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Title: Ultra-Efficient Cascaded Buck-Boost Converter.
Creator: Ashok Pise, Anirudh, Batarseh, Issa, Mikhael, Wasfy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: This thesis presents various techniques to achieve ultra-high-efficiency for Cascaded-Buck-Boost converter. A rigorous loss model with component non linearity is developed and validated experimentally. An adaptive-switching-frequency control is discussed to optimize weighted efficiency. Some soft-switching techniques are discussed. A low-profile planar-nanocrystalline inductor is developed and various design aspects of core and copper design are discussed. Finite-element-method is used to...
Show moreThis thesis presents various techniques to achieve ultra-high-efficiency for Cascaded-Buck-Boost converter. A rigorous loss model with component non linearity is developed and validated experimentally. An adaptive-switching-frequency control is discussed to optimize weighted efficiency. Some soft-switching techniques are discussed. A low-profile planar-nanocrystalline inductor is developed and various design aspects of core and copper design are discussed. Finite-element-method is used to examine and visualize the inductor design. By implementing the above, a peak efficiency of over 99.2 % is achieved with a power density of 6 kW/L and a maximum profile height of 7 mm is reported. This converter finds many applications because of its versatility: allowing bidirectional power flow and the ability to step-up or step-down voltages in either direction.
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Date Issued: 2017
Identifier: CFE0007277, ucf:52181
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007277

Title: Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty.
Creator: Zhan, Yiduo, Zheng, Qipeng, Vela, Adan, Garibay, Ivan, Sun, Wei, University of Central Florida
Abstract / Description: In this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments,...
Show moreIn this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments, and need to stay in operation for a long time and accommodate many different scenarios in the future. The uncertainties we are addressing in this dissertation mainly include demands, electricity prices, investment and maintenance costs of power generation units. To address these future uncertainties in the decision-making process, this dissertation adopts two different optimization approaches: decision-dependent stochastic programming and adaptive robust optimization. In the decision-dependent stochastic programming approach, we consider the electricity prices and generation units' investment and maintenance costs being endogenous uncertainties, and then design probability distribution functions of decision variables and input parameters based on well-established econometric theories, such as the discrete-choice theory and the economy-of-scale mechanism. In the adaptive robust optimization approach, we focus on finding the multistage adaptive robust solutions using affine policies while considering uncertain intervals of future demands.This dissertation mainly includes three research projects. The study of each project consists of two main parts, the formulation of its mathematical model and the development of solution algorithms for the model. This first problem concerns a large-scale investment problem on both thermal and wind power generation from an integrated angle without modeling all operational details. In this problem, we take a multistage decision-dependent stochastic programming approach while assuming uncertain electricity prices. We use a quasi-exact solution approach to solve this multistage stochastic nonlinear program. Numerical results show both computational efficient of the solutions approach and benefits of using our decision-dependent model over traditional stochastic programming models. The second problem concerns the long-term investment planning with detailed models of real-time operations. We also take a multistage decision-dependent stochastic programming approach to address endogenous uncertainties such as generation units' investment and maintenance costs. However, the detailed modeling of operations makes the problem a bilevel optimization problem. We then transform it to a Mathematic Program with Equilibrium Constraints (MPEC) problem. We design an efficient algorithm based on Dantzig-Wolfe decomposition to solve this multistage stochastic MPEC problem. The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems' expansion planning.
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Date Issued: 2016
Identifier: CFE0006676, ucf:51248
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006676

Title: Online Neuro-Adaptive Learning For Power System Dynamic State Estimation.
Creator: Birari, Rahul, Zhou, Qun, Sun, Wei, Dimitrovski, Aleksandar, University of Central Florida
Abstract / Description: With the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring...
Show moreWith the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring System (WAMS), it has become possible to avail rapid real-time measurements at the network nodes. These measurements can be exploited for better estimates of system dynamic states. In this research, we have proposed a novel Artificial Intelligence (AI) based real-time neuro-adaptive algorithm for rotor angle and speed estimation of synchronous generators. Generator swing equations and power flow models are incorporated in the online learning. The algorithm learns and adapts in real-time to achieve accurate estimates. Simulation is carried out on 68 bus 16 generator NETS-NYPS model. The neuro-adaptive algorithm is compared with classical Kalman Filter based DSE. Applicability and accuracy of the proposed method is demonstrated under the influence of noise and faulty conditions.
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Date Issued: 2017
Identifier: CFE0006858, ucf:51747
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006858

