1,720,965 research outputs found
Modelling and Simulation of Fuel Cell Based DC Microgrid
No full textME, EIEDIn the modern power scenario the deployment of distributed energy resources are gradually
increasing day by day to find out the most suitable alternatives from the several choices of DERs. The natural uncertainty is inevitable in renewable energy resources such as solar and wind energy based systems. In this perspective, the Fuel Cell technology is considered as one of the promising DER which is almost free from the effect of climatic conditions. In this context, this dissertation presents a detailed literature survey on fuel cell technology and its
promising applications in microgrid. This research proposes a model of Fuel Cell based DC microgrid supplying resistive loads and rotational machines such as separately excited DC motor using Matlab/Simulink software. This proposed model has the feature of critical and non-critical load selectivity which is an important property of microgrid. In addition, this work shows the scopes of future research on Fuel Cell based microgrid aiming maximum utilization of this technology as a part of solution to energy crisis in the near future
Comparison between Perturb & Observe Method and Incremental Conductance Method for Maximum Power Point Tracking of PV Module
No full textDue to the mounting demand of electricity, inadequate reserve of fossil fuel and rising prices
of conventional sources, photovoltaic (PV) energy becomes a promising substitute. It is a
prevalent means of producing clean and renewable power and due to this reason, the demand
of PV generation systems increases and there is a need to extract maximum power from them.
So there is need of maximum power point tracking (MPPT) in a PV system. It is a technique
that can operate solar PV systems in such a way that they produce maximum power that can
be generated. MPPT tracking system works based on a tracking algorithm which is provided
through a control system. In this dissertation, a comparison is made between perturb and
observe MPPT method and incremental conductance MPPT method to make clear
understanding about their behavior for tracking maximum power point (MPP) of PV module
under constant and variable irradiation. For this purpose, a simple and accurate model of
photovoltaic module is proposed and simulated. The output of the proposed module is
connected with boost converter whose switching is controlled by the above mention MPPT
techniques to ensure the satisfactory operation of PV module at MPP
Fault Detection and Identification for DC Microgrid with Wavelet-Based Artificial Neural Networks
No full textThe widespread implementation of DC microgrids (DCMGs) is a significant step
toward future power systems' ability to match load requirements with distributed generation
precisely. DC microgrid has become a viable alternative for increasing DC applications and
load needs compared to AC microgrid. However, the DC microgrid system's thriving
potential is hampered by the significant challenges associated with its protection. The
challenges arise due to the time constraints imposed by rapidly increasing fault currents in
DC systems, an absence of frequency and phasor information, and the absence of a natural
zero crossing of DC fault current. Furthermore, altering DC microgrid topologies impacts the
existing protection mechanisms significantly. As a result, for the DC microgrid to operate
adequately, an intelligent protection strategy is required.
This work presents intelligent fault detection and identification approach for DC
microgrids based on wavelet transform (WT) and artificial neural networks (ANNs). In this
work, firstly, the wavelet transform is applied for pre-processing the current signals to
determine the detailed wavelet coefficients. Then, the maximum value among the detailed
coefficients, which provides critical information during fault events, is used to construct the
input feature vector. After extracting the fault characteristics, the data set, consisting of input
feature vector along with output tags, is utilized for training an Artificial Neural Network
(ANN) model to identify and categorize faults in DCMGs. For data collection, the study
simulates diverse fault types, fault locations, fault resistance, and fault incident time and nofault
(load variation) scenarios under both grid-parallel and off-grid operating modes.
MATLAB/Simulink software has been used to run the simulations on a PV-based DC
microgrid. The proposed scheme's test analysis using ANN verifies the scheme's reliability
and efficiency in providing potential DC microgrid protection
Simulation of a PV based microgrid assisted by fuel cell in Indian scenario
No full textME, EIEDThe first part of dissertation begins with introducing the concept of microgrid based on renewable energy resources, its relevance in Indian context with respect to application areas, benefits and challenges faced while building any microgrid plan. There is also asmall brief regarding Govt. of India’s movement towards smartgrid initiatives andrelated programs which are in pipeline.
