Development of system separation strategy using synchrophasor data

In the current market system, power systems are operated at higher loads for economic reasons. Power system stability becomes a genuine concern in such operating conditions. In case of failure of any larger component, the system may become stressed. These events may start cascading failures, which may lead to blackouts. One of the main reasons of the major recorded blackout events has been the unavailability of system-wide information. Synchrophasor technology has the capability to provide system-wide real time information. Phasor Measurement Units (PMUs) are the basic building block of this technology, which provide the Global Positioning System (GPS) time-stamped voltage and current phasor values along with the frequency. It is being assumed that synchrophasor data of all the buses is available and thus the whole system is fully observable. This information can be used to initiate islanding or system separation to avoid blackouts. A system separation strategy using synchrophasor data has been developed to answer the three main aspects of system separation: (1) When to separate: One class support machines (OC-SVM) is primarily used for the anomaly detection. Here OC-SVM was used to detect wide area instability. OC-SVM has been tested on different stable and unstable cases and it is found that OC-SVM has the capability to detect the wide area instability and thus is capable to answer the question of “when the system should be separated”. (2) Where to separate: The agglomerative clustering technique was used to find the groups of coherent buses. The lines connecting different groups of coherent buses form the separation surface. The rate of change of the bus voltage phase angles has been used as the input to this technique. This technique has the potential to exactly identify the lines to be tripped for the system separation. (3) What to do after separation: Load shedding was performed approximately equal to the sum of power flows along the candidate system separation lines should be initiated before tripping these lines. Therefore it is recommended that load shedding should be initiated before tripping the lines for system separation.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

EXPLORATION OF THE MTSAT2 SATELLITE CAPABILITIES FOR REAL TIME DETECTION AND CHARACTERIZATION OF VOLCANIC EMISSIONS

In this report, we attempt to define the capabilities of the infrared satellite remote sensor, Multifunctional Transport Satellite-2 (MTSAT-2) (i.e. a geosynchronous instrument), in characterizing volcanic eruptive behavior in the highly active region of Indonesia. Sulfur dioxide data from NASA's Ozone Monitoring Instrument (OMI) (i.e. a polar orbiting instrument) are presented here for validation of the processes interpreted using the thermal infrared datasets. Data provided from two case studies are analyzed specifically for eruptive products producing large thermal anomalies (i.e. lava flows, lava domes, etc.), volcanic ash and SO2 clouds; three distinctly characteristic and abundant volcanic emissions. Two primary methods used for detection of heat signatures are used and compared in this report including, single-channel thermal radiance (4-µm) and the normalized thermal index (NTI) algorithm. For automated purposes, fixed thresholds must be determined for these methods. A base minimum detection limit (MDL) for single-channel thermal radiance of 2.30E+05 Wm- 2sr-1m-1 and -0.925 for NTI generate false alarm rates of 35.78% and 34.16%, respectively. A spatial comparison method, developed here specifically for use in Indonesia and used as a second parameter for detection, is implemented to address the high false alarm rate. For the single-channel thermal radiance method, the utilization of the spatial comparison method eliminated 100% of the false alarms while maintaining every true anomaly. The NTI algorithm showed similar results with only 2 false alarms remaining. No definitive difference is observed between the two thermal detection methods for automated use; however, the single-channel thermal radiance method coupled with the SO2 mass abundance data can be used to interpret volcanic processes including the identification of lava dome activity at Sinabung as well as the mechanism for the dome emplacement (i.e. endogenous or exogenous). Only one technique, the brightness temperature difference (BTD) method, is used for the detection of ash. Trends of ash area, water/ice area, and their respective concentrations yield interpretations of increased ice formation, aggregation, and sedimentation processes that only a high-temporal resolution instrument like the MTSAT-2 can analyze. A conceptual model of a secondary zone of aggregation occurring in the migrating Kelut ash cloud, which decreases the distal fine-ash component and hazards to flight paths, is presented in this report. Unfortunately, SO2 data was unable to definitively reinforce the concept of a secondary zone of aggregation due to the lack of a sufficient temporal resolution. However, a detailed study of the Kelut SO2 cloud is used to determine that there was no climatic impacts generated from this eruption due to the atmospheric residence times and e-folding rate of ~14 days for the SO2. This report applies the complementary assets offered by utilizing a high-temporal and a high-spatial resolution satellite, and it demonstrates that these two instruments can provide unparalleled observations of dynamic volcanic processes.