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.

The power grid is interconnected in many ways; so that when disturbances occur in a small region, their effects can be seen across large areas causing major blackouts. In order to isolate the fault, measurements taken at different times throughout the blackout need to be collected and analyzed. With each measurement device having its own time source, time alignment can be a quite tedious and lengthy process. The need for a new time synchronized measurement device has arrived. The Phasor Measurement Units (PMU) is not only GPS time synchronized, but it also takes measurements as voltage and current phasors. PMUs are becoming an integral part in many power system applications from load flow analysis and state estimation to analyzing blackout causes. Phasor Data Concentrators (PDC) collect and process PMU data. As such, it is important that PMU and PDC communication is seamless. PDCs are set up at multiple utilities and power authorities and also need to be able to communicate and send data to one another seamlessly to encompass analysis of large measurement systems. If these devices are not working similarly when processing and sending/receiving data, unnecessary problems may arise. Therefore it is important that there is an expectation as to how they should work. However, what is expected from these devices is not entirely clear. For this reason, standards such as IEEE C37.118.2-2011 [5] have been proposed to help make operation as uniform as possible. Unfortunately, the standards for PDCs are lacking and tend to only set up communication protocols. To help normalize PDCs, these standards need to be expanded to include all PDC operations and give little room for discrepancy as to what a PDC should do in any given situation. Tests have been performed on PDCs not only to see how they match up to current standards but on how they act outside of the standards. / Master of Science

Today's electric power system is operated under increasingly stressed conditions. As electrical demand increases, the existing grid is operated closer to its stable operating limits while maintaining high reliability of electric power delivery to its customers. Protective schemes are designed to account for pressures towards unstable operation, but there is always a tradeoff between security and dependability of this protection. Adaptive relaying schemes that can change or modify their operation based on prevailing system conditions are an example of a protective scheme increasing reliability of the power system. The purpose of this thesis is to validate and analyze implementation of the Security-Dependability Adaptive Voting Scheme. It is demonstrated that this scheme can be implemented with a select few Phasor Measurement Units (PMUs) reporting positive sequence currents to a Phasor Data Concentrator (PDC). At the PDC, the state of the power system is defined as Stressed or Safe and a set of relays either vote or perform normal operation, respectively. The Adaptive Voting Scheme was implemented using two configurations: hardware- and software-based PDC solutions. Each was shown to be functional, effective, and practical for implementation. Practicality was based on the latency of Wide Area Measurement (WAM) devices and the added latency of relay voting operation during Stressed conditions. Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs), and relay operation delays were quantified to determine the benefits and limitations of WAMS protection and implementation of the voting scheme. It is proposed that the delays injected into the existing protection schemes would have minimal effect on the voting scheme but must be accounted for when implementing power system controls due to the real-time requirements of the data. / Master of Science

This report presents a simulation of a real-time phasor measurement unit (PMU) using Matlab, designed to adhere to the IEEE C.37.118 standard. A PMU utilizes measured voltage or current on the power grid and calculates the phasor, frequency and rate of change of frequency (ROCOF). They are crucial in smart grid applications, used to minimize losses and prevent damage to hardware and blackouts. The project was issued by Hitachi Energy with the purpose of utilizing the model for simulating and analyzing real-time data, assessing the PMU's response to different scenarios on the power grid. The results are intended to verify their current system's implementation. The Matlab implementation correctly calculated the phasor, phase-shift, frequency and ROCOF within the requirements of the standard. The associated Total Vector Error (TVE) also complied with the standard. However, the real-time aspect of the PMU did not comply with the standard for several reasons. Specifically the Hilbert transform and FIR filter introduced calculation and filtering delays in addition to internet transmission delays associated with the UDP-interface in Matlab. However, Matlab support confirmed that there are known performance issues with the UDP-interface. It was concluded that the model provided a solid groundwork for its intended use, though the model has yet to be tested with real data from the grid, and would benefit from additional work on optimization.

