Inverter dead-band time, which is intended to ensure safe device operation also has been observed to induce sub-harmonic oscillations. This research project investigates the emergence of sub-harmonic oscillations in the line currents of induction motor drives operated in open-loop when lightly-loaded. We aim to identify the parameters responsible for these oscillations and estimate the regions of oscillatory behaviour using analytical methods. This analytical approach enables us to identify these regions efficiently without the need for time-consuming and resource-intensive testing at various flux levels ( V/f ratios ) and fundamental frequencies. We consider the machine parameters, DC-bus voltage, switching frequency, dead-band time, fundamental frequency of operation, and moment of inertia of the machine in our analysis. Furthermore, we compare the obtained analytical results with experimental waveforms of motor currents analyzed using Fast Fourier Transform (FFT). The insights gained from this investigation help to unveil the factors and regions of sub harmonic oscillations while exploring effective mitigation strategies.

An increase in the consumption of electricity in the traction sector has also resulted in power quality issues due to the single-phase and non-linear nature of traction loads. Due to such loading on the secondary side phases of the traction transformer, negative sequence currents and harmonics are drawn from the primary side. It results in voltage imbalance at the point of common coupling (PCC) located between the grid and traction substation. There are different ways to compensate for power quality issues. They are system configuration-based solutions, power source-based solutions, and equipment-based solutions. The system configuration based focuses on modifying the design of the system like phase shifting, three-phase train implementation, etc. Power source solutions have different special connections of the traction transformer like Delta–wye, Scott, Le Blanc, and Wood Bridge types. The equipment-based classification includes implementing auxiliary devices like static VAR compensator (SVC), railway static power conditioner (RPC), active power quality conditioner (APQC), and hybrid power quality conditioner (HPQC). The study focused on understanding the power quality issues and solutions provided in the literature. Performance of the system has been improved by using RPC along with Scott traction transformer. The same has been validated through simulation.

Protection of traditional distribution systems is relatively easier as they are set up in a radial configuration with the primary source supplying power. The protection system incorporates various components like fuses, auto re-closers, overcurrent relays and circuit breakers. However, the integration of bidirectional power sources such as DGs and EVs can result in various protection challenges, like increasing short-circuit level of the system, change in power flow direction, loss of coordination within relays, protection blinding, sympathetic tripping, unsynchronised reclosing, etc. The study of the impact of EV penetration and DG integration is carried out in four different scenarios, namely Grid to Vehicle (G2V) unidirectional charging mode, G2V charging along with DG integration (G2V+DG), Vehicle to grid (V2G) bidirectional charging, and V2G charging along with DG integration (V2G+DG). The simulation study is carried out on a detailed dynamic model of a real-life distribution system of Delhi developed in DIgSILENT PowerFactory. The analysis of the obtained results facilitates the validation changes required in the existing protection system in response to changing scenarios in real-world distribution networks, such as the integration of DGs and the rise in EV adoption.

The penetration of inverter-based generation has risen exponentially due to environ-mental and economic reasons. Phasing out of synchronous generators can pose a potential threat for the grid, causing problems due to the low overload capacity, lack of inertia, etc., in inverters. Grid forming inverters have proved promising in filling these gaps, and hence, their deployment is slowly increasing across vast geographical boundaries. However, a number of such inverters spread out over large geographical regions give rise to complex control problems. Therefore, the challenge lies in the development of communication-less strategies to solve these issues. The work involves the development of advanced decentralized control strategies for grid forming inverters that improve steady state and transient responses in addition to power quality. Extensive simulations and experimental validations prove the efficacy of the proposed control strategies.

High penetration of inverter interfaced distributed generators (IIDGs) in the distribution network has evolved the concept of power islands, which can operate independently to feed the local network when grid is unavailable. The unconventional fault signatures of IIDGs adversely affects the conventional protective relaying. Also, low short circuit current contribution and control dependent fault behavior of IIDGs creates protection challenges mainly for these power islands. First, the failure of phase and sequence components based conventional fault detection schemes in presence of IIDGs is presented. Then, to address this issue, a negative sequence power-based fault detection method is proposed, which only requires local voltage and current at the relaying point, hence does not depend upon any type of communication. The scheme is validated on an islanded 18-bus CIGRE benchmark network for various fault cases at different inception angles, for various fault resistances and load variations.

