Large scale outages have occurred worldwide in recent decades with some impacting 15-25% of a nation’s population. The complexity of blackouts has been extensively studied but many questions remain. As there are no perfect solutions to prevent blackouts, usually caused by a complex sequence of cascading events, a number of different measures need to be undertaken to minimize impact of future disturbances. Increase in deployment of phasor measurement units (PMUs) across the grid has given power industry an unprecedented technology to study dynamic behavior of the system in real time. Integration of large scale synchronized measurements with SCADA system requires a careful roadmap and methodology. When properly engineered, tested, and implemented, information extracted from synchrophasor data streams provides realtime observability for transmission system. Synchrophasor data can provide operators with quick insight into precursors of blackout (e.g., angular divergence) which are unavailable in traditional SCADA systems. Current visualization tools and SE functions, supported by SCADA, provide some basic monitoring. Inaccuracies in measurements and system models, absence of redundancy in the measured parameters or breaker statuses in most cases, and lack of synchronization and time resolution in SCADA data result in limited functionality and precision for a typical EMS required in today’s operating environment of tighter margins that require more frequent and more precise data. Addition of synchrophasor data, typically having several orders of magnitude higher temporal resolution, (i.e., 60 to 120 measurements per second as opposed to one measurement every 4 to 8 seconds), can help detect higher speed phenomena and system oscillations. Also, time synchronization to one micro-second allows for accurate comparison of phase angles across the grid and identification of major disturbances and islanding. This dissertation proposes a more comprehensive, holistic set of criteria for optimizing PMU placement with consideration for diverse factors that can influence PMU siting decision-making process and incorporates several practical implementation aspects. An innovative approach to interoperability testing is presented and solutions are offered to address the challenges. The proposed methodology is tested to prove the concept and address real-life implementation challenges, such as interoperability among the PMUs located across a large area.

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As the power industry enters the new century, powerful driving forces, uncertainties and new services and functions are compelling electric utilities to make dramatic changes in the way they communicate. Expanding network services such as real time monitoring are also driving the need for more increasing bandwidth in the communication network backbone. These needs will grow further as new remote real-time protection and control applications become more feasible and pervasive. This dissertation addresses two main issues for the future power system information infrastructure: communication network infrastructure and associated power system applications. Optical network no doubt will become the predominate network for the next generation power system communication. The rapid development of fiber optic network technology poses new challenges in the areas of topology design, network management and real time applications. Based on advanced fiber optic technologies, an all-fiber network was investigated and proposed. The study will cover the system architecture and data exchange protocol aspects. High bandwidth, robust optical network could provide great opportunities to the power system for better service and efficient operation. In the dissertation, different applications were investigated. One of the typical applications is the SCADA information accessing system. An Internet-based application for the substation automation system will be presented. VLSI (Very Large Scale Integration) technology is also used for one-line diagrams auto-generation. High transition rate and low latency optical network is especially suitable for power system real time control. In the dissertation, a new local area network based Load Shedding Controller (LSC) for isolated power system will be presented. By using PMU and fiber optic network, an AGE (Area Generation Error) based accurate wide area load shedding scheme will also be proposed. The objective is to shed the load in the limited area with minimum disturbance. / Ph. D.

For decades, researchers have sought to make the North American power system as reliable as possible with many security measures in place to include redundancy. Yet the increasing number of blackouts and failures have highlighted the areas that require improvement. Meeting the increasing demand for energy and the growing complexity of the loads are two of the main challenges faced by the power grid. In order to prepare for contingencies and maintain a secure state, power engineers must perform simulations using steady state and dynamic models of the system. The results from the contingency studies are only as accurate as the models of the grid components. The load components are generally the most difficult to model since they are controlled by the consumer. This study focuses on developing static and dynamic load models using advanced mathematical approximation algorithms and wide area measurement devices, which will improve the accuracy of the system analysis and hopefully decrease the frequency of blackouts. The increasing integration of phasor measurement units (PMUs) into the power system allows us to take advantage of synchronized measurements at a high data rate. These devices are capable of changing the way we manage online security within the Energy Management System (EMS) and can enhance our offline tools. This type of data helps us redevelop the measurement-based approach to load modeling. The static ZIP load model composition is estimated using a variation of the method of least squares, called bounded-variable least squares. The bound on the ZIP load parameters allows the measurement matrix to be slightly correlated. The ZIP model can be determined within a small range of error that won't affect the contingency studies. Machine learning is used to design the dynamic load model. Neural network training is applied to fault data obtained near the load bus and the derived network model can estimate the load parameters. The neural network is trained using simulated data and then applied to real PMU measurements. A PMU algorithm was developed to transform the simulated measurements into a realistic representation of phasor data. These new algorithms will allow us to estimate the load models that are used in contingency studies. / Ph. D.

