Since the capacitor current can reflect load variations faster than the peak inductor current, incorporating the capacitor current into control loops helps to improve the transient response of the dc-dc converter. In this paper, a coupled inductor single input dual output (CI-SIDO) buck converter is investigated under capacitor current ripple (CCR) control. A precise small-signal model for a CCR-controlled CI-SIDO buck converter operating in continuous conduction mode (CCM) is developed. The accurate small-signal model is obtained by substituting the derived CCR controller expressions in the CI-SIDO buck converter state-space model. The CCR controller equations specify the duty ratios as functions of the circuit variables namely the capacitor currents and the output voltages. It is observed that the CI-SIDO buck converter with the CCR controller exhibits instability when both or either of the duty ratios are greater than 0.7 or their sum is greater than 1. The results of the PLECS STANDALONE simulation validate the theoretical propositions as regards the switching instability of the controlled converter.

The Distributed Static Compensator (DSTATCOM) is being recognized as a shunt compensator in the power distribution networks (PDN). In this research study, the superconducting magnetic energy storage (SMES) is deployed with DSTATCOM to augment the assortment compensation capability with reduced DC link voltage. The proposed SMES is characterized by a DC-DC converter with different circuit elements like one inductor, two diodes and two insulated gate bipolar transistors. The Deep Bayesian Active Learning algorithm is suggested to operate SMES supported DSTATCOM for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor, load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the DSTATCOM. To show the power quality effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC- 61000-1 grid code. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Turbine blades, which carry approximately one-third of a turbine weight, are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. Early detection of blade damage is crucial for preventing catastrophic failures that can lead to downtime, repair costs, and even injury or loss of life. This manuscript aims to explore an image analytics-based deep learning framework for wind turbine blade erosion detection. Turbine blade images are captured via drones/unmanned aerial vehicles during the data collection phase. Upon inspection, it was found that the image dataset was limited; hence, image augmentation was applied to improve the blade image dataset. The approach is modeled as a multiclass supervised learning problem where different turbine blade surface damage scenarios are considered. The potential capability of transfer learning methods such as VGG16-RCNN and AlexNet are tested against a convolutional neural network for detecting the blade's surface damage. Results reveal the VGG16-RCNN model as the best classifier among the tested ones with the highest accuracy and precision score. To validate the effect of image augmentation on the training data, the accuracy of the proposed VGG16-RCNN framework is assessed via sensitivity analysis, and results of the same reveal that horizontal and vertical flip together with zoom and rotation brings out an efficiency of 93.8%. However, a more generic model that works well with turbines located in different topological regions could be of more importance. © 2024 IEEE.

As the capacitor current can respond to load fluctuations more rapidly than the peak inductor current, incorporating the capacitor current into the current control loop enhances the transient response in DC-DC converters as well as ensures over-current protection and noise immunity. This paper presents a comprehensive small-signal model (SSM) for a capacitor current ripple controlled (CCR) coupled inductor single-input dual-output (CI-SIDO) buck converter. The complete SSM is derived by unifying the developed SSM of the CCR controller with the SSM of the considered power converter using state-space averaging technique. In CCR control, the amalgamation of the comparator and SR flip-flop is accountable for producing the duty cycle. The proposed SSM is of immense usefulness in designing the outer loop controller, deriving switching instability conditions, and analyzing the dynamic characteristics of the capacitor current controlled CI-SIDO buck converter. To evaluate the advantages of this current controller to CI-SIDO buck converter, a load regulation analysis using the SSM of CCR controller is provided and thereafter verified through simulations in MATLAB/Simulink. It is observed that the low frequency gain of the cross and self regulation transfer functions is substantially less signifying promising dynamic and load disturbance rejection capability of the CCR control driven CI-SIDO buck converter.

The introduction of alternative vehicle technologies, such as Electrical Vehicles (EVs) is a practical endeavour to minimize CO 2 and NO X emissions. Therefore, EVs raise concerns about vehicle charging and management. The deployment of charging stations (CS) for EVs is explored in this study due to the importance of charging station infrastructure. Furthermore, its objective is to examine the economic and technical aspects of the placement of fast charging stations, the investment cost necessary to install the charging station is considered under the economic aspect, whereas distribution system energy loss and voltage variation at buses are considered under the technical aspect to construct CS at optimal locations. First, a mathematical formulation of the problem is developed to deploy the CS in the distribution system. A novel AI-based hybrid technique of gray wolf optimization and particle swarm optimization (HGWOPSO) is used to determine the optimal location of CSs. The developed method is tested by simulation on a 33-IEEE bus. Furthermore, after integrating renewable energy sources at the CSs, 10.55% energy loss is reduced and also improved voltage profile of the proposed system.

