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Output power control of wind energy system by tip speed ratio control using fractional PI^βD^α controller

International Journal of Information technology (Singapore) 2021年第1期13卷 299-305页

作者： Pathak, Diwaker Gaur, Prerna Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi Delhi India

Various non-linear systems are well designed using a non-integer order mathematical model based differential and integral components. The fractional-order concept provides an effective method for turning the technology robust in various industry applications. This paper presents a fractional proportional-integral-derivative (FPID) controller and its benefits in variable speed wind turbine (WT) application. The FPID controller improves the operational environment of WT from the initiation of two more tuning parameters i.e., the fractional-order integral and derivative gains. These gains are well-tuned using the manual tuning method. For analysis of the effectiveness of the FPID controller, a comparison has been carried out between FPID and conventional PID controllers. The overshoot, rise time and settling time in various parameter responses like rotor speed, electromagnetic torque, and load power are considered for comparison of effectiveness of both the controllers. Performance indexes are also calculated and compared for both the controllers. © 2020, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Fractional PID controller PID controller Renewable energy Wind turbine

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On comparing the performances of MLP and RBFN on sales forecasting problem

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International Journal of Information technology (Singapore) 2022年第1期14卷 301-309页

作者： Tiwari, Raman Kumar, Rajesh Gera, Rajat Srivastava, Smriti Faculty of Management Studies Manav Rachna University Faridabad India Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Faculty of Management Studies Manav Rachna International Institute of research and studies Faridabad India Division of Instrumentation and Control Netaji Subhas University of Technology (formerly Netaji Subhas Institute of Technology) New Delhi 110078 India

This paper addresses the problem of predicting the sales by developing two sales forecasting models based on multi-layered perceptron (MLP) and radial basis function network (RBFN). The performance of both these models have been thoroughly compared in predicting the sales. To update the parameters in these models we have used dynamic back-propagation (DBP) method. Simulation results obtained shows that MLP has performed better than the RBFN. © 2019, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Dynamic back-propagation algorithm MLP prediction RBFN Sales forecasting

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Modified variable step incremental-conductance MPPT technique for photovoltaic system

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International Journal of Information technology (Singapore) 2021年第6期13卷 2483-2490页

作者： Singh, Priyanka Shukla, Nitin Gaur, Prerna Instrumentation & Control Engineering Netaji Subhas Institute of Technology New Delhi India

Maximum power point (MPP) Tracking, which is usually referred as MPPT, is a setup that forces the photovoltaic (PV) module to operate in such a way that the PV module will yield the maximum power, which in turn depend upon the solar illumination and surrounding temperature. Amidst all the MPPT designs, perturb & observe and incremental conductance (INC) algorithms are mostly selected due to easy implementation and good tracking credibility. The conventional MPPTs based on fixed step size give less power output and at every step tracking is required for new location due to random variation is a tedious job. In this paper the variable step size INC-MPPT technique is proposed which automatically tracks the next step size to maximize the power output. The comprehensive comparison is presented using MATLAB/SIMULINK between conventional and improved MPPT. © 2020, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Incremental-conductance (INC) Maximum power point (MPP) Maximum power point tracking (MPPT) Photovoltaic (PV) Variable step size INC

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A novel approach to predict stock market price using radial basis function network

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International Journal of Information technology (Singapore) 2021年第6期13卷 2277-2285页

作者： Kumar, Rajesh Srivastava, Shefali Dass, Anuli Srivastava, Smriti Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and Technology (Deemed to be University) Patiala 147004 India Division of Information Technology Netaji Subhas Institute of Technology New Delhi 110078 India Division of Instrumentation and Control Netaji Subhas Institute of Technology New Delhi 110078 India

In the financial sector, the sales price forecasting is a hot issue. Since the indices associated with the stock are nonlinear and are affected by various internal and external factors, they are very difficult to model and pose a difficult problem to be solved by the researchers. This paper is devoted in designing an intelligent prediction model based on the radial basis function network (RBFN). To tune its parameters a learning algorithm is developed using the back-propagation (BP) method. The performance of the proposed method is also compared with that of the multi-layered feed-forward neural network (MLFFNN) containing only single hidden layer and the results obtained from the simulation study indicate that the performance of RBFN is better as compared to the MLFFNN model. © 2019, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Back-propagation Multilayer feed forward neural network Prediction Radial basis function network Stock market data

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On Comparing the Performance of Deep Learning Techniques for Modeling of Non-Linear Systems

On Comparing the Performance of Deep Learning Techniques for...