In the next part of the thesis, we move towards modelingand simulation of a proposed photovoltaic based microgrid modelassisted by fuel cell and battery to secure the load demands of Thapar University hostel building. The simulated model achieves effective synchronization of the renewable energy source with grid and is able to study the exchange of bi-directional power flow with the grid, throughout 24 hours of a given sample day. This model is robust in nature considering the fact that the proposed systemwas simulated with dynamically varying input irradiance level and temperature throughout the day based on real ground data of the site.
The modeled system is able to show satisfactory behavior while operating in grid connected as well as islanded mode and is able to meet the load demands of hostel. The simulation results are found satisfactory at this preliminary level of planning and design and this approach would be useful as a base to simulate and study various aspects of microgrid operation prior to actual future on-site installation.Electrical and Instrumentation Engineering Department, Thapar Universit
Modelling and Optimization Of Biomass Based System In Microgrid
No full textThe handling of biomass like MSW and rice straw becomes a challenge in the present time due to
various economic and environmental challenges. Finding an economical and environmentally
sustainable MSW and rice straw processing system is necessary. For hybrid power generation systems,
biomass can act as a balancing system to wind and solar resources due to its different properties related
to reliability. The utilization of MSW and rice straw in a microgrid system will open new doors for its
sustainable processing and energy generation. This study proposes modelling and simulation of MSW
and rice straw-based microgrids along with their economic and environmental evaluation. The
technologies are selected after a comparative analysis of various technologies in economic and
environmental terms for a particular type of biomass (MSW and rice straw).
The characteristic assessment and comparative analysis of various municipal solid waste (MSW) and
rice straw processing technologies are proposed to fulfill this aim. In the case of MSW, landfilling is
taken as the common way to handle MSW. In contrast, the technologies such as anaerobic digestion
(AD), compost, refuses derived fuel (RDF), incineration, and gasification are selected as the alternate
systems to process MSW. Along with the electricity generation technologies (AD, Gasification, and
incineration), other technologies such as RDF and composting are selected to compare and find the most
sustainable means to process the MSW. Considering the virtual operation of the existing plants, the
techno-economic and environmental parameters of all the selected technologies are assessed in the
Indian scenario. For evaluation of each system, the industrial ecology-based symbiotic system approach
along with Life cycle assesment (LCA) is applied to Indian MSW taking a population size of one
million. All the selected technologies are evaluated in detail at each step of the whole process and
compared with each other in economic and environmental terms. In a similar approach, the characteristic
assessment and comparative analysis of various rice straw processing technologies are also proposed.
The same methodology is applied to evaluate rice straw processing techniques using incineration,
gasification, AD, fermentation, and integrated operation of fermentation and AD.