Title: Smart Grid Demonstration: Distributed Active and Reactive Power Control.
Creator: Vellakovil Rajamani, Siddarth, Qu, Zhihua, Simaan, Marwan, Sun, Wei, University of Central Florida
Abstract / Description: The present infrastructure of energy delivery was designed over 60 years ago with the goal to be centralized. However, it is aging and is under-utilized, which will potentially limit the world's ability to achieve its energy objective. The lack of vibrant control on the grid makes it difficult to stop cascading power failure, and to achieve high penetration of renewable energy resources, such as wind and solar thus resulting in grid instability. A decentralized and distributed control...
Show moreThe present infrastructure of energy delivery was designed over 60 years ago with the goal to be centralized. However, it is aging and is under-utilized, which will potentially limit the world's ability to achieve its energy objective. The lack of vibrant control on the grid makes it difficult to stop cascading power failure, and to achieve high penetration of renewable energy resources, such as wind and solar thus resulting in grid instability. A decentralized and distributed control mechanism implemented with a definite communication protocol solves the issues mentioned above. The electric power grid going into the future is expected to consists of distributed generators and loads. The implementation of a distributed control will benefit utility services and will create financial advantages. One of the best solutions is to organize these distributed generators (DG) in a micro-grid structure which will then connect to the main grid through the point of common coupling (PCC). A proper organization and control of the Microgrid is always a big challenge. To overcome this, using cooperative control makes it possible to bring together different agents in the networked systems as a group and realize the desired objective. The micro grid power objective is set by a virtual leader and is transferred to the other agents in the system through a local communication channel. A distributed cooperative control is formulated to effectively organize all the DGs in the Microgrid to produce the necessary active and reactive power to satisfy multiple objectives. It not only satisfies the active power flow from the main grid to a constant but also reduces the reactive power flow to the main grid. Moreover, the algorithm can be used to implement the demand response continuously using a combination of DGs and their local controllable loads. The approach is to use distributed inverters with the aid of multiple local communication channels for active power compensation of the micro-grid in real-time in a distributed and cooperative manner.
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Date Issued: 2016
Identifier: CFE0006513, ucf:51362
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006513

Title: A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation.
Creator: Hong, Jungseok, Sun, Wei, Zhou, Qun, Zheng, Qipeng, University of Central Florida
Abstract / Description: Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS...
Show moreNatural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints.The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation.
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Date Issued: 2017
Identifier: CFE0006746, ucf:51856
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006746

