Efficient System Research Articles

Non-parametric efficiency analysis of urban transport systems

ABSTRACT To promote territorial mobility and reduce environmental impacts at the same time, transport policy often targets (energy-)efficiency of urban transport systems. Thus far though, related studies are focused on particular transport modes or operators. Given, the multi-modal character of urban mobility, the present study suggests broadening the perspective and accounting for public and private transport. Based on a two-stage non-parametric technique, the efficiency of 49 German municipalities alongside transport systems’ inefficiency determinants is assessed. In line with previous studies, we find highly populated municipalities to operate more efficiently than smaller ones. Second, efficiency is driven by modal split and individual mobility patterns, with a positive correlation between territorial mobility and trips made by bike or public transport. Finally, the results suggest that negative impacts of trip density (e.g. congestion) might counter the positive influence of higher compactness on efficiency, once a certain threshold of urbanization is reached.

Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.

Optimum Planning of Carbon Capture and Storage Network Using Goal Programming

Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goal programming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models’ results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic–environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the very important strategies of mitigating climate change and reducing global warming.

Dual-channel infrared OPO lidar optical system for remote sensing of greenhouse gases in the atmosphere: Design and characteristics

Current global warming and climate change under the greenhouse effect call for a thorough understanding of the spatial distribution of greenhouse gases in different atmospheric layers. Lidar systems are the most effective for remote greenhouse gas monitoring. We design a two-channel infrared OPO lidar optical system for remote DIAL/DOAS sensing of carbon dioxide and water vapor in the atmosphere. In this work, optimal geometric parameters of the transceiving channel of the lidar optical system are chosen, a need to focus laser radiation at a distance of 1 km from an observation point is demonstrated, and numerical simulations confirm the possibility of detecting lidar signals ranging from 10−7 to 10−10 W in the informative spectral range 4800–5100 cm−1 (1960–2083 nm). Laboratory experiments with the main components of the lidar system with experimentally confirmed parameters, which simulate atmospheric measurements of CO2 absorption at a calibrated sensing wavelength of 2005 nm (4987 cm−1) (pressure of 1 atm; CO2 concentration corresponding to the midlatitude summer background atmosphere) in the informative spectral range of the lidar system, enable selecting a pair of wavelengths with resonant absorption of the target gas near 2005 nm to study the background state of the atmosphere in the surface layer. Efficiency of the lidar optical system is confirmed by in situ test experiments, where backscattering signals from a topographic target with an albedo of ∼0.15 spaced 168 m apart from an observer are recorded at 60 mV when operating along a horizontal atmospheric path. The lidar system we design can be used in measuring complexes at carbon test sites. It can also be used for atmospheric monitoring in industrial centers, at background measuring stations, and in swamp areas.

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of desalination plants is a key task for designing and implementing these facilities. In this regard, artificial intelligence techniques have proven to be powerful assets in this field. These methods offer an expedited and effective means of estimating effective parameters, thus catalyzing their implementation in real-world scenarios. In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. The next phase of this research employed the SHAP interpretability method to illustrate the impact of individual variables on the model's output. Moreover, the predictive performance of the developed frameworks was evaluated using a set of five dependable statistical measures: RMSE, NS, MAE, MAPE and R 2. These indicators were utilized to provide a robust means of gauging the precision of the model's forecasts. A comparative analysis of the outcomes, as measured by the RMSE criteria, revealed that the SVR technique (RMSE = 0.125 L/(h·m2)) exhibited superior performance compared to NGBoost (RMSE = 0.163 L/(h·m2)), AdaBoost (RMSE = 0.219 L/(h·m2)), CatBoost (RMSE = 0.149 L/(h·m2)), GPR (RMSE = 0.156 L/(h·m2)), and ERT (RMSE = 0.167 L/(h·m2)) methodologies in predicting permeate flow rates. The modelling outcomes obtained during the evaluation stage demonstrated the efficacy of the SVR algorithm in enhancing the precision of permeate flow forecasts, utilizing relevant input variables.

Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.

Analysis of 3k Experiments Applied to Railway Braking: Influence of Contaminants and Train Speed

The presence of contaminants influences braking efficiency in the railway system because it alters the adhesion at the wheel–rail interface. It is essential to study this phenomenon, as contaminants reduce the friction between wheels and rails, which impacts braking and transport safety. In addition, these contaminants increase the risk of derailments. The objective of the research was to determine the impact of different contaminants and operating speeds on the critical braking system’s responses. Using the 3k full factorial experimental design methodology, with analysis of variance (ANOVA) and linear and quadratic regressions, visualized using surface graphs, the effects of three operating conditions were studied: clean rails, with sand and sawdust, and driving the train at three operating speeds. These conditions gave rise to variations in braking distances, maximum creep, wheel slip times, and maximum peaks of electric current when braking in each experiment. The tests were carried out on the straight section of a β-shaped track and a railway vehicle, designed at a scale of 1:20. The analysis reveals that the braking distance increases significantly with surface roughness (clean track < sawdust < sand). At 0.75 m/s, the sawdust track reduces braking distance by 21% compared with the clean track; at 1.00 m/s, the reduction is 19%; and at 1.30 m/s, it is 35%.

