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检索条件"任意字段=International Conference on Innovations in Power and Advanced Computing Technologies"

共 2836 条记录，以下是81-90 订阅

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Cost Minimization in Residential Hybrid Energy Systems Using advanced Scheduling Algorithms

Cost Minimization in Residential Hybrid Energy Systems Using...

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Advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Chodagam Srinivas A. V. Pavan Kumar V. Pranay Teja Reddy B Pavan K Himasagar Reddy Y Yugandhar Reddy Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This study proposes an advanced optimization methodology for managing a grid-connected residential hybrid thermal and electrical energy system, incorporating a combined heat and power (CHP) fuel cell and a battery-based energy storage system (ESS). A predictive scheduling framework is designed to optimize the operational plan for distributed energy resources (DER) over a 24-hour period. The main objective is to reduce the operational expenses of a smart home by strategically allocating resources while considering dynamic electricity tariffs and the efficiency of the ESS. To achieve this, an enhanced Adaptive Gravitational Search Algorithm (AGSA) is employed. The research also includes a comparative evaluation of the AGSA against conventional Gravitational Search Algorithm (GSA) and Harmony Search Algorithm (HSA) methods. This comparison underscores the AGSA's superior performance in optimizing residential energy systems. The findings offer significant insights into the application of optimization algorithms for improving cost-efficiency and energy management in modern smart homes.

关键词： Costs Cogeneration Heuristic algorithms Tariffs Fuel cells Smart homes Optimal scheduling Batteries Distributed power generation Thermal energy

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A Review of AI-Driven Techniques for power System Insulation Coordination and Surge Protection

A Review of AI-Driven Techniques for Power System Insulation...

引用

IT Innovation and Knowledge Discovery (ITIKD), international conference on

作者： Mark Anthony G. Teodoro Ian B. Benitez Electrical Engineering Department FEU Institute of Technology Manila Philippines

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Insulation coordination and surge protection devices (SPDs) are fundamental to the safety and reliability of modern power systems, especially in renewable energyintegrated grids. These systems protect critical electrical infrastructure from transient overvoltages, ensuring stable and sustainable operation. However, traditional methods, such as simulation-based analyses and manual fault detection, face challenges in scalability, adaptability, and efficiency, particularly in dynamic energy environments. Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have introduced transformative capabilities for insulation coordination and SPDs. AI techniques, such as machine learning and neural networks, enable precise fault prediction, real-time monitoring, and adaptive control, significantly enhancing grid reliability. IoT-enabled SPDs further improve operational efficiency through predictive maintenance and continuous performance monitoring, aligning with sustainable energy goals. These innovations also address the needs of resilient infrastructure development, smart grid implementation, and urban sustainability. This paper explores the evolution of these systems, emphasizing the shift from traditional to AI-driven and hybrid approaches. By integrating advanced technologies, power systems can achieve enhanced reliability, efficiency, and resilience.

关键词： Insulation Technological innovation Renewable energy sources Surge protection power system reliability Internet of Things Reliability Transient analysis Sustainable development Surges

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Improvement of power Quality of On-Board Charger for Electrical Vehicular Applications: A Review

Improvement of Power Quality of On-Board Charger for Electri...

引用

Device Intelligence, computing and Communication technologies, (DICCT), international conference on

作者： Vipul Gupta Bhoopendra Singh Sachin Tiwari Department of Electrical Engineering UIT RGPV Bhopal India Department of Technical Vocational Education and Research NITTTR Bhopal India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In this paper, we give a thorough classification of power quality (PQ) enhancement strategies for OBCs in light of electric vehicle (EV) applications. With the scroll to EVs gathers momentum, so does the demand for smart and trustworthy charging mechanism. With their involvement in charging batteries by converting the grid's ac power to dc power, OBCs are crucial to the process, and poorly designed OBCs can harm grid stability and system efficiency. Nevertheless, keeping high PQ standards is a challenge due to problems that include harmonic distortion, low power factor, electromagnetic interference (EMI), as well as transient events. This paper reviews some of the solutions, such as active and passive filtering, power factor correction (PFC) methods and advanced converter topologies to mitigate these problems. It reviews trends towards boosting efficiency and minimize losses with wide-bandgap (WBG) materials such as gallium nitride (GaN) and silicon carbide (SiC), as well as emerging trends like bidirectional OBCs and wireless power transfer (WPT). Furthermore, the potential for improved PQ in future OBC systems through the integration of renewable energy sources and AI-based control strategies is also assessed. This review aims to highlight current gaps and future directions that will contribute to the establishment of sustainable and efficient EV charging infrastructure.

