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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11-20 订阅

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Frequency Regulation of Multi-Source Power System Through Aggregated Thermostatically controlled Loads

Frequency Regulation of Multi-Source Power System Through Ag...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

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

ISBN: (纸本)9798331540821

The use of thermostatically controlled loads like inverter air conditioners (IACs) is widespread and can be leveraged for frequency regulation services. This paper focuses on load frequency control (LFC) for single area multi-source system comprising thermal, hydro and gas sources. The proportional integral (PI) and fractional order PI (FOPI) controllers are proposed using the particle swarm optimization (PSO) algorithm. The PSO based PI/FOPI strategy determines controller parameters by optimizing ITAE performance index. MATLAB/SIMULINK results demonstrate the significant improvements in overall system dynamics. The robustness of the proposed controllers is assessed under non-linear conditions, including generation rate constraints (GRC), governor dead band (GDB) and time delays. The PSO-FOPI controller exhibits superior robustness and dynamic response among the considered controllers. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Economical Demand-Side Management with Distributed Energy Resources

Economical Demand-Side Management with Distributed Energy Re...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Vishal Kumar, Sachin Sagar, Harsh Kumar Gaurav Agrawal, Alok Netaji Subhas University of Technology Department of Electrical Engineering New Delhi India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9798350364866

A potent technology for demand response is proposed in this paper. The proposed algorithm is believed to bring significant monetary increments and improvements in grid stability, especially within the emerging context of renewables as well as Electric Vehicles (EVs). This research presents an innovative strategy for finding residential participants with load profiles alone for maintaining grid stability, which is useful in data-scarce countries like India. The proposed algorithm aimed at maximizing customer and grid benefits. Additionally, the adoption of Distributed Energy Resources (DERs) will make Demand Side Management (DSM) strategies more effective, thus underlining their significance in molding a sustainable energy environment. More importantly, the study contributes to the knowledge of economic DSM practices outlining DER integration for future energy resilience and sustainability. © 2024 IEEE.

关键词： Demand response

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Solar-Powered EV Charging and Adaptive Irrigation System

Solar-Powered EV Charging and Adaptive Irrigation System

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Kumar, Shashwat Miglani, Yash Tomar, Anuradha Netaji Subhas University of Technology Dept. of Instrumentation & Control Engineering New Delhi India

ISBN: (纸本)9798350364866

Solar photovoltaic (PV) irrigation systems are emerging as a promising technology for regions with high solar irradiance and unreliable grid electricity. However, their intermittent use necessitates capacity optimization. To address this issue, this paper proposes a solar-powered system that integrates adaptive irrigation and electric vehicle (EV) charging. In order to maximize utilization of energy depending upon the demand in real time, the system dynamically divides power from the PV array between an EV battery and an induction motor for irrigation. This study demonstrates that when the PV power decreases from 2.6 kW to 1.58 kW, the EV battery compensates by discharging approximately 1 kW. This illustrates how the EV battery adjusts for periods of lower PV output by transferring energy to the irrigation motor, guaranteeing continued optimal functioning. The irrigation system employs a fuzzy logic controller (FLC) to optimize water flow rate by adjusting rotor speed via the voltage-hertz (V/f) method, based on soil moisture and temperature. At 44°C and 50% soil moisture level, the FLC evaluates a reference synchronous speed of 1200 RPM to precisely align the water flow rate with soil requirements. This integrated approach addresses inefficiencies in the conventional PV irrigation model. The proposed system is validated through MATLAB Simulink®. © 2024 IEEE.

关键词： Soil moisture

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control of PV Based Water Pumping System Optimized Using Neural Network 4

Control of PV Based Water Pumping System Optimized Using Neu...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Saritraj Kumar, Shubham Dhingra, Vikhyat Dahiya, Aman Tomar, Anuradha Netaji Subhas University of Technology Electrical Engineering India Netaji Subhas University of Technology Instrumention and Control Engineering India

ISBN: (纸本)9798350364729

The objective of this study is to enhance the efficiency of a solar-powered water pumping system through the integration of advanced control methodologies. The study focuses on harnessing the potential of photovoltaic (PV) panels by employing Maximum Power Point Tracking (MPPT) with incremental conductance method to maximize power extraction under varying irradiance conditions. The irradiance values obtained from the solar panels are utilized as inputs to a neural network (NN) predictive model, which accurately forecasts power output. The predicted power values are then transformed into reference speeds and employed within a field-oriented control (FOC) framework to regulate the operation of the induction motor (IM). This integrated approach aims to enhance the efficiency and reliability of the system by dynamically adjusting motor speed based on real-time irradiance fluctuations and mitigating the impact of changing environmental conditions. The findings contribute to the advancement of control strategies for renewable energy systems, particularly in remote agricultural applications where reliable water supply is crucial for sustainable development. © 2024 IEEE.