Short-term power load forecast using OOA optimized bidirectional long short-term memory network with spectral attention for the frequency domain

Accurate short-term power load forecasting is crucial for maintaining the stability and efficiency of modern power systems, especially in the face of increasing volatility and complexity. This paper introduces a novel approach for short-term power load forecasting by integrating the Osprey Optimization Algorithm (OOA) with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a spectral attention mechanism. The OOA optimizes the hyperparameters of the BiLSTM, improving the model's global search capability and avoiding local optima. The spectral attention mechanism further refines the model by focusing on key frequency components in the time series data. Experimental results demonstrate the superior performance of the proposed model, achieving an RMSE of 0.1382, MAE of 0.0659, and MAPE of 1.14 %, significantly outperforming traditional methods. The model's effectiveness is particularly notable in capturing complex patterns during critical peak and trough periods, making it a valuable tool for enhancing grid operation and management.

Spatiotemporal variation in grain production performance and efficiency of the cultivated landscapes in Upper Blue Nile Basin of Ethiopia: the impact of residual moisture-based farming on water and food security

Analysis of grain production performance can provide reference information to explore multiple cropping options and further improve the resource use efficiency of farming methods. This study investigated the spatiotemporal dynamics of grain production performance and efficiency of major crop production systems (CPS) in the Ethiopia’s Blue Nile Basin. The results show that only 39% of the basin is currently cultivated, although a significant cropland expansion (10%) was recorded between 1985 and 2020. The study identified 11 major CPS, mostly practiced in the basin. Of these, single cropping based on the main rainy season (Meher-Only) covers the largest area (26%), followed by Meher-Residual-Intermittent (12%) and Meher-Belg-Dependable (11%). Extended-Meher, Meher-Residual-Dependable, Meher-Residual-Intermittent, and Meher-Belg-Dependable are the four more powerful CPS with higher efficiency. Comparatively, CPS practiced in Wet-Woyna-Dega and Wet-Dega have better overall performance. Findings confirm that agricultural space management (land) and green-water (rainfall) utilization are the most influential factors, followed by land use planning and land use systems (CPS) invention. As landscape suitability for grain production governs future performance, in the low elevation and flood plains parts of the basin, the possibility of creating additional space into the food system is very high. In mountainous and high-altitude regions, the efficiency of grain production will decrease because incorporating additional arable land into the food system is trivial. In the last three decades, in BNB, only 10% of arable land (equivalent to 30 million quintals of food) has been added to the good system, which can support approximately 6 million people. Compared to the population growth of the basin (12 million 1985–2020), its contribution to the food system was less than 50%. This confirms that multiple cropping systems, such as Residual moisture-based CPS, have played a significant role in boosting the food system in the basin. Therefore, improving grain production performance/efficiency requires targeted investments, including the invention of more adaptable crop varieties, efficient cropping practices, and the introduction of advanced agricultural space and water management technologies. The results of the study will help identify important policy gaps and suggest possible options to enhance residual farming and other multiple cropping systems.

Analysis of War Optimization Algorithm in a Multi-Loop Power System Based on Directional Overcurrent Relays

In electrical power systems, ensuring a reliable, precise, and efficient relay strategy is crucial for safe and trustworthy operation, especially in multi-loop distribution systems. Overcurrent relays (OCRs) have emerged as effective solutions for these challenges. This study focuses on optimizing the coordination of OCRs to minimize the overall operational time of main relays, thereby reducing power outages. The optimization problem is addressed by adjusting the time multiplier setting (TMS) using the War Strategy Optimization (WSO) algorithm, which efficiently solves this constrained problem. This algorithm mimics ancient warfare strategies of attack and defense to solve complex optimization problems efficiently. The results show that WSO provides superior performance in minimizing total operating time and achieving global optimum solutions with reduced computational effort, outperforming traditional optimization methods (i.e., SM, HPSO, GA, RTO, and JAYA). The proposed algorithm shows a net time gains of 7.77 s, 2.57 s, and 0.8484 s when compared to GA, RTO, and JAYA respectively. This robust protection coordination ensures better reliability and efficiency in multi-loop power systems.

The Role of Heater Size and Location in Modulating Natural Convection Behavior in Cu-Water Nanofluid-Loaded Square Enclosures

Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H2O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ lh ≤ 1.0, 0.125 ≤ hc ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process.

