Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression.

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter's future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Agreement between non-invasive and invasive arterial blood pressure during surgery in the prone position: an error grid analysis.

PURPOSE: Prone position has recently gained renewed importance as a treatment for acute respiratory distress syndrome and spine and brain surgeries. Our study aimed to perform an error grid analysis to examine the clinical discrepancies between arterial blood pressure (ABP) and non-invasive blood pressure (NIBP) in the prone position and to investigate the risk factors influencing these differences. METHODS: Error grid analysis was performed retrospectively on 1389 pairs of 100 consecutive prone positioning cases. This analysis classifies the difference between the two methods into five clinically relevant zones, from "no risk" to "dangerous risk". Additionally, multivariable ordinal logistic regression analysis was conducted to evaluate the relationship between the risk zones of mean blood pressure (MBP), as classified by error grid analysis and the covariate of interest. RESULTS: Error grid analysis showed that the proportions of measurement pairs in risk zones A-E for systolic blood pressure were 96.8%, 3.2%, 0.1%, 0%, and 0%, respectively. In contrast, the MBP proportions were 74.0%, 25.1%, 0.9%, 0.1%, and 0%. Multivariable ordinal logistic regression analysis revealed that the position of arms (next to the head) was a significant factor (adjusted odds ratio: 4.35, 95% CI: 2.38-8.33, P < 0.001). CONCLUSION: Error grid analysis revealed a clinically unacceptable discrepancy between ABP and NIBP for MBP during prone positioning surgery. The position of the arms next to the head was associated with increased clinical discrepancy between the two MBP measurement methods.

Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform.

Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis. The CNN input consists of two-dimensional scalograms generated using Continuous Wavelet Transform (CWT). The proposed diagnosis technique demonstrated superior performance compared to benchmark architectures, namely MobileNet, NASNetMobile, and InceptionV3, achieving higher test accuracies and lower losses on binary and multi-fault classification tasks on balanced, unbalanced, and noisy datasets. Further, a quantitative comparison is conducted with similar recent studies. The obtained results indicate good performance and high reliability of the proposed fault diagnosis method.

Correlation analysis of occupational stress and metabolic syndrome among employees of a power grid enterprise in China.

BACKGROUND: Being in a state of high occupational stress may disrupt the metabolic balance of the body, thus increasing the risk of metabolic diseases. However, the evidence about the relationship between occupational stress and metabolic syndrome was limited. OBJECTIVES: To explore the association between occupational stress and metabolic syndrome (MetS) in employees of a power grid enterprise. METHODS: A total of 1091 employees were recruited from a power grid enterprise in China. Excluding those who failed to complete the questionnaire and those who had incomplete health check-ups, 945 subjects were included in the study. Assessment of occupational stress was used by job demand-control (JDC) and effort-reward imbalance (ERI) questionnaires, respectively. The information on body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. The levels of high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting blood glucose (FBG) in the fasting venous blood samples were measured. Logistic regression analysis and multiple linear regression methods were used to analyze the correlation between JDC and ERI models of occupational stress, metabolic syndrome, and its components, respectively. RESULTS: The prevalence of MetS was 8.4% and 9.9% in JDC and ERI model high occupational stress employees, respectively. ERI model occupational stress and smoking are significantly associated with the risk of MetS. ERI ratio was significantly associated with lower HDL-C levels. Gender, age, marital status, smoking, high-temperature and high-altitude work were significantly associated with metabolic component levels. CONCLUSION: Our study revealed a high detection rate of occupational stress in both JDC and ERI models among employees of a power grid enterprise. ERI model occupational stress, demanding more attention, was associated with the risk of MetS as well as its components such as HDL-C.

Volume replacement in tumor plastic surgery and breast-conserving surgery using 3D grid and strip­shaped acellular dermal matrix: Two case reports.

The present study was driven by the scarcity of suitable materials for mending partial breast defects and the imperative considerations of safety and durability. The current study presents findings from two female patients, aged 59 and 40, who underwent breast cancer treatment. Patient 1 underwent a mastectomy with a sentinel lymph node biopsy, while patient 2 underwent a partial mastectomy with axillary lymph node dissection. Core needle biopsy confirmed invasive ductal carcinoma in both cases. Breast ultrasound revealed hypoechoic lesions with smooth edges. The reconstruction of the breast defect employed an acellular dermal matrix, and the safety and cosmetic outcomes for each patient were analyzed. At 3 months post-radiotherapy, neither patient experienced significant complications. The preservation of breast contour and volume was satisfactory, with no postoperative tumor recurrences detected. In summary, utilizing an acellular dermal matrix with a three-dimensional grid design for partial breast defect reconstruction offers a viable alternative that aligns with oncological safety standards and provides good cosmetic results.