The world of power systems is ever changing; ever evolving. One such evolution was the advent of Phasor Measurement Units (PMUs). With the introduction of PMUs in the field, power system monitoring and control changed for the better. Innovative and efficient algorithms that used synchrophasors came to be written. To make these algorithms robust, it became necessary to remove errors that crept into the power system with time and usage. Thus the process of calibration became essential when practical decisions started being made based on PMU measurements. In the context of this thesis ‘calibration’ is the method used to estimate a correction factor which, when multiplied with the respective measurement, negates the effect of any errors that might have crept into them due to the instrument transformers located at the inputs of a PMU or the PMU device itself. Though this thesis mainly deals with the calibration of instrument transformers, work has been done previously for calibrating other components of a power system. A brief description of those methods have been provided along with a history on instrument transformer calibration. Three new methodologies for instrument transformer calibration have been discussed in details in this thesis. The first method describes how only voltage transformers can be calibrated by placing optimal number of good quality voltage measurements at strategic locations in the grid, in presence of ratio errors in the instrument transformers and Gaussian errors in the PMUs. The second method provides a way to calibrate all instrument transformers (both current and voltage) in presence of only one good quality voltage measurement located at the end of a tie-line. This method assumes that all the instrument transformers have ratio errors and the PMUs have quantization errors. The third method attains the same objective as the second one, with the additional constraint that the data obtained from the field may be contaminated. Thus, the third method shows how calibration of all the instrument transformers can be done with data that is intermittent and is therefore, the most practical approach (of the three) for instrument transformer calibration. / Master of Science

As power system grids continue to grow in order to support an increasing energy demand, the system's behavior accordingly evolves, continuing to challenge designs for maintaining security. It has become apparent in the past few years that, as much as discovering vulnerabilities in the power network, accurate simulations are very critical. This study explores a classification method for validating simulation models, using disturbance measurements from phasor measurement units (PMU). The technique used employs the Random Forest learning algorithm to find a correlation between specific model parameter changes, and the variations in the dynamic response. Also, the measurements used for building and evaluating the classifiers were characterized using Prony decomposition. The generator model, consisting of an exciter, governor, and its standard parameters have been validated using short circuit faults. Single-error classifiers were first tested, where the accuracies of the classifiers built using positive, negative, and zero sequence measurements were compared. The negative sequence measurements have consistently produced the best classifiers, with majority of the parameter classes attaining F-measure accuracies greater than 90%. A multiple-parameter error technique for validation has also been developed and tested on standard generator parameters. Only a few target parameter classes had good accuracies in the presence of multiple parameter errors, but the results were enough to permit a sequential process of validation, where elimination of a highly detectable error can improve the accuracy of suspect errors dependent on the former's removal, and continuing the procedure until all corrections are covered. / Ph. D.