Energy harvesting has emerged as a potential method for powering low-power devices like sensor nodes in wireless sensor networks and biomedical electronic implants. Currently, batteries are the main power source for such devices. Replacing or recharging these batteries located in difficult-to-access locations is time and cost intensive. Thus, using ambient energy or waste energy to recharge these batteries is seen as a reliable solution. The underlying principle is to capture and convert ambient energy to a useful form. Along with this, proper power conditioning is required to suit the load specifications. This study is based on the utilization of the piezoelectric effect for converting vibrational kinetic energy to electrical energy. A model of the piezoelectric device is developed. It is interfaced with a resonant inverter for maximum power extraction from the ambient source. Zero voltage switching is employed to reduce switching losses during inverter operation. Another DC-DC buck converter is used to supply power to the load as per its requirements.

Carbon emission and climate change was not always the matter of concern for human kinds. But in recent years or decades, ppm of carbon in the atmosphere has reached an absolute peak, which leads to global warming, climate change, extinction of creatures etc. Thus it becomes essential not to add more carbon to the atmosphere. The power sector played the most important role in emitting carbon on generating energy from fossil fuels. In the past, energy generation was planned by considering the cheapest cost and availability of sources which resulted in the overuse of available fossil resources and hence high carbon concentration. It is essential to plan the power sector so that generation should meet the energy demand and emission target. Since demand for electrical energy is growing in every region of the world, renewable energy is an alternate option that can be used to generate electricity at a very low emission factor, but at the same time, cost associated with the installation of renewable is not always economical. This paper aims to develop a model that gives the optimum energy mix for long-term future energy demand. Since planning for the long term from the base year will not give a feasible solution because of the long-year gap and variations in several parameters. Therefore, short-term planning is used to satisfy the energy demand of the next period. Hence a multi-period approach is considered to determine the optimum energy mix for every period. In this work, we have taken different scenarios of future energy demand between 2021 to 2050, different scenarios for reducing demand side emission factor, and determined the optimum path to reach our target demand. The carbon emission pinch analysis technique determines the minimum requirement of low-carbon resources. All the results obtained are verified by other linear programming techniques like the simplex method. When the emission factor is more stringent, the share of renewable becomes more critical in generating sources.

The existing scenario of conventional sources of energy getting extinct prioritizes the need to use energy from renewable resources. Due to the renewable energy is intermittent Thermal energy storage technologies have earned their importance as promising storage systems in the present and future eras. Single medium thermocline tanks have the popularity to achieve thermal stratification. The result shows that the single medium thermocline tank with a distributor reduces the thermocline thickness and increases the efficiency of the system, visualizing the fluid motion into the porous media Particle image velocimetry measurement is the best method. Investigating the turbulence of the fluid by taking free flow and pore medium with a 5 mm cubic structure glass ball can be analyzed experimentally. PIV is used to measure the fluid velocity when turbulence interacts with the wall model (surface and subsurface) thus, investigation of the turbulence experimental will give porosity and permeability of media. When the laser passes two pulses at 250 Hz after a precise time difference at the same moment, the camera will able able to capture the two images/frame (1280 X 800 pixels) of the scattered particles. Cross correlation of the two frames when they correlated at peak then it will give the velocity of particles one is (U =∆X/∆T) AND (V =∆Y/∆T).

The Indian Navy is working towards having a Green Strike Group by the end of this decade in accordance with the National Bio-Fuel Policy and the Indian Navy Environment Conservation Roadmap (INECR). Therefore it has become imperative to identify and implement clean burning fuels without compromising on India’s maritime dominance in the blue waters. The Indian Navy is presently using High Flash High Speed Diesel (HFHSD) for propulsion and power generation. Target of this project is to identify fuel blends (with bio-fuels) which may provide performance comparable to FSHSD whilst reducing emissions without undertaking any major modifications to the marine engines and associated shipboard systems. Ternary blends (FSHSD + Bio-Diesel + Bio-Ethanol) were arrived upon as the tentative solution towards countering the limitations of using standalone Bio-Diesel blends. Further, considering sustainability as one of the cornerstones for evaluation as per INECR, Used Cooking Oil (UCO) based Bio-Diesel and Bio-Ethanol (from waste food crops) are being used as the components of the targeted Ternary blend. The most suited blend will be selected based on experimental results of trials on test engines and 1-D combustion simulation. This blend will then be further assessed from the standpoint of shelf life and long term utility.