A spatial transformation-based CAN model for information integration within grid cell modules.

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

Assessing coastal population capacity in Tsunami-prone areas: A grid-based approach.

Tambakrejo Beach in Blitar Regency is classified as an area that is very vulnerable to tsunami catastrophes. Many researchers have conducted studies on regions impacted by the tsunami. However, more studies into the link between the outcomes of social and spatial studies still need to be carried out because these are two different perspectives with different parameters and variables. The novel approach in this research involves delineating tsunami-affected areas and assessing population capacity in coastal regions. The hazard maps and livelihood asset variables using grid cells of a specific size have been used to identify risk levels. The grid cells used are 50 m2 × 50 m² so that they are expected to represent the minor units on the face of the earth, such as buildings, assets, property or land parcels, for capacity assessments or measuring the level of threat to disasters and are no longer based on regional administrative boundaries. Contribution: The research results show that using grid cells to analyse areas affected by the tsunami can provide excellent and informative results. Research findings at the research location regarding community preparedness in facing tsunamis show that communities at risk of being affected by the tsunami need to increase their capacity because the majority of communities in coastal areas, especially in the Sidorejo sub-village, have been identified as having low capacity according to several livelihood asset parameters such as financial capital in income. By increasing individual capacity, it is hoped that society will be able to avoid the threat of tsunami waves better.

Combining the Fragment Molecular Orbital and GRID Approaches for the Prediction of Ligand-Metalloenzyme Binding Affinity: The Case Study of hCA II Inhibitors.

Polarization and charge-transfer interactions play an important role in ligand-receptor complexes containing metals, and only quantum mechanics methods can adequately describe their contribution to the binding energy. In this work, we selected a set of benzenesulfonamide ligands of human Carbonic Anhydrase II (hCA II)-an important druggable target containing a Zn2+ ion in the active site-as a case study to predict the binding free energy in metalloprotein-ligand complexes and designed specialized computational methods that combine the ab initio fragment molecular orbital (FMO) method and GRID approach. To reproduce the experimental binding free energy in these systems, we adopted a machine-learning approach, here named formula generator (FG), considering different FMO energy terms, the hydrophobic interaction energy (computed by GRID) and logP. The main advantage of the FG approach is that it can find nonlinear relations between the energy terms used to predict the binding free energy, explicitly showing their mathematical relation. This work showed the effectiveness of the FG approach, and therefore, it might represent an important tool for the development of new scoring functions. Indeed, our scoring function showed a high correlation with the experimental binding free energy (R2 = 0.76-0.95, RMSE = 0.34-0.18), revealing a nonlinear relation between energy terms and highlighting the relevant role played by hydrophobic contacts. These results, along with the FMO characterization of ligand-receptor interactions, represent important information to support the design of new and potent hCA II inhibitors.

Comparative evaluation of image quality between virtual grid and grid portable radiographic systems.

OBJECTIVE: Virtual Grid (VG) is an image processing technique designed to address scattered radiation from radiographic systems without a physical grid. It aims to eliminate artifacts caused by grid misalignment and enhance radiographic workflow efficiency. We intend to evaluate image quality between Virtual Grid and grid-based radiographic systems across various patient thicknesses. Approach: A Fujifilm Virtual Grid and GE AMX-4 portable radiographic system was used. Image quality was assessed using MTF, NPS, LCR, and CNR. MTF calculations employed an edge-device method with a 0.1mmCu sheet. For NPS evaluation, uniform images were acquired with multiple 30x30cm solid water blocks (2cm thick), overlaid in 2cm increments to simulate patient sizes from 2cm to 40cm. LCR and CNR were evaluated using a CIRS test plate with 9-hole depths for a hole diameter of 0.375". The test object was placed on top of the detector then water blocks, while maintaining the same SID, beam quality, and exposure between the units. Visual assessments were conducted by four readers, quantifying perceived hole numbers. The weighted Cohen's Kappa and Welch's T-test were utilized for statistical analysis. Main results: At 80% MTF, VG exhibited high contrast resolution of 1.1 lp/mm compared to 1.2 lp/mm for the grid system. VG demonstrated lower noise levels across all frequencies for equivalent patient thicknesses. Welch's T-test indicated no significant differences in LCR (P=0.31) and CNR (P=0.34) between the systems. However, qualitative observation demonstrated VG's better low contrast response for patient sizes ≥10cm. The average weighted Cohen's Kappa value was 0.78. Significance: This work indicates the Virtual Grid technology can effectively mitigate scattered radiation to improve granularity and low-contrast resolution in an image compared to a grid system. Furthermore, it can potentially reduce patient dose.