Study of the Microbiological Efficiency of a Hospital Ventilation System With Hepa Filtration in the Prevention of Airborne Pathogens

Objective: To evaluate the microbiological efficiency in air conditioning systems with HEPA filtration in an infectious disease hospital. Theoretical Framework: Air conditioning in intensive care units and wards must consider the spread of microorganisms through respiratory pathways, as both patients and healthcare professionals are exposed to infectious agents transmitted by bioaerosols. Due to the possibility of environmental infection, there is a growing recognition that poorly designed air conditioning systems enhance the transmission of pathogens in areas with constant flow of individuals, particularly immunocompromised patients, who are more susceptible to infections. Air conditioning with HEPA filtration is a hospital engineering technique that shows great potential for filtering airborne contaminants, thereby reducing their dispersion in the environment. Method: Air samples were collected using the active impaction method over a 32-week period in the intensive care unit and wards of a reference hospital for infectious diseases. Results and Discussion: In the hospital environment, 67% of fungi and 33% of bacteria were identified, along with their respective subcategories. The colony-forming unit count exceeded the standards established by current regulations. The study highlights that hospital air is a pathway for the transmission and persistence of pathogenic microorganisms. Research Implications: The importance of ensuring a safe and healthy hospital environment is emphasized. Originality/Value: There are few studies addressing pathogens transmitted by bioaerosols. This study contributes to expanding knowledge on this topic and suggests the need for a review of current regulatory standards.

Toward Carbon‐Negative Methanol Production from Biogas: Intensified Membrane Reactor

AbstractThe modern world's major challenges, such as global warming, air pollution, and increasing energy demands, escalate the importance of sustainable development and transition toward renewables using innovative and environmentally friendly solutions, such as intensifying chemical processes, to reduce carbon footprints effectively. Aiming to enhance the process toward negative carbon emissions, this perspective explores the intensified membrane reactors for reducing the energy intensity of converting biogas into methanol, a versatile chemical feedstock, and renewable liquid fuel. Syngas and methanol synthesis processes, catalysts, and membranes were explored, and novel reactor designs were proposed. Introduction of selective membranes into the catalytic reaction zone to combine synthesis separation steps could enhance the system efficiency and intensify the process by recycling energy and materials, besides reducing costs and required energy for the separation process: the continuous harnessing of products shifts reactions toward desired species while recycling energy and materials enhances the process efficiency, and separating water from methanol reduces the required energy and costs of extra processes for methanol separation. The successful implementation of this technology holds significant promise for sustainable developments in producing chemicals and renewable fuel from renewable biogas and reducing methane and carbon dioxide emissions toward achieving carbon‐negative technologies.

An Optimized Multi-Level Control Method for Wireless Power Transfer System Using the Particle Swarm Optimization Algorithm

A Wireless Power Transfer (WPT) system, known for its contactless power delivery, is extensively used for power supply in spacecraft applications. Achieving efficient and stable power transfer necessitates the integration of DC/DC converters on both the primary and secondary sides of WPT systems for power conversion and control. Traditional efficiency optimization methods primarily focus on impedance matching within the wireless power resonance network, often neglecting the overall efficiency optimization of multi-stage DC-DC and WPT systems. This oversight results in suboptimal overall system efficiency despite optimal efficiency in the wireless transmission segment. Additionally, the time-varying nature of mutual inductance and load parameters during power transmission in WPT systems presents challenges for maximum efficiency tracking and power control. This paper introduces a multi-level coordinated control efficiency optimization method for WPT systems utilizing the particle swarm optimization (PSO) algorithm. This method takes into account the transmission losses across all power conversion units within the WPT system, establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. The PSO algorithm is then employed to solve this optimization model using estimated mutual inductance and load values. By adjusting the DC/DC converters on both sides, the method ensures optimal overall system efficiency and consistent power transmission. Experimental results indicate that under varying load and mutual inductance conditions, a Series–Series (SS) compensated WPT system using this method achieves a 200 W power output with maximum efficiency tracking, a power output error of 0.63%, and an average transmission efficiency of 86.2%. This demonstrates superior power transmission stability and higher efficiency compared to traditional impedance matching methods.

Quasicrystalline 30° twisted bilayer graphene: fractal patterns and electronic localization properties

The recently synthesized 30° twisted bilayer graphene (30°-TBG) systems are unique quasicrystal systems possessing dodecagonal symmetry with graphene’s relativistic properties. We employ a real-space numerical atomistic framework that respects both the dodecagonal rotational symmetry and the massless Dirac nature of the electrons to describe the local density of states of the system. The approach we employ is very efficiency for systems with very large unit cells and does not rely on periodic boundary conditions. These features allow us to address a broad class of multilayer two-dimensional crystal with incommensurate configurations, particularly TBGs. Our results reveal that the 30°-TBG electronic spectrum consist of extended states together with a set of localized wave functions. The localized states exhibit fractal patterns consistent with the quasicrystal tiling.

