Data-Driven Event Identification Using Deep Graph Neural Network and PMU Data

Phasor measurement units (PMUs) are being widely installed on power transmission systems, which provides a unique opportunity to enhance wide-area situational awareness. One essential application is to utilize PMU data for real-time event identification. However, taking full advantage of all PMU data in event identification is still an open problem. Hence, we propose a novel event identification method using multiple PMU measurements and deep graph neural network techniques. Unlike the previous models that rely on data from single PMU and ignore the interactive relationships between different PMUs or use multiple PMUs but determine the functional connectivity manually, our method performs interactive relationship inference in a data-driven manner. To ensure the optimality of the interactive inference procedure, the proposed method learns the interactive graph jointly with the event identification model. Moreover, instead of generating a single statistical graph to represent pair-wise relationships among PMUs during different events, our approach produces different event identification-specific graphs for different power system events, which handles the uncertainty of event location. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. The numerical results validate that our method has higher identification accuracy compared to the existing methods

A power system and synchrophasor communication network co-simulation testbed with a real-time cyber security application

The development of smart grids facilitates the deployment of phasor measurement units (PMUs) to improve the system stability and reliability. The growing installation of PMUs provides grid operators wide-area situational awareness while introducing additional vulnerabilities to power systems from the cyber security point of view. Thus, not only the online method to handle such vulnerabilities real-time but also the corresponding power system simulation environments with appropriate time-fidelity are needed. This thesis presents two major works: an interactive, extensible environment for power system simulation and a real-time malicious PMU data detection method. The first part introduces such an environment that operates with power system models in the PMU time frame, including data visualization and interactive control action capabilities. The flexible and extensible capabilities are demonstrated by interfacing with a synchrophasor communication network simulation, which is a testbed for developing real-time PMU data related applications. The second part proposes an online method to detect ongoing contingencies in the system and malicious data attack on its underlying synchrophasor communication network. To do so, the principal component analysis is applied to leverage the spatial and temporal correlations among the PMU data, and the method is implemented in the synchrophasor network simulation for data collection and tests. Pattern match and data reconstruction are proposed to identify incident types and find their most possible locations. The thesis illustrates the extensibility of the interactive simulation environment and the effectiveness of the proposed method with a 150 buses case

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information