http://localhost:8080/xmlui/handle/123456789/384 2026-04-26T08:24:52Z 2026-04-26T08:24:52Z Monakanti, Baba Fakruddin http://localhost:8080/xmlui/handle/123456789/3491 2025-10-28T09:46:52Z 2024-01-01T00:00:00Z

Title: IMPROVED HIGH GAIN DC-DC CONVERTERS FOR MICROGRID AND ELECTRIC VEHICLE APPLICATIONS Authors: Monakanti, Baba Fakruddin Abstract: The world-wide energy consumption raised by 34% with associated carbon dioxide gas emissions increased by 15% from 35.7 billion metric tons (bmt) to projected 41 bmt in 2050. These factors of increased energy consumption and CO2 emission have attracted the alternate green energy sources with zero carbon emission regarding environmental protection concerns. The aforementioned micro sources are mostly at consumer premises and can be able to form dc microgrids. The merits of green energy micro sources are zero-emission, highly consistent, low cost and on the other hand major limitation of these sources is the low voltage at their output terminals. In order to comply with the applicability concerns power electronic-based power conditioning is required for these micro sources in terms of the low terminal dc voltage. The amplification can be done by two types of power electronic converters i.e., isolated and nonisolated dc-dc converters. In isolated converters, the voltage transformation ratio depends on the magnetic coupling i.e., with transformer or coupled inductor technologies prime focus being on the turns ratio. These topologies are often in danger if their leakage flux is not properly processed. Moreover, this leakage flux also causes voltage spikes across the switch at the high-voltage side. The aforementioned constraints demand a peculiar design of magnetic components which in turn dictates the cost, density, efficiency, and scalability of the magnetically coupled dc-dc converters. The nonisolated topologies i.e., non-magnetically coupled dc-dc converters are more in demand due to the absence of the aforementioned constraints. These nonisolated converters have inherent features such as simplicity in construction, compactness, efficiency, and low cost concerns. The primitive classic boost converter is a simple solution but it has the adverse effects of drastically decreasing efficiency at extreme duty ratios at which it has to be operated to attain high and or ultra-high voltage gains. The later evaluated versions of boost converters like passive, active, and hybrid switched inductor converters, switched capacitor topologies, and voltage lift-voltage multiplier-based converters have major demerits such as high current stress, elevated component count, and reduced efficiency. Recent elegant interleaved integrated passive-switched-inductor topologies of common grounding with split duty and iii reduction in long conducting intervals for switches are the viable alternate solutions for the aforementioned converters. The interleaved split duty-based converters possess the feature of low duty for the switches but the overall cumulative duty cycle is still high with a relatively high element count and elevated output capacitor inrush currents at the end of each switching cycle. In addition, the overall elevated duty ratios make the efficiency of the converter to be less followed by the heating concerns. This typical concern demands the evolution of dc-dc converters consisting of high boosting factors at low duty ratios, such analogous featured converters are quadratic boost dc-dc converters. This converter features a high boost factor at a low duty which in turn lowers the voltage and current stresses, and improves the efficiency. Here much attention is required for the design of the second inductor because of its high voltage excitation and high sizing requirements. The feature of high boost factor at low duty especially at lowered upper bound limit is also possible with impedance (L-C) network-based Z-Source converters that are highly volatile by having discontinuous input currents, this makes them less adoptable for majority applications. To overcome this demerit an analogous converter featuring similar voltage gain and more importantly, a series inductor at the input terminals is reported namely by a Quasi Z Source dc-dc converter, nevertheless because of the low charging interval for the inductors due to their limited upper bound on duty cycle makes the inductors to be bulky and henceforth associated packaging concerns, economical and spacing concerns will persist. In this regard, a single switched inductor modified Sheppard Taylor-based converter is proposed which surpasses the high inductor size and packaging concerns. The recent evolution of dc-dc converters is also abundantly emphasizing the bidirectional dc-dc converters (BDC) for hybrid energy source-based electric vehicle propulsion systems. Among the plethora of BDC’s quadratic boost-buck converters are popular because of their paramount amplification and attenuation consisting of a wide range of duty ratio flexibility, featuring simple topological synthesis and economical concerns towards the stated applications. Description: NITW

2024-01-01T00:00:00Z Vikram Raju, Gotte http://localhost:8080/xmlui/handle/123456789/3490 2025-10-28T09:43:29Z 2024-01-01T00:00:00Z