Enhanced RF Energy Harvesting System Utilizing Piezoelectric Transformer

RF energy harvesting converts ambient signals into electrical power, providing a sustainable energy source. This study demonstrates the use of a piezoelectric transformer for efficient RF energy harvesting. In this work, a piezoelectric transformer (PT) is employed as a high-gain, efficient inverting amplifier to enhance RF wireless energy harvesting. The PT, composed of lead zirconate titanate (PZT), is placed after the receiving loop antenna, with its output connected to an AC-to-DC converter circuit. Maximum harvested power was observed at the PT’s resonance frequency of 50 kHz, with an optimal load of 40 kΩ. The system, comprising the antenna, transformer, and rectifier circuit, continues to resonate at 50 kHz, as confirmed by input impedance measurements, demonstrating stable and effective performance. The overall system efficiency was characterized to be 88%.

Enhancement efficiency of flow and irreversibility system for MHD Buongiorno's nanofluid in complex peristaltic tapered channel with electroosmosis forces

Abstract Magnetohydrodynamic flow efficiency and irreversibility improvement research are multiple problems that arise when electroosmosis forces affect Buongiorno's nanofluid in a complicated peristaltic tapered channel. Thermal energy and temperature gradients cause nanoparticles to migrate randomly, affecting flow efficiency and irreversibility. Sometimes the Infected veins generate complex peristaltic waves on its walls. The mathematical model that characterizes the motion of Jeffrey magnetohydrodynamic Buongiorno's nanofluid inside a complex tapered peristaltic channel, considering the effects of electroosmotic forces is discussed. The long wavelength and low Reynolds numbers approximation is considered. The approximate solution of the nonlinear system of partial differential formulas is obtained using the Adomian decomposition method. Also, the irreversibility of the system and entropy generation are being studied. Flow characteristics with biophysical and thermal parameters are plotted and discussed. The improvement in the interstitial distances between the molecules that make up the nanofluid, which in turn works to enhance the Bejan numbers. So, one important result is by growing in both Brownian motion and thermophoresis, the Bejan numbers rise clearly. Both the Jeffrey parameter and Debye–Huckel parameter work to upsurge the loss of kinetic energy within the molecules, which reduces the temperatures inside the nanofluid and thus reduces the entropy rate, in contrast to the rest of the parameters that raise the kinetic energy inside the molecules that make up the nanofluid.

Affective Computing in Mental Health: The Role of Facial Expression Recognition

Affective computing has emerged as one of the leading interdisciplinary disciplines that utilize technology to detect and interpret human emotions, and so it provides critical information for the mental health monitoring process. The present study addresses the facial expression recognition as an important tool in the diagnosis and treatment of mental health disorders. By discussing the physiological and psychological implications that facial expressions may have, we analyze how FER systems can supplement the traditional approach to mental health evaluation by providing real-time feedback and timely intervention. Improved algorithms for machine learning and computer vision techniques in FER systems enhance the accuracy and efficiency of FER systems, thus increasing their applicability in teletherapy, clinical assessment, and personal well-being applications. This discussion paper shall also touch on the issues and ethical concerns of FER technology, such as privacy and the potential for misinterpretation. Our results indicate that FER can be a meaningful contributor to care for personal mental health by continuous monitoring and understanding of emotional well-being.

Evaluation of the Development Effectiveness of Intellectual Property Operation for Science and Technology-based Small and Medium-sized Enterprises: Based on the Research Data of Qinchuangyuan Innovation Driving Platform

Science and technology-based enterprises promote national economic growth and facilitate scientific and technological innovation. This study takes the development of Intellectual Property (IP) operation of science and technology-based enterprises as the research object and constructs a relevant evaluation index system. It also takes the data of 112 small and medium-sized science and technology-based enterprises within the Qinchuangyuan Innovation Driving Platform as an example, conducts statistical analysis and case analysis, and concludes development strategies. The results of the study provide policy suggestions for further strengthening and promoting the development of intellectual property operation by enhancing the efficiency of the IP operation system and policy services for small and medium-sized enterprises (SMEs) in science and technology, promoting the construction of supporting facilities and improving employee satisfaction; as well as simplifying the IP filing procedures, optimizing the financing environment, and establishing a platform for regular evaluation and information sharing to accelerate the transformation of IP; and at the same time, implementing a mechanism for efficient policy fulfillment and departmental linkages to enhance the implementation of the policies and form a high-quality business environment that will help SMEs in their development and technological innovation of SMEs.

Fault in Converter Interfaced Micro Grid Using Detection and Identification of Hybrid Technique

ABSTRACTThis paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments.