Matching Items (11)
Filtering by
- All Subjects: Electrical Engineering
- Genre: Masters Thesis
Description
Nowadays, the widespread introduction of distributed generators (DGs) brings great challenges to the design, planning, and reliable operation of the power system. Therefore, assessing the capability of a distribution network to accommodate renewable power generations is urgent and necessary. In this respect, the concept of hosting capacity (HC) is generally accepted by engineers to evaluate the reliability and sustainability of the system with high penetration of DGs. For HC calculation, existing research provides simulation-based methods which are not able to find global optimal. Others use OPF (optimal power flow) based methods where
too many constraints prevent them from obtaining the solution exactly. They also can not get global optimal solution. Due to this situation, I proposed a new methodology to overcome the shortcomings. First, I start with an optimization problem formulation and provide a flexible objective function to satisfy different requirements. Power flow equations are the basic rule and I transfer them from the commonly used polar coordinate to the rectangular coordinate. Due to the operation criteria, several constraints are
incrementally added. I aim to preserve convexity as much as possible so that I can obtain optimal solution. Second, I provide the geometric view of the convex problem model. The process to find global optimal can be visualized clearly. Then, I implement segmental optimization tool to speed up the computation. A large network is able to be divided into segments and calculated in parallel computing where the results stay the same. Finally, the robustness of my methodology is demonstrated by doing extensive simulations regarding IEEE distribution networks (e.g. 8-bus, 16-bus, 32-bus, 64-bus, 128-bus). Thus, it shows that the proposed method is verified to calculate accurate hosting capacity and ensure to get global optimal solution.
too many constraints prevent them from obtaining the solution exactly. They also can not get global optimal solution. Due to this situation, I proposed a new methodology to overcome the shortcomings. First, I start with an optimization problem formulation and provide a flexible objective function to satisfy different requirements. Power flow equations are the basic rule and I transfer them from the commonly used polar coordinate to the rectangular coordinate. Due to the operation criteria, several constraints are
incrementally added. I aim to preserve convexity as much as possible so that I can obtain optimal solution. Second, I provide the geometric view of the convex problem model. The process to find global optimal can be visualized clearly. Then, I implement segmental optimization tool to speed up the computation. A large network is able to be divided into segments and calculated in parallel computing where the results stay the same. Finally, the robustness of my methodology is demonstrated by doing extensive simulations regarding IEEE distribution networks (e.g. 8-bus, 16-bus, 32-bus, 64-bus, 128-bus). Thus, it shows that the proposed method is verified to calculate accurate hosting capacity and ensure to get global optimal solution.
ContributorsYuan, Jingyi (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Khorsand, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2018
Description
The concept of the microgrid is widely studied and explored in both academic and industrial societies. The microgrid is a power system with distributed generations and loads, which is intentionally planned and can be disconnected from the main utility grid. Nowadays, various distributed power generations (wind resource, photovoltaic resource, etc.) are emerging to be significant power sources of the microgrid.
This thesis focuses on the system structure of Photovoltaics (PV)-dominated microgrid, precisely modeling and stability analysis of the specific system. The grid-connected mode microgrid is considered, and system control objectives are: PV panel is working at the maximum power point (MPP), the DC link voltage is regulated at a desired value, and the grid side current is also controlled in phase with grid voltage. To simulate the real circuits of the whole system with high fidelity instead of doing real experiments, PLECS software is applied to construct the detailed model in chapter 2. Meanwhile, a Simulink mathematical model of the microgrid system is developed in chapter 3 for faster simulation and energy management analysis. Simulation results of both the PLECS model and Simulink model are matched with the expectations. Next chapter talks about state space models of different power stages for stability analysis utilization. Finally, the large signal stability analysis of a grid-connected inverter, which is based on cascaded control of both DC link voltage and grid side current is discussed. The large signal stability analysis presented in this thesis is mainly focused on the impact of the inductor and capacitor capacity and the controller parameters on the DC link stability region. A dynamic model with the cascaded control logic is proposed. One Lyapunov large-signal stability analysis tool is applied to derive the domain of attraction, which is the asymptotic stability region. Results show that both the DC side capacitor and the inductor of grid side filter can significantly influence the stability region of the DC link voltage. PLECS simulation models developed for the microgrid system are applied to verify the stability regions estimated from the Lyapunov large signal analysis method.
