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- All Subjects: engineering
- Creators: Sugar, Thomas
- Member of: ASU Electronic Theses and Dissertations
Description
Transmission expansion planning (TEP) is a complex decision making process that requires comprehensive analysis to determine the time, location, and number of electric power transmission facilities that are needed in the future power grid. This dissertation investigates the topic of solving TEP problems for large power systems. The dissertation can be divided into two parts. The first part of this dissertation focuses on developing a more accurate network model for TEP study. First, a mixed-integer linear programming (MILP) based TEP model is proposed for solving multi-stage TEP problems. Compared with previous work, the proposed approach reduces the number of variables and constraints needed and improves the computational efficiency significantly. Second, the AC power flow model is applied to TEP models. Relaxations and reformulations are proposed to make the AC model based TEP problem solvable. Third, a convexified AC network model is proposed for TEP studies with reactive power and off-nominal bus voltage magnitudes included in the model. A MILP-based loss model and its relaxations are also investigated. The second part of this dissertation investigates the uncertainty modeling issues in the TEP problem. A two-stage stochastic TEP model is proposed and decomposition algorithms based on the L-shaped method and progressive hedging (PH) are developed to solve the stochastic model. Results indicate that the stochastic TEP model can give a more accurate estimation of the annual operating cost as compared to the deterministic TEP model which focuses only on the peak load.
ContributorsZhang, Hui (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Thesis advisor) / Mittelmann, Hans D (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2013
Description
In modern electric power systems, energy management systems (EMSs) are responsi-ble for monitoring and controlling the generation system and transmission networks. State estimation (SE) is a critical `must run successful' component within the EMS software. This is dictated by the high reliability requirements and need to represent the closest real time model for market operations and other critical analysis functions in the EMS. Tradi-tionally, SE is run with data obtained only from supervisory control and data acquisition (SCADA) devices and systems. However, more emphasis on improving the performance of SE drives the inclusion of phasor measurement units (PMUs) into SE input data. PMU measurements are claimed to be more accurate than conventional measurements and PMUs `time stamp' measurements accurately. These widely distributed devices meas-ure the voltage phasors directly. That is, phase information for measured voltages and currents are available. PMUs provide data time stamps to synchronize measurements. Con-sidering the relatively small number of PMUs installed in contemporary power systems in North America, performing SE with only phasor measurements is not feasible. Thus a hy-brid SE, including both SCADA and PMU measurements, is the reality for contemporary power system SE. The hybrid approach is the focus of a number of research papers. There are many practical challenges in incorporating PMUs into SE input data. The higher reporting rates of PMUs as compared with SCADA measurements is one of the salient problems. The disparity of reporting rates raises a question whether buffering the phasor measurements helps to give better estimates of the states. The research presented in this thesis addresses the design of data buffers for PMU data as used in SE applications in electric power systems. The system theoretic analysis is illustrated using an operating electric power system in the southwest part of the USA. Var-ious instances of state estimation data have been used for analysis purposes. The details of the research, results obtained and conclusions drawn are presented in this document.
ContributorsMurugesan, Veerakumar (Author) / Vittal, Vijay (Committee member) / Heydt, Gerald (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the increased penetration of solar PV, it has become considerable for the system planners and operators to recognize the impact of PV plant on the power system stability and reliable operation of grid. This enforced the development of adequate PV system models for grid planning and interconnection studies. Western Electricity Coordinating Council (WECC) Renewable Energy Modeling Task Force has developed generator/converter, electrical controller and plant controller modules to represent positive sequence solar PV plant model for grid interconnection studies. This work performs the validation of these PV plant models against the field measured data. Sheer purpose of this validation effort is to authenticate model accuracy and their capability to represent dynamics of a solar PV plant. Both steady state and dynamic models of PV plant are discussed in this work. An algorithm to fine tune and determine the electrical controller and plant controller module gains is developed. Controller gains as obtained from proposed algorithm is used in PV plant dynamic simulation model. Model is simulated for a capacitor bank switching event and simulated plant response is then compared with field measured data. Validation results demonstrate that, the proposed algorithm is performing well to determine controller gains within the region of interest. Also, it concluded that developed PV plant models are adequate enough to capture PV plant dynamics.
ContributorsSoni, Sachin (Author) / Karady, George G. (Thesis advisor) / Undrill, John (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis presents research on innovative AC transmission design concepts and focused mathematics for electric power transmission design. The focus relates to compact designs, high temperature low sag conductors, and high phase order design. The motivation of the research is to increase transmission capacity with limited right of way.