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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Verma, Mohit Srivastava, Smriti Netaji Subhas University of Technology Instrumentation and Control Engineering Division New Delhi110078 India

ISBN: (纸本)9781665439862

This paper involves the modeling and prediction of non linear systems. The modelling of a nonlinear system will be accomplished using deep learning techniques. The dataset that is considered in this case is of time series which is stock price dataset. In this paper, deep learning techniques are implemented to predict and calculate the various types of performance metrics on a given dataset. © 2021 IEEE.

关键词： Nonlinear systems

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Image Analysis for E-Healthcare Systems using Multi-Biometric Recognition Model

Image Analysis for E-Healthcare Systems using Multi-Biometri...

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2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

作者： Bansal, Naman Arora, Pranav Sharma, Deepak Kumar Gupta, Koyel Datta Kuntala, Chandana Netaji Subhas Institute of Technology University of Delhi Department of Instrumentation and Control Engineering New Delhi India Indira Gandhi Delhi Technical University for Women Department of Information Technology New Delhi India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350396485

With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly emerging new technologies and continuous innovation. Many hospitals keep the records on paper, which raises many challenges, including but not limited to a significant amount of time for searching and retrieving a specific record, high maintenance costs, lack of backup, and limited security. Although the inclusion of technology has made this task far more efficient and secure, there is still much that can be done to improve it. This paper proposes a double index-based approach for mapping medical records directly to a patient's biometrics which would take advantage of the uniqueness of biometrics to identify a patient. The proposed multi-biometric model achieves an accuracy of 97% and an F 1 score of 0.9814. © 2023 IEEE.

关键词： Biometrics

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PATH PLANNING OF A MULTI DIMENSIONAL ROBOT IN STATIC ENVIRONMENT USING META HEURISTIC TECHNIQUES 8

PATH PLANNING OF A MULTI DIMENSIONAL ROBOT IN STATIC ENVIRON...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Dubey, Navanit Pankaj Rani, Asha Singh, Vijander Instrumentation and Control Engineering Division Netaji Subhas University of Technology New Delhi110078 India

ISBN: (纸本)9781665435642

This paper focuses on finding the shortest and correct path for the robots without any collision with the objects in its environment. Generally global (static) and local environments are used for path planning of mobile robots. In this work, static environment is considered and an optimized path is evaluated for efficient robot movement from the start position to the end position. Different classical and evolutionary based path search algorithms are used so as to find the optimized path, with reduced computation time and path length of the mobile robot. The results reveal that evolutionary-based algorithms provide a more effective solution to the problem under consideration. © 2021 IEEE

关键词： Genetic algorithms

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

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

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

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

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.

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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...

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Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), International Conference on

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.

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Enhancing transparency in global horizontal irradiance estimation with tree based machine learning algorithms and Shapley additive explanations framework

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International Journal of Ambient Energy 2025年第1期46卷

作者： Gupta, Rahul Ganvir, Chaitanya Jha, S.K. Singh, Rahul Baghel, Raj Kumar Department of Electrical and Electronics Engineering G L Bajaj Institute of Technology and Management Greater Noida India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Dwarka India Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar India

The estimation of global horizontal irradiance (GHI) is crucial for assessing solar energy potential, especially for investment purposes in specific regions. This study employs two feature selection techniques such as recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) to identify key variables from two datasets, which are then used to train four machine learning (ML) models such as Decision Tree (DT), Random Forest (RF), Extreme Gradient Boost (XGB), and Extra Trees (ET) regressors. The performance of these models is evaluated using three statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R2). The results show that the ET regressor, when combined with LASSO, achieves the best predictive performance, with an MAE of 1.36 W/m² and an RMSE of 2.46 W/m2. The study further employs Shapley Additive Explanations (SHAP) to interpret the model, revealing that parameters like Diffuse Horizontal Irradiance, Solar Zenith Angle, and Direct Normal Irradiance significantly impact GHI prediction accuracy. The combination of feature selection, advanced ML models, and SHAP analysis offers a comprehensive and transparent framework for solar energy resource assessment, addressing the need for accuracy and interpretability in GHI estimation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Solar irradiance

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