关键词： Solar concentrators

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Three Phase Charging System using Grid to Vehicle and Vehicle to Grid technology 9

Three Phase Charging System using Grid to Vehicle and Vehicl...

引用

9th IEEE India International Conference on Power Electronics, IICPE 2023

作者： Singh, Minakshi Mittal, Alok Prakash Singh, Ankit Kumar Netaji Subhas University of Technology Instrumentation and Control Delhi India Netaji Subhas University of Technology Electrical Engineering Delhi India

ISBN: (纸本)9798350307252

The research study presents a complex system that uses cutting-edge converters to control energy transfer. It is called Grid to Vehicle (G2V) and Vehicle to Grid (V2G). A three-phase bidirectional AC-DC converter and a DC-DC buck-boost converter are used in the arrangement. A bidirectional AC-DC converter is used in the G2V process to transform a 415V, 50 Hz AC power source into an 800V DC output, guaranteeing a sinusoidal current and unity power factor. On the other hand, during V2G, the vehicle's batteries' stored energy can be returned to the power grid, helping to stabilize the entire power system. The DC-DC buck-boost converter has two functions: it functions as a boost converter during battery discharge and as a buck converter during battery charging. PI controllers are used to regulate the charging and discharging procedures with regard to voltage and current. The study demonstrates the efficacy of the suggested algorithm through simulations, demonstrating the system's usefulness in realistic applications. Offering a viable option for bidirectional energy transfer between electric vehicles and the power grid, this cutting-edge device demonstrates the potential for improved energy management and grid stability. © 2023 IEEE.

关键词： Buck-Boost converter

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Rearrangement of the PV Array to Reduce the Effect of Partial Shading Using Meta-Heuristic Techniques 1

引用

3rd Electric Power and Renewable Energy Conference, EPREC 2022

作者： Dhariwal, Reeva Kumar, Bhavnesh Electrical Engineering Department Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Department Netaji Subhas University of Technology Delhi India

ISBN: (数字)9789811966057

ISBN: (纸本)9789811966040

Non-uniform illumination decreases the efficiency of the photovoltaic (PV) array by reducing the current through the entire string, creating a hot spot in the PV module, and producing multiple peaks in the power-voltage characteristic which results in decreased output power through the PV array. So, it becomes necessary to reduce these effects due to uneven illumination. Many techniques are used for mitigating this effect and one of them is the rearrangement of the PV array. This helps in dispersing the shade which increases the overall power of the PV system. In this paper, two meta-heuristic optimization techniques namely genetic algorithm (GA) and particle swarm optimization (PSO) are used for rearranging the PV array. The connections considered for the analysis of these techniques are TCT, BL-TCT, and HC-TCT. The result shows that rearrangement obtain through PSO when applied to TCT gives better results followed by HC-TCT when compared with the GA technique. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Integrated CNN-LSTM Model for Emotion Detection Using Physiological Signals from Wearables 6th

Integrated CNN-LSTM Model for Emotion Detection Using Physio...

引用

6th International conference on communication and computational technologies, ICCCT 2024

作者： Ankita Singh, Inderveer Nandini, Durgesh Yadav, Jyoti Instrumentation and Control Engineering Department Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India

ISBN: (纸本)9789819774258

Emotion recognition from physiological signals is pivotal for enhanced human–computer interaction. However, recognizing emotions using conventional electroencephalogram (EEG) signals offers limited wearability in day-to-day scenarios. In addition, the recent advancements in wearable fitness tracking devices offer non-intrusive, real-time insights into individuals’ health parameters. Thus, modeling diverse multimodal physiological modalities to recognize human emotions can enable an emotionally enriched user experience. The work proposes an automated emotion recognition system utilizing the multimodal physiological signals acquired using the wearable smartwatch. The study utilizes the publicly available EMOGNITION database comprising diverse physiological parameters. The parameters considered in this work include Heart Rate, Blood Volume Pulse, 3-axis Accelerometer, 3-axis Gyroscope, and 4-axis Rotation, acquired via the Samsung Galaxy smartwatch wearable device. The work examines the variations in the considered signals to determine nine discrete emotions. The preprocessed signal modalities are investigated using the hybrid Convolutional Neural Networks (CNNs) and one layer of Long Short-term Memory (LSTM) deep learning (DL) architecture. The adopted DL architecture obviated the requirement of manual feature extraction. The developed system successfully determines nine discrete emotions with an average accuracy and F1 score of 96.82% and 96.74%, respectively. The obtained results outperform the existing methods in the literature, demonstrating potential in emotion recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Comparative Study of ANN and Incremental Conductance MPPT for Solar Water Pump

Comparative Study of ANN and Incremental Conductance MPPT fo...