Regarding compact phase spacing, insight into the possibility of increasing the security rating of transmission lines is the primary focus through increased mutual coupling and decreased positive sequence reactance. Compact design can reduce the required corridor width to as little as 31% of traditional designs, especially with the use of inter-phase spacers. Typically transmission lines are built with conservative clearances, with difficulty obtaining right of way, more compact phase spacing may be needed. With design consideration significant compaction can produce an increase by 5-25% in the transmission line security (steady state stability) rating. In addition, other advantages and disadvantages of compact phase design are analyzed. Also, the next two topics: high temperature low sag conductors and high phase order designs include the use of compact designs.
High temperature low sag (HTLS) conductors are used to increase the thermal capacity of a transmission line up to two times the capacity compared to traditional conductors. HTLS conductors can operate continuously at 150-210oC and in emergency at 180-250oC (depending on the HTLS conductor). ACSR conductors operate continuously at 50-110oC and in emergency conditions at 110-150oC depending on the utility, line, and location. HTLS conductors have decreased sag characteristics of up to 33% compared to traditional ACSR conductors at 100oC and up to 22% at 180oC. In addition to what HTLS has to offer in terms of the thermal rating improvement, the possibility of using HTLS conductors to indirectly reduce tower height and compact the phases to increase the security limit is investigated. In addition, utilizing HTLS conductors to increase span length and decrease the number of transmission towers is investigated. The phase compaction or increased span length is accomplished by utilization of the improved physical sag characteristics of HTLS conductors.
High phase order (HPO) focuses on the ability to increase the power capacity for a given right of way. For example, a six phase line would have a thermal rating of approximately 173%, a security rating of approximately 289%, and the SIL would be approximately 300% of a double circuit three phase line with equal right of way and equal voltage line to line. In addition, this research focuses on algorithm and model development of HPO systems. A study of the impedance of HPO lines is presented. The line impedance matrices for some high phase order configurations are circulant Toeplitz matrices. Properties of circulant matrices are developed for the generalized sequence impedances of HPO lines. A method to calculate the sequence impedances utilizing unique distance parameter algorithms is presented. A novel method to design the sequence impedances to specifications is presented. Utilizing impedance matrices in circulant form, a generalized form of the sequence components transformation matrix is presented. A generalized voltage unbalance factor in discussed for HPO transmission lines. Algorithms to calculate the number of fault types and number of significant fault types for an n-phase system are presented. A discussion is presented on transposition of HPO transmission lines and a generalized fault analysis of a high phase order circuit is presented along with an HPO analysis program.
The work presented has the objective of increasing the use of rights of way for bulk power transmission through the use of innovative transmission technologies. The purpose of this dissertation is to lay down some of the building blocks and to help make the three technologies discussed practical applications in the future.
Regarding compact phase spacing, insight into the possibility of increasing the security rating of transmission lines is the primary focus through increased mutual coupling and decreased positive sequence reactance. Compact design can reduce the required corridor width to as little as 31% of traditional designs, especially with the use of inter-phase spacers. Typically transmission lines are built with conservative clearances, with difficulty obtaining right of way, more compact phase spacing may be needed. With design consideration significant compaction can produce an increase by 5-25% in the transmission line security (steady state stability) rating. In addition, other advantages and disadvantages of compact phase design are analyzed. Also, the next two topics: high temperature low sag conductors and high phase order designs include the use of compact designs.
High temperature low sag (HTLS) conductors are used to increase the thermal capacity of a transmission line up to two times the capacity compared to traditional conductors. HTLS conductors can operate continuously at 150-210oC and in emergency at 180-250oC (depending on the HTLS conductor). ACSR conductors operate continuously at 50-110oC and in emergency conditions at 110-150oC depending on the utility, line, and location. HTLS conductors have decreased sag characteristics of up to 33% compared to traditional ACSR conductors at 100oC and up to 22% at 180oC. In addition to what HTLS has to offer in terms of the thermal rating improvement, the possibility of using HTLS conductors to indirectly reduce tower height and compact the phases to increase the security limit is investigated. In addition, utilizing HTLS conductors to increase span length and decrease the number of transmission towers is investigated. The phase compaction or increased span length is accomplished by utilization of the improved physical sag characteristics of HTLS conductors.
