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- All Subjects: Electrical Engineering
- Genre: Masters Thesis
- Resource Type: Text
Description
Network-on-Chip (NoC) architectures have emerged as the solution to the on-chip communication challenges of multi-core embedded processor architectures. Design space exploration and performance evaluation of a NoC design requires fast simulation infrastructure. Simulation of register transfer level model of NoC is too slow for any meaningful design space exploration. One of the solutions to reduce the speed of simulation is to increase the level of abstraction. SystemC TLM2.0 provides the capability to model hardware design at higher levels of abstraction with trade-off of simulation speed and accuracy. In this thesis, SystemC TLM2.0 models of NoC routers are developed at three levels of abstraction namely loosely-timed, approximately-timed, and cycle accurate. Simulation speed and accuracy of these three models are evaluated by a case study of a 4x4 mesh NoC.
ContributorsArlagadda Narasimharaju, Jyothi Swaroop (Author) / Chatha, Karamvir S (Thesis advisor) / Sen, Arunabha (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2012
Description
The accurate monitoring of the bulk transmission system of the electric power grid by sensors, such as Remote Terminal Units (RTUs) and Phasor Measurement Units (PMUs), is essential for maintaining the reliability of the modern power system. One of the primary objectives of power system monitoring is the identification of the snapshots of the system at regular intervals by performing state estimation using the available measurements from the sensors. The process of state estimation corresponds to the estimation of the complex voltages at all buses of the system. PMU measurements play an important role in this regard, because of the time-synchronized nature of these measurements as well as the faster rates at which they are produced. However, a model-based linear state estimator created using PMU-only data requires complete observability of the system by PMUs for its continuous functioning. The conventional model-based techniques also make certain assumptions in the modeling of the physical system, such as the constant values of the line parameters. The measurement error models in the conventional state estimators are also assumed to follow a Gaussian distribution. In this research, a data mining technique using Deep Neural Networks (DNNs) is proposed for performing a high-speed, time-synchronized state estimation of the transmission system of the power system. The proposed technique uses historical data to identify the correlation between the measurements and the system states as opposed to directly using the physical model of the system. Therefore, the highlight of the proposed technique is its ability to provide an accurate, fast, time-synchronized estimate of the system states even in the absence of complete system observability by PMUs.
The state estimator is formulated for the IEEE 118-bus system and its reliable performance is demonstrated in the presence of redundant observability, complete observability, and incomplete observability. The robustness of the state estimator is also demonstrated by performing the estimation in presence of Non-Gaussian measurement errors and varying line parameters. The consistency of the DNN state estimator is demonstrated by performing state estimation for an entire day.
The state estimator is formulated for the IEEE 118-bus system and its reliable performance is demonstrated in the presence of redundant observability, complete observability, and incomplete observability. The robustness of the state estimator is also demonstrated by performing the estimation in presence of Non-Gaussian measurement errors and varying line parameters. The consistency of the DNN state estimator is demonstrated by performing state estimation for an entire day.
ContributorsChandrasekaran, Harish (Author) / Pal, Anamitra (Thesis advisor) / Sen, Arunabha (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2020
Description
Due to the rapid penetration of solar power systems in residential areas, there has
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
ContributorsHashmy, Syed Muhammad Yousaf (Author) / Weng, Yang (Thesis advisor) / Sen, Arunabha (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019