Title: nanoengineered energy harvesting and storage devices.
Creator: Li, Chao, Thomas, Jayan, Zhai, Lei, Yang, Yang, Gesquiere, Andre, Dong, Yajie, Sun, Wei, University of Central Florida
Abstract / Description: Organic and perovskite solar cells have recently attracted significant attention due to itsflexibility, ease of fabrication and excellent performance. In order to realize even betterperformance for organic and perovskite solar cells, rejuvenated effort towards developingnanostructured electrodes and high quality active layer is necessary.In this dissertation, several strategic directions of enhancing the performance of organicand perovskite solar cells are investigated. An introduction and...
Show moreOrganic and perovskite solar cells have recently attracted significant attention due to itsflexibility, ease of fabrication and excellent performance. In order to realize even betterperformance for organic and perovskite solar cells, rejuvenated effort towards developingnanostructured electrodes and high quality active layer is necessary.In this dissertation, several strategic directions of enhancing the performance of organicand perovskite solar cells are investigated. An introduction and background of organic andperovskite solar cells, which includes motivation, classification and working principles,nanostructured electrode materials and solvent effect on active materials, and devices fabrication,are presented. A facile method, called Spin-on Nanoprinting (SNAP), to fabricate highly orderedZnO-AgNW-ZnO electrode is introduced to enhance the performance of organic solar cell.Subsequently, a ternary solvent method is developed to fabricate high Voc thieno[3,4-b]thiophene/benzodithiophene (PTB7) and indene-C60 bisadduct (ICBA)solar cells. Theperformance of the devices improved about 20% compared to those made by binary solventmethod. In order to understand the fundamental properties of the materials ruling theperformance of the PSCs tested, AFM-based nanoscale characterization techniques includingPulsed-Force-Mode AFM (PFM-AFM) and Mode-Synthesizing AFM (MSAFM) are introduced.These methods are used to study the morphology and physical properties of the structuresconstitutive of the active layers of the PSCs. Conductive-AFM (cAFM) studies reveal localvariations in conductivity in the donor and acceptor phases as well as an increase in photocurrentmeasured in the PTB7:ICBA sample obtained with the ternary solvent processing technique.Moreover, efficient perovskite solar cells with good transparency in the visible wavelength rangehave been developed by a facile and low-temperature PCBM-assisted perovskite growth method.This method results in the formation of perovskite-PCBM hybrid material at the grain boundaries which is observed by EELS mapping and confirmed by steady-state photoluminescence (PL)spectra and transient photocurrent (TP) measurements. This method involves fewer steps andtherefore is less expensive and time consuming than other reported methods. In addition, wereport an all solid state, energy harvesting and storing (ENHANS) filament which integratesperovskite solar cell (PSC) on top of a symmetric supercapacitor (SSC) via a copper filamentwhich works as a shared electrode for direct charge transfer. Developing ENHANS on a copperfilament provides a low-cost solution for flexible self-sufficient energy systems for wearablesand other portable devices. Finally, a summary of this dissertation as well as some potentialfuture directions are presented.
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Date Issued: 2016
Identifier: CFE0006693, ucf:51912
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006693

Title: Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid.
Creator: Golshani, Amir, Sun, Wei, Qu, Zhihua, Vosoughi, Azadeh, Zhou, Qun, Zheng, Qipeng, University of Central Florida
Abstract / Description: Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This...
Show moreLarge power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
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Date Issued: 2017
Identifier: CFE0007284, ucf:52169
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007284

Title: Managing IO Resource for Co-running Data Intensive Applications in Virtual Clusters.
Creator: Huang, Dan, Wang, Jun, Zhou, Qun, Sun, Wei, Zhang, Shaojie, Wang, Liqiang, University of Central Florida
Abstract / Description: Today Big Data computer platforms employ resource management systems such as Yarn, Torque, Mesos, and Google Borg to enable sharing the physical computing among many users or applications. Given virtualization and resource management systems, users are able to launch their applications on the same node with low mutual interference and management overhead on CPU and memory. However, there are still challenges to be addressed before these systems can be fully adopted to manage the IO resources...
Show moreToday Big Data computer platforms employ resource management systems such as Yarn, Torque, Mesos, and Google Borg to enable sharing the physical computing among many users or applications. Given virtualization and resource management systems, users are able to launch their applications on the same node with low mutual interference and management overhead on CPU and memory. However, there are still challenges to be addressed before these systems can be fully adopted to manage the IO resources in Big Data File Systems (BDFS) and shared network facilities. In this study, we mainly study on three IO management problems systematically, in terms of the proportional sharing of block IO in container-based virtualization, the network IO contention in MPI-based HPC applications and the data migration overhead in HPC workflows. To improve the proportional sharing, we develop a prototype system called BDFS-Container, by containerizing BDFS at Linux block IO level. Central to BDFS-Container, we propose and design a proactive IOPS throttling based mechanism named IOPS Regulator, which improves proportional IO sharing under the BDFS IO pattern by 74.4% on an average. In the aspect of network IO resource management, we exploit using virtual switches to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Third, to solve the data migration problem in small-medium sized HPC clusters, we propose to construct a sided IO path, named as SideIO, to explicitly direct analysis data to BDFS that co-locates computation with data. By experimenting with two real-world scientific workflows, SideIO completely avoids the most expensive data movement overhead and achieves up to 3x speedups compared with current solutions.
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Date Issued: 2018
Identifier: CFE0007195, ucf:52268
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007195