The electric power grid is operated with exceptionally high levels of reliability, yet recent large-scale outages have highlighted areas for improvement in operation, control, and planning of power systems.  Synchrophasor technology may be able to address these concerns, and Phasor Measurement Units (PMUs) are actively being deployed across the Western Interconnection and North America.  Initiatives such as the Western Interconnection Synchrophasor Program (WISP) are making significant investments PMUs with the expectation that wide-area, synchronized, high-resolution measurements will improve operator situational awareness, enable advanced control strategies, and aid in planning the grid. This research is multifaceted in that it focuses on improved operator awareness and alarming as well as innovative remedial controls utilizing synchrophasors.  It integrates existing tools, controls, and infrastructure with new technology to propose applications and schemes that can be implemented for any utility.  This work presents solutions to problems relevant to the industry today, emphasizing utility design and implementation.  The Bonneville Power Administration (BPA) and Western Electricity Coordinating Council (WECC) transmission systems are used as the testing environment, and the work performed here is being explored for implementation at BPA.  However, this work is general in nature such that it can be implemented in myriad networks and control centers. A Phase Angle Alarming methodology is proposed for improving operator situational awareness.  The methodology is used for setting phase angle limits for a two-tiered angle alarming application.  PMUs are clustered using an adapted disturbance-based probabilistic rms-coherency analysis.  While the lower tier angle limits are determined using static security assessment between the PMU clusters, the higher tier limits are based on pre-contingency operating conditions that signify poorly damped post-contingency oscillation ringdown.  Data mining tools, specifically decision trees, are employed to determine critical indicators and their respective thresholds.  An application is presented as a prototype; however, the methodology may be implemented in online tools as well as offline studies. System response to disturbances is not only dependent on pre-contingency conditions but also highly dependent on post-contingency controls.  Pre-defined controls such as Special Protection Schemes (SPSs) or Remedial Action Schemes (RAS) have a substantial impact on the stability of the system.  However, existing RAS controls are generally event-driven, meaning they respond to predetermined events on the system.  This research expands an existing event-driven voltage stability RAS to a response-based scheme using synchrophasor measurements.  A rate-of-change algorithm is used to detect substantial events that may put the WECC system at risk of instability.  Pickup of this algorithm triggers a RAS that provides high-speed wide-area reactive support in the BPA area.  The controls have proved effective for varying system conditions and topologies, and maintain stability for low probability, high consequence contingencies generally dismissed in today's deterministic planning studies. With investments being made in synchrophasor technology, the path of innovation has been laid; it's a matter of where it goes.  The goal of this research is to present simple, yet highly effective solutions to problems.  Doing so, the momentum behind synchrophasors can continue to build upon itself as it matures industry-wide. / Ph. D.

Phasor Measurement Units (PMUs) are widely acknowledged as one of the most significant developments in the field of real-time monitoring of power system. By aligning time stamps of voltage and current phasor measurements, which are consistent with Coordinated Universal Time (UTC), a coherent picture of the power system state can be achieved through either direct measurements or simple linear calculations. With the growing number of PMUs installed or planned to be installed in the near future, both utilities and research institutions are looking for novel applications of synchrophasor measurements from these widely installed PMUs. In this dissertation, the author proposes two new PMUs measurements applications: three-phase instrument transformer calibration, and three-phase line parameter calculation with instrument transformers. First application is to calibrate instrument transformers. Instrument transformers are the main sensors used in power systems. They provide isolation between high voltage level of primary side and metering level of the secondary side. All the monitoring and measuring systems obtain input signals from the secondary side of instrument transformers. That means when instrument transformers are not accurate, all the measurements used in power system are inaccurate. The most important job of this dissertation is to explore a method to automatically calibrate all the instrument transformers in the power system based on real-time synchrophasor measurements. The regular instrument transformer calibration method requires the instrument transformer to be out of service (offline) and calibrated by technicians manually. However, the error of instrument transformer changes when environment changes, and connected burden. Therefore, utilities are supposed to periodically calibrate instrument transformers at least once a year. The high labor and economic costs make traditional instrument transformer calibration method become one of the urgent problems in power industry. In this dissertation we introduce a novel, low cost and easy method to calibrate three-phase instrument transformers. This method only requires one three-phase voltage transformer at one bus calibrated in advance. All other instrument transformers can be calibrated by this method as often as twice a day, based on the synchrophasor measurements under different load scenarios. Second application is to calculate line parameters during calibrating instrument transformers. The line parameters, line impedance and line shunt admittance, as needed by utilities are generated by the computer method. The computer method is based on parameters, such as the diameter, length, material characteristics, the distance among transmission line, the distance to ground and so on. The formulas to calculate line parameters have been improved and re-modeled from time to time in order to increase the accuracy. However, in this case, the line parameters are still inaccurate due to various reasons. The line parameters errors do affect the instrument transformers calibration results (with 5% to 10% error). To solve this problem, we present a new method to calculate line parameters and instrument transformers in the same processing step. This method to calibrate line parameter and instrument transformers at the same time only needs one pre-calibrated voltage transformer and one pre-calibrated current transformer in power system. With the pre-calibrated instrument transformers, the line parameter as well as the ratio correction factors of all the other instrument transformers can be solved automatically. Simulation results showed the errors between calculated line parameters and the real line parameter, the errors between calibrated ratio correction factors and the real ratio correction factors are of the order of 10e-10 per unit. Therefore, high accuracy line parameters as well as perfectly calibrated instrument transformers can be obtained by this new method. This method can run automatically every day. High accuracy and dynamic line parameters will significantly improve power system models. It will also increase the reliability and speed of the relay system, enhance the accuracy of power system analysis, and benefit all other researches using line parameters. New methods of calculating line parameter and the instrument transformer calibrations will influence the whole power industry significantly. / Ph. D.