Pyrometry is a crucial technique for measuring flame temperature in various applications such as furnaces, laboratories, and reaction energetics. Two-color pyrometry is a non-contact method that uses two different wavelengths of light to determine an object's temperature. Color ratio pyrometry, a subset of two-color pyrometry, uses the intensity ratio of two colors to calculate an object's temperature. The study explores the possibility of using a smartphone camera as a pyrometer, as it measures three wavelengths (Red, Green, and Blue). Three smartphones are used and calibrated with a monochromator to obtain the camera sensor's spectral response curves. These curves are used with power measurements to plot color ratio curves like R/G against the wavelength. Wavelength vs temperature curves are later obtained for each phone using a blackbody calibration furnace. The final product is a mobile application that inputs a raw image and outputs a detailed temperature analysis of the flame. The study also explores the possibility of creating common calibration data for all smartphones and using the Raspberry Pi (RPi) and its camera module as a pyrometer instead of a smartphone.

Marine Gas Turbine (GT) has been the workhorse of propulsion and power generation onboard Indian Navy warships over the past six decades. DT59 (engine index number) GTs are being presently utilized for propulsion of P15, P15A and P15B class of ships of the Indian Navy. GTs have an intricate design which entails operators and maintainers to possess the highest level of professional competence for seamless operation. Analysis and prediction of key operational parameters and maintenance routines of these GTs is vital towards improving their utilization and reducing down time. Therefore, the target of this project is to predict the performance parameters of a DT59 gas turbine using a machine learning model. This project is the first of its kind utilizing Machine learning to optimize the output parameters and operational availability DT59 Gas Turbine engines. Archived and present-day data from engine log books is being compiled from the ships for training the model. This template can also be utilized for predicting the performance of other machinery fitted onboard Indian Navy warships by applying the concepts of machine learning. This project will also provide one of the early templates of python libraries to be imported and required codes in which only the input data in required format will have to be substituted to get the desired output. This may be considered as a major step in using machine learning to optimize the performance and maintenance routines of machinery for maximum operational availability.

Reactivity Controlled Compression Ignition (RCCI) is an attractive low temperature combustion (LTC) technology that exhibits potential for reducing nitrogen oxides (NOx), and soot emissions while simultaneously enhancing fuel conversion efficiency at certain engine operating conditions, compared to conventional diesel engines. Effective control of the maximum pressure rise rate (MPRR) is critical for the safe and efficient operation of RCCI engines at varying engine loads. This study employs a data-driven modeling (DDM) approach that utilizes a least square-support vector machines (SVM) algorithm to develop a linear parameter-varying (LPV) representation of MPRR, mid-point of combustion (CA50), and indicated mean effective pressure (IMEP) for RCCI combustion. The LPV model is integrated into a model predictive controller (MPC) to regulate CA50 and IMEP while constraining MPRR. The performance of the proposed controller is evaluated using closed-loop simulations, where a neural network model is trained, with data available in literature, to accurately predict CA50, IMEP, MPRR and cylinder pressure and temperature at the start of combustion (SOC). The trained neural network model achieved high prediction accuracy with R2 values greater than 0.9 for all features. The proposed DDM approach has the potential to enhance the control of RCCI combustion, leading to improved engine performance and reduced emissions.

