检索结果-内蒙古大学图书馆

Single-Sensor based CSPO Algorithm for Maximum Power Point Tracking under Dynamic Shading Conditions

Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 841-854页

作者： Bashista, Prateek Mishra, Anurag Kumar, Bhavnesh Tyagi, Arjun Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi India

One of the most significant and difficult challenges in solar systems is determining the maximum power point. This process becomes more difficult when the array of solar modules is subjected to non-uniform shade. A unique hybrid maximum power point tracking (MPPT) approach is presented in this research. The suggested approach, cuckoo search–perturb observe, is created by combining the traditional perturb & observe (P&O) and cuckoo search algorithms (CSPO). A hybrid algorithm has been developed to capitalize on the benefits of both algorithms while overcoming their respective disadvantages. The proposed algorithm attempts to incorporate the most prominent aspects of both algorithms while also attempting to address the drawbacks of both algorithms and improving the accuracy of the new approach by resolving the issue of P&O getting stuck on local maxima during non-uniform irradiance and reducing the resource-intensive approach of cuckoo search by limiting its number of iterations. Furthermore, the suggested approach locates the maximum power point using just voltage sensors, decreasing sensor requirements. The simulation findings for maximum power point tracking performance are also presented. © The Institution of Engineers (India) 2024.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Power of Advanced Deep Learning Algorithms for Foretelling Wind Speed

Harnessing the Power of Advanced Deep Learning Algorithms fo...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

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

ISBN: (纸本)9798331540821

Wind speed estimation is crucial for the stability and long-term planning of the electrical grid. Wind power has positive environmental effects and allows grid operators to anticipate energy generation trends and make real-time adjustments. This study investigates four deep learning algorithms for predicting wind speed: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. The LSTM model shows the best R2 value and the lowest RMSE value, demonstrating its better predictive ability. These models capture complex temporal correlations in wind speed data, demonstrating their utility as reliable time-series analysis tools. The recognition of LSTM networks as the best wind speed predictors paves the way for cutting-edge forecasting techniques and a robust energy infrastructure that can meet the demands of a changing energy landscape. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Power Management and Power Quality control in Hybrid Unified-MIMO System Based Microgrid

Power Management and Power Quality Control in Hybrid Unified...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Verma, Akash Deep Gaur, Prerna Tomar, Anuradha Netaji Subhas University of Technology Instrumentation & Control Engineering Department Delhi India Netaji Subhas University of Technology Electrical Engineering Department Delhi India

ISBN: (纸本)9798350364866

Low voltage next-generation unified multi-input multi-output (U-MIMO) system-based AC-DC microgrids (MGs), equipped with distributed renewable resources (DERs), significantly contribute to sustainable development by offering reliable, decentralized and scalable electricity. It alleviates the burden on the main grid and promotes socio-economic development. However, challenges persist in power management, power quality and the high installation costs of battery management systems (BMS). To address these issues, this study introduces a modified hybrid U-MIMO system to optimize MGs. This system provides an all-weather synchronized solution, integrating multi-located PV nanoarrays and single-phase grid supply as multi-input sources for AC-DC multi-output loads with a bi-directional BMS. Grid interaction reduces the required battery capacity and thus lowers installation costs. The modified U-MIMO system enhances power management by accounting for BMS state of charge (SOC) and power generation from DERs. This study uses a single-phase induction motor and a rectifier with inductive load as non-linear AC loads, while an EV battery serves as the DC load. Multi-control structures within the hybrid U-MIMO system ensure a constant DC bus voltage, adhering to IEEE 519 standards. A comprehensive analysis with two modes and one scenario demonstrates effective power management, synchronization and power quality control, validating the system's capability to support diverse loads and applications efficiently. © 2024 IEEE.

关键词： Induction motors

来源：评论

学校读者我要写书评

暂无评论

Wind Speed Prediction Using Fuzzy C Means Clustering with LSTM 3

Wind Speed Prediction Using Fuzzy C Means Clustering with LS...

引用

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

作者： Singh, Saumya Srivastava, Smriti Netaji Subhas University of Technology Instrumentation and Control Engineering New Delhi India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387193

This paper presents a clustering approach before long short-term memory (LSTM) modeling for preprocessing to enhance the model's predictability without increasing the complexity of the LSTM model. Relevant features are selected, and principal component analysis (PCA) is performed before clustering to focus on the main features in the data. The results of the clustering, including cluster centres for each principal component and membership, are then given to the LSTM for model training. The performance of fuzzy C-means in integration with long short-term memory (FCM-LSTM) model is compared with other deep learning models in terms of performance metrics. The superior performance of the proposed model is visualized through prediction graphs and performance metrics. Thus, using an unsupervised method like clustering before modeling provides an advantage in detecting anomalies or hidden patterns in wind data. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Prediction of PV Power Considering Dust and Environmental Factors 4

Machine Learning-Based Prediction of PV Power Considering Du...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Nanda, Saksham Tomar, Anuradha Kumar, Hritik Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Instrumentation & Control Engineering Delhi India

ISBN: (纸本)9798331528171

Solar energy is one of the most important forms of renewable energy for generating electricity. The efficiency of Photovoltaic (PV) panels that produce electricity depends on the continuously changing weather parameters like dust, wind speed, wind direction, ambient temperature, module temperature, relative humidity and most importantly the solar irradiation. The dust particles in the atmosphere, like the other weather parameters, is also changing continuously and plays a major role in reducing the sunlight on the PV panels by scattering, absorbing or reflecting the incoming sunlight. This leads to reduced efficiency in the production of electricity and consequently the DC power generated from the PV panels will be less. This paper aims to build a model using machine learning algorithms to predict the output DC power due to the dust (PM 2.5 and PM 10 concentrations are considered in the dataset), wind speed, wind direction, the ambient temperature of the region where the PV panels are placed, the module temperature, the relative humidity. Using this dataset, a random forest model (by implementing a machine learning algorithm known as the Random Forest Algorithm) was developed and implemented. This model achieved a mean absolute error (MAE) of 482.02 Watts and an R-squared score of 0.873 and can be used to achieve optimized performance and efficient energy conversion of PV systems. © 2024 IEEE.

关键词： Photovoltaics

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of RUL and SOH Prediction Algorithms using Machine Learning Techniques 4

Comparative Analysis of RUL and SOH Prediction Algorithms us...

引用

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

作者： Gupta, Manvi Arya, Archie Varshney, Uday Mittal, Jishnu Tomar, Anuradha Netaji Subhas University of Technology Electrical Engineering Department Delhi India Netaji Subhas University of Technology Instrumentation and Control Engineering Department Delhi India

ISBN: (纸本)9798350364729

This study explores data-driven methodologies for State of Health (SOH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries, vital for electric vehicle (EV) adoption. Leveraging NASA and Oxford Battery Degradation datasets, advanced state of the art machine learning techniques like LSTM networks and Transformers and Gated Recurrent Unit (GRU) are integrated to develop and validate a predictive model. Challenges such as non-linear behavior and limited data are addressed with a novel neural network. A comprehensive literature review along with comparative analysis is done to assess the better performing model and it's strengths compared to others. The study advances battery health prediction technologies, enhancing EV battery reliability and efficiency. © 2024 IEEE.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

Secondary Droop control Supported Cascaded PR controller for SCIM-Based Water Pumping System 4

Secondary Droop Control Supported Cascaded PR Controller for...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

ISBN: (纸本)9798350383997

The squirrel cage induction machine (SCIM) based water pumping system (WPS) remains a cost-effective and robust option compared to other alternatives. However, the voltage-to-frequency-based controllers and direct-on-line (DOL) start of SCIM within solar PV-based microgrids (MGs) result in voltage dips and harmonic disturbances. Hence, other electrical applications can't be used in parallel. The SCIM needs a dedicated system that increases the overall cost. Hence, maintaining load voltage and frequency constant in a power system with IEEE 519 standards is very critical with a SCIM-based WPS. To address this issue, a secondary droop-control supported cascaded proportional resonant (CPR) controller has been proposed and tested for a three-phase voltage source inverter (VSI) in a grid-interactive solar PV system for SCIM-based WPS. The objective is to keep constant load voltage and frequency with enhanced power quality as per IEEE 519 standards and optimize power management so that other applications can also be used in parallel with the same controller. Additionally, a 'Grid-Start Grid-Run' mode has been discussed to protect solar modules from over-current during the DOL start of the WPS. The DC link voltage is regulated to ensure the motor torque and speed remain within specified operational parameters. The proposed control is stable, robust, and cost-effective, offering the flexibility to operate in islanded mode when necessary. © 2024 IEEE.

关键词： Squirrel cage motors

来源：评论

学校读者我要写书评

暂无评论

Correlating Solar PV Technical Metrics and Financial Outcomes: A Year-Long Study of System Performance 4

Correlating Solar PV Technical Metrics and Financial Outcome...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Tomar, Anuradha Kumar, Hritik Netaji Subhas University of Technology Department of Instrumentation & Control Engineering Delhi India

ISBN: (纸本)9798331528171

The efficiency of photovoltaic (PV) systems is an important component in delivering high revenue to achieve the necessary goals at minimum cost and to overcome various climatic conditions in different areas. This paper aims to provide detailed techno-economic investigation of a PV system installed in a university in Delhi, India for one year. The analysis targets fundamental technical parameters namely AC power inverter indications, solid and baseline solar irradiation data, real-time yield as well as possible output. A comparison is then made of the expected production levels at 100% efficiency, 80% efficiency, and 60% efficiency then comparing the forecasted values with the production rates while considering breakdown losses, performance deviations, and energy losses during communication downtimes. In parallel, qualitative assessment validates the impact of operational inefficiencies on the profitability of the organization, and numerical assessment of ROI, the operation and manufacturing costs, and consequences of production inefficiency. The additional combination of both technical and financial data provides a comprehensive understanding of system performance by showing how technical variations including those in the solar irradiation and in the efficiency of the system have impacts on financial aspects in the long run. Thus, providing correlation of these technical and financial parameters of this study will create a framework of how to enhance performance of the PV system and how to make good financial decisions. The findings help to enhance the knowledge of actual performance of PV systems and provide useful information for enhancement of PV systems design, control and economic assessment in the renewable power generation industry. © 2024 IEEE.

关键词： Solar irradiance

来源：评论

学校读者我要写书评

暂无评论

Improved Grey Wolf Optimization Abetting Fuzzy Logic controllers Used for Load Frequency control in Two-Area Interlinked Reheat Thermal Power Plants applying Governor Dead Band and Generation Rate Constraint Non-Linearity 2

Improved Grey Wolf Optimization Abetting Fuzzy Logic Control...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Shukla, Shasya Jha, Shyam Kant Netaji Subhas University of Technology Instrumentation and Control Engineering Department New Delhi India

ISBN: (纸本)9798350306415

The need of a fast, effective and economical controller for load frequency control has been established in this paper. The primary aim is to conduct an investigation into the effectiveness of P.I.D. and FUZZY LOGIC controller applied to load frequency control (LFC) of interlinked power system with non-linearity by applying optimization. As it is already known when certain input variables and the related output variables do not have a linear or proportional connection, this is referred to as non-linearity in power systems. In other words, changes in input parameters do not always correspond to changes in output parameters in a straight proportion. Instead, the system's behaviour displays nonlinear traits, which can make analysis and control more difficult and complicated. In order to preserve system stability, efficiency, and the capacity to maintain a constant frequency and voltage, dealing with non-linearity in power systems is crucial. Fuzzy logic, optimization, and adaptive control are examples of sophisticated modelling and control approaches that engineers and researchers frequently use to better understand and handle non-linear phenomena and guarantee the stable functioning of power systems. Considering the domain of global optimization and engineering design challenges, the Improved Grey Wolf Optimization (I-GWO) offers a highly effectual and sturdy approach. This improvement seeks to solve some of the drawbacks of the conventional Grey Wolf Optimizer (GWO) method while applying it to a system having GRC and GDB non-linearity. © 2024 IEEE.

关键词： Fuzzy controller I-GWO Load frequency control non-linearity optimization

来源：评论

学校读者我要写书评

暂无评论

Cost Optimization of EV Charging Station Integrated with Solar PV 3

Cost Optimization of EV Charging Station Integrated with Sol...

引用

3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Srivastava, Prabhat Agrawal, Alok Gaur, Prerna 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)9798350390728

Mobile charging stations (MCS) consist of vehicle equipped with battery banks and rapid chargers. The idea of using static charging station is to refuel electric cars. These charging stations are scheduled and routed to meet the needs of electric vehicle users who are heterogeneous in terms of geography and time. In order to meet the requirements of mobile charging stations, charging requests deadlines, cost estimation, acceptance of both the user etc. conditions should be required. Also, to optimize the problem to turn down the cost of charging, it should require to get the solar enabled charging station that provide surplus of energy at the peak hours and increase the reliability of system. Numerical findings demonstrate that the approach can significantly lower the cost of charging at peak hours and off peak too and also improving customer satisfaction by improvising the charging strategy. Solar based MCS have the potential to significantly contribute to the acceleration of EV adoption by offering charging services based on the location and time-agnostic. © 2024 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：