Title: Advanced Control Techniques for Efficiency and Power Density Improvement of a Three-Phase Microinverter.
Creator: Tayebi, Seyed Milad, Batarseh, Issa, Mikhael, Wasfy, Sundaram, Kalpathy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: Inverters are widely used in photovoltaic (PV) based power generation systems. Most of these systems have been based on medium to high power string inverters. Microinverters are gaining popularity over their string inverter counterparts in PV based power generation systems due to maximized energy harvesting, high system reliability, modularity, and simple installation. They can be deployed on commercial buildings, residential rooftops, electric poles, etc and have a huge potential market....
Show moreInverters are widely used in photovoltaic (PV) based power generation systems. Most of these systems have been based on medium to high power string inverters. Microinverters are gaining popularity over their string inverter counterparts in PV based power generation systems due to maximized energy harvesting, high system reliability, modularity, and simple installation. They can be deployed on commercial buildings, residential rooftops, electric poles, etc and have a huge potential market. Emerging trend in power electronics is to increase power density and efficiency while reducing cost. A powerful tool to achieve these objectives is the development of an advanced control system for power electronics. In low power applications such as solar microinverters, increasing the switching frequency can reduce the size of passive components resulting in higher power density. However, switching losses and electromagnetic interference (EMI) may increase as a consequence of higher switching frequency. Soft switching techniques have been proposed to overcome these issues. This dissertation presents several innovative control techniques which are used to increase efficiency and power density while reducing cost. Dynamic dead time optimization and dual zone modulation techniques have been proposed in this dissertation to significantly improve the microinverter efficiency. In dynamic dead time optimization technique, pulse width modulation (PWM) dead times are dynamically adjusted as a function of load current to minimize MOSFET body diode conduction time which reduces power dissipation. This control method also improves total harmonic distortion (THD) of the inverter output current. To further improve the microinverter efficiency, a dual-zone modulation has been proposed which introduces one more soft-switching transition and lower inductor peak current compared to the other boundary conduction mode (BCM) modulation methods.In addition, an advanced DC link voltage control has been proposed to increase the microinverter power density. This concept minimizes the storage capacitance by allowing greater voltage ripple on the DC link. Therefore, the microinverter reliability can be significantly increased by replacing electrolytic capacitors with film capacitors. These control techniques can be readily implemented on any inverter, motor controller, or switching power amplifier. Since there is no circuit modification involved in implementation of these control techniques and can be easily added to existing controller firmware, it will be very attractive to any potential licensees.
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Date Issued: 2017
Identifier: CFE0007136, ucf:52328
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007136

Title: Analysis, Design and Efficiency Optimization of Power Converters for Renewable Energy Applications.
Creator: Chen, Xi, Batarseh, Issa, Zhou, Qun, Mikhael, Wasfy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: DC-DC power converters are widely used in renewable energy-based power generation systems due to the constant demand of high-power density and high-power conversion efficiency. DC-DC converters can be classified into non-isolated and isolated topologies. For non-isolated topologies, they are typically derived from buck, boost, buck-boost or forth order (such as Cuk, Sepic and Zeta) converters and they usually have relatively higher conversion efficiency than isolated topologies. However, with...
Show moreDC-DC power converters are widely used in renewable energy-based power generation systems due to the constant demand of high-power density and high-power conversion efficiency. DC-DC converters can be classified into non-isolated and isolated topologies. For non-isolated topologies, they are typically derived from buck, boost, buck-boost or forth order (such as Cuk, Sepic and Zeta) converters and they usually have relatively higher conversion efficiency than isolated topologies. However, with the applications where the isolation is required, either these topologies should be modified, or alternative topologies are needed. Among various isolated DC-DC converters, the LLC resonant converter is an attractive selection due to its soft switching, isolation, wide gain range, high reliability, high power density and high conversion efficiency.In low power applications, such as battery chargers and solar microinverters, increasing the switching frequency can reduce the size of passive components and reduce the current ripple and root-mean-square (RMS) current, resulting in higher power density and lower conduction loss. However, switching losses, gate driving loss and electromagnetic interference (EMI) may increase as a consequence of higher switching frequency. Therefore, switching frequency modulation, components optimization and soft switching techniques have been proposed to overcome these issues and achieve a tradeoff to reach the maximum conversion efficiency.This dissertation can be divided into two categories: the first part is focusing on the well-known non-isolated bidirectional cascaded-buck-boost converter, and the second part is concentrating on the isolated dual-input single resonant tank LLC converter. Several optimization approaches have been presented to improve the efficiency, power density and reliability of the power converters. In the first part, an adaptive switching frequency modulation technique has been proposed based on the precise loss model in this dissertation to increase the efficiency of the cascaded-buck-boost converter. In adaptive switching frequency modulation technique, the optimal switching frequency for the cascaded-buck-boost converter is adaptively selected to achieve the minimum total power loss. In addition, due to the major power losses coming from the inductor, a new low profile nanocrystalline inductor filled with copper foil has been designed to significantly reduce the core loss and winding loss. To further improve the efficiency of the cascaded-buck-boost converter, the adaptive switching frequency modulation technique has been applied on the converter with designed nanocrystalline inductor, in which the peak efficiency of the converter can break the 99% bottleneck.In the second part, a novel dual-input DC-DC converter is developed according to the LLC resonant topology. This design concept minimizes the circuit components by allowing single resonant tank to interface with multiple input sources. Based on different applications, the circuit configuration for the dual-input LLC converter will be a little different. In order to improve the efficiency of the dual-input LLC converter, the semi-active rectifiers have been used on the transformer secondary side to replace the low-side bridge diodes. In this case, higher magnetizing inductance can be selected while maintaining the same voltage gain. Besides, a burst-mode control strategy has been proposed to improve the light load and very light load efficiency of the dual- input LLC converter. This control strategy is able to be readily implemented on any power converter since it can be achieved directly through firmware and no circuit modification is needed in implementation of this strategy.
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Date Issued: 2019
Identifier: CFE0007612, ucf:52531
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007612