Load models have evolved from simple ZIP model to composite model that incorporates the transient dynamics of motor loads. This research utilizes the latest trend on Machine Learning and builds reliable and accurate composite load model. A composite load model is a combination of static (ZIP) model paralleled with a dynamic model. The dynamic model, recommended by Western Electricity Coordinating Council (WECC), is an induction motor representation. In this research, a dual cage induction motor with 20 parameters pertaining to its dynamic behavior, starting behavior, and per unit calculations is used as a dynamic model. For machine learning algorithms, a large amount of data is required. The required PMU field data and the corresponding system models are considered Critical Energy Infrastructure Information (CEII) and its access is limited. The next best option for the required amount of data is from a simulating environment like PSSE. The IEEE 118 bus system is used as a test setup in PSSE and dynamic simulations generate the required data samples. Each of the samples contains data on Bus Voltage, Bus Current, and Bus Frequency with corresponding induction motor parameters as target variables. It was determined that the Artificial Neural Network (ANN) with multivariate input to single parameter output approach worked best. Recurrent Neural Network (RNN) is also experimented side by side to see if an additional set of information of timestamps would help the model prediction. Moreover, a different definition of a dynamic model with a transfer function-based load is also studied. Here, the dynamic model is defined as a mathematical representation of the relation between bus voltage, bus frequency, and active/reactive power flowing in the bus. With this form of load representation, Long-Short Term Memory (LSTM), a variation of RNN, performed better than the concurrent algorithms like Support Vector Regression (SVR). The result of this study is a load model consisting of parameters defining the load at load bus whose predictions are compared against simulated parameters to examine their validity for use in contingency analysis. / Master of Science / Independent system Operators (ISO) and Distribution system operators (DSO) have a responsibility to provide uninterrupted power supply to consumers. That along with the longing to keep operating cost minimum, engineers and planners study the system beforehand and seek to find the optimum capacity for each of the power system elements like generators, transformers, transmission lines, etc. Then they test the overall system using power system models, which are mathematical representation of the real components, to verify the stability and strength of the system. However, the verification is only as good as the system models that are used. As most of the power systems components are controlled by the operators themselves, it is easy to develop a model from their perspective. The load is the only component controlled by consumers. Hence, the necessity of better load models. Several studies have been made on static load modeling and the performance is on par with real behavior. But dynamic loading, which is a load behavior dependent on time, is rather difficult to model. Some attempts on dynamic load modeling can be found already. Physical component-based and mathematical transfer function based dynamic models are quite widely used for the study. These load structures are largely accepted as a good representation of the systems dynamic behavior. With a load structure in hand, the next task is estimating their parameters. In this research, we tested out some new machine learning methods to accurately estimate the parameters. Thousands of simulated data are used to train machine learning models. After training, we validated the models on some other unseen data. This study finally goes on to recommend better methods to load modeling.

Given the ability of the Phasor Measurement Unit (PMU) to directly measure the system state and the increasing implementation of PMUs across the electric power industry, a natural expansion of state estimation techniques would be one that employed the exclusive use of PMU data. Dominion Virginia Power and the Department of Energy (DOE) are sponsoring a research project which aims to implement a three phase linear tracking state estimator on Dominion's 500kV network that would use only PMU measurements to compute the system state. This thesis represents a portion of the work completed during the initial phase of the research project. This includes the initial development and testing of two applications: the three phase linear state estimator and the topology processor. Also presented is a brief history of state estimation and PMUs, traditional state estimation techniques and techniques with mixed phasor data, a development of the linear state estimation algorithms and a discussion of the future work associate with this research project. / Master of Science