关键词： Renewable energy sources Reviews Silicon carbide Electromagnetic interference power quality power factor correction Harmonic distortion Topology Transient analysis Standards

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Dual Source Renewable Charger For Electrical Vehicles

Dual Source Renewable Charger For Electrical Vehicles

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advanced computing technologies (ICoACT), international conference on

作者： Sanmugavalli P Vidhya Shree S Vaisri S Vasuki M Suriya N Department of ECE M. Kumarasamy college of engineering Karur India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The study introduces a novel Dual Source Renewable Charger for electric vehicles (EVs) that uses wind and solar energy to improve charging dependability and efficiency. Under ideal circumstances, the system's overall energy efficiency ranges from 85 to 90%, which is a 15–20% increase over single-source renewable charging systems. A typical EV battery can be charged in 6–8 hours thanks to its 1.5–2.5 kW combined power output under average sun irradiation (800 W/m 2 ) and wind speeds (5–7 m/s). The system includes a hybrid renewable energy model with solar panels and a vertical-axis wind turbine, as well as an intelligent power management unit that dynamically prioritizes energy sources according to availability and environmental circumstances. The design incorporates a 10 kWh energy storage system to mitigate energy intermittency, guaranteeing continuous charging for 4–6 hours during periods of low renewable energy supply. By restricting charge/discharge cycles to 95% depth of discharge (DoD), the adaptive charging approach incorporates a bidirectional DC-DC converter, prolonging battery longevity by 20–25% in comparison to traditional methods. The charger also lowers operating expenses and carbon impact by reducing grid reliance by 60–70% during periods of peak renewable energy output. The system's Internet of Things (IoT) features allow for real-time monitoring and optimization, improve user comfort and grid compatibility, and reduce energy losses by 5–10% through adaptive power management and predictive maintenance.

关键词： Renewable energy sources Vehicle-to-grid Wind energy power system management Wireless power transfer Control systems Hybrid power systems Batteries Internet of Things Predictive maintenance

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Data-Driven Capacitor Placement for power Loss Minimization: A Machine Learning Approach

Data-Driven Capacitor Placement for Power Loss Minimization:...

引用

Advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： M.Rama Prasad Reddy Madana Boyana Hemanth Kumar Golla Indra Kumar M Chaitanya G.Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper introduces an innovative framework for optimal capacitor placement in radial distribution systems, combining Random Forest (RF) as a predictive modelling tool with Coral Reef Optimization (CRO) for optimization. The proposed methodology is evaluated on IEEE 33-bus and IEEE 69-bus systems under two scenarios: the placement of two and three capacitors. The integration of RF allows accurate and rapid estimation of power losses and voltage profiles for capacitor configurations, significantly reducing computational overhead associated with repeated load flow analyses. CRO, inspired by natural reef ecosystems, effectively determines the optimal locations and sizes of capacitors to minimize active power losses and enhance voltage stability. The adaptive mechanisms of CRO ensure a robust balance between exploration and exploitation, leading to efficient and reliable solutions. Comparative analyses with existing techniques demonstrate that the proposed RF -CRO approach achieves superior performance in terms of loss reduction and voltage profile improvement. The findings underscore the potential of hybridizing predictive modeling with optimization algorithms, paving the way for advanced solutions in power system optimization and distribution network efficiency.

关键词： Radio frequency Capacitors Marine vegetation Estimation Voltage Predictive models power system stability Prediction algorithms Minimization Optimization

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Sustainable Energy Generation Trends - Insights from Python Analysis

Sustainable Energy Generation Trends - Insights from Python ...

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advanced innovations in Smart Cities (ICAISC), international conference on

作者： Hasanur Zaman Anonto Md Ismail Hossain Al Shahriar Zishan Md Akteruzzaman Md Ashikul Islam Abu Shufian Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Research Centre for Green Energy Electro-Anatomy and Digital Intelligence (ReGED) Dhaka Bangladesh Dept. of Electrical and Computer Engineering Lamar University Beaumont Texas United States

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Energy systems around the world have relied heavily on fossil fuels, contributing to greenhouse gas emissions and environmental challenges. However, in alignment with global climate objectives, many regions are accelerating their transition toward renewable energy sources. This study examines the evolving energy production landscape in the Netherlands, where fossil fuel-based technologies are steadily being replaced with intermittent renewable sources, particularly solar and wind. This research uses Python for data preprocessing, trend analysis, and visualization to explore key temporal patterns in energy generation. The results reveal that by 2023, nearly half of the Netherlands' electricity demand will be met by renewable sources, with solar and wind energy contributing approximately 17.6% and 15%, respectively. These gains are driven mainly by photovoltaic (PV) projects and offshore wind installations. Despite these positive trends, challenges such as grid stability and negative pricing during periods of excess supply present significant obstacles. Addressing these issues will require innovative solutions to increase the reliability and efficiency of renewable energy supplies. This paper discusses potential strategies, including advanced energy storage and adaptive grid management systems, which can enhance renewable integration and support a resilient infrastructure.