Then selecting the best techniques, modelling, and simulation of MSW and rice straw-based microgrids
are proposed. In case of MSW the proposed microgrid is capable of processing the urban waste of a
small city of 0.1 million population and fulfilling the electricity demand of a nearby village having 225
houses. The proposed microgrid consists of MSW processing techniques: AD (processes a wet portion
of MSW) and gasification (processes a dry portion of MSW) that are further integrated with solar,
battery, and the main grid. After selecting the gasification technique, modelling and simulation of a
small-scale rice straw-based microgrid is proposed. This microgrid is capable to process the surplus rice
straw of a village having 250 houses and 900 acres of cultivated land. The system processes 1128
tonne/year of surplus rice straw and meets the load demand of the same village. The gasification
technique that processes rice straw to produce electricity is further integrated with solar, battery, and the
utility grid. The modelling and simulation of both the systems are done using Matlab software. The size
of the microgrids is optimized using an artificial bee colony (ABC) algorithm. The effectiveness of the
adopted technique is verified by comparing the results with the particle swarm optimization (PSO)
algorithm. The economic evaluation of the proposed systems is done based on net present cost (NPC),
annualized system cost (ASC), and the levelized cost of electricity (LCOE). The landfill avoided cost is
also calculated along with the waste processing cost. The environmental impact of the proposed
microgrids is also evaluated using the LCA methodology
Design of Adaptive Protection Schemes for Microgrids
No full textWith the integration of small energy sources that can feed loads independently constitutes a microgrid. If those small energy sources or distributed generators (DGs) are of different nature like photovoltaic or wind or any other types of distributed energy source; then that microgrid is termed as hybrid microgrid. After the integration of DGs into the existing system, the conventional protection schemes may fail to provide reliable operation. The dynamic behavior of microgrid system under faulty conditions makes adaptive protection a general necessity for reliable microgrid operation. In design of adaptive protection, the grid-connected and islanded modes have immense importance including grid-connected mode without DGs in microgrid. In this thesis, a new adaptive protection scheme is proposed based on the above-mentioned modes of microgrid operation. The proposed method considers nature of DGs connected, fault location detection and fault nature identification based on quadrature and zero sequence components of fault current considering impact of X/R ratio of DGs. The proposed methodologies for adaptive protection schemes are verified in Matlab-Simulink environment and the results are found to be satisfactory while various faults are simulated at different nodes of the microgrid model. At the time of verification of effectiveness of the proposed methodologies, the time derivative of quadrature and zero-axis components of fault current are considered sufficient to instantaneously detect the fault location and fault nature in microgrid system. Types I, III and IV wind distributed generators have a different and wide range of current sharing capacity during the fault occurrence. For the design of the proper protection scheme in a hybrid microgrid, it becomes important to study different wind distributed generators. In a hybrid microgrid consisting of single and doubly-fed induction generators and photovoltaic distributed generators, the fault current in a feeder shows different behavior which changes as per the type of distributed generators and grid/islanded connection of microgrid operation. Based on the type of wind and photovoltaic distributed generators, a provision of a new adaptive protection scheme should be the primary concern for updating the relay settings as per the change like distributed generators, a distance of the fault from the point of common coupling and nature of the loads in the microgrid system. The q0 components of fault current are used for detecting the low X/R ratio of distributed generators, modes of operation, transient reactance during the series and shunt faults in a hybrid microgrid. The novel contribution in this part of research work is the implementation of a fuzzy-based adaptive protection scheme through analysis of the q0 components. In addition, the relay current shared by different distributed generators is derived for the q0 components in
terms of the transient's component is another contribution. Considering q0 components and
transient reactance, a new relation between the relay current settings and modes of operation has
been identified for adaptive relaying. The effectiveness of the proposed adaptive protection scheme
for the hybrid microgrid is verified through a simulation case study using Matlab–Simulink
software.
In a Hybrid microgrid, the overcurrent relays sense the changes in the fault currents while the
microgrid switches from the grid-connected to islanded mode of operation. Further, for the
different types of distributed generator, such as PV, Wind turbines of types I, III, and IV; the
variation in fault currents are detected by the relays. This leads to delays and inappropriate
coordination in conventional protection schemes. In this thesis, an adaptive protection scheme with optimal settings is proposed for phase and earth fault detection. It also takes care of different nature of distributed generators (DGs), all feasible operating modes of hybrid microgrid with only q component of fault current while zero component is used to differentiate between earth and phase faults. Also, a new strategy is proposed that optimizes the coordination time of fuses as a backup to primary and backup relays with new coordination time interval constraints. A differential evolutionary algorithm is proposed for determination of optimal settings for the directional overcurrent relays
Development of Ester Oil Based Nanofluid as Liquid Insulation for Power Transformers
No full textThe conventional mineral oil is the most widely used insulating oil in transformers. The reason for its wide usage is its easy availability and good insulating and cooling properties. However, with these superior qualities, the usage of mineral oil poses some serious threats like the depletion of its resources, its non bio-degradibilty and high inflammability. Due to these issues, there has been a continuous increase in research for finding suitable alternative insulating fluids which can possess comparable insulating and cooling properties and can also eliminate the risk of threats posed by the usage of mineral oil. Vegetable oils are gaining popularity nowadays as alternatives to conventional mineral oil for transformers. Also known as natural ester oils, the dielectric properties of these oils are very much comparable to the mineral oil and in many cases better than the conventional mineral oil. For example, the dielectric strength of vegetable oils is generally better than the mineral oil. However, in a few qualities like total acidity and oxidation stability, these vegetable oils lack behind the conventional mineral oil. The latest trend in research these days is to further enhance these qualities of vegetable oils by dispersing suitable nanomaterials in them in order to enable the researchers to synthesize 3rd generation insulating fluids for transformers and to increase the transformer voltage ratings.