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images.

BACKGROUND: Polycystic Ovary Syndrome (PCOS) is a medical condition that causes hormonal disorders in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS mainly suffer from extreme weight gain, facial hair growth, acne, hair loss, skin darkening, and irregular periods, leading to infertility in rare cases. Doctors usually examine ultrasound images and conclude the affected ovary but are incapable of deciding whether it is a normal cyst, PCOS, or cancer cyst manually. OBJECTIVE: To have access to the high-risk crucial PCOS and to detect the condition and the treatment aimed at mitigating health hazards such as endometrial hyperplasia/cancer, infertility, pregnancy complications, and the long-term burden of chronic diseases such as cardiometabolic disorders linked with PCOS. METHODS: The proposed Self-Defined Convolution Neural Network method (SD_CNN) is used to extract the features and machine learning models such as SVM, Random Forest, and Logistic Regression are used to classify PCOS images. The parameter tuning is done with lesser parameters in order to overcome over-fitting issues. The self-defined model predicts the occurrence of the cyst based on the analyzed features and classifies the class labels effectively. RESULTS: The Random Forest Classifier was found to be the most reliable and accurate among Support Vector Machine (SVM) and Logistic Regression (LR), with accuracy being 96.43%. CONCLUSION: The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction.

Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach.

Next generation electrical grid considered as Smart Grid has completely embarked a journey in the present electricity era. This creates a dominant need of machine learning approaches for security aspects at the larger scale for the electrical grid. The need of connectivity and complete communication in the system uses a large amount of data where the involvement of machine learning models with proper frameworks are required. This massive amount of data can be handled by various process of machine learning models by selecting appropriate set of consumers to respond in accordance with demand response modelling, learning the different attributes of the consumers, dynamic pricing schemes, various load forecasting and also data acquisition process with more cost effectiveness. In connected to this process, considering complex smart grid security and privacy based methods becomes a major aspect and there can be potential cyber threats for the consumers and also utility data. The security concerns related to machine learning model exhibits a key factor based on different machine learning algorithms used and needed for the energy application at a future perspective. This work exhibits as a detailed analysis with machine learning models which are considered as cyber physical system model with smart grid. This work also gives a clear understanding towards the potential advantages, limitations of the algorithms in a security aspect and outlines future direction in this very important area and fast-growing approach.

Levers and obstacles for implementing public engagement practices in electricity grid development.

The scale of change required through the development of new energy infrastructure throughout Europe is vast. The societal dimensions of the energy transition are increasingly recognised as centrally important and approaches to infrastructure development which seek to incorporate such considerations are warranted. EirGrid - Ireland's national electricity transmission operator - through their own historical context, have undergone a journey to develop new strategies for citizen and community engagement with relation to energy grid developments. Here, we reflect upon this journey, situating it within their previous failures and the national context. This process of reflective practice seeks to provide findings for other organisations internationally undertaking a journey towards establishing new engagement practices. The establishment of such practices is critical for enabling deeper societal engagement on the energy transition. A research gap exists in relation to the organisational development of new public engagement practices within institutions tasked with developing infrastructure associated with the energy transition. This creates a challenge whereby ever-increasing calls for public engagement are made, but no lessons exist with relation to how such new practices can be embedded within an organisational strategy. We contribute to this space through answering the research question: what are the key levers and barriers for organisation change towards new forms of public engagement in infrastructure delivery? The reflections outlined through this paper have been provided by individuals in different positions across the organisation. The paper develops key findings which add to the literature in relation to levers and obstacles for implementing public engagement and associated factors.

Application and comparison of several adaptive sampling algorithms in reduced order modeling.

