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检索条件"任意字段=2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023"

共 3602 条记录，以下是21-30 订阅

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Design and Analyze of Coordinated Control of Hybrid PV-Wind power Generation System

Design and Analyze of Coordinated Control of Hybrid PV-Wind ...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Sridhar, H.S. Mohiyuddin, T.D. Siddaganga Institute of Technology Dept. of Electrical & Electronics Engineering Karnataka Tumakuru India

ISBN: (纸本)9798350333244

The energy which is produced using conventional sources has done an irreparable damage to the environment. So, to ameliorate the existing conditions, it is very much needed to come up with methodologies which are environmentally friendly and which reduce our dependence on these conventional sources of energy. The proposed project is an attempt in this direction. It combines solar and wind energy systems, which is currently called as Hybrid System, to produce electricity for meeting the existing energy demand and to supply electric power to the needful areas, it can also interface with the utility power grid if surplus power is produced. This approach is yet to acquire commercial dimension in India. In the project, independently a load will be met by photovoltaic and wind energy system. Project proposes an algorithm for tracking maximum power output and simulation will be accepted for analyze the results. The project also aims to integrate the solar photovoltaic (SPV) with wind energy conversion schemes to meet consumer load and also design a supervisory control method for wind energy system to regulate both active power and reactive power towards the load, and further analyze the system steadiness in grid-connected mode. © 2023 IEEE.

关键词： Wind power

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Beyond the Pixels: Detecting Originality and Assessing Product Condition using Digital Image Processing Techniques 7

Beyond the Pixels: Detecting Originality and Assessing Produ...

引用

7th international conference on circuit power and computing technologies, iccpct 2024

作者： Arhatia, Ishit Abraham, Bryan SRM Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798350372816

This paper introduces a novel approach using digital image processing for product management, focusing on authenticity verification and quality assessment. Advanced algorithms distinguish genuine from fraudulent products and evaluate their condition. The study highlights the potential of digital image processing to enhance supply chain security and consumer trust, promising significant business impacts by reducing counterfeit circulation, improving brand reputation, and optimizing inventory management © 2024 IEEE.

关键词： Inventory control

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Utilization of Adaptive Neuro Fuzzy Inference System Integrated Unified power Quality Conditioner for Enhancement of power Quality in Distribution System

Utilization of Adaptive Neuro Fuzzy Inference System Integra...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Padhi, Sobhanjit Mohanty, Bismit Mohapatra, Abhimanyu Swain, Rudra Prasad Banerjee, Arishmita Mishra, Siddhant Odisha University of Technology and Research Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350333244

The growing use of nonlinear electronic loads has created significant challenges for utilities and end-users alike when it comes to maintaining power quality. As a result, there is a growing focus on improving the quality of the electricity supply, with custom power devices being used to safeguard sensitive loads and enhance the quality and reliability of the power supply. One such custom power device is the Unified power Quality Conditioner (UPQC), which has become widely used for mitigating power quality issues. ANFIS is a powerful tool for modelling and analysing complex systems, and its application to the UPQC is expected to yield valuable insights into the device's performance in mitigating power quality issues. In this paper, the UPQC is modelled and analysed using the Adaptive Neuro-Fuzzy Inference Method (ANFIS) to improve the power quality of a 16-bus distribution system. The consequence of this analysis will become a milestone for comparing different power quality improvement techniques and identifying the most effective strategies for enhancing power quality in distribution systems. All the performance analysis has been carried out on the MA TLAB/Simulink platform. © 2023 IEEE.

关键词： power quality

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power Factor Correction by ANN Based Bridgeless Modified Landsman Converter

Power Factor Correction by ANN Based Bridgeless Modified Lan...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Vinoth, K. Sivaramakrishnan, G. Chandra Sekar, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350333244

An increased power factor (PF) frontend battery is used to design a new Electric Vehicle (EV) charger in this paper. In general, Diode Bridge Rectifier (DBR) is used for generating current with low harmonics, but due to its excessive conduction loss, a Modified Landsman converter is established. power Factor Correction (PFC) is recommended using a Landsman converter with a Bridgeless (BL) architecture for low power applications. The proposed converter solves various power Quality (PQ) issues like, fluctuations, harmonics, device stress and current ripples. In the proposed work, EV battery which is a non-linear load causes PQ troubles on the input side resulting in input supply deterioration. To make the supply stable, the suggested converter adopts an Artificial Neural Network (ANN) controller. The minimized error from the ANN controller is fed to the PWM generator through which the distortions gets eliminated and a stabilized supply with unity PF operation is obtained. The entire work for enhancing PQ through Bridgeless Modified Landsman Converter (BLML) is carried out in MATLAB and simulation responses are obtained. © 2023 IEEE.

