Advanced Wide-Area Monitoring System Design, Implementation, and Application

Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below: First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed

Analysis and techniques of data compression in smart grids in the context of IEC 61850 communication protocol

A smart grid is characterized by a two-way communication between the generation and the loads in addition to the distributed energy resources, which dictated the integration of smart monitoring devices to achieve full observability of the network. In smart grid operation, the monitoring and the measuring devices such as smart meters, phasor measurement units (PMUs) and the intelligent electronic devices (IEDs) typically record the data and share the information across each level of the grid. In distribution substations, the data and hence the information is then further transferred throughout the supervisory control and data acquisition (SCADA) and the data control center using the communication protocols. As a result, a large amount of data is transferred among different monitoring devices, data control centers, and SCADA within the smart grid, which calls for new requirements for the communication channels and the storage capacities. Data compression is considered promising techniques to reduce the burden on the communication channels as well as the storage in particular during the smart grid operation. This thesis focuses on studying the data compression techniques when applied to power system disturbances in the context of the IEC 61850 communication protocol, which is extensively used in smart distribution substation automation. A proposed approach for data compression is introduced in this work and it is based on a combined wavelet-surrogate binary regression tree and a hybrid thresholding method. The proposed approach for data compression is tested experimentally in real-time in the context of IEC 61850 communication protocol and the performance is compared to the existing approaches for data compression. The results have shown that the implementation of the proposed data compression approach may lead to significant reduction in the number and sizes of the Generic Object-Oriented Substation Event (GOOSE) messages, which are exchanged between the IEDS and the SCADA within the smart distribution substation automation in smart grids