关键词： Renewable energy sources Technological innovation Wind energy power system stability Wind power generation Market research Stability analysis Fossil fuels Meteorology Energy storage

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Modeling of Grid-Connected SPV System and Performance Analysis Using advanced Controller Under Varying Environmental Conditions

Modeling of Grid-Connected SPV System and Performance Analys...

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Advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Veenita Swarnkar Mahesh Singh Shimpy Ralhan Department of EEE Shri Shankaracharya Technical Campus Bhilai Chhattisgarh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This study investigates the dynamic performance of a 255kW grid-connected solar photovoltaic (SPV) system through mathematical modeling and simulation. A novel hybrid algorithm, the Adaptive Grasshopper Optimization Algorithm combined with a Recurrent Neural Network (AGO-RNN), is employed to evaluate the system's performance under three distinct scenarios: (i) standard temperature and irradiance (G), (ii) varying temperature and irradiance (G), and (iii) simultaneous changes in temperature and irradiance (G). The AGO-RNN approach is utilized to calculate the Total Harmonic Distortion (THD) in each scenario, with the results compared against those achieved using the Particle Swarm Optimization (PSO) method. The proposed system incorporates a 255 kW solar PV model, Maximum power Point Tracking (MPPT), and a three-level Neutral Point Clamped (NPC) inverter. Simulation outcomes demonstrate that the AGO-RNN controller reduces THD by 1.6% compared to PSO, meeting the requirements of IEC 61727 by minimizing current magnitude and ensuring improved grid performance.

关键词： Photovoltaic systems Maximum power point trackers Total harmonic distortion Recurrent neural networks Heuristic algorithms System performance Simulation Reliability IEC Standards Standards

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Enhanced Economic Load Dispatch Using a Novel Optimization Approach: A Cost Minimization Perspective

Enhanced Economic Load Dispatch Using a Novel Optimization A...

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Advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： V B Thurai Raaj Chodagam Srinivas A. V. Pavan Kumar A.Venkata Subba Rao V. Sindhuja C.Venkata Navya Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper proposes an advanced African Vulture Optimization (AVO) algorithm integrated with Gaussian Process Regression (GPR) for the economic load dispatch (ELD) problem in power systems. The proposed method aims to enhance the ELD's efficiency by minimizing operational costs while maintaining power system constraints. The AVO algorithm optimizes the power generation across various units, and the GPR model is incorporated to improve convergence and accuracy in capturing the cost function's nonlinearity. The IEEE 30-bus system is used as a test case, examining three distinct load demand scenarios to evaluate the algorithm's robustness under varied conditions. Additionally, three different population sizes are applied to investigate the effects on algorithmic performance, specifically focusing on convergence rate and solution quality. Results demonstrate that the AVO-GPR method effectively reduces computational costs while achieving feasible and optimal power dispatch, outperforming traditional methods in terms of both accuracy and computational efficiency. This study contributes to the development of reliable and adaptive optimization techniques in economic load dispatch, supporting the operational efficiency of modern power systems.

关键词： Costs Accuracy power system dynamics Gaussian processes Minimization Computational efficiency Optimization power generation Convergence Genetic algorithms

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Data-Driven Capacitor Placement for power Loss Minimization: A Machine Learning Approach 5

Data-Driven Capacitor Placement for Power Loss Minimization:...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Reddy, M.Rama Prasad Kumar, Madana Boyana Hemanth Kumar, Golla Indra Chaitanya, M. G.Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India

ISBN: (纸本)9798350356236

关键词： Load flow optimization

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advanced Temperature Forecasting in Major Indian Cities: A Comparative Study of SARIMA, Prophet, and XGBoost Models

Advanced Temperature Forecasting in Major Indian Cities: A C...

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power, Control and computing technologies (ICPC2T), conference on

作者： Ashutosh Mishra Aaron D’Souza Jatoth Ravi Kumar Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

This paper presents an analysis of temperature forecasting models across major Indian cities: Mumbai, Kolkata, Delhi, and Chennai by three methodologies that are Seasonal Autoregressive Integrated Moving Average (SARIMA), Prophet, and XGBoost. By analyzing historical temperature data, we implement a methodology that includes data collection, preprocessing, model selection, training, and evaluation. To evaluate the stationarity of the dataset, the Augmented Dickey-Fuller test is used, and seasonal differencing is applied to achieve stationarity. Both seasonal and non-seasonal components are fitted in the SARIMA model. The Prophet model is also fitted in handling missing data and trends, and XGBoost through machine learning techniques. The performance of each of the three models is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The forecasts are generated for six years and this study contributes to climate science by a comprehensive evaluation of temperature forecasting methodologies, that could be helpful in urban planning and policy-making.

关键词： Training Analytical models Temperature distribution Urban planning power control Predictive models Market research Data models Temperature control Forecasting

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