Title: Microgrid Control and Protection: Stability and Security.
Creator: Keshavarztalebi, Morteza, Behal, Aman, Haralambous, Michael, Sun, Wei, Jain, Amit Kumar, Kutkut, Nasser, University of Central Florida
Abstract / Description: When the microgrid disconnects from the main grid in response to, say, upstream disturbance orvoltage fluctuation and goes to islanding mode, both voltage and frequency at all locations in themicrogrid have to be regulated to nominal values in a short amount of time before the operation ofprotective relays. Motivated by this, we studied the application of intelligent pinning of distributed cooperative secondary control of distributed generators in islanded microgrid operation in a power...
Show moreWhen the microgrid disconnects from the main grid in response to, say, upstream disturbance orvoltage fluctuation and goes to islanding mode, both voltage and frequency at all locations in themicrogrid have to be regulated to nominal values in a short amount of time before the operation ofprotective relays. Motivated by this, we studied the application of intelligent pinning of distributed cooperative secondary control of distributed generators in islanded microgrid operation in a power system. In the first part, the problem of single and multi-pinning of distributed cooperative secondary control of DGs in a microgrid is formulated. It is shown that the intelligent selection of a pinning set based on the number of its connections and distance of leader DG/DGs from the rest of the network, i.e., degree of connectivity, strengthens microgrid voltage and frequency regulation performance both in transient and steady state. The proposed control strategy and algorithm are validated by simulation in MATLAB/SIMULINK using different microgrid topologies. It is shown that it is much easier to stabilize the microgrid voltage and frequency in islanding mode operationby specifically placing the pinning node on the DGs with high degrees of connectivity than byrandomly placing pinning nodes into the network. In all of these research study cases, DGs areonly required to communicate with their neighboring units which facilitates the distributed controlstrategy.Historically, the models for primary control are developed for power grids with centralized powergeneration, in which the transmission lines are assumed to be primarily inductive. However, fordistributed power generation, this assumption does not hold since the network has significant resistive impedance as well. Hence, it is of utmost importance to generalize the droop equations, i.e., primary control, to arrive at a proper model for microgrid systems. Motivated by this, we proposed the secondary adaptive voltage and frequency control of distributed generators for low and medium voltage microgrid in autonomous mode to overcome the drawback of existing classical droop based control techniques. Our proposed secondary control strategy is adaptive with line parameters and can be applied to all types of microgrids to address the simultaneous impacts of active and reactive power on the microgrids voltage and frequency. Also, since the parameters in the network model are unknown or uncertain, the second part of our research studies adaptive distributed estimation/compensation. It is shown that this is an effective method to robustly regulate the microgrid variables to their desired values.The security of power systems against malicious cyberphysical data attacks is the third topic of this dissertation. The adversary always attempts to manipulate the information structure of the power system and inject malicious data to deviate state variables while evading the existing detection techniques based on residual test. The solutions proposed in the literature are capable of immunizing the power system against false data injection but they might be too costly and physically not practical in the expansive distribution network. To this end, we define an algebraic condition for trustworthy power system to evade malicious data injection. The proposed protection scheme secures the power system by deterministically reconfiguring the information structure and corresponding residual test. More importantly, it does not require any physical effort in either microgrid or network level. The identification scheme of finding meters being attacked is proposed as well. Eventually, a well-known IEEE 30-bus system is adopted to demonstrate the effectiveness of the proposed schemes.
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Date Issued: 2016
Identifier: CFE0006338, ucf:51569
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006338