关键词： power quality

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Enhancing power Factor Correction by ANN-Based Bridgeless Improved Landsman Converter

Enhancing Power Factor Correction by ANN-Based Bridgeless Im...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Sangeetha, B. Parvathi Ashwin, S. Pughalenthi, V. Farhan, H. Aarupadai Veedu Institute of Technology Department of Electrical and Electronics Engineering Paiyanoor India

ISBN: (纸本)9798350333244

In recent times, there has been a surge in interest surrounding Electric Vehicles (EVs) due to their numerous advantages such as environmental friendliness and impressive battery storage capacity. EVs have brought about a revolutionary change in both transportation and power industries, offering the potential to positively impact and enhance both sectors through their incorporation. The integration of EVs with power grid necessitates the adoption of effective power Factor Correction (PFC) techniques to address power quality challenges at supply end, which are inherent to this coupling. In this particular study, a superior bridgeless landsman converter is introduced to enhance power factor correction. By optimizing the conduction of current through minimal semiconductor components, the proposed system effectively regulates the link voltage, resulting in enhanced efficiency and minimized losses. To accomplish this, an Artificial Neural Network (ANN) based Proportional Integral (PI) controllers is utilised. This controller aids in predicting and categorizing responses based on their respective timeframes. A hysteresis controller is seamlessly integrated with a PWM generator, enabling accurate estimation of the converter's steady state switching frequency and delivering reliable outcomes. The outcomes obtained through this approach demonstrate that it effectively maintains a sustainable environment, offering improved efficiency and reduced harmonics. © 2023 IEEE.

关键词： Neural networks

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Artificial Intelligence Based Reduced Switch Multilevel Inverter for Grid Connected PV Applications

Artificial Intelligence Based Reduced Switch Multilevel Inve...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Thangam, Thomas Rathika, N. Sivasubramanian, M. Kumar, R. Tharwin Manohar, V. Joshi Kavyalakshmi, A. International Maritime College of Oman Department of Process Engineering National University of Science and Technology Sohar Oman Mahendra Engineering College for Women Department of Electrical & Electronics Engineering Namakkal Tiruchengode India Sri Venkateswara College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Thiruvallur India Puducherry Technological University Department of Electrical and Electronics Engineering Puducherry India Presidency University Department of Electrical & Electronics Engineering Bengaluru India Government College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350333244

Due to the rise in computer power, tools, and data collection, artificial intelligence (AI) is becoming more and more prevalent in diverse photovoltaic (PV) system applications. The basics of grid-connected multilevel inverters for PV systems is provided, together with information on the drivers, characteristics, evaluation criteria, topologies, modulation schemes, and selection criteria for various applications. The results of a thorough reliability investigation of basic 15L multilevel inverter (MLI) are examined in this study. The inverter is the most crucial component of a grid-connected PV system. This study provides a survey of the system topologies and grid-connected PV inverters utilized for PV systems linked to the grid. © 2023 IEEE.

关键词： Maximum power point trackers

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Review of Maximum power Point Tracking Based Novel Converters Used in EV Application

Review of Maximum Power Point Tracking Based Novel Converter...

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2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Kavin, K.S. Subha Karuvelam, P. Santhoshi, B. Kavya Lakshmi, D. Sivasubramanian, M. Suresh, R. Government College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India Department of Electrical and Electronics Engineering Andhra Pradesh Rajahmundry India AMET Deemed to Be University Department of Electrical and Electronics Engineering Tamil Nadu India Sri Venkateswara College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Thiruvallur India Department of Electrical and Electronics Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350333244

The usage of renewable energy plays a significant part to make a pollution-free environment. The best solution for regulating DC voltage and maximising the performance of RES is to employ DC-DC converters. A Maximum power Point Tracking (MPPT) controller is used to monitor Photovoltaic (PV) panel's maximum output power. Here, an evaluation is done to identify the most effective controller acting as MPPT with Converter is discussed. An evaluation of several dc-dc converter models took into account a number of factors, including price, the materials utilised, current ripple, and efficiency. Lastly, we demonstrate comparative analysis to find out the most effective conversion for PV-fed applications. From the comparison result, the Artificial Neural Network (ANN) technique obtained better tracking accuracy for MPPT method. © 2023 IEEE.