Real-time monitoring of electromechanical oscillations is of great significance for power system operators; to this aim, software solutions (algorithms) that use synchrophasor measurements have been developed for this purpose. This thesis investigates different approaches for improving mode estimation process by offering new methods and deepening the understanding of different stages in the mode estimation process. One of the problems tackled in this thesis is the selection of synchrophasor signals used as the input for mode estimation. The proposed selection is performed using a quantitative criterion that is based on the variance of the critical mode estimate. The proposed criterion and associated selection method, offer a systematic and quantitative approach for PMU signal selection. The thesis also analyzes methods for model order selection used in mode estimation. Further, negative effects of forced oscillations and non-white noise load random changes on mode estimation results have been addressed by exploiting the intrinsic power system property that the characteristics of electromechanical modes are predominately determined by the power generation and transmission network. An improved accuracy of the mode estimation process can be obtained by intentionally injecting a probing disturbance. The thesis presents an optimization method that finds the optimal spectrum of the probing signals. In addition, the probing signal with the optimal spectrum is generated considering arbitrary time domain signal constraints that can be imposed by various probing signal generating devices. Finally, the thesis provides a comprehensive description of a practical implementation of a real-time mode estimation tool. This includes description of the hardware, software architecture, graphical user interface, as well as details of the most important components such as the Statnett’s SDK that allows easy access to synchrophasor data streams. / <p>The Doctoral Degrees issued upon completion of the programme are issued by Comillas Pontifical University, Delft University of Technology and KTH Royal Institute of Technology. The invested degrees are official in Spain, the Netherlands and Sweden, respectively.</p><p>QC 20160218</p> / FP7 iTesla

The motivation for this research is to leverage the increasing deployment of the phasor measurement unit (PMU) technology by electric utilities in order to improve situational awareness in power systems. PMUs provide unprecedentedly fast and synchronized voltage and current measurements across the system. Analyzing the big data provided by PMUs may prove helpful in reducing the risk of blackouts, such as the Northeast blackout in August 2003, which have resulted in huge costs in past decades. In order to provide deeper insight into early warning signs (EWS) of catastrophic events in power systems, this dissertation studies changes in statistical properties of high-resolution measurements as a power system approaches a critical transition. The EWS under study are increases in variance and autocorrelation of state variables, which are generic signs of a phenomenon known as critical slowing down (CSD). Critical slowing down is the result of slower recovery of a dynamical system from perturbations when the system approaches a critical transition. CSD has been observed in many stochastic nonlinear dynamical systems such as ecosystem, human body and power system. Although CSD signs can be useful as indicators of proximity to critical transitions, their characteristics vary for different systems and different variables within a system. The dissertation provides evidence for the occurrence of CSD in power systems using a comprehensive analytical and numerical study of this phenomenon in several power system test cases. Together, the results show that it is possible extract information regarding not only the proximity of a power system to critical transitions but also the location of the stress in the system from autocorrelation and variance of measurements. Also, a semi-analytical method for fast computation of expected variance and autocorrelation of state variables in large power systems is presented, which allows one to quickly identify locations and variables that are reliable indicators of proximity to instability.

Master of Science / Department of Electrical and Computer Engineering / Shelli Starrett / Variability in power systems is increasing due to pushing the system to limits for economic purposes, the inclusion of new energy sources like wind turbines and photovoltaic, and the introduction of new types of loads such as electric vehicle chargers. In this new environment, system monitoring and control must keep pace to insure system stability and reliability on a wide area scale. Phasor measurement unit technology implementation is growing and can be used to provide input signals to new types of control. Fuzzy logic based power system stabilizer (PSS) controllers have also been shown effective in various studies. This thesis considers several choices of input signals, composed assuming phasor measurement availability, for fuzzy logic-based controllers. The purpose of the controller is to damp power systems’ low frequency oscillations. Nonlinear transient simulation results for a 4-machine two-area system and 50 machine system are used to compare the effects of input choice and controller type on damping of system oscillations. Reactive power in the system affects voltage, which in turn affects system damping and dynamic stability. System stability and damping can be enhanced by deploying SVC controllers properly. Different types of power system variables play critical role to damp power swings using SVC controller. A fuzzy logic based static var compensator (SVC) was used near a generator to damp these electromechanical oscillations using different PMU-acquired inputs. The goal was again improve dynamic stability and damping performance of the system at local and global level. Nonlinear simulations were run to compare the damping performance of different inputs on the 50 machine system.

abstract: The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures. This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs. The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced. The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks. The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017