This thesis focuses on the system structure of Photovoltaics (PV)-dominated microgrid, precisely modeling and stability analysis of the specific system. The grid-connected mode microgrid is considered, and system control objectives are: PV panel is working at the maximum power point (MPP), the DC link voltage is regulated at a desired value, and the grid side current is also controlled in phase with grid voltage. To simulate the real circuits of the whole system with high fidelity instead of doing real experiments, PLECS software is applied to construct the detailed model in chapter 2. Meanwhile, a Simulink mathematical model of the microgrid system is developed in chapter 3 for faster simulation and energy management analysis. Simulation results of both the PLECS model and Simulink model are matched with the expectations. Next chapter talks about state space models of different power stages for stability analysis utilization. Finally, the large signal stability analysis of a grid-connected inverter, which is based on cascaded control of both DC link voltage and grid side current is discussed. The large signal stability analysis presented in this thesis is mainly focused on the impact of the inductor and capacitor capacity and the controller parameters on the DC link stability region. A dynamic model with the cascaded control logic is proposed. One Lyapunov large-signal stability analysis tool is applied to derive the domain of attraction, which is the asymptotic stability region. Results show that both the DC side capacitor and the inductor of grid side filter can significantly influence the stability region of the DC link voltage. PLECS simulation models developed for the microgrid system are applied to verify the stability regions estimated from the Lyapunov large signal analysis method.
ContributorsXu, Hongru (Author) / Chen, Yan (Thesis advisor) / Johnson, Nathan (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
The most important metrics considered for electric vehicles are power density, efficiency, and reliability of the powertrain modules. The powertrain comprises of an Electric Machine (EM), power electronic converters, an Energy Management System (EMS), and an Energy Storage System (ESS). The power electronic converters are used to couple the motor with the battery stack. Including a DC/DC converter in the powertrain module is favored as it adds an additional degree of freedom to achieve flexibility in optimizing the battery module and inverter independently. However, it is essential that the converter is rated for high peak power and can maintain high efficiency while operating over a wide range of load conditions to not compromise on system efficiency. Additionally, the converter must strictly adhere to all automotive standards.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
ContributorsMullangi Chenchu, Hemanth (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Switching surges are a common type of phenomenon that occur on any sort of power system network. These are more pronounced on long transmission lines and in high voltage substations. The problem with switching surges is encountered when a lot of power is transmitted across a transmission line
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
ContributorsShaikh, Mohammed Mubashir (Author) / Qin, Jiangchao (Thesis advisor) / Heydt, Gerald T (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
The following report details the motivation, design, analysis, simulation and hardware implementation of a DC/DC converter in EV drivetrain architectures. The primary objective of the project was to improve overall system efficiency in an EV drivetrain. The methodology employed to this end required a variable or flexible DC-Link voltage at the input of the inverter stage. Amongst the several advantages associated with such a system are the independent optimization of the battery stack and the inverter over a wide range of motor operating conditions. The incorporation of a DC/DC converter into the drivetrain helps lower system losses but since it is an additional component, a number of considerations need to be made during its design. These include stringent requirements on power density, converter efficiency and reliability.
These targets for the converter are met through a number of different ways. The switches used are Silicon Carbide FETs. These are wide band gap (WBG) devices that can operate at high frequencies and temperatures. Since they allow for high frequency operation, a switching frequency of 250 khz is proposed and implemented. This helps with power density by reducing the size of passive components. High efficiencies are made possible by using a simple soft switching technique by augmenting the DC/DC converter with an auxiliary branch to enable zero voltage transition.