A mass of abnormal cells that form inside or outside the brain is called a brain tumor. Adults are at high risk of developing brain tumors, which can cause serious organ dysfunction and even death. Detecting tumors manually is a tedious and difficult method that might yield erroneous findings. As a result, these tumors must be meticulously classified to offer a complete medical diagnosis and design an appropriate treatment plan. Estimating the patient's chances of survival is difficult since tumors are uncommon and can vary greatly in size, location, and history. In order to address these issues, the use of two different deep learning frameworks for multi-class brain tumor classification utilizing Magnetic Resonance Imaging (MRI) data was examined in this study. Significant evaluation metrics, including F1 score, recall, accuracy, and precision, were applied to these models. Both models demonstrated significant improvements over prior brain tumor classification studies, illustrating that deep learning algorithms may be used in the future to accurately diagnose brain tumors and enable medical personnel to make well-informed judgments regarding patients' treatment courses. This study proposes two classification algorithms: ResNet50, that obtained a success rate of 99.39%, and EfficientNetB0, obtained accuracy rate of 99.75%.

This paper presents a novel coronavirus herd immunity optimization (CHIO) algorithm for tuning the proportional-integral-derivative (PID) controller for the DC-DC boost converter. The closed-loop control action using the PID controller is designed to regulate the output voltage of DC-DC boost converter across the load end. CHIO is a nature-inspired meta-heuristic optimization algorithm formulated based on the way humankind handled the coronavirus pandemic (COVID-19) in recent years. This optimization algorithm exploits the herd immunity and social distancing concepts. The optimization algorithm has been developed on MATLAB/Simulink software for obtaining the optimum PID controller gains. Extensive simulations are conducted under (i) start-up response, (ii) reference voltage change (iii) load resistance change, and (iv) input voltage change to find the performance of the proposed controller. The obtained results indicate a successful convergence and satisfactory dynamic response of the output voltage under wide variation in the operating points.

Persistent diabetes results in diabetic retinopathy (DR), affecting the retinal blood vessels (BVs), causing lesions. Rapid identification and treatment are crucial for preventing vision loss. Low ophthalmologist to patient’s ratio results automating the DR detection a dire need. Therefore, a feature extraction method is proposed using a Mamdani fuzzy inference system (FIS) classifier for efficient identification. Methods: Mathematical morphology, region growth, and 12-region search computation have been used to mask the BVs and macula. The masked green plane image was subjected to Nick's thresholding to locate the dark lesions, from which statistical features were extracted and employed in the Mamdani FIS to classify the DR. Results: On evaluating a total of 909 images from the MESSIDOR database shows, average sensitivity, specificity, area under the curve receiver operating characteristics, and accuracy of 99.7%, 99.8%, 99.4%, and 99.6%, respectively. The algorithm performs well in real-time images from two local hospitals. Conclusion: The proposed technique provides a powerful yet flexible tool for improving the diagnosis and treatment of this condition that threatens vision, as it combines the strengths of fuzzy logic, clinical knowledge, and adaptive learning to provide precise, timely, non-invasive, and economical solutions.

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

A battery charging topology has been designed and developed for the fast charging of Li-Ion batteries. The charging circuitry comprises of a Proportional-Integral-Derivative (PID) controlled DC-DC buck converter system for reducing the charging time in Li-Ion batteries. Battery charging time depends on several factors and the charging current is one of the major criteria. In this work, the buck converter is used to attain a high charging current, besides providing the regulated voltage to the battery. Initially, the AC supply obtained from the mains is converted to DC using an AC-DC rectifier. The rectifier output is further fed to the buck converter to increase the output current of the circuit. The buck converter reduces the output voltage and increases through it. The circuit parameters are designed by considering the commercially available Lithium-ion battery LIR18650 as the load with a capacity of 2600 mAh and a nominal voltage of 3.7 V. The considered battery requires a standard charging current of 0.5 A, however the circuit is designed to provide the rapid charge current of 1.3 A as the output by using the buck converter. The converter is operated in continuous conduction mode and helps in charging the battery under constant current mode. In order to avoid interruption to the charging current when there is a simultaneous discharge of the battery, further improvement in the closed-loop control action is made by employing PID controller. Extensive simulation work have been conducted using the MATLAB/Simulink tool. The results obtained suggests there is a significant reduction of charging time under different conditions compared to the conventional method of battery charging.

This paper presents an exhaustive search approach to determine the best location and size of PV placement for power loss minimization of radial distribution networks. In this approach, the network power loss is determined by placing PV in each location, one at a time, and the size of PV in the same location is varied between 10 and 300 kW with an increment of 10 kW. The combination of location and size of PV which provides the minimum network power loss can be the best location and size of PV for power loss minimization of radial distribution networks. The forward–backward sweep load flow algorithm is used to incorporate the PV model. The 33-bus radial distribution network is used to demonstrate the approach. The simulation results show that the placement of a suitable size of PV in some specific locations significantly reduces the network power loss.

