Matching Items (216)
Description
The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
ContributorsSinghal, Nikita Ghanshyam (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Committee member) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
ContributorsNATH, ANUBHAV (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2019
Description
Alternative sources of power generation interconnected at the transmission level have witnessed an increase in investment in the last few years. On the other hand, when the power systems are being operated close to their limits, power system operators and engineers face the challenge of ensuring a safe and reliable supply of electricity. In such a scenario, the reliability of the transmission system is crucial as it ensures secure transfer of uninterrupted power from the generating sources to the load centers. This thesis is aimed at ensuring the reliability of the transmission system from two perspectives. First, this work monitors power system disturbances such as unintentional islanding to ensure prompt detection and implementation of restorative actions and thus, minimizes the extent of damage. Secondly, it investigates power system disturbances such as transmission line outages through reliability evaluation and outage analysis in order to prevent reoccurrence of similar failures.
In this thesis, a passive Wide Area Measurement System (WAMS) based islanding detection scheme called Cumulative Sum of Change in Voltage Phase Angle Difference (CUSPAD) is proposed and tested on a modified 18 bus test system and a modified IEEE 118 bus system with various wind energy penetration levels. Comparative analysis between accuracies of the proposed approach and the conventional relative angle difference approach in presence of measurement errors indicate a superior performance of the former. Results obtained from the proposed approach also reveal that power system disturbances such as unintentional island formations are accurately detected in wind integrated transmission systems.
Quantitative evaluation of the transmission system reliability aids in the assessment of the existing system performance. Further, post-mortem analysis of failures is an important step in minimizing recurrent failures. Reliability evaluation and outage analysis of transmission line outages carried out in this thesis have revealed chronological trends in the system performance. A new index called Outage Impact Index (OII) is also been proposed which can identify and prioritize outages based on their severity. This would serve as a baselining index for assessing and monitoring future transmission system performances and will facilitate implementation of reliability improvement measures if found necessary.
In this thesis, a passive Wide Area Measurement System (WAMS) based islanding detection scheme called Cumulative Sum of Change in Voltage Phase Angle Difference (CUSPAD) is proposed and tested on a modified 18 bus test system and a modified IEEE 118 bus system with various wind energy penetration levels. Comparative analysis between accuracies of the proposed approach and the conventional relative angle difference approach in presence of measurement errors indicate a superior performance of the former. Results obtained from the proposed approach also reveal that power system disturbances such as unintentional island formations are accurately detected in wind integrated transmission systems.
Quantitative evaluation of the transmission system reliability aids in the assessment of the existing system performance. Further, post-mortem analysis of failures is an important step in minimizing recurrent failures. Reliability evaluation and outage analysis of transmission line outages carried out in this thesis have revealed chronological trends in the system performance. A new index called Outage Impact Index (OII) is also been proposed which can identify and prioritize outages based on their severity. This would serve as a baselining index for assessing and monitoring future transmission system performances and will facilitate implementation of reliability improvement measures if found necessary.
ContributorsBarkakati, Meghna (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith E. (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
The lifetime of a transformer is essentially determined by the life of its insulation
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
ContributorsVaidya, Chinmay Vishwas (Author) / Holbert, Keith E. (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
Transmission line parameters play an important role in state estimation, dynamic line rating, and fault analysis. Because of this, several methods have been proposed in the literature for line parameter estimation, especially using synchrophasor data. However, success of most prior research has been demonstrated using purely synthetic data. A synthetic dataset does not have the problems encountered with real data, such as invariance of measurements and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. On the other hand, the true values of the line parameters are unknown and therefore the algorithms cannot be directly implemented on real data. A multi-stage test procedure is developed in this work to circumvent this problem.
In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented.
Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.
In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented.
Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.
ContributorsMansani, Prashanth Kumar (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith E. (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2018
Description
Breast microcalcifications are a potential indicator of cancerous tumors. Current visualization methods are either uncomfortable or impractical. Impedance measurement studies have been performed, but not in a clinical setting due to a low sensitivity and specificity. We are hoping to overcome this challenge with the development of a highly accurate impedance probe on a biopsy needle. With this technique, microcalcifications and the surrounding tissue could be differentiated in an efficient and comfortable manner than current techniques for biopsy procedures. We have developed and tested a functioning prototype for a biopsy needle using bioimpedance sensors to detect microcalcifications in the human body. In the final prototype a waveform generator sends a sin wave at a relatively low frequency(<1KHz) into the pre-amplifier, which both stabilizes and amplifies the signal. A modified howland bridge is then used to achieve a steady AC current through the electrodes. The voltage difference across the electrodes is then used to calculate the impedance being experienced between the electrodes. In our testing, the microcalcifications we are looking for have a noticeably higher impedance than the surrounding breast tissue, this spike in impedance is used to signal the presence of the calcifications, which are then sampled for examination by radiology.
ContributorsWen, Robert Bobby (Co-author) / Grula, Adam (Co-author) / Vergara, Marvin (Co-author) / Ramkumar, Shreya (Co-author) / Kozicki, Michael (Thesis director) / Ranjani, Kumaran (Committee member) / School of Molecular Sciences (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Buck converters are a class of switched-mode power converters often used to step down DC input voltages to a lower DC output voltage. These converters naturally produce a current and voltage ripple at their output due to their switching action. Traditional methods of reducing this ripple have involved adding large discrete inductors and capacitors to filter the ripple, but large discrete components cannot be integrated onto chips. As an alternative to using passive filtering components, this project investigates the use of active ripple cancellation to reduce the peak output ripple. Hysteretic controlled buck converters were chosen for their simplicity of design and fast transient response. The proposed cancellation circuits sense the output ripple of the buck converter and inject an equal ripple exactly out of phase with the sensed ripple. Both current-mode and voltage-mode feedback loops are simulated, and the effectiveness of each cancellation circuit is examined. Results show that integrated active ripple cancellation circuits offer a promising substitute for large discrete filters.
ContributorsWang, Ziyan (Author) / Bakkaloglu, Bertan (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05