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检索条件"机构=Instrumentation and Control Engineering Netaji Subhas University of Technology"

共 350 条记录，以下是41-50 订阅

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Analysis of Bifacial Photovoltaic Panel Under Different Reflective Surfaces 3

Analysis of Bifacial Photovoltaic Panel Under Different Refl...

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3rd IEEE International Conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Kathuria, Harit Singh, Ikjot Gupta, Aditya Puniya, Gaurav Kumar, Bhavnesh Netaji Subhas University of Technology Instrumentation and Control Engineering Department Sector-3 New Delhi Dwarka110078 India

ISBN: (纸本)9798350387193

With developments in bifacial solar panels, the solar photovoltaics interest has started shifting from monofacial designs to bifacial solar panels. This paper analyses and compares the performance between a bifacial and a monofacial solar panel system. The bifacial design offers different performance characteristics based on the reflective properties of the base material. This paper reviews and compares the system's performance on various reflective surfaces. This study employs a comparative outlook on performance measures such as current, voltage, power and thermal outputs. Taking the global energy crisis into consideration, solar panels prove to be an important replacement for the conventional ways of electricity generation and bifacial solar panels improve the efficiency of a conventional solar panel. Therefore, this study is anticipated to serve as a guide for integrating bifacial photovoltaic systems in general use, increase its efficiency and also promote research in this domain. © 2024 IEEE.

关键词： Bifacial Gain Bifacial Solar Panels Monofacial Solar Panels Photovoltaic System Reflector

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An Intelligent MPPT Technique for a Three-Stage Battery Charge controller for Standalone System 2nd

An Intelligent MPPT Technique for a Three-Stage Battery Char...

引用

2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Hussain, Alina Manisha Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi110078 India

ISBN: (纸本)9789819909681

This paper presents a comparative analysis between two different MPPT algorithms for a 3-stage battery charge controller (BCC) using a standalone system. A DC–DC buck converter is used as a step-down converter. Two different algorithms are used to extract the maximum power from solar PV panel. The maximum power from the solar photovoltaic panel is extracted using a conventional approach of Perturbation and Observation, as well as an intelligent MPPT technique called Fuzzy logic control. A battery charge controller (BCC) is used to charge the battery by using three different stages of the charging strategy. The different stages of charging incorporate Stage1—Bulk charging, Stage2—Absorption charging and Stage3—Float charging stage. The overall performance of the model is measured in terms of MPP tracking, lead-acid battery charging and controller efficiency. The output shows that the MPPT charge controller can track the MPP within 0.5 s regardless of solar irradiation variation. The concept of charging the battery in stages is implemented. The efficiency of the battery charge controller is attained up to 98.86% with a Fuzzy logic controller. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Maximum power point trackers

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Comparative Analysis of Machine Learning Models for Solar Power Prediction Using Time Series Weather Data 3

Comparative Analysis of Machine Learning Models for Solar Po...

引用

3rd IEEE International Conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Kane, Sumedh Nilesh Rathore, Akshit Abhijeet Anees, Sanya Kane, Arati Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Bharati Vidyapeeth College of Engineering Delhi India

ISBN: (纸本)9798350387193

Smart Grids preside over traditional power grids due to their efficiency and effectiveness. Smart grid implementation has benefited all components of the power system. These grids usually generate energy from renewable energy resources. However, the stability, reliability, and seamless functioning of the grid are adversely affected by the highly weather-dependent characteristics of renewable energy sources. As a result, accurate energy generation forecasting is an essential requirement for the grid's steady and reliable operation. The forecasting techniques for the quantity of electricity produced by solar power plants are discussed in this study. First, the impact of weather on solar power generation is investigated. Second, a machine learning method for solar panel output power prediction based on weather conditions is proposed. Finally, an ensemble approach utilizing different algorithms for machine learning is utilized to boost the accuracy of the proposed model. For the objective of creating prediction models, we utilized a variety of techniques, including temporal convolutional neural networks, fine trees, support vector machines with different kernel functions, artificial neural networks, and long-short-term memory RNNs. Each model's prediction accuracy has been assessed by comparing and contrasting these different methods. A comparative investigation indicated that the Temporal Convolutional Network-based model trumps the others in terms of efficiency. The proposed ensemble model exceeded the conventional individual models after performance comparison with the standard approaches. © 2024 IEEE.