The efficacy of the approach is tested through simulation and hardware implementation of two different prototypes. The Gen-I prototype was a single soft switched synchronous boost converter rated at 2.5kw. Both the motoring mode and regenerative modes of operation (Boost and Buck) were hardware tested for over 2kw and efficiency results of over 98.15% were achieved. The Gen-II prototype and the main focus of this work is an interleaved soft switched synchronous boost converter. This converter has been implemented in hardware as well and has been tested at 6.7kw and an efficiency of over 98% has been achieved in the boost mode of operation.
These targets for the converter are met through a number of different ways. The switches used are Silicon Carbide FETs. These are wide band gap (WBG) devices that can operate at high frequencies and temperatures. Since they allow for high frequency operation, a switching frequency of 250 khz is proposed and implemented. This helps with power density by reducing the size of passive components. High efficiencies are made possible by using a simple soft switching technique by augmenting the DC/DC converter with an auxiliary branch to enable zero voltage transition.
The efficacy of the approach is tested through simulation and hardware implementation of two different prototypes. The Gen-I prototype was a single soft switched synchronous boost converter rated at 2.5kw. Both the motoring mode and regenerative modes of operation (Boost and Buck) were hardware tested for over 2kw and efficiency results of over 98.15% were achieved. The Gen-II prototype and the main focus of this work is an interleaved soft switched synchronous boost converter. This converter has been implemented in hardware as well and has been tested at 6.7kw and an efficiency of over 98% has been achieved in the boost mode of operation.
ContributorsRaza, Bassam (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Switching regulator has several advantages over linear regulator, but the drawback of switching regulator is ripple voltage on output. Previously people use LDO following a buck converter and multi-phase buck converter to reduce the output voltage ripple. However, these two solutions also have obvious drawbacks and limitations.
In this thesis, a novel mixed signal adaptive ripple cancellation technique is presented. The idea is to generate an artificial ripple current with the same amplitude as inductor current ripple but opposite phase that has high linearity tracking behavior. To generate the artificial triangular current, duty cycle information and inductor current ripple amplitude information are needed. By sensing switching node SW, the duty cycle information can be obtained; by using feedback the amplitude of the artificial ripple current can be regulated. The artificial ripple current cancels out the inductor current, and results in a very low ripple output current flowing to load. In top level simulation, 19.3dB ripple rejection can be achieved.
In this thesis, a novel mixed signal adaptive ripple cancellation technique is presented. The idea is to generate an artificial ripple current with the same amplitude as inductor current ripple but opposite phase that has high linearity tracking behavior. To generate the artificial triangular current, duty cycle information and inductor current ripple amplitude information are needed. By sensing switching node SW, the duty cycle information can be obtained; by using feedback the amplitude of the artificial ripple current can be regulated. The artificial ripple current cancels out the inductor current, and results in a very low ripple output current flowing to load. In top level simulation, 19.3dB ripple rejection can be achieved.
ContributorsYang, Zhe (Author) / Bakkaloglu, Bertan (Thesis advisor) / Seo, Jae-Sun (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Prior work in literature has illustrated the benefits of using surge arrester as a way to improve the lighting performance of the substation and transmission line. Installing surge arresters would enhance the system reliability but it comes with an extra capital expenditure. This thesis provides simulation analysis to examine substation-specific applications of surge arrester as a way of determining the optimal, cost-effective placement of surge arresters. Four different surge arrester installation configurations are examined for the 500/230 kV Rudd substation which belongs to the utility, Salt River Project (SRP). The most efficient configuration is identified in this thesis. A new method “voltage-distance curve” is proposed in this work to evaluate different surge arrester installation configurations. Simulation results show that surge arresters only need to be equipped on certain location of the substation and can still ensure sufficient lightning protection.
With lower tower footing resistance, the lightning performance of the transmission line can typically be improved. However, when surge arresters are installed in the system, the footing resistance may have either negative or positive effect on the lightning performance. Different situations for both effects are studied in this thesis.