In the field of nanomaterials, a recent in-trend nanomaterial, i.e., graphene oxide, has created a boom in research. Graphene oxide is constantly emerging as an attractive alternative to various other materials due to its low cost and large-scale production. Graphene oxide is currently becoming a basis for exploring innovative opportunities in the fields of emerging trends of research. While being extensively researched in the fields of medicine, chemistry and physics, the role of graphene oxide in insulation systems still remains unexplored. Moreover, till date, almost all of the research in the field of developing 3rd generation insulating fluids is based on dispersing conducting and /or semi-conducting nanomaterials in either mineral oil or in vegetable oil and to study the dielectric properties of synthesized nanofluids. The 3rd generation transformer oils refer to nanoparticles dispersed insulating fluids which have improved dielectric as well as cooling properties. However, for this research, a natural ester oil based blend having vegetable oil as a primary oil and mineral oil as a secondary oil has been optimized on the basis of its dielectric strength and the optimized blend has been chosen as the base fluid for dispersing the nanomaterials. The selected oil blend has been dispersed with three different types of nanomaterials on the basis of their conductivity – non-conductive graphene oxide (GO) nanoparticles, semi-conductive titanium di-oxide (TiO2) nanoparticles and conductive zinc oxide (ZnO) nanoparticles. The main focus is kept on the graphene oxide dispersed nanofluid and its dielectric as well as physio-thermal properties are extensively studied and compared with TiO2 and ZnO dispersed nanofluids. The effects of these nanofluids on transformer solid insulation before and after subjecting the liquid-solid insulation to extreme thermal stress conditions are also studied. The insulation design of transformers is analyzed when proposed to be filled with the synthesized nanofluids and the proposed changes in transformer design have been formulated.
Results indicate that dispersion of nanomaterials results in an overall enhancement in the dielectric properties of nanofluids. The results have shown that with the addition of GO nanoparticles, the breakdown voltage of nanofluids increases up to 42% as compared to pure mineral oil, 17% as compared to pure ester oil and 15% as compared to the base oil-blend. The breakdown voltage of TiO2 dispersed nanofluid is reported to be about 30% greater than pure mineral oil, 24% greater than pure ester oil, and 17% greater than the oil-blend. Likewise, the breakdown voltage of ZnO dispersed nanofluid is observed to be around 28% greater than pure mineral oil, 19% greater than pure ester oil, and 16% greater than the oil-blend. The other dielectric properties like relative permittivity as well as the dissipation factor also improve significantly. In terms of physio-thermal properties, the viscosities of GO and TiO2 dispersed nanofluids show no significant change whereas the viscosity of ZnO dispersed nanofluid keeps on increasing with the increase in concentration level, thus making it a ‘bad’ coolant. Results also show that at peak values, the ac breakdown voltage of nanofluids decreases by just 2.6% after ageing when compared to the base oil for which the ac breakdown voltage decreases by 8.4% after ageing. Similarly, at peak values, the impulse breakdown voltage of nanofluid decreases by just 2.9% after ageing as compared to the base oil for which the impulse breakdown voltage decreases by 7.3%. The dispersion of GO also leads to the possibility of reduction in core size by 14.3% when compared to the base oil. Thus, the developed nanofluids can be used in transformers with successful operation
Frequency Control of Microgrid under Islanded Condition Using Fuzzy-PI based Controller
No full textMaster of Engineering (Power Electronics and Drives)With increasing power demand, the pressure on conventional sources of energy has increased
leading to depletion of fossil fuels. Burning of fossil fuels is one major factor that contributes
to pollution and global warming. All these issues related with conventional sources of energy
has led to a new concept of power generation which employs the use of renewable energy
resources (RESs). The use of RES’s lead to issue of power fluctuation in the system which