关键词： Prediction models

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Particle Swarm Optimization Based control of a Differential Drive Mobile Wheeled Robot 14

Particle Swarm Optimization Based Control of a Differential ...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sonowal, Arpita Rani, Asha Singh, Vijander Netaji Subhas University of Technology Department of Electrical Engineering New Delhi Dwarka India Netaji Subhas University of Technology Department of Electrical Engineering and Instrumentation and Control Engineering New Delhi Dwarka India

ISBN: (纸本)9798350335095

This work aims at the efficient control of a mobile robot using optimised PID controller. The two DC motors are controlled precisely which in turn regulate the movement of the robot. Numerous techniques have been developed to enhance the PID control strategy applied to linear and non-linear systems. In this work, first the actuator model of the mobile robot is simulated and then the optimal computation techniques i.e., Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are considered to tune the gains of PID controller. ITAE is considered as the objective function for optimization. The comparative study reveals that PSO tuned PID provides the least ITAE and is thus used for the mobile robot. The velocity plots are analysed for various positions of wheels. The motion characteristics for different paths are also analysed which justify the effectiveness of the PSO-PID controller over the other considered techniques. © 2023 IEEE.

关键词： Soft computing

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Multi-scale Architecture for High Performance RSVP Classification 3rd

Multi-scale Architecture for High Performance RSVP Classific...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Chaudhary, Priyanshu Dhankhar, Nischay Singhal, Amit Rana, K.P.S. Department of Electronics and Communication Engineering Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India

ISBN: (纸本)9789819725076

The ability to directly control external equipment using electrical impulses to translate brain activity into commands or information has made brain-computer interfaces (BCI) an increasingly relevant research topic. Numerous uses for this technology have been discovered, including cursor control, wheelchair navigation, and use in diverse sectors. In this paper, a unique EEGNet-inspired Multi-Scale architecture is proposed, which is intended to boost Rapid Serial Visual Presentation (RSVP) task categorization performance. The performance of our suggested architecture is assessed using the datasets from BCI Competitions II and III, and accuracy of 76.3%, 84.2%, and 90.5% is achieved for subjects II-A, II-B, and IIb, respectively. The average symbols recognized under repetition metric is also used to evaluate the proposed architecture. These results show a notable improvement in accuracy and other evaluation metrics compared to previous research. Overall, the proposed architecture advances the field of BCI technology and offers a fresh, potentially effective method for enhancing RSVP categorization task performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain computer interface

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Global Horizontal Irradiance Estimation Using Bi-LSTM Algorithm 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Gupta, Rahul Yadav, Anil Kumar Jha, Shyama Kant Department of Electrical Engineering Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Instrumentation & Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144008 India Department of Instrumentation & Control Engineering Netaji Subhas University of Technology New Delhi Dwarka India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Accurate global horizontal irradiance (GHI) estimation is crucial for integrating renewable energy sources into the primary electrical system because it has an impact on grid operations, stability, and planning. Solar power is a significant component of the mix of energy sources since it has benefits for the environment and can reduce dependency on fossil fuels. With the aid of precise GHI projections, grid operators are able to forecast energy generation and adjust in real time to maintain a stable power supply. The planning of conventional power plants and efficient energy flow management is impacted by accurate GHI estimates. The balance between power production and consumption is essential for the stability of the grid. This research explores the potential of deep learning algorithms such as unilateral-LSTM and bilateral-LSTM networks for GHI prediction in Rajasthan, India, utilizing their inherent memory and adaptability. The min–max scaler technique is applied for preprocessing datasets obtained from the National Solar Radiation Database (NSRDB). To check the predictive performance and limitations of each model, this study analyzes various statistical parameters such as RMSE, MAE, and R2. The Bi-LSTM model using "RELU" as a gate activation function outperforms the Uni-LSTM due to getting the lowest values of MAE and RMSE. The highest R2 value obtained by the Bi-LSTM model indicates that its predictive performance is better than Uni-LSTM. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Modelling of a Non-Linear Dynamic System Using Long Short-Term Memory 4

Modelling of a Non-Linear Dynamic System Using Long Short-Te...

引用

4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sahu, Richa Srivastava, Smriti Kumar, Rajesh Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Control and Instrumentation Engineering Delhi India National Institute of Technology Department of Electrical Engineering Kurukshetra India

ISBN: (纸本)9798350306118

Long Short-Term Memory (LSTM) is a Recurrent Neural Network (RNN) that overcomes typical neural network constraints. Because of its ability to record temporal dependencies and solve nonlinear equations, long-Term data can be simply managed. LSTM is intended to alleviate the problems of RNN's vanishing gradient and exploding gradient difficulties. In this paper, we used LSTM to solve nonlinear plant equations with a gradient descent-based back propagation approach and retrieved the plant's output as well as other performance measures including Average Mean Square Error (AMSE) and Total Mean Average Error (TMAE). When the output of an LSTM is compared to that of a feed-forward Neural Network (FFNN), the LSTM outshines the FFNN. All the parameters of both models, such as iteration count and learning rate are kept constant. © 2023 IEEE.

