http://localhost:8080/xmlui/handle/123456789/31 2026-05-08T13:44:00Z 2026-05-08T13:44:00Z http://localhost:8080/xmlui/handle/123456789/6070 2026-04-20T07:25:30Z 2025-05-01T00:00:00Z

Design and analysis of effective controller for deregulated power system The framework of the electric utilities has been radically changed from conventional to modern competitive structure which is known as Deregulated Power System (DPS). In conventional power system Vertically Integrated Utility (VIU) regulates generation, transmission and distribution. In DPS, VIU which is monopolistic replaced by autonomous strong competitors’ entities like Transmission Companies (TRANSCOs), Distribution Companies (DISCOs), Generation Companies (GENCOs), self-sufficient operators known as Independent System Operators (ISOs).A detailed literature review of DPS shows that ISO is responsible for providing many auxiliary services and one among them is the Automatic Generation Control (AGC). The primary objective of AGC is to regulate the area frequency deviation by providing the balance between active power generation in amid to fluctuating load demands. Power systems could encounter a significant instability issue with a considerable decline in the frequency. The ongoing expansion in dimensions and intricacy, randomly varying power requirements, system modeling inaccuracies and changes in electrical power system have made the AGC task into a difficult one. Due to this, traditional control methods might not be able to manage such erratic changes in an AGC system. AGC problem for deregulated power system environment requires innovative tactics that combine information, strategies, and procedures for diverse sources to address the AGC issues of power system efficiently. Therefore, the objective of this research is to suggest various forms of novel controller frameworks for different types of restructured power systems. Five different power system being used for this research work are (i) three-area single-source hydro-hydro, (ii) two-area DPS having thermal-GTPP and diesel-GTPP generating units, (iii) two-area DPS having thermal reheat generating units, (iv) two-area DPS hydro-thermal reheat generating units and (v) two-area DPS having Thermal-Hydro-Gas (THG) generating unit. Initially, Optimal Control (OC) has been designed for AGC of three-area hydrohydro DPS and efficacy of OC is analysed under different market power transactions scenarios. Further, efficacy has been tested by incorporating system nonlinearities, such as Time Delay (TD), Generation Rate Constraints (GRC) and Governor Dead Band (GDB). Finally, sensitivity analysis has been performed to test the efficacy of OC under system parameter variation. Next OC approach has been designed for AGC of two-area DPS having GTPPthermal and GTPP- diesel. The proposed OC mechanism satisfied AGC requirement under various market scenarios, including poolco-based and poolco bilateral transactions with contract violations. Further, a novel control strategy for improving AGC performance of two -area single-source thermal reheat and two-area multi-source thermal-hydro power systems iv in a deregulated environment has been analysed. This approach introduces a Cascade Optimal Control –Proportional Integral with Derivative Filter (COC-PIDN) controller, in which master /primary OC has been designed using full-state vector feedback approach and slave/secondary controller has been optimized through the Salp Swarm Algorithm (SSA). The COC-PIDN controller outperform OC by providing lower frequency undershoot/overshoot, better stability margins when simulated and analysed in similar environment. COC-PIDN proves its robustness when tested by incorporating various system’s nonlinearities. Additionally, sensitivity analysis of COC-PIDN controller shows its resilience to variations in system parameters. This research work further extended by designing Cascade Optimal Control – Fractional Order Derivative (COC-FOD) controller to enhance the AGC performance of two-area DPS having THG generating units. The Primary controller is optimized with full-state feedback control, and the secondary FOD controller’s gains are optimized using SSA. The COC-FOD controller outperform OC by providing better Dynamic Performance (DP) in similar environment. Additionally, In presence of COC-FOD a minimal impact on DP of DPS have been observed when tested under variations of system parameters. Further in this research work SSA optimized PID/FOPID controllers has been designed and analysed for AGC enhancement of two-area DPS having THG generating units. Results show that the SSA-tuned PID/FOPID controller achieves a lower cost function value, indicating better overall DP and stability for multi-area THG-DPS systems. Proposed control mechanism of this research work are integrated with Energy Storage Devices (ESDs) for AGC enhancement. It has been observed that ESDs, including Battery Energy Storage (BES), Redox Flow Batteries (RFB), Flywheel Energy Storage (FES),Superconducting Magnetic Energy Storage (SMES) and Capacitive Energy Storage (CES) improves system stability by reducing peak deviations, oscillations, and settling time. Further, it has also been analysed that among the different ESDs, RFB exhibits the better performance with minimal frequency deviations. This research work also demonstrates significant performance improvements of the proposed controllers such as COC-PIDN and FOPID when coupled with RFB, in two-area single/multi-source DPS. Furthermore, under varying load conditions, the integration of RFB and SMES as Hybrid Energy Storage Systems (HESS), demonstrated superior performance compared to systems without ESDs. In a nutshell findings underscore the potential of proposed control strategies integrated with ESDs to enhance stability, improved DP, and robustness against parameter changes and providing a pathway for more reliable and efficient power systems in a deregulated environment. Jain, Sheilza