further causes frequency deviations. The load frequency changes abnormally, which is fuzzy
in nature, due to low system inertia and unpredictable variation in wind and solar irradiance
level. The frequency fluctuation caused on account of integration of RESs is a well
recognized phenomenon. To control these fluctuations storage units can be added in the
system. Integration of storage units in the system also increases the reliability of the system
by ensuring continuous supply of power. However, apart from integrating storage units in the
system a proper control scheme can be developed for frequency control of the microgrid. In
this work a fuzzy-PI based control scheme is proposed, which automatically updates the
control parameters on occurrence of any disturbance in the system. A microgrid under
islanded condition consisting of photovoltaic and wind generator units along with diesel
generator set and storage units is considered. Further, performance of the proposed fuzzy-PI
controller is verified with that of an autotuned PI controller, which is readily available in
MATLAB/simulink library to get faster response. The steady state response is found
minimum in case of autotuned PI controller as compared to fuzzy-PI controller.The proposed
fuzzy-PI controller is validated based on ITAE (4-7%) which is higher than that attained from
autotuned-PI controller. The control schemes are validated for single area hybrid microgrid
and two area microgrid system. The developed model is simulated in MATLAB/Simulink
environment.Electrical and Instrumentation Engineering Department, Thapar Universit
Forecasting Of Renewable Energy and Load Using Sliding Window and Neural Network Approach for Microgrid
Get PDFThis dissertation presents the study of application of Sliding Window Approach for forecasting. The past data can be utilized for predicting the future data. The data from 18th to 31st January and from 25th to 31st of January have been considered to forecast the data on 1st Feb. The current year’s variation throughout the week is being matched with that of the previous year by using the mean of Sliding Window Approach and the best window is selected for forecasting. The selected window and the current year’s weekly variations are used for the purpose of forecasting. The first objective of the work is to study the application of Sliding Window Approach for forecasting and the second objective is to propose a Sliding Window based algorithm for forecasting of data of Patiala in India using Matlab. The third objective is to compare the method of forecasting. The result for both the methods is compared and it is found satisfactory
Power Quality Enhancement In PV Based AC Microgrid Using ANN Controller
No full textSolar power is considered a very promising source for electric power generation. The
increased penetration of PV based microgrid in distributed feeder system leads the power
quality issues, especially under islanded condition. So there is need for efficient controlling
scheme which can efficiently regulate the output of the system along with it improves the
power quality. In this dissertation, artificial neural network is proposed as the inverter control
system in PV based microgrid as an alternative to standard PI inverter control scheme. PV
microgrid is connected at optimally position in 13 IEEE node feeder system. The controller
model has nine numbers of input signals and generates twelve pulse output signals for
inverter circuit. Artificial neural network is most suitable for nonlinear and complex system
and its response is better than other controlling scheme. Due to this feature, ANN is proposed
as inverter control scheme in system and it improves the performance and efficiency of the
inverter and enhances the power quality. The proposed ANN trains feedforward network
using Levenberg-Marquardt algorithm. The proposed system is verified through MATLAB
simulink and the results are compared between the PI and neural network based controllers.
The total harmonic distortion, voltage and phase angle variations are monitored and
maintained within satisfactory range for the enhancement of microgrid power quality during
grid-connected and islanded modes of operation.Thapar Institute of Engineering and Technology, Patiala, Punjab, Indi
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