High phase order (HPO) focuses on the ability to increase the power capacity for a given right of way. For example, a six phase line would have a thermal rating of approximately 173%, a security rating of approximately 289%, and the SIL would be approximately 300% of a double circuit three phase line with equal right of way and equal voltage line to line. In addition, this research focuses on algorithm and model development of HPO systems. A study of the impedance of HPO lines is presented. The line impedance matrices for some high phase order configurations are circulant Toeplitz matrices. Properties of circulant matrices are developed for the generalized sequence impedances of HPO lines. A method to calculate the sequence impedances utilizing unique distance parameter algorithms is presented. A novel method to design the sequence impedances to specifications is presented. Utilizing impedance matrices in circulant form, a generalized form of the sequence components transformation matrix is presented. A generalized voltage unbalance factor in discussed for HPO transmission lines. Algorithms to calculate the number of fault types and number of significant fault types for an n-phase system are presented. A discussion is presented on transposition of HPO transmission lines and a generalized fault analysis of a high phase order circuit is presented along with an HPO analysis program.
The work presented has the objective of increasing the use of rights of way for bulk power transmission through the use of innovative transmission technologies. The purpose of this dissertation is to lay down some of the building blocks and to help make the three technologies discussed practical applications in the future.
ContributorsPierre, Brian J (Author) / Heydt, Gerald (Thesis advisor) / Karady, George G. (Committee member) / Shunk, Dan (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2015
Description
The high penetration of photovoltaic (PV) both at the utility and at the distribu-tion levels, has raised concerns about the reliability of grid-tied inverters of PV power systems. Inverters are generally considered as the weak link in PV power systems. The lack of a dedicated qualification/reliability standard for PV inverters is a main barrier in realizing higher level of confidence in reliability. Development of a well-accepted design qualification standard specifically for PV inverters will help pave the way for significant improvement in reliability and performance of inverters across the entire industry. The existing standards for PV inverters such as UL 1741 and IEC 62109-1 primarily focus on safety. IEC 62093 discusses inverter qualification but it includes all the balance of sys-tem components and therefore not specific to PV inverters. There are other general stan-dards for distributed generators including the IEEE1547 series of standards which cover major concerns like utility integration but they are not dedicated to PV inverters and are not written from a design qualification point of view. In this thesis, some of the potential requirements for a design qualification standard for PV inverters are addressed. The IEC 62093 is considered as a guideline and the possible inclusions in the framework for a dedicated design qualification standard of PV inverter are discussed. The missing links in existing PV inverter related standards are identified by performing gap analysis. Dif-ferent requirements of small residential inverters compared to large utility-scale systems, and the emerging requirements on grid support features are also considered. Electric stress test is found to be the key missing link and one of the electric stress tests, the surge withstand test is studied in detail. The use of the existing standards for surge withstand test of residential scale PV inverters is investigated and a method to suitably adopt these standards is proposed. The proposed method is studied analytically and verified using simulation. A design criterion for choosing the switch ratings of the inverter that can per-form reliably under the surge environment is derived.
ContributorsAlampoondi Venkataramanan, Sai Balasubramanian (Author) / Ayyanar, Raja (Thesis advisor) / Vittal, Vijay (Committee member) / Heydt, Gerald (Committee member) / Arizona State University (Publisher)
Created2011
Description
The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The Smart Grid environment is envisioned to include distributed generation, flexible and controllable loads, bidirectional communications using smart meters and other technologies. Sensory technology may be utilized as a tool that enhances operation including operation of the distribution system. Addressing this point, a distribution system state estimation algorithm is developed in this thesis. The state estimation algorithm developed here utilizes distribution system modeling techniques to calculate a vector of state variables for a given set of measurements. Measurements include active and reactive power flows, voltage and current magnitudes, phasor voltages with magnitude and angle information. The state estimator is envisioned as a tool embedded in distribution substation computers as part of distribution management systems (DMS); the estimator acts as a supervisory layer for a number of applications including automation (DA), energy management, control and switching. The distribution system state estimator is developed in full three-phase detail, and the effect of mutual coupling and single-phase laterals and loads on the solution is calculated. The network model comprises a full three-phase admittance matrix and a subset of equations that relates measurements to system states. Network equations and variables are represented in rectangular form. Thus a linear calculation procedure may be employed. When initialized to the vector of measured quantities and approximated non-metered load values, the calculation procedure is non-iterative. This dissertation presents background information used to develop the state estimation algorithm, considerations for distribution system modeling, and the formulation of the state estimator. Estimator performance for various power system test beds is investigated. Sample applications of the estimator to Smart Grid systems are presented. Applications include monitoring, enabling demand response (DR), voltage unbalance mitigation, and enhancing voltage control. Illustrations of these applications are shown. Also, examples of enhanced reliability and restoration using a sensory based automation infrastructure are shown.
ContributorsHaughton, Daniel Andrew (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015