2025-05-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/6069 2026-04-20T07:21:05Z 2024-11-01T00:00:00Z

Analysis and design of neurodegenerative disorder detection mechanism Neurodegenerative disorders represent a major global health challenge characterized by the progressive degeneration of neurons in the central nervous system. These conditions primarily impact neural networks and can manifest in a variety of symptoms, including cognitive decline, behavioral changes, and movement disorders. The degeneration associated with these diseases often leads to complications that may result in disability and, ultimately, death. Some of the most common neurodegenerative disorders include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. Out of all neurodegenerative disorders, Alzheimer’s and Parkinson’s disease are the most prominent because they affect a significant portion of the population. Their high prevalence, combined with the profound effects on cognitive and motor functions, makes them critical concerns for public health and caregiving systems. Recent advancements in neuroimaging techniques have enhanced the ability to visualize brain changes associated with neurodegeneration. These techniques allow for the observation of atrophy in brain tissue and deposition of abnormal proteins. Additionally, neuroimaging techniques help assess changes in brain activity and connectivity, offering valuable information about the progression of the disease. In recent literature, numerous machine learning methods have been proposed to enhance the identification of neurodegenerative disorders through neuroimaging modalities. However, these advances are combined with several research gaps. These include small sample sizes, limited incorporation of white matter features, and insufficient integration of clinical test scores into classifiers. Many studies focus on region-of-interest features, neglecting the potential of whole-brain analysis, and often fail to adequately identify prodromal stages of disease. Additionally, the use of separate algorithms for feature selection and classification can lead to the loss of critical information. Distinguishing neurodegenerative diseases from others with similar symptoms is challenging, compounded by the lack of standardized preprocessing pipelines for imaging data. Addressing these issues could significantly enhance the diagnostic accuracy of predictive models. This thesis explores the application of machine learning techniques to analyze neuroimaging data, aiming to enhance the identification and understanding of neurodegenerative diseases. It presents three proposed mechanisms for detecting disorders such as Alzheimer’s and Parkinson’s disease, specifically addressing the gaps identified in the existing literature. The first proposed mechanism presents an approach for detecting brain regions that contribute to Alzheimer’s disease using support vector machine classifiers and the recently developed Self Regulating Particle Swarm Optimization (SRPSO) algorithm. The classifiers for distinguishing subjects into AD iv patients and Cognitively Normal (CN) individuals were built using Gray Matter (GM) and White Matter (WM) volumetric features extracted from structural magnetic resonance images. It could be observed from results that the classifier built using both GM and WM features provided better accuracy than the performance of classifier built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features further improves the accuracy of the classifier. In order to identify the brain regions that are important for AD vs CN classification problem, SRPSO is used to extract GM and WM features that are important for better classification performance. This helped in reducing the number of features by a factor of hundred. The features identified by SRPSO were also mapped back to the brain using automatic anatomic labeling template to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD. The second proposed mechanism presents a new approach to distinguish progressive Mild Cognitively Impaired (pMCI) subjects who eventually develop Alzheimer’s disease from stable MCI (sMCI) subjects whose situation does not deteriorate into AD. The proposed approach combines the discriminating capabilities of classifiers and representation learning capacities of autoencoders into a unified architecture, and is hence termed as Joint Autoencoder and Classifier Deep Neural Network (JACDNN). JACDNN employs a single classifier and multiple autoencoders that are trained together to perform pattern classification. The autoencoders in JACDNN, regularizes individual layers in the network used for classification to learn representations useful for reconstructing a given input. Performance of JACDNN has been evaluated on several machine learning problems pertaining to dementia, namely AD vs CN, AD vs sMCI, CN vs pMCI and pMCI vs sMCI. These problems are targeted using two datasets. The first dataset consist of GM features of subjects and the second dataset consist of combination of GM and WM features. It is observed that better classification results are obtained when the classifier is built on GM and WM as compared to GM features alone. Performance comparison of JACDNN with other existing approaches has been conducted for these problems. The results clearly indicate that JACDNN performs better than other existing approaches for these problems. The third proposed mechanism presents a novel deep neural network architecture for distinguishing subjects of Parkinson’s disease from cognitively normal cohort. The architecture combines the representation learning capacities and discriminating capabilities of convolutional neural network to improve classification performance of the network. For this purpose, the network consists of a classification sub-network and multiple reconstruction sub-networks. The classification sub-network discriminates v between PD and CN subjects and the reconstruction sub-networks are used to regularize the individual layers of the classification sub-network. All sub-networks are trained simultaneously to optimize the customized objective function of the network. The network is trained on Single Photon Emission Computerized Tomography (SPECT) images obtained from baseline study of Parkinson’s Progression Marker Initiative (PPMI). When compared to recent state of the art methods for PD detection, the proposed mechanism reports upto 10% improvement in the classification accuracy Gupta, Shailender