关键词： Backpropagation

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Modelling of a MIMO Non-Linear System using a Double Internal Loop Recurrent Neural Network

Modelling of a MIMO Non-Linear System using a Double Interna...

引用

2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

ISBN: (纸本)9781665493826

A Double Internal Loop Recurrent Neural Network (DILRNN) model is proposed for Multiple Input Multiple Output (MIMO) system to obtain improved output using Gradient Descent based Back Propagation Algorithm. In the modelling of DILRNN, three feedback loops are taken i.e., the first feedback is taken from the unit delay of the output to the hidden layer, the second feedback is taken from the previous value of hidden layer to itself and the last feedback is taken from the previous value of output to output. The weight is updated with the help of the algorithm after every iteration. The output of the proposed DILRNN model reduces error significantly vis-à-vis other Recurrent Neural Network (RNN) models for constant value of iterations and epochs. The statistical parameters such as RMSE and TMAE for the proposed model are greatly in comparison to other models and thereby enhances the accuracy of the model. © 2023 IEEE.

关键词： Recurrent neural networks

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A Comparative Analysis of Different Batteries technology on Off-Grid Hybrid System 2

A Comparative Analysis of Different Batteries Technology on ...

引用

2nd IEEE International Conference on Measurement, instrumentation, control and Automation, ICMICA 2023

作者： Gaurav, Avinash Tyagi, Arjun Kumar, Bhavnesh Jha, Shyama Kant Netaji Subhash University of Technology Department of Electrical Engineering New Delhi110078 India University of Delhi Faculty of Technology Department of Electrical Engineering Delhi110007 India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9798350355048

Several rural communities are still living with disrupted power supplies. Consequently, diesel generators are used for getting regular power supply to provide effective healthcare services. Usage of diesel generator not only have deteriorating impact on the environment, but also is an expensive alternative to grid supply. Usage of renewable energy systems with battery are becoming popular solution for feeding such kind of loads in the remote areas. In this work, an off-grid hybrid electrification system (HES) is designed which consists of Solar PV-Diesel Generator-Batteries for the regular supply of electricity to the available load of PHC, located in a remote area of Kaimur district in Bihar, India. Investigation is carried out for five different technologies of batteries Lead-acid (LA), Lithium-ion (LI), Vanadium flow (VF), Zinc bromide (ZB) and Nickel-iron (NI) which are incorporated with HES. An optimum configuration for a PHC has been designed taking all major techno-economic factors and renewable penetration into consideration. Investigation reveals that a vanadium flow-based hybrid electrification system is the most feasible solution with respect to the techno-economic perspective among all considered configurations. © 2024 IEEE.

关键词： Lithium-ion batteries

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A Transfer Function Approach to Modeling of Thermostatically controlled Loads for the Power System Frequency Regulation

A Transfer Function Approach to Modeling of Thermostatically...

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2023 International Conference on Power Energy, Environment and Intelligent control, PEEIC 2023

作者： Gupta, Sweta Harish, V.S.K.V. Sharma, Rajneesh Netaji Subhas University of Technology Department of Electrical Engineering Sector-3 New Delhi Dwarka110078 India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering Sector-3 NewDelhi Dwarka110078 India

ISBN: (纸本)9798350357769

To keep the balance between generation and consumption power, frequency regulation plays a significant role. The Thermostatically controlled Loads (TCLs), like heating systems, refrigerators, and air conditioning units can be employed as the regulation tool to address the problem of distribution system fluctuation since these loads can be managed without compromising customer comfort due to their flexible operation. The task of research is to create a suitable controller by regulating the combined TCLs to work with conventional generators to maintain the consistency in the frequency of a single area system and then by using the model of proportional-integral (PI) control method, a transfer function approach must be formulated. The simulated results show that the suggested approach and models are adequate for providing frequency control to enable higher renewable energy penetrations and could be done by analyzing and comparing the system frequency response. © 2023 IEEE.

关键词： frequency regulation single area system the aggregated model thermostatically controlled loads

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