Title: Protection of Different Configured Transmission Lines and Frequency Control of Microgrid Using Artificial Intelligent Techniques Authors: Vikram Raju, Gotte Abstract: Protection and control are crucial to maintain the stable operation of the power systems. Growth in global electricity consumption due to urbanization and industrialization, demands enhanced generation and transmission capacities with higher order network configurations. Power transmission network plays a vital role in transmitting power to distant areas or load centres. Often, renewable energy integration into power systems is encouraged due to low carbon emissions. There is a steady growth in the amount of renewable energy generation every year. The geographically dispersed nature and intermittent generation of renewables require increased transmission capabilities to move excess energy to distant load centres. Rising power demand and renewable integration are a challenge to the power system's protection and control. In order to have improved system stability, reduced service disruptions, and enhanced power delivery efficiency, the protection of transmission lines and frequency control of the system are vital. This work focuses on artificial intelligent protection schemes for various transmission line configurations (double circuit three-phase, single circuit six-phase, and single circuit three-phase), to ensure reliable and secure power transmission and control strategy for frequency control of microgrid. The main aim of the transmission line protection scheme is to identify and isolate the fault as quickly as possible to maintain the stability of the system. The quick detection and classification of faults help the repairmen or maintenance crew to improve the service restoration time. An intelligent protection scheme is proposed based on a single fuzzy inference system and discrete Fourier transform towards the faulty phase detection and classification on the mutually coupled double circuit lines. This proposed protection technique uses the magnitude of the pre-processed current information measured at the sending end bus only. This is implemented in the MATLAB/Simulink environment on a 400 kV, 50 Hz, and 300 km double circuit transmission line model. The efficacy of the proposed scheme has been tested by performing a wide range of simulation studies concerning different types of faults viz. common short circuit faults, cross-country and evolving faults, and high impedance faults. Typical fault scenarios viz. current transformer saturation, noisy environment, and faults occurring during power swing scenarios with variations in different fault parameters and operating conditions were also studied. The results presented confirm that the proposed method detects/classifies all types of faults within one cycle time and is reliable with a detection/classification accuracy of 99.75%. It is found immune to the variations in fault vi parameters and for varying operating conditions. Also, it is not affected by the zero-sequence mutual impedance of the line and does not require any training and communication link. Furthermore, the performance is also appraised with other training-based protection schemes. The enhanced power transfer capability is possible with the six-phase transmission system but it did not gain popularity due to the lack of a proper protection scheme to secure the line for 120 types of different possible short circuit faults. This work presents a comprehensive protection scheme utilizing discrete wavelet transform (db4 mother wavelet) and artificial neural networks (ANNs). Levenberg-Marquardt algorithm is used for training the ANNs. This protection scheme requires only pre-processed current information of the sending end bus. For fault detection and classification of all 120 types of faults, a single ANN module is implemented with six inputs and six outputs. For the estimation of fault location in each phase, 11 ANN modules with six outputs are used viz. one for each of the 11 types of combination of faults. The proposed protection scheme is implemented on a six-phase Allegheny power transmission system using MATLAB/Simulink platform. The simulation results prove its efficiency and effectiveness in detecting and classifying all types of faults with varying parameters. All fault types are detected/classified within one cycle time and the detection/classification accuracy is found to be 99.76%. It is found that the performance of the fault location estimation modules is better with the training data and moderate with the testing data. The integration of renewable energy sources (RES), such as solar and wind power, into power systems presents unique challenges for transmission line protection. Traditional distance protection schemes may not be adequately sensitive or adaptable to the dynamic characteristics of RES-connected lines. To address these challenges, this work proposes an intelligent novel protection scheme that combines the fuzzy logic system for fault detection/classification with regression-based bagged ensemble learning for fault location estimation. The proposed scheme utilizes voltage signals of the bus connected to renewable energy sources processed with discrete Fourier transform (DFT) to extract relevant features for fault diagnosis. A Mamdani based fuzzy inference system is implemented to analyze the DFT-extracted features and make decisions regarding fault occurrence and type. A bagged ensemble learning approach, incorporating multiple regression trees, is employed to accurately estimate the fault location along the transmission line. The performance and efficacy of the proposed protection scheme are verified through extensive vii MATLAB/Simulink simulations on the transmission line model integrated with renewable energy sources (solar and wind). The simulations were carried out considering the variations in fault parameters with different solar irradiations and wind speeds. The results demonstrate that the scheme effectively detects and classifies various fault types in one cycle time, even under dynamic RES generation conditions. The proposed scheme achieved 99.56% accuracy in fault detection/classification confirming its reliable operation. Further, the proposed fault location estimation approach approximates the fault location within ±5% error band and the Chi-square test is performed to assess its reliability. However, apart from the protection of transmission lines, there is another equally concerned issue as much as protection i.e., frequency control of microgrid. Microgrid (MG) is a combination of diesel engine generators, renewable energy sources, loads and various energy storage systems. The low inertia of the microgrid system, stochastic loads and intermittent/discontinuous generation of renewables create complications in the frequency control of microgrid. Massive frequency deviations will cause stability and reliability problems and sometimes may lead to microgrid blackouts. A more rugged and efficient control action is needed to ameliorate the frequency stability of the microgrid. Therefore, a multi-stage PID controller whose parameters are optimized by the moth flame optimization (MFO) algorithm is proposed to control the frequency of an islanded Bella Coola microgrid. This microgrid has renewable energy sources and coordinated control of plug-in hybrid electric vehicles with diesel engine generators. Some popular meta-heuristic based PID control techniques viz. PSO-PID, TLBO-PID, and GOA-PID are also applied to assess the superior performance of the MFO algorithm. The effectiveness of the proposed control method is evaluated on the Bella Coola microgrid to obtain its dynamic response considering the simultaneous changes in renewable energy sources, load, and parametric uncertainties. The dynamic response of the microgrid is enhanced significantly which is confirmed through MATLAB/Simulink simulation results. Moreover, the proposed multi-stage PID controller is robust towards parametric uncertainties of microgrid and plug-in hybrid electric vehicles as compared to other PID controllers. The stability and comparison analysis prove that the proposed method works efficiently. Description: NITW

2024-01-01T00:00:00Z Vijay, Vutla http://localhost:8080/xmlui/handle/123456789/3489 2025-10-28T09:37:29Z 2024-01-01T00:00:00Z

Title: Investigations on the Optimal Planning of Electric Vehicle Charging Stations in coupled network using Meta Heuristic Techniques Authors: Vijay, Vutla Abstract: Growing oil prices, rapid depletion of fossil fuels, and emission of Green House Gases have compelled a shift from conventional combustion engine to Electric Vehicle (EV). It is anticipated that the share of EV is going to rise within a short time. However, driving range and charging time are issues that limit the share of EV. Providing proper charging infrastructure along the road side of urban roads and utilization of advanced technology in charging could tackle the above mentioned issues. There are many charging methods available for EVs, and one of them is DC Rapid Charging, which can quickly charge an EV battery. The performance of the distri bution system is impacted by increased power loss and voltage deviation caused by additional load brought on by EVs. Furthermore, positioning charging stations haphaz ardly throughout a power distribution network does great harm. In addition, planning of charging station considering only distribution networks is not a reliable approach. Moreover, the location of the charging station should offer convenience to the EV user in a given EVdriving range andbenefits the charging station owner. All of the aforemen tioned issues were the rationale for the thesis, which aims to plan charging stations in a coupled network involving power distribution network and road transportation network. Initially, a multi-objective approach for optimal planning of Rapid Charging Stations (RCSs) and distributed generators (DGs) was proposed. The method suggested aims to achieve reduced active power loss, EV user costs, and voltage deviation for effective RCSs and DGs planning. IEEE 33 bus power distribution network superimposed with a 25-node road transportation network was considered as the test system. Rao 3 algo rithm was applied for optimization, and the results were compared with PSO and JAYA algorithms. The number of charging connectors at charging station not only impacts the station installation cost but also waiting time. Hence, determination of optimal number of con nectors is necessary in optimal planning. Therefore, a two-stage optimal planning is proposed in this thesis to address the issues stated above. In the first stage, simultaneous optimal planning of RCSs and distributed generators is done to minimize active power loss, voltage deviation, EV user cost and to maximize voltage stability index. In the second stage, an optimal number of connectors was decided to minimize the installation cost and waiting time in queue at RCS. Here, M1/M2/C queuing model was considered to determine the waiting time. vi In addition, integration of D-STATCOMs was done along with charging station and DGs to improve the performance of distribution system through a two stage approach. In Stage 1, RCSs, DGs, and D-STATCOMs were planned optimally by improving voltage stability index, active power loss, voltage deviation, and EV user cost. RCS connectors count was identified in Stage 2 by reducing building cost and waiting time. Further, network reconfiguration was employed along with optimal planning of RCSs, DGs, and D-STATCOMs to improve the performance of the distribution system. Mini mization of active power loss, voltage deviation, EV user cost, waiting time, installation cost, and improvement of voltage stability index were considered in optimal planning. The proposed approach was tested using an IEEE 33 bus RDN coupled with transporta tion network. Daily load variation at buses and hourly charging probability of EVs were used in the analysis. The optimization problem was solved using the novel Multi Objective Rao 3 Algorithm (MORA), and the solutions validated using NSGA-II. The results demonstrate the effectiveness of the suggested strategy by MORA in determining optimal sizes and locations to benefit EV users, charging station owners, and distribution network operators Description: NITW

