Search

Abstract The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios.

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/

This paper presents a robust hybrid super-twisting reaching law-based sliding mode controller in conjunction with a complementing recursive backstepping controller for DC-DC boost converters with constant power loads (CPLs). The key control goal is to keep the DC-bus voltage steady and constant while continuously powering the CPL. The nonlinear dynamical model of the DC-DC boost converter is translated into Brunovsky’s canonical forms, utilizing the precise feedback linearization technique to attain this control goal. It is worth mentioning that the dynamical model has been modified with bounded external uncertainties to demonstrate the restraint performance for huge loads and input voltage variations. The proposed hybrid controller is then constructed using this model. Furthermore, to ensure the large signal stability of the whole system under the designed control law, the Lyapunov stability theory is used. Numerical simulation analysis are carried out in MATLAB/Simulink to validate the performance of the designed robust controller for a DC–DC boost converter feeding a CPL when the operating point change. The results show that the designed controller outperforms both existing nonlinear controllers and a traditional PI controller in terms of ensuring the system’s stability and fast response in a variety of operating modes. Finally, in order to confirm the theoretical design and simulation results of the designed robust hybrid controller, experimental results are also provided in this paper.

Abstract In this paper, a Backstepping Global Integral Terminal Sliding Mode Controller (BGITSMC) with the view to enhancing the dynamic stability of a hybrid AC/DC microgrid has been presented. The proposed approach controls the switching signals of the inverter, interlinking the DC-bus with the AC-bus in an AC/DC microgrid for a seamless interface and regulation of the output power of renewable energy sources (Solar Photovoltaic unit, PMSG-based wind farm), and Battery Energy Storage System. The proposed control approach guarantees the dynamic stability of a hybrid AC/DC microgrid by regulating the associated states of the microgrid system to their intended values. The dynamic stability of the microgrid system with the proposed control law has been proved using the Control Lyapunov Function. A simulation analysis was performed on a test hybrid AC/DC microgrid system to demonstrate the performance of the proposed control strategy in terms of maintaining power balance while the system’s operating point changed. Furthermore, the superiority of the proposed approach has been demonstrated by comparing its performance with the existing Sliding Mode Control (SMC) approach for a hybrid AC/DC microgrid.

In this paper, a composite nonlinear controller is developed for stabilizing a constant power load (CPL) feeding DC-DC boost converter (DBC) for DC microgrid applications. If the boost converter is not properly controlled, the negative incremental impedance (NII) of CPLs can cause DC-bus voltage instability. As the original model is not adequate for designing the proposed controller, the nonlinear dynamical model of the DBC is transformed into Brunovsky’s canonical forms. Large load and input voltage changes hinder the controller’s performance. Therefore, the dynamical model has been updated to include constrained external uncertainty. Then, the proposed hybrid controller, which is a combination of the nonlinear backstepping controller and the sliding mode controller, is designed. The sliding mode controller has a double-integral-based sliding surface and a fast power-based reaching law to improve hybrid controller performance. The global stability of the proposed system is analyzed using the control Lyapunov function theory. Lastly, simulation studies in MATLAB/Simulink and experiments on an in-house hardware platform show that the proposed composite controller works well even when there are big changes.

Abstract In the design procedure of a PV‐based microgrid, optimal sizing of its components plays a significant role, as it ensures optimum utilization of the available solar energy and associated storage devices. This in turn ensures efficient and economic operation of the microgrid. Various approaches have been reported in the literature in order to approach the sizing problem in PV‐based microgrids. However, the existing approaches are subjective to the requirements of the microgrid and there is no unique framework for approaching the microgrid sizing problem. In this study, a comprehensive review of the existing approaches used for sizing of PV‐based microgrids with a summary of the commonly adopted design considerations has been presented. Also, the optimization problem, including the usually considered designed objectives and constraints, for PV‐based microgrid sizing have been thoroughly reviewed in this study. Furthermore, the adopted approaches for solving the optimization problem associated with the sizing of a PV‐based microgrid system available in the literature have been reviewed comprehensively. With a view to present a generic framework for the optimal sizing of a PV‐based microgrid, this study further presents a framework based on the comprehensive review of the existing literature. The developed framework will be useful for both the practising engineers and academics to understand and apply the usual practices for optimal sizing of PV‐based microgrids.

DNA methylation is an epigenetic modification that plays a crucial role in various biological processes, including gene expression regulation, cell differentiation, and the development of diseases such as cancer. Identifying DNA methylation patterns is essential for understanding its functional implications. Traditional experimental methods for detecting DNA methylation are costly, time-consuming, and inefficient for analyzing large-scale sequencing data. In this research, we explore the application of machine learning techniques to accurately identify DNA methylation sites. Our research aims to develop a Particle Swarm Optimization-Assisted Multilayer Ensemble Model (PSO-MEM) with several significant contributions. These include extracting semantic features from genetic sequences, optimizing feature dimensions to reduce classification errors, developing a multilayer dynamic approach that transfers learned information between layers during classification, and incorporating ensemble techniques for improved prediction and optimal results. To evaluate the performance of our proposed model, we compare it with existing models using eight publicly available datasets. The results demonstrate the efficacy of our approach, with AUC values of 91.99%, 92.80%, 90.28%, 91.03%, 93.09%, 90.79%, 90.68%, and 91.88% for the C. elegans, D. melanogaster, A. thaliana, E. coli, G. subterraneus, G. pickeringi, F. vesca, and R. chinensis datasets, respectively. The results highlight the potential of machine learning techniques for efficient and reliable identification of DNA methylation sites in large-scale genomic data, facilitating advancements in understanding epigenetic modifications and their functional implications.

Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers’ mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches.

This paper proposed a grid connected solar Photovoltaic (PV) Systems with a new voltage balancing converter suitable for Neutral-Point-Clamped (NPC) Multilevel Inverter (MLI). The switched-capacitors used in the proposed converter is able to balance the DC link capacitor voltage effectively by using proper switching states. The proposed balancing converter can be extended to any higher levels and it can boost the DC input voltage to a higher voltage levels without using any magnetic components. This feature allows the converter to operate with the boosting capability of the input voltage to the desired output voltage while ensuring the self-balancing. In this paper the proposed converter is used for a grid connected solar PV system with NPC multilevel inverter, which is controlled using vector control scheme. The proposed grid connected solar PV system with associated controllers and maximum power point tracking (MPPT) is implemented in Matlab/SimPowerSystem and experimentally validated using dSPACE system and designed converters. The simulation and experimental results show that the proposed topology can effectively balance the DC link voltage, extract maximum power from PV module and inject power to the grid under varying solar irradiances with very good steady state and dynamic performances.

The problem of maximizing lifetime of a sensor network is still challenging mainly due to the stringent delay-deadline of real-time applications and heterogeneity of sensor devices. The problem is further complicated when the network contains many obstacles. In maximizing network lifetime, existing literature works either merely address issues of application delay-deadline and presence of obstacles, or analyze primitive data collection approaches for such an environment. In this paper, we formulate optimal data collection schedule of a mobile sink in an obstructed sensor network as a mixed-integer linear programming (MILP) problem. The proposed data collection scheduling finds an optimal set of rendezvous nodes over a preformed Starfish routing backbone, and corresponding sojourn duration so as to maximize the network lifetime while maintaining delay-deadline constraint in an obstructed network. The proposed Starfish-scheduling ensures a loop-free traveling path for a mobile sink across the network. The results of performance evaluation, performed in network simulator-2, depict the suitability of Starfish scheduling as it outperforms state-of-the-art-works in terms of extending network lifetime and data delivery throughput as well as reducing average end-to-end delay.