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PMU DATA APPLICATIONS IN SMART GRID: LOAD MODELING, EVENT DETECTION AND STATE ESTIMATION
Title
PMU DATA APPLICATIONS IN SMART GRID: LOAD MODELING, EVENT DETECTION AND STATE ESTIMATION
Creator
Ge, Yinyin
Date
2016, 2016-05
Description
The thesis mainly includes four parts of research, event detection, data archival reduction, load modeling, state estimation. Firstly, we...
Show moreThe thesis mainly includes four parts of research, event detection, data archival reduction, load modeling, state estimation. Firstly, we present methods on real-time event detection and data archival reduction based on synchrophasor data produced by phasor measurement unit (PMU). Event detection is performed with Principal Component Analysis (PCA) and a second order difference method with a hierarchical framework for the event notification strategy on a small-scale Microgrid. Compared with the existing methods, the proposed method is more practical and efficient in the combined use of event detection and data archival reduction. Secondly, the proposed method on data reduction, which is an “Event oriented auto-adjustable sliding window method”, implements a curve fitting algorithm with a weighted exponential function-based variable sliding window accommodating different event types. It works efficiently with minimal loss in data information especially around detected events. The performance of the proposed method is shown on actual PMU data from the IIT campus Microgrid, thus successfully improving the situational awareness (SA) of the campus power system network. Thirdly, we present a new “event-oriented” method of online load modeling for the IIT Microgrid based on synchrophasor data produced PMU. Several load models and their parameter estimation methods are proposed. It is given great importance on choosing the best models for the detected events. The online load modeling process is based on an adjustable sliding window applied to two different types of load step changes. The load modeling tests and related analysis on the synchrophasor data of the IIT Microgrid are demonstrated in this paper. Finally, we present a three-phase unbalanced distribution system state estimation (DSSE) method based on Semidefinitetheir parameter estimation methods are proposed. It is given great importance on choosing the best models for the detected events. The online load modeling process is based on an adjustable sliding window applied to two different types of load step changes. The load modeling tests and related analysis on the synchrophasor data of the IIT Microgrid are demonstrated in this paper. Finally, we present a three-phase unbalanced distribution system state estimation (DSSE) method based on Semidefinite Programming (SDP). A partitioning strategy with the aid of PMU and another distributed optimization algorithm alternating direction method of multipliers (ADMM) are also proposed for large-scale DSSE. Compared with a traditional weighted least square (WLS) method based on the Gauss-Newton iteration, the proposed DSSE by SDP method delivers a more accurate estimation, and the application of ADMM can lead to high performance for large scale DSSE while deriving satisfying estimation.
Ph.D. in Electrical Engineering, May 2016
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SEQUENTIAL MONTE CARLO METHODS FOR PARAMETER ESTIMATION, DYNAMIC STATE ESTIMATION AND CONTROL IN POWER SYSTEMS
Title
SEQUENTIAL MONTE CARLO METHODS FOR PARAMETER ESTIMATION, DYNAMIC STATE ESTIMATION AND CONTROL IN POWER SYSTEMS
Creator
Maldonado, Daniel Adrian
Date
2017, 2017-05
Description
The estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the...
Show moreThe estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the introduction of technologies such as distributed generation and demand-side management, the ability of system operators to forecast the dynamic behavior of the system will deteriorate and as a result, the cost of keeping the system together will increase. Sequential Monte Carlo or Particle Filtering is a family of algorithms to efficiently perform inference in non-linear dynamic systems by exploiting their structure without assuming any linearity or normality structure. In this thesis we provide two novel ways of employing these algorithms for inference and control of power systems. First, we motivate the use Bayesian statistics in load modelling by introducing a novel statistical model to capture the aggregated response of a set of loads. We then use the model to characterize load with measurement data and prior information using the Sequential Monte Carlo algorithm. Second, we introduce the Model Predictive Control for power system stabilization. We present the use of the Sequential Monte Carlo algorithm as a way of solving the stochastic Model Predictive Control problem and we compare its performance to existing regulators. In addition, Model Predictive Control is applied to load shedding Finally, we test the performance of the algorithm in a large power system scenario.
Ph.D. in Electrical Engineering, May 2017
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