关键词： DC-DC converters

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High Gain Luo Converter for ANFIS-GWO optimized PV Based Grid Tied Dstatcom Module

High Gain Luo Converter for ANFIS-GWO optimized PV Based Gri...

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Mohammed Ovaiz, A. Senthil Kumar, R. Shanmugapriya, M. Jayasurya, E. Narendra, Kocherla Saravanan, R. Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Electronics and Communication Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350333244

Due to its utilization of renewable energy sources for the production of electricity, the solar Photovoltaic (PV) energy generation system has come under scrutiny as a major energy system by electric utilities all over the world. As these kinds of systems expand in the modern day, more study is required to advance modern technologies, such as extremely efficient batteries or solar panels. In this study, the PV system is now equipped with a Luo converter, which enhances the PV system's output voltage gain. The converter uses Pulse Width Modulation (PWM) controller to enhance device efficiency overall and track the maximum power point tracker (MPPT). Increase the accuracy of MPPT controller adopting Adaptive Neuro-Fuzzy (ANFIS), Gray Wolf Optimization (GWO). The input DC voltage is inverted using a three phase Voltage Source Inverter (VSI) and a three phase induction motor, and the resultant harmonics are reduced using an LC filter. MATLAB is used to execute the simulation, and a minimum Total Harmonic Distortion (THD) value of 1.24% was found. © 2023 IEEE.

关键词： Maximum power point trackers

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Modular Multilevel Converter for Renewable System Towards THD Mitigation

Modular Multilevel Converter for Renewable System Towards TH...

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2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Prakash, S. Mohamed, M.B. Nainar Aarupadai Veedu Institute of Technology Department of Electrical and Electronics Engineering Vinayaka Missions Research Foundation Tamil Nadu India

ISBN: (纸本)9798350333244

Reconfigurable property, flexibility and simplicity in operation of Modular Multilevel Converter (MMC) have attained great importance in the research field. Since, it functions at Low frequency, the performance of the converter gets enhanced. Moreover, many techniques has been implemented for reducing the harmonic contents in MMC. In proposed work, a novel technique is introduced for mitigating the harmonic issues in MMC. The output from the photovoltaic system (PV) is given to the voltage stabilizer, which balances the voltage and gives it to the High Frequency Converter (HFC) of the MMC. Low frequency DC is converted by the HFC converter into high frequency DC, which is then fed to the Low Frequency Converter (LFC), which changes the high frequency DC into low frequency AC before being delivered to the grid. The P&O greatest power Point Tracking (MPPT) algorithm is implemented to monitor PV panel's greatest power output. PI controller based closed loop technique is used to regulate MMC and achieve grid synchronisation. To evaluate the proposed work's effectiveness, MATLAB/Simulink is employed to implement it. Using the defined work, better reduction of THD is achieved. © 2023 IEEE.

关键词： Maximum power point trackers

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Machine Learning for Analysis and Prediction of Wind Energy

Machine Learning for Analysis and Prediction of Wind Energy

引用

2023 international conference on circuit power and computing technologies, iccpct 2023

作者： Mohd, Yasir Singh, Harvinder Chandigarh University Department of Electrical Engineering Mohali Punjab Gharuan140413 India

ISBN: (纸本)9798350333244

A key and expanding field in the renewable energy sector is wind energy prediction. Determining the quantity of energy that will be provided is essential to reducing the cost of the wind farm's output and guaranteeing the security of grid access since biofuels are included into already-existing programs and integrated with conventional sources. In this research, we'll apply several cutting-edge Deep Learning techniques to a number of machine learning issues, such as the development of a prediction model for wind energy output. This paper delves into the application of machine learning techniques especially CNN for wind energy forecasting, with a focus on addressing key challenges and improving prediction accuracy. The study highlights the vanishing gradient issue and weight initialization as critical factors influencing the success of wind energy predictions. A detailed analysis of predicted values and forecasts for power generation, wind speed, wind pressure, and air temperature has been provided. These results showcase the efficacy of the machine learning model in estimating wind energy output and offer insights into the temporal variations observed throughout the year. Accurate wind energy forecasting is essential for effective integration and management of renewable energy sources, contributing to overall energy sustainability. The findings contribute to the optimization of wind energy resource utilization and advancements in the renewable energy domain. This research underscores the potential of machine learning to enhance forecasting models, enabling informed decision-making and fostering the growth of renewable energy technologies. © 2023 IEEE.

关键词： Wind power

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