Title: Applied Advanced Error Control Coding for General Purpose Representation and Association Machine Systems.
Creator: Dai, Bowen, Wei, Lei, Lin, Mingjie, Rahnavard, Nazanin, Turgut, Damla, Sun, Qiyu, University of Central Florida
Abstract / Description: General-Purpose Representation and Association Machine (GPRAM) is proposed to be focusing on computations in terms of variation and flexibility, rather than precision and speed. GPRAM system has a vague representation and has no predefined tasks. With several important lessons learned from error control coding, neuroscience and human visual system, we investigate several types of error control codes, including Hamming code and Low-Density Parity Check (LDPC) codes, and extend them to...
Show moreGeneral-Purpose Representation and Association Machine (GPRAM) is proposed to be focusing on computations in terms of variation and flexibility, rather than precision and speed. GPRAM system has a vague representation and has no predefined tasks. With several important lessons learned from error control coding, neuroscience and human visual system, we investigate several types of error control codes, including Hamming code and Low-Density Parity Check (LDPC) codes, and extend them to different directions.While in error control codes, solely XOR logic gate is used to connect different nodes. Inspired by bio-systems and Turbo codes, we suggest and study non-linear codes with expanded operations, such as codes including AND and OR gates which raises the problem of prior-probabilities mismatching. Prior discussions about critical challenges in designing codes and iterative decoding for non-equiprobable symbols may pave the way for a more comprehensive understanding of bio-signal processing. The limitation of XOR operation in iterative decoding with non-equiprobable symbols is described and can be potentially resolved by applying quasi-XOR operation and intermediate transformation layer. Constructing codes for non-equiprobable symbols with the former approach cannot satisfyingly perform with regarding to error correction capability. Probabilistic messages for sum-product algorithm using XOR, AND, and OR operations with non-equiprobable symbols are further computed. The primary motivation for the constructing codes is to establish the GPRAM system rather than to conduct error control coding per se. The GPRAM system is fundamentally developed by applying various operations with substantial over-complete basis. This system is capable of continuously achieving better and simpler approximations for complex tasks.The approaches of decoding LDPC codes with non-equiprobable binary symbols are discussed due to the aforementioned prior-probabilities mismatching problem. The traditional Tanner graph should be modified because of the distinction of message passing to information bits and to parity check bits from check nodes. In other words, the message passing along two directions are identical in conventional Tanner graph, while the message along the forward direction and backward direction are different in our case. A method of optimizing signal constellation is described, which is able to maximize the channel mutual information.A simple Image Processing Unit (IPU) structure is proposed for GPRAM system, to which images are inputted. The IPU consists of a randomly constructed LDPC code, an iterative decoder, a switch, and scaling and decision device. The quality of input images has been severely deteriorated for the purpose of mimicking visual information variability (VIV) experienced in human visual systems. The IPU is capable of (a) reliably recognizing digits from images of which quality is extremely inadequate; (b) achieving similar hyper-acuity performance comparing to human visual system; and (c) significantly improving the recognition rate with applying randomly constructed LDPC code, which is not specifically optimized for the tasks.
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Date Issued: 2016
Identifier: CFE0006449, ucf:51413
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006449

Adaptive Control (1)	+ -
Advanced Coding (1)	+ -
Artificial Intelligence (1)	+ -
Bus voltage control (1)	+ -
Cascaded Buck Boost Converter (1)	+ -

Component Non-Linearity (1)	+ -
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