2024-01-01T00:00:00Z M. L., Parvathy http://localhost:8080/xmlui/handle/123456789/3458 2025-10-27T10:51:32Z 2023-01-01T00:00:00Z

Title: EFFECTIVE MODEL PREDICTIVE CURRENT CONTROL SCHEMES FOR PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE Authors: M. L., Parvathy Abstract: Out of the total electrical energy generated worldwide, 46% is consumed by electrical machines leading to the emission of 6040 megatons of carbon dioxide gas. Therefore, it is essential to use motors with higher efficiency for the conservation of energy, protection of the environment, and sustainable development. Permanent Magnet Synchronous Motor (PMSM) is favorable for industrial and transportation applications due to its compact size, higher torque to weight ratio, efficiency, and reliability. In recent years, with the progression in digital signal processing technology, an effective and advanced control strategy like Model Predictive Control (MPC) which is computationally exhaustive has received wider attention. It offers the feasibility to use multiple constraints, multiple objectives, and multiple variables while maintaining its simplicity and intuitiveness. The most prominent MPC schemes are Model Predictive Torque Control (MPTC) and Model Predictive Current Control (MPCC). The MPTC scheme has the objective function defined using the torque and flux variables. However, in MPCC, the cost function is defined using stator currents as control variables. This eliminates the problem of tedious tuning of the weighting factor. The MPCC scheme offers excellent dynamic performance through the indirect control of torque and flux variables using the stator currents. However, the main challenges observed in the MPCC scheme are larger ripples in torque and flux ripples under steady-state conditions. To overcome this, the application of two or more voltage vectors is widely researched. However, the main challenge for the real-time application of multiple voltage vector-based control schemes is the increased computational burden. Thus, the wider application of the MPC scheme is still under the radar due to its higher computational complexity. This thesis proposes control strategies to improve the steady-state performance of the MPCC-controlled PMSM drive and addresses the limitation of increased computational complexity. iii To improve the steady-state torque and flux performance of the MPCC controlled PMSM drive, a dual voltage vector concept is implemented. The cause of increased ripples in torque and flux is identified to be the application of a single voltage vector for the entire control period irrespective of the magnitude of the error between control variables. Thus, to control the magnitude of the optimum voltage vector, a null vector is added to it. The duration for which the optimum vector is applied is determined based on the deadbeat principle. The duty ratio calculation used is simple and less sensitive to parameter variations. The application of dual voltage vectors undeniably improves the performance of the drive at the expense of increased computational time. Thus, a low complex dual voltage vector application scheme is evaluated in this research, which reduces the number of voltage vectors used for prediction, cost function evaluation, and optimization to three. The voltage vector preselection does not require additional determination of sector or reference vectors. This reduces the computational time and with the application of an active and null voltage vector, the steady-state drive performance is improved. The duration of application of the active vector is determined using the rms ripple minimization technique. In certain operating conditions where the error magnitude is large, it is required to apply more than two voltage vectors in a control period. Thus, multiple voltage vector application strategies are investigated using the virtual voltage vector concept. However, with the augmentation of the control set using the virtual voltage vectors, there would be a catalyzed increase in the computational burden. To limit the increase in the computational complexity, a voltage vector preselection scheme is employed which effectively locates the optimum voltage vector that minimizes the error. The duration of application of voltage vectors is determined using the average error minimization technique. The application of two or more voltage vectors in a sample time can provide a better steady-state response with an increase in computational complexity. To address this, a simplified voltage vector selection-based MPCC is proposed which directly evaluates the optimum voltage vector without prediction, cost function evaluation, and optimization. The multiple voltage vector application is then achieved by determining the position of the first optimum voltage vector using a modified position determination approach. The proposed MPCC improves the iv steady-state performance with reduced computational complexity. The duration of active voltage vectors is directly evaluated using the cost function ratios. This reduces the parameter sensitivity of the calculation. The proposed MPCC schemes are developed using MATLAB/Simulink. The real-time implementation of the control schemes is achieved using the dSPACE 1104 control platform. The effectiveness of the control techniques is evaluated using comprehensive comparisons with the existing scheme Description: NITW

2023-01-01T00:00:00Z