2024-11-01T00:00:00Z Rani, Pratibha http://localhost:8080/xmlui/handle/123456789/6068 2026-04-20T07:16:59Z 2025-06-01T00:00:00Z

Design and analysis of massive MIMO communication system Rani, Pratibha Over the past decade, we have witnessed an extraordinary surge in the proliferation of connected wireless devices, the sheer volume of which has reached billions. Amidst this rapid technological growth, another pressing concern has emerged: the energy consumption associated with protocols for communication. In this context, the massive MIMO (mMIMO) technology has emerged as a beacon of promise. By equipping base sataion (BS) with an extensive array of antennas—whether collocated or dispersed— mMIMO enables the simultaneous servicing of huge amount of users within the specific time-frequency resource. This innovative approach aligns perfectly with the aforementioned requirements and positions itself as a frontrunner for driving the evolution of 5G and beyond. In this dissertation, we delve deeply into the nuanced performance metrics of mMIMO systems. Our exploration extends to the introduction of an innovative pilot assignment scheme aimed at enhancing channel estimation (CE) and signal detection accuracy. By meticulously investigating these elements, we strive to support the ongoing development and optimization of the transformative technology, ensuring it meets the robust demands of tomorrow’s wireless communication landscape. In this comprehensive study, an innovative space-time transimission scheme (STTS) is introduced that aims to surmount the inherent problems associated with channel estimation, particularly when utilizing orthogonal pilot information in both collocated and distributed MIMO systems equipped with numerous transmitting and receiving antennas. The fundamental challenge arises from the necessity of acquiring accurate channel information through orthogonal pilots, which invariably introduces pilot overhead for channel estimation. This overhead can lead to critical bandwidth insufficiencies, prompting a delicate trade-off between the required quantity of pilots for effective CE and the overall spectral efficiency (SE) of the system. The issue of data symbol detection is tackled, the MLD method, a robust approach is employed that consistently addresses the complexities associated with parameter estimation challenges. Moreover, singular value decomposition (SVD) is used to derive the MGF, a mathematical tool crucial for calculating the symbol error rate (SER) using M-ary phase shift key (M-PSK). The ergodic capacity emerges as a pivotal parameter for ensuring reliable communication within this framework. The plot for ergodic capacity, offering a tangible visualization of the method’s efficacy in enhancing communication reliability is obtained. In the analysis, the MGF of the instantaneous signal to noise (SNR) is harnessed to develope an approximate expression for the SER in the proposed STTS framework. Interestingly, it is discovered that the diversity order is one less than the amount of receiver antennas utilized in this innovative scheme, highlighting an intricate relationship iv between these parameters. Furthermore, an exhaustive examination of the effects of pilot sequence length on the overall execution of the proposed transmission scheme is done, delving into the nuances of communication theory concepts such as the probaibility density function (PDF) and cumulcative distribution function (CDF). Throughrigorous simulations, PDF and CDF plots across various degrees of freedom and system configurations are obtained. The findings reveal that as the degree of freedom improves, the suitably normalized sum of the channel information exhibits a tendency to converge towards a normal distribution within the STTS framework. The architecture of our transmitter is notably sophisticated, featuring a substantial array of antennas specifically designed to accommodate multiple users. Each user is assigned a distinct group of antennas, and the transmitter adeptly employs tailored beamforming vectors for every user group to broadcast signals. On the receiving end, unique combining vectors are implemented for accurate signal detection, ensuring optimal performance. To effectively nullify interference from the signals of unintended users, our proposed scheme capitalizes on the concept of a null space, an advanced technique that enhances signal clarity and reliability. The computation of the combining vector is anchored in the maximum eigenvalue criterion, an established method that augments the scheme’s effectiveness. The extensive simulations and analyses unequivocally demonstrate that the proposed STTS exhibits significant improvements in performance when a higher amount of antennas are implemented at either the transmitter (Tx) or the user end, aptly showcasing the potential of this advanced transmission scheme in modern communication systems. The STTS, combined with DNN, play a crucial role in estimating communication channels in OFDM systems. This approach allows for more accurate and efficient channel estimation, which is essential for improving signal quality and minimizing interference in wireless communications. By leveraging the powerful pattern recognition capabilities of DNNs, the STTC can enhance the performance of OFDM systems, ensuring reliable data transmission even in challenging environments. Arti, M.K. and Dimri, Pradeep Kumar

2025-06-01T00:00:00Z Thusoo, Ritika http://localhost:8080/xmlui/handle/123456789/3411 2025-05-31T09:49:09Z 2023-09-01T00:00:00Z

Analysis and control of unmanned aerial vehicle for path planning using IoT Thusoo, Ritika Jain, Sheilza and Kalra, Sakshi

2023-09-01T00:00:00Z