision-making (MCDM) frameworks, hybrid approaches integrating fuzzy logic and GIS, heuristic/metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), and artificial-intelligence-based models for predictive site selection. GIS-MCDM hybrid approaches were the most prevalent, offering improved robustness in spatial decision-making. Nevertheless, the literature reveals persistent gaps, including limited empirical validation, insufficient use of real-time data, and weak integration with smart-grid planning. This review provides a structured methodological classification, highlights sustainability considerations, and outlines a research roadmap toward intelligent, data-driven, and sustainable EV infrastructure planning aligned with global energy-transition goals.
This paper aims to develop an efficient finite-set model predictive control (FS-MPC) strategy for DC-DC boost converters to improve voltage regulation while reducing computational complexity. The proposed approach introduces a split cost function that decouples voltage and current regulation, providing a simpler alternative to conventional long-horizon FS-MPC schemes used to address the converter’s non-minimum-phase (NMP) behavior. A current estimation technique is incorporated to eliminate the need for additional sensors, lowering hardware cost and improving robustness. Unlike existing FS-MPC methods that rely on horizon extension or extra measurements, the proposed strategy leverages the split cost structure to achieve comparable NMP compensation with significantly lower computational effort. The controller is implemented in real time using a hardware-in-the-loop (HIL) setup on a ZedBoard platform, with accurate data acquisition provided by an external ADC. Experimental results demonstrate that the proposed approach enhances voltage-tracking performance, eliminates overshoot and undershoot, reduces settling time by over 40%, and decreases computational effort by more than 80% compared to traditional FS-MPC methods.
This article explores the problem of adaptive control for nonlinear dynamic systems operating under uncertainty. It presents a model reference adaptive control (MRAC) method that integrates a neuro-fuzzy network with B-spline basis functions. The proposed approach allows effective approximation of nonlinear behaviors and ensures high control accuracy despite external disturbances and structural uncertainties within the system. The paper compares the performance of conventional linear MRAC with the neuro-fuzzy controller. Simulation results demonstrate that the neuro-fuzzy MRAC achieves superior stability and accuracy in closed-loop control. Additionally, the study examines the system’s local stability under specific conditions of the learning rate. To address the challenge of computational complexity, a decomposition strategy dividing the controller into smaller sub-models is introduced, effectively mitigating the “curse of dimensionality.” The findings support the applicability of neuro-fuzzy controllers for the intelligent control of a wide range of nonlinear systems.
Energy system operation relies on synchronous protection and safety, with stable networks able to handle disruptions without abrupt changes. Due to the complexity of modern electrical systems, contingency analysis is essential for addressing issues in power system analysis. This study presents a comprehensive method for identifying the initial causes of cascading system failures in power systems. Case studies utilizing IEEE test systems with various cascade models demonstrate the effectiveness of this approach. The analysis aids in evaluating the possible impacts on the system and informs preventive strategies to avoid failures. This study integrates voltage stability and reactive power performance indices to create a framework for assessing the reliability of a medium-voltage power network operating at 33 kV. It evaluates N-1 and N-2 contingencies due to line and generator outages to identify and rank critical network components. In contrast to traditional reliability evaluations, the approach identifies weaknesses connected to voltage that impact system resilience. The proposed approach enables improved ranking of severe contingencies beyond conventional methods, supporting targeted reinforcement and enhancing voltage stability and overall system reliability.
This paper explores a simulation-based study on a renewable energy system that integrates wind energy with a single-ended primary inductor converter (SEPIC) to drive a brushless DC (BLDC) motor for water pumping applications. The proposed system addresses the challenge of regulating the variable output of wind turbines by employing a SEPIC converter to provide a stable direct current (DC) voltage supply to the BLDC motor. The novelty of this work lies in the combined modeling and performance analysis of the wind turbine, SEPIC converter, BLDC motor, and electronic commutation in MATLAB/Simulink, optimized for energy-efficient off-grid pumping. Simulation results demonstrate that the SEPIC converter effectively stabilizes the wind-generated voltage, ensuring reliable motor operation under varying wind conditions. The proposed system exhibits high efficiency, stable dynamic response, and low maintenance requirements, making it a practical solution for water pumping in wind-rich regions where solar irradiance is limited, particularly for off-grid water pumping applications.