Model Order Reduction (MOR) techniques have extensive applications across scientific and engineering disciplines, such as neutron field reconstruction of nuclear reactor cores, thermoelastic field reconstruction, fluid, and solid mechanics. In the process of building a Reduced Order Model (ROM), the selection of the basis functions in the offline stage is crucial and directly depends on the parameter space sampling strategy. This problem has always been a challenge in MOR. Research into adaptive sampling algorithms has become a hot topic in recent years. To better understand the application of these algorithms to MOR, this paper focuses on three prevalent adaptive sampling algorithms: pseudo-gradient sampling, adaptive sparse grid sampling, adaptive training set extension. These have been successfully applied in various applications, including nuclear reactor cores, molten salt reactor system, power system for convection problems. We systematically assess and compare their performance, finding that adaptive sampling algorithms excel in sampling divergent and oscillating areas and are generally better than the standard sampling strategy. Specifically, the pseudo-gradient sampling algorithm is effective for small-scale scenarios, while the other two algorithms are designed for large-scale sampling. Their practicality is confirmed through successful applications in nuclear reactor cores.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning.

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer. High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system. The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation. The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Optogenetic silencing of medial septal GABAergic neurons disrupts grid cell spatial and temporal coding in the medial entorhinal cortex.

The hippocampus and medial entorhinal cortex (MEC) form a cognitive map that facilitates spatial navigation. As part of this map, MEC grid cells fire in a repeating hexagonal pattern across an environment. This grid pattern relies on inputs from the medial septum (MS). The MS, and specifically GABAergic neurons, are essential for theta rhythm oscillations in the entorhinal-hippocampal network; however, the role of this population in grid cell function is unclear. To investigate this, we use optogenetics to inhibit MS-GABAergic neurons and observe that MS-GABAergic inhibition disrupts grid cell spatial periodicity. Grid cell spatial periodicity is disrupted during both optogenetic inhibition periods and short inter-stimulus intervals. In contrast, longer inter-stimulus intervals allow for the recovery of grid cell spatial firing. In addition, grid cell phase precession is also disrupted. These findings highlight the critical role of MS-GABAergic neurons in maintaining grid cell spatial and temporal coding in the MEC.

Determinantal point process attention over grid cell code supports out of distribution generalization.

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.

Self-folding RCA product into a parallel monolayer DNA nanoribbon and woven into a nano-fence structure by a short bridge strand.

The universal programmed construction of patterned periodic self-assembled nanostructures is a technical challenge in DNA origami nanotechnology but has numerous potential applications in biotechnology and biomedicine. In order to circumvent the dilemma that traditional DNA origami requires a long unusual single-stranded virus DNA as the scaffold and hundreds or even thousands of short strands as staples, we report a method for constructing periodically-self-folded rolling circle amplification products (RPs). The repeating unit is designed to have 3 intra-unit duplexes (inDP1,2,3) and 2 between-unit duplexes (buDP1,2). Based on the complementary pairing of bases, RPs each can self-fold into a periodic grid-patterned ribbon (GR) without the help of any auxiliary oligonucleotide staple. Moreover, by using only an oligonucleotide bridge strand, the GRs are connected together into the larger and denser planar nano-fence-shaped product (FP), which substantially reduces the number of DNA components compared with DNA origami and eliminates the obstacles in the practical application of DNA nanostructures. More interestingly, the FP-based DNA framework can be easily functionalized to offer spatial addressability for the precise positioning of nanoparticles and guest proteins with high spatial resolution, providing a new avenue for the future application of DNA assembled framework nanostructures in biology, material science, nanomedicine and computer science that often requires the ordered organization of functional moieties with nanometer-level and even molecular-level precision.

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI).

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

Energy harvesting from fuel cell bicycles for home DC grids using soft switched DC-DC converter.

Fuel cell vehicles (FCVs) are gaining significance due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. Their efficient fuel cell cycle makes them ideal for last-mile transportation, offering zero emissions and longer range compared to battery electric vehicles. Additionally, the generation of electricity through fuel cell stacks is becoming increasingly popular, providing a clean energy source for various applications. This paper focuses on utilizing the energy from fuel cycle bicycles when it's not in use and feeding it into the home DC grid. To achieve this, a dual-phase DC to DC converter is proposed to boost stack voltage and integrate with the 24 V DC home grid system. The converter design is simulated using the PSIM platform and tested in a hardware-in-the-loop (HIL) environment with real-time simulation capabilities.