This thesis proposes a surge arrester installation strategy for the overhead transmission line lightning protection. In order to determine the most efficient surge arrester configuration of transmission line, the entire transmission line is divided into several line sections according to the footing resistance of its towers. A line section consists of the towers which have similar footing resistance. Two different designs are considered for transmission line lightning protection, they include: equip different number of surge arrester on selected phase of every tower, equip surge arresters on all phases of selected towers. By varying the number of the towers or the number of phases needs to be equipped with surge arresters, the threshold voltage for line insulator flashover is used to evaluate different surge arrester installation configurations. The way to determine the optimal surge arresters configuration for each line section is then introduced in this thesis.
With lower tower footing resistance, the lightning performance of the transmission line can typically be improved. However, when surge arresters are installed in the system, the footing resistance may have either negative or positive effect on the lightning performance. Different situations for both effects are studied in this thesis.
This thesis proposes a surge arrester installation strategy for the overhead transmission line lightning protection. In order to determine the most efficient surge arrester configuration of transmission line, the entire transmission line is divided into several line sections according to the footing resistance of its towers. A line section consists of the towers which have similar footing resistance. Two different designs are considered for transmission line lightning protection, they include: equip different number of surge arrester on selected phase of every tower, equip surge arresters on all phases of selected towers. By varying the number of the towers or the number of phases needs to be equipped with surge arresters, the threshold voltage for line insulator flashover is used to evaluate different surge arrester installation configurations. The way to determine the optimal surge arresters configuration for each line section is then introduced in this thesis.
ContributorsXia, Qianxue (Author) / Karady, George G. (Thesis advisor) / Ayyanar, Raja (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the increasing penetration of Photovoltaic inverters, there is a necessity for recent PV inverters to have smart grid support features for increased power system reliability and security. The grid support features include voltage support, active and reactive power control. These support features mean that inverters should have bidirectional power and communication capabilities. The inverter should be able to communicate with the grid utility and other inverter modules.
This thesis studies the real time simulation of smart inverters using PLECS Real Time Box. The real time simulation is performed as a Controller Hardware in the Loop (CHIL) real time simulation. In this thesis, the power stage of the smart inverter is emulated in the PLECS Real Time Box and the controller stage of the inverter is programmed in the Digital Signal Processor (DSP) connected to the real time box. The power stage emulated in the real time box and the controller implemented in the DSP form a closed loop smart inverter.
This smart inverter, with power stage and controller together, is then connected to an OPAL-RT simulator which emulates the power distribution system of the Arizona State University Poly campus. The smart inverter then sends and receives commands to supply power and support the grid. The results of the smart inverter with the PLECS Real time box and the smart inverter connected to an emulated distribution system are discussed under various conditions based on the commands received by the smart inverter.
This thesis studies the real time simulation of smart inverters using PLECS Real Time Box. The real time simulation is performed as a Controller Hardware in the Loop (CHIL) real time simulation. In this thesis, the power stage of the smart inverter is emulated in the PLECS Real Time Box and the controller stage of the inverter is programmed in the Digital Signal Processor (DSP) connected to the real time box. The power stage emulated in the real time box and the controller implemented in the DSP form a closed loop smart inverter.
This smart inverter, with power stage and controller together, is then connected to an OPAL-RT simulator which emulates the power distribution system of the Arizona State University Poly campus. The smart inverter then sends and receives commands to supply power and support the grid. The results of the smart inverter with the PLECS Real time box and the smart inverter connected to an emulated distribution system are discussed under various conditions based on the commands received by the smart inverter.
ContributorsThiagarajan, Ramanathan (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rapid advancement in the technologies related to renewable energies such
as solar, wind, fuel cell, and many more, there is a definite need for new power con
verting methods involving data-driven methodology. Having adequate information is
crucial for any innovative ideas to fructify; accordingly, moving away from traditional
methodologies is the most practical way of giving birth to new ideas. While working
on a DC-DC buck converter, the input voltages considered for running the simulations
are varied for research purposes. The critical aspect of the new data-driven method
ology is to propose a machine learning algorithm. In this design, solving for inductor
value and power switching losses, the parameters can be achieved while keeping the
input and output ratio close to the value as necessary. Thus, implementing machine
learning algorithms with the traditional design of a non-isolated buck converter deter
mines the optimal outcome for the inductor value and power loss, which is achieved
by assimilating a DC-DC converter and data-driven methodology.
