10 results on '"Mandava, Srihari"'
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2. Optimizing home energy management: Robust and efficient solutions powered by attention networks
- Author
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Nutakki, Mounica and Mandava, Srihari
- Published
- 2024
- Full Text
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3. Review on optimization techniques and role of Artificial Intelligence in home energy management systems
- Author
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Nutakki, Mounica and Mandava, Srihari
- Published
- 2023
- Full Text
- View/download PDF
4. Speed and current feedback loops control for permanent magnet synchronous motor using PID controller.
- Author
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Mandava, Srihari and Nippatla, Venkata Ramanaiah
- Subjects
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PID controllers , *PERMANENT magnet motors , *SWITCHED reluctance motors , *INDUSTRIAL robots , *RELUCTANCE motors , *SPEED - Abstract
The major contribution of this paper is shows how to control the speed of Permanent Magnet Synchronous Motor (PMSM) by using Proportional Integral and Derivative (PID) controller. In this work, proposed PID controller is used in both speed and current feedback loops to control the speed and torque respectively. The proposed PID controller having the advantages which are less oscillatory response, less overshoots and takes less settling time for the speed response compared with existing PID controller used in speed control loop. The PMSM drive is used in electric vehicle, electric traction, robotics and industrial dive applications. The PMSM having the salient features which are high efficiency, high torque density, low torque ripple, high power density and robust construction compare to dc, induction, Switched Reluctance Motor (SRM) and Brush Less Direct Current (BLDC) motors. In this paper, the proposed PID controller is developed based on Field Oriented Control (FOC) strategy is well used to achieve the good speed response of PMSM drive using MATLAB R2019b/Simulink software. The proposed PID controller provides an excellent steady state and dynamic speed response in terms of rise time, peak time, settling time and overshoots. In addition, proposed PID controller has overcome the drawbacks of Proportional (P) and Proportional Integral (PI) controllers. The P and PI controllers which process only first order unstable systems can be stabilized and higher order processes cannot be stabilized respectively. These controllers compromised if the system dynamics change over time. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Brain tumour detection using deep learning.
- Author
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Balajee, Aishwarya, Bharat, Mounica, Nutakki, and Mandava, Srihari
- Subjects
BRAIN tumors ,CONVOLUTIONAL neural networks ,DEEP learning ,MAGNETIC resonance imaging ,USER interfaces - Abstract
The growth of abnormal cells in the brain causes brain tumours. Statistical calculations on the population of the United States reveal that amongst those diagnosed with a brain tumour, the percentage of patients who have for five years or more after the tumour is discovered is only approximately 36 diagnosis of a brain tumour leads to a decreased chance of a patient's demise. The classic procedure used in general hospitals to diagnose brain tumours is magnetic resonance imaging. Once the MRI reveals the presence of abnormal cell growth, a biopsy is performed on the tissue sample of the suspected tumour mass. Examination of biopsy results helps classify the type of tumour. The aim of this project is split into two sections. The first one is to use MRI scans of the brain to identify the exact type of tumour present in the brain using a Convolutional Neural Network with a static Graphic User Interface to make the results easily accessible. The second section of this project is to compare different types of convolutional neural networks (Modified CNN, VGG16 and ALEXNet) on various parameters to determine which of those would be the most helpful with our project necessities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models.
- Author
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K, Karan Kumar, Nutakki, Mounica, Koduru, Suprabhath, and Mandava, Srihari
- Subjects
DEEP learning ,ENERGY consumption forecasting ,SUPPORT vector machines ,ENERGY management ,ELECTRIC power consumption ,QUANTUM computing - Abstract
The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
7. Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques.
- Author
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Nutakki, Mounica and Mandava, Srihari
- Subjects
ENERGY management ,RECURRENT neural networks ,ENERGY consumption ,POWER resources ,ENERGY demand management ,SMART homes ,MACHINE learning - Abstract
The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. Comparative vector control study on speed of PMSM drive using sensorless and machine learning techniques: review.
- Author
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Nippatla, V. Ramanaiah and Mandava, Srihari
- Subjects
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MACHINE learning , *VECTOR control , *TRAFFIC safety , *PERMANENT magnet motors , *FUZZY logic - Abstract
The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM's are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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9. Non-Isolated Multiport Converter for Renewable Energy Sources: A Comprehensive Review.
- Author
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Narayanaswamy, Jayakumar and Mandava, Srihari
- Subjects
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RENEWABLE energy sources , *ELECTRONIC circuits , *INTEGRATED circuits , *DC-to-DC converters , *SUSTAINABLE development , *ENERGY harvesting - Abstract
A key focus of the Sustainable Development Goals (SDGs) is to harvest electricity from renewables. The fields of renewable energy sources (RES) and harvesting methods have seen tremendous growth. Multiport power electronic converter circuits integrate multiple energy sources. This integration aids in powering the load with different required voltage ratings. A simple and reliable control scheme for this system can maintain high power transfer efficiency and reliability. To integrate RES, non-isolated (NI) DC-DC multiport converters (MPC) are introduced; however, the main objective is to concentrate on NI MPC which provide a reliable output. In this paper non-isolated MPC topologies are reviewed and analyzed based on difference parameters considering circuit configuration; operating principal; number of components; complexity; and efficiency. This comprehensive review can be used as a guide for the suitable selection of converter topology to RES integration. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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10. Integration of AI with reduced order generalized integrator controller for power system harmonic reduction.
- Author
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Mandava, Srihari, Medarametla, Praveen Kumar, Gudipalli, Abhishek, Saravanan, M, and Sudheer, P
- Subjects
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INTEGRATORS , *REACTIVE power , *FUZZY logic , *ELECTRIC power filters , *ARTIFICIAL intelligence , *VOLTAGE control - Abstract
The increased use of electronics for control and use of nonlinear type loads by consumers in the present power system network are injecting harmonics into the power signal, due to which the power quality issue has become more challenge for the present researchers. In this work, the reduced order generalized integrator (ROGI) and fuzzy logic control (FLC) are collectively used to reduce the current harmonics in the power system. FLC‐SVM technique is used to maintain the process of SAF—Shunt active filter with fixed switching frequency. This proposed control technique consists of control loop for current to have fast and effective control. The Proportional Resonant controller is used to have control on voltage for a slow and effective control process which is also used to compensate reactive power. The shunt active filter is designed with the help of ROGI by integrating PI controller for fuzzy‐based SVM to reduce the current harmonics on the load side. The results are achieved in MATLAB/SIMULINK software. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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