327 research outputs found
Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration
This dissertation is motivated by the lack of combined physics-based and data-driven
framework for solving power system challenges that are introduced by the integration of
new devices and new system components. As increasing number of stochastic generation,
responsive loads, and dynamic measurements are involved in the planning and operations
of modern power systems, utilities and system operators are in great need of new analysis
framework that could combine physical models and measuring data together for solving
challenging planning and operational problems.
In view of the above challenges, the high-level objective of this dissertation is to develop
a framework for integrating measurement data into large physical systems modeled
by dynamical equations. To this end, the dissertation first identifies four critical tasks
for the planning and operations of the modern power systems: the data collection and
pre-processing, the system situational awareness, the decision making process, as well as
the post-event analysis. The dissertation then takes one concrete application in each of
these critical tasks as the example, and proposes the physics-based/data-driven approach
for solving the challenging problems faced by this specific application.
To this end, this dissertation focuses on solving the following specific problems using
physics-based/data-driven approaches. First, for the data collection and pre-processing
platform, a purely data-driven approach is proposed to detect bad metering data in the
phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data
quality. Second, for the situational awareness platform, a physics-based voltage stability
assessment method is presented to improve the situational awareness of system voltage
instabilities. Third, for the decision making platform, a combined physics-based and
data-driven framework is proposed to support the decision making process of PMU-based
power plant model validation. Forth, for the post-event analysis platform, a physics-based
post-event analysis is presented to identify the root causes of the sub-synchronous oscillations
induced by the wind farm integration.
The above problems and proposed solutions are discussed in detail in Section 2 through
Section 5. The results of this work can be integrated to address practical problems in
modern power system planning and operations
Advanced Wide-Area Monitoring System Design, Implementation, and Application
Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below:
First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed.
Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed.
Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated.
Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances.
Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed
Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter
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