Due to low temperature in the surrounding of crude oil transportation pipelines, wax starts to aggregate at the inner surface of the pipelines and forms a thick layer which restricts the flow of oil and at times even clogs the pipeline. The conventional processes to restart the clogged pipeline are costly and difficult to perform. PPDs (Pour point depressants) are a cheaper and better alternative for preventing this wax precipitation. To study the effect of PPDs on each crude oil and to test or experiment all the PPDs on a crude oil is difficult and time consuming. Hence to overcome this problem, a machine learning approach is implemented. This will find the general trends of PPDs and crude oils and will predict the suitable PPDs for corresponding crude oils, based on the training data. This data is collected from various research articles and used for training different supervised and unsupervised machine learning models. Hence given the composition and other data of crude oils such as the API gravity, pour point and weight percentages of asphaltene and wax present, etc, the corresponding suitable PPDs are predicted. And for testing the accuracy, the results from all the models are crosschecked.

This work presents a simulation-based study of a light duty mini truck operating on diesel fuel in the Indian context, as part of a life cycle greenhouse gas emission analysis. The study involved the design of a Mahindra Maxximo vehicle in AVL Cruise software, which was used to produce emissions characteristics over a legislated driving cycle. Initially, a conventional diesel engine vehicle was built in AVL Cruise and emission maps were generated under steady-state operating conditions. This was followed by the inclusion of maps generated on low-temperature combustion (LTC) regime data gathered from a dual-fuel reactivity-controlled compression ignition (RCCI) engine. For emission analysis, an after-treatment system was built in AVL Boost and integrated with the existing AVL Cruise model. The results of the study revealed that the LTC operation maps produced lower NOx and soot emissions and higher CO and hydrocarbon emissions compared to the conventional diesel engine maps. Additionally, the effect of after treatment system in AVL Boost was investigated. The study provides valuable insights into the potential of LTC and after-treatment systems in reducing greenhouse gas emissions from mini trucks in the Indian context. The methodology presented in this work could be extended to other vehicle types and driving cycles, contributing to the development of cleaner transportation systems.

In light of increasing environmental degradation and decline of nonrenewable energy sources over the last few decades, hydrogen fuel has been seen as a key alternative source of renewable energy. To attain the desired hydrogen economy, several materials for hydrogen storage have been developed. Magnesium hydride has become a hot spot in the research of hydrogen storage materials, due to its high theoretical hydrogen storage capacity and also high gravimetric and volumetric hydrogen capacities of ca. 7.6 wt.% and 111 g/L, respectively. However, the bottleneck to the wide applications of Magnesium hydride as a commercial source of hydrogen fuel is the slow kinetics of hydrogen de/absorption MXene, a novel 2D transition metal carbide has demonstrated the dual roles of storing hydrogen and enhancing the hydrogen evolution reactions of lightweight metal hydrides especially Magnesium Hydride. Therefore, two batches of TiC2Tx MXenes were prepared by adding 1 g of Titanium Aluminium Carbide powder to 10 mL of 40% HF-etchant and kept for stirring at room temperature for 36h and 24h. MXene and Mg nanocomposites were prepared by ball milling for 60h. This project, provides insights into MXenes, their recent applications as potential materials for storing hydrogen, and as functional additives for enhancing the hydrogen reaction of Magnesium Hydride.

PEM Electrolyzers play a cardinal role in the hydrogen economy due to their high operating current densities, fast dynamic response, and the possibility of operating at high differential pressures (cathode pressure higher than anode) to reduce the energy needed for compression of hydrogen. However, operating at high cathode pressure necessitates using a thicker electrolyte membrane to mitigate gas crossover and withstand differential pressures, which decreases the voltage efficiency of the electrolyzer cell. In this study, a PEM electrolyzer system is modeled to evaluate the overall system efficiency, which is maximized to find optimum cathode pressure and membrane thickness across a range of current densities, subject to constraints on the anodic hydrogen volume fraction (maximum 2%) to ensure safe operating condition, and on the operating potential (maximum 2.2V) to avoid catalyst degradation. From optimum cathode pressure obtained for various membrane thicknesses across a range of current densities, indicated that the Nafion 115 membrane with a thickness of 127 μm proved to be an ideal balance between ensuring safe and efficient operation across the current density range of 0.001-3 A/cm2. Through the proposed methodology, we can choose the right membrane thickness and operating pressure for a PEM electrolyzer.

The escalating transport demand over the past decade has intensified the need to replace internal combustion (IC) engines with better alternatives like fuel cells that can simultaneously provide superior performance and diminished emissions. The radiators that are used in fuel cell-based electric vehicles are at least twice the size of IC engine radiators. This leads to an increase in mass, volume, and fuel consumption. The undertaken study proposes a reduction in radiator size by storing thermal energy in phase change materials. This work presents a generalized sizing method for the recommended thermal management system, using a simple mathematical approach based on Pinch Analysis. The optimization studies show the possibility to bring down the minimum radiator area requirement in a fuel cell vehicle like the 2022 Toyota Mirai by 5.5 times by using around 13 kg of Paraffin Wax along with it. The proposed methodology is realized by analysing a few representative parameters like ambient temperature, drag area and curb mass of the vehicle, maximum allowable temperature rise in fuel cells, etc. In addition, the study also estimates the minimum required radiator areas for a few commercial fuel cell vehicles in the Indian environment.

Lithium cobalt oxide (LCO) is a well-known cathode due to its distinctive structure and superior electrochemical relationship. However, electrochemical operation at a voltage of more than 4.5 V and at a high rate, a requirement of today's battery technology, LCO losses its structural stability and related battery safety. The LCO cathode has been coated with a thin layer of lithium cobalt manganese oxide (LiCo0.5Mn1.5O4, or LCMO), and synthesized using microwave-assisted heating. The as-prepared cathode structure revealed very less manganese (III) ion (Mn3+) diffusion into the LCO, which increased its lattice parameter and favored the Li-ion diffusion in the LCO cathode, enabling a faster cycling rate for longer periods. The full cell is fabricated using LCMO-coated LCO as a cathode and graphite as an anode. The n/p ratio was optimized to achieve superior full-cell performance. To demonstrate the practical relevance of the research, we developed a 2 Ah pouch cell. The as developed cathode material was used to fabricate all solid-state cell and its electrochemical characterization was carried out. The interface between Li metal and solid-state electrolyte was stabilized through lithiophilic and lithiophobic kind of gradient morphology.

Metal-air batteries propose an option for high energy density and low-cost storage. Developing a high-efficiency and durable bifunctional electrocatalyst for both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is critical for the widespread application of rechargeable zinc−air (Zn−air) batteries. Herein, we propose a controllable interface (nickel cobalt layered double hydroxide with cerium oxide) engineering concept applied on nitrogen-doped Vulcan carbon (N@C) to design a noble-metal-free bidirectional catalyst (N@C_LDH-CeO2) via two steps reproducible wet chemical route. The multifunctional hybrids interface may serve as a hydrogen acceptor and thus optimize the adsorption behaviors to break the OH*— OOH*, which can scaling-up the relationship in OER. Whereas dopant pyridinic N in N@C, creates the active sites for oxygen reduction reaction, carbon atoms next to pyridinic N are recommended to be the active sites with Lewis’s basicity at which O2 molecules are adsorbed as the initial step of the ORR. Benefiting from the advantages mentioned above of interfaces, N@C_LDH-CeO2 offers superior oxygen reduction reaction and oxygen evolution reaction catalytic activity with an ultralow polarization gap (∆E) of 0.76 V, as well as preferable durability and rapid reaction kinetics.

Natural circulation loop are gravity-based heat exchangers, driven on buoyancy, and utilized in a variety of commercial settings. The idea of passive heat exchange in NCL systems is appealing since the fluid circulation can be maintained without the assistance of an externally driven device (like a pump). The generation of time-varying non-linearities in the two-phase dynamical system, causing the flow to change from a steady-state regime to either a flow-reversal or oscillatory regime, both being not acceptable, is one potential issue with relying solely on buoyancy. This study aims to perform bifurcation analysis of a two-phase flow system using the MATCONT software. The two-phase flow system under consideration is modelled by a set of ordinary differential equations, which describe the dynamics of the system. A one-dimensional homogeneous equilibrium model is used to describe the density wave oscillation in the heated channel. In this analysis, the different types of bifurcations that can occur in the system, such as saddle-node, Hopf, and pitchfork bifurcations will be explored. We will also investigate the effect of varying system parameters on the stability of the system. The results of this analysis will provide insight into the behaviour of the two-phase flow system and help to identify regions of parameter space where the system is stable or unstable.