引用

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

作者： Bharti, Kanchan Singh, Sarvendra Kumar Kant Jha, Shyama Netaji Subhas University of Technology Electrical Engineering New Delhi India Netaji Subhas University of Technology Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9781665493826

For measuring the peak power output of PV panel, maximum power point tracking (MPPT) method based on artificial neural networks (ANN) and Incremental Conductance (INC) is describe in this paper. It is precise to use MPPT for the measurement of maximum possible power point as the weather variable such as temperature and sun irradiation changes. The research design and simulation of PV Array with MPPT controller are presented in this paper. The goal of this study is to develop an MPPT controller using an artificial neural network approach for PV applications. A comparison between an ANN-based MPPT controller's performance and that of the conventional INC MPPT method is made. The MATLAB software is used to run simulations and analyze the results. In this article, MATLAB simulation design of the ANN and INC MPPT for obtaining maximum output power and voltage is discussed in detail and coupled with a boost converter. © 2023 IEEE.

关键词： MATLAB

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Predictive Performance of EV Charging Behaviour in COVID-19 3

Predictive Performance of EV Charging Behaviour in COVID-19

引用

3rd IEEE International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Gupta, Manvi Mittal, Jishnu Tomar, Anuradha Netaji Subhas University of Technology Electrical Engineering New Delhi India Netaji Subhas University of Technology Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9798350319972

Transportation is an essential prerequisite for modern life, however, the conventional combustion engine is rapidly losing its relevance. Vehicles powered by petrol and diesel emit a substantial amount of pollutants and are currently being rapidly replaced by fully electric vehicles (EVs). These EVs are emission-free and significantly more eco-friendly. The COVID-19 pandemic has had a significant impact on the global EVs market. The widespread adoption of EVs faces various challenges, including vehicle scheduling due to the limited EV charging facilities. Smart scheduling algorithms can address this issue by managing to improve EV scheduling by utilizing tools and algorithms fueled by data-driven approaches to predict EV charging demand. Previous research has utilized historical EV charging data to predict the behavior of departure time and energy needs. However, unexpected events like a pandemic can lead to changes in behavior, making predictions more challenging. This paper proposes using pre-COVID and COVID-period historical charging data to predict EV session duration using machine learning algorithms like Decision Tree Regressor, XGBoost, and Deep Neural Networks. XGBoost achieved the best predictive performance, with R2 scores of 0.99 for session duration, outperforming existing literature. The study demonstrates significant improvement in both predictions compared to previous work and conducts a comparative analysis of EV behavior during normal and unexpected circumstances. Overall as a result of the COVID-19 pandemic, the global EV market has experienced a decline in growth. With factory closures, supply chain disruptions, and reduced consumer demand, the production and sales of EVs have been negatively impacted. © 2023 IEEE.

关键词： Forecasting

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An NFNet-Based Approach for Enhanced Bearing Fault Classification Using Vibrational Signal Analysis 14

An NFNet-Based Approach for Enhanced Bearing Fault Classific...

引用

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

作者： Jain, Vidhi Dixit, Mayank Misra, Vihaan Bhutani, Geetansh Singh, Vijander Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9798350335095

The present work proposes a novel approach for detecting bearing faults in induction motors using vibrational signals. The proposed method employs a Normalizer-Free ResNet (NFNet) architecture to extract relevant features from 2D image representations of raw vibration data and a classification model to identify the presence of a fault. We convert the vibrational signals into 2D images by applying the Short-Time Fourier Transform (STFT) technique, which offers a time-frequency analysis of the signals. The proposed method improves accuracy over conventional approaches along with reduced computational overhead by using high-performance NFNet architecture for classification. The effectiveness of the proposed method is evaluated on the Case Western Reserve university (CWRU) and Intelligent Maintenance Systems (IMS) datasets of vibration signals from induction motors, experiencing various bearing faults. The results indicate that the suggested framework outperforms the existing methods in terms of accuracy and efficiency, achieving state-of-the-art results. This method can be used for real-time monitoring of induction motors to detect bearing faults and prevent potential equipment failures, making it highly useful for industrial applications. © 2023 IEEE.

关键词： Computer vision

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