With the rapidly expanding infrastructure of the solar energy system, the need for an accurate solar prediction has become an essential part of the renewable energy sector. Over the past decade, various machine learning (ML) algorithms have been used for this purpose. Although the prediction of solar irradiance forecasting has been discussed in a large number of studies, the use of meta-heuristic optimization techniques has not been explored to select features for the forecasting model. This study comprises two meta-heuristic optimization techniques such as simulated annealing (SA) and ant colony optimization (ACO) for feature selection. The results show that feature selection based on meta-heuristics gave better results than models without feature selection. Amongst the two optimization methods, ACO outperformed SA with some exceptions. For SA, the declining order of performance observed is extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), decision tree (DT) and support vector regression (SVR), while for ACO the declining order observed is XGBoost followed by MLP, RF, DT and SVR. This manuscript indicates the potential capability of meta-heuristic techniques for accurate prediction of global horizontal irradiance (GHI) given a wide array of feature variables.

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An adaptive controller is proposed for DC–DC buck converters featuring prescribed time bound estimation of unknown system uncertainties and exogenous disturbances followed by nominal output performance recovery. The objective of the proposed control is to attain a robust output voltage tracking in buck converter in presence of parametric, non-parametric, matched and mismatched perturbations across wide operating range. Different from neural network estimators and characterizing substantially low computational complexity, an online estimator is presented to reconstruct the incurred uncertainty. The estimated additive uncertainty is thereafter fed to the nominal backstepping controller for subsequent compensation in finite time. Exact recovery of nominal output voltage tracking is claimed in a piecewise sense owing to the accuracy and precise estimation of the unknown unparametrized lumped uncertainty manifested in the form of large sudden variations in load and input voltage. Rigorous performance and stability analysis of the online estimator, along with similar analysis of the overall tracking control system are undertaken. Extensive numerical study is carried out to investigate the performance of the proposed control scheme. Further, experimentation of the proposed controller on a dc–dc buck converter using control desk DS1103 with an embedded TMS320F240 processor has been performed. The obtained experimental results demonstrate a good agreement with the simulation findings.

This paper deals with the output voltage regulation problem of dc-dc boost converter feeding a resistive load. A new control mechanism based on Chebyshev neural network embedded in an adaptive backstepping framework is proposed for the boost converter control. Since the converter is complex, time varying and non-linear in nature, it exhibits high sensitivity to unanticipated disturbances in the load current. Hence, designing a robust control mechanism to attain a satisfactory transient and steady state performance over a wide range of operating points is a challenging task. In this work, a control law is derived based on the systematic and recursive design strategy of adaptive backstepping method. A single layer functional link Chebyshev neural network is employed for a fast estimation of uncertain and time varying load profile of the boost converter. The stability of overall converter equipped with the proposed controller is proved using Lyapunov stability criterion. Further, in order to validate the proposed methodology, the boost converter is simulated in MATLAB/Simulink software and is subjected to different load perturbations. The efficacy of the proposed control is highlighted by evaluating it against the conventional adaptive backstepping control under identical conditions. The results obtained reveals that the proposed control is much faster in estimating the unknown load parameter and offers satisfactory output voltage tracking, yielding fast response and low peak overshoot/undershoot in the event of unknown load perturbations. Experimental investigation using dspace DS1103 controller is further carried out to validate the efficacy of proposed control scheme.

DC-DC step-down/buck converters are prominent part of DC power supply system. The dynamics of DC-DC step down converter are nonlinear in nature and are largely influenced from both parametric and external load perturbations. Under its closed loop operation, obtaining a precise output voltage tracking besides satisfactorily inductor current response is a challenging control objective. In this regard, this article proposes a novel Laguerre neural network estimation technique for the approximation of unknown and uncertain load function, followed by its subsequent compensation in the adaptive backstepping controller. A detailed design of the proposed estimator and adaptive backstepping controller along with closed loop asymptotic stability have been presented. Further, the proposed control mechanism is evaluated through extensive numerical simulations while subjecting the converter to input voltage, reference voltage and load resistance perturbations. Furthermore, the results are verified by testing the proposed controller on a laboratory prototype with DSP based TM320F240 controller board. The transient performance metrics such as settling time and peak overshoot/undershoot are evaluated and compared against adaptive backstepping control and PID control methods. Finally, the analysis of results reveals that the proposed control methodology for DC-DC step down converter offers a faster transient output voltage tracking with smooth and satisfactory inductor current response over a wide operating range.

An affiliation for Dr. Tousif Khan Nizami is missing from the original article. In addition to the Department of Electronics and Electrical Engineering at Indian Institute of Technology, Dr. Tousif Khan Nizami is affiliated with the Department of Electrical and Electronics Engineering at SRM University-AP, Amaravati 522 502, India.