The present thesis investigates the different outcomes from machine learning al
gorithms in comparison with the dynamic equations. Specifically, the DC-DC buck
converter will be focused on the thesis. In order to determine the most effective way
of keeping the system in a steady-state, different circuit buck converter with different
parameters have been performed.
At present, artificial intelligence plays a vital role in power system control and
theory. Consequently, in this thesis, the approximation error estimation has been
analyzed in a DC-DC buck converter model, with specific consideration of machine
learning algorithms tools that can help detect and calculate the difference in terms
of error. These tools, called models, are used to analyze the collected data. In the
present thesis, a focus on such models as K-nearest neighbors (K-NN), specifically
the Weighted-nearest neighbor (WKNN), is utilized for machine learning algorithm
purposes. The machine learning concept introduced in the present thesis lays down
the foundation for future research in this area so that to enable further research on
efficient ways to improve power electronic devices with reduced power switching losses
and optimal inductor values.
as solar, wind, fuel cell, and many more, there is a definite need for new power con
verting methods involving data-driven methodology. Having adequate information is
crucial for any innovative ideas to fructify; accordingly, moving away from traditional
methodologies is the most practical way of giving birth to new ideas. While working
on a DC-DC buck converter, the input voltages considered for running the simulations
are varied for research purposes. The critical aspect of the new data-driven method
ology is to propose a machine learning algorithm. In this design, solving for inductor
value and power switching losses, the parameters can be achieved while keeping the
input and output ratio close to the value as necessary. Thus, implementing machine
learning algorithms with the traditional design of a non-isolated buck converter deter
mines the optimal outcome for the inductor value and power loss, which is achieved
by assimilating a DC-DC converter and data-driven methodology.
The present thesis investigates the different outcomes from machine learning al
gorithms in comparison with the dynamic equations. Specifically, the DC-DC buck
converter will be focused on the thesis. In order to determine the most effective way
of keeping the system in a steady-state, different circuit buck converter with different
parameters have been performed.
At present, artificial intelligence plays a vital role in power system control and
theory. Consequently, in this thesis, the approximation error estimation has been
analyzed in a DC-DC buck converter model, with specific consideration of machine
learning algorithms tools that can help detect and calculate the difference in terms
of error. These tools, called models, are used to analyze the collected data. In the
present thesis, a focus on such models as K-nearest neighbors (K-NN), specifically
the Weighted-nearest neighbor (WKNN), is utilized for machine learning algorithm
purposes. The machine learning concept introduced in the present thesis lays down
the foundation for future research in this area so that to enable further research on
efficient ways to improve power electronic devices with reduced power switching losses
and optimal inductor values.
ContributorsAlsalem, Hamad (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Kozicki, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
As the world becomes more electronic, power electronics designers have continuously designed more efficient converters. However, with the rising number of nonlinear loads (i.e. electronics) attached to the grid, power quality concerns, and emerging legislation, converters that intake alternating current (AC) and output direct current (DC) known as rectifiers are increasingly implementing power factor correction (PFC) by controlling the input current. For a properly designed PFC-stage inductor, the major design goals include exceeding minimum inductance, remaining below the saturation flux density, high power density, and high efficiency. In meeting these goals, loss calculation is critical in evaluating designs. This input current from PFC circuitry leads to a DC bias through the filter inductor that makes accurate core loss estimation exceedingly difficult as most modern loss estimation techniques neglect the effects of a DC bias. This thesis explores prior loss estimation and design methods, investigates finite element analysis (FEA) design tools, and builds a magnetics test bed setup to empirically determine a magnetic core’s loss under any electrical excitation. In the end, the magnetics test bed hardware results are compared and future work needed to improve the test bed is outlined.
ContributorsMeyers, Tobin (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2019