Real-time monitoring of the power electric grid is more important than ever, to preventcatastrophic failures and to support fast-acting power electronic devices, renewable energy resources, and extreme weather conditions. Accordingly, there is an emerging need to a new class of wide-area monitoring sensors that can capture time-synchronized voltage and current waveforms. This thesis is about the new frontier in the power system monitoring using synchronized waveform measurements.
Waveform Measurement Units (WMUs) are a new class of smart grid sensors thatprovide precise time-synchronized voltage waveform and current waveform measurements, also known as synchro-waveform measurements. WMUs can show the wave-shape of the voltage and current at very high resolutions. Further, the waveform measurements are precisely synchronized across different WMUs. The very high reporting rate of WMUs and the fact that we have access to synchronized waveform measurements, can significantly enhance our understanding and awareness about the status of the power electric grid and its components. However, the sole availability of such huge amount of data in itself is not sufficient; we need to translate the WMUs data to actionable information to be useful. This thesis provides new methodologies for the practical applications of synchro- waveform measurements in event detection, event classification, event location identification, and event-based network parameters estimation. An event is defined as any sort of change in any component across the power electric grid, with focus on sub-cycle events; which are the type of events that call for the use of waveform measurements. This thesis also presents real-life applications of synchronized waveform data for asset monitoring and wildfire monitoring.
State estimation is a fundamental task in power system monitoring. The focus in this thesis is on Distribution System State Estimation (DSSE). One of the main challenges in DSSE is the lack of observability due to the small number of sensor installations in practical power distribution circuits, where the number of measurements is far fewer than the number of state variables. In this thesis, our goal is to develop DSSE methods which address the low-observability challenges.First, we leverage the high reporting rate of a small number of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, to unmask and characterize sparsity patterns among the state variables in radial power distribution systems. Accordingly, the DSSE problem is formulated over the differential synchrophasors as an adaptive group sparse recovery problem to track the changes that are made in the states of the system and captured by D-PMU measurements. To enhance the performance of the proposed method, the formulated DSSE is further augmented by the side information on the support of the vector of unknowns that is obtained from the outcome of an event-zone identification analysis prior to solving the DSSE problem. Second, to capture the dynamics of the power distribution system, we model the DSSE problem under an event-triggered setting, where we use the estimations of the state variables during the previous events as priori information to predict the state variables at the current event. Accordingly, a novel data-driven method based on elastic net regression is proposed to learn the event-triggered state transition matrix; despite the low-observability in the system. Here, in the absence of direct power measurements, we enhance our ability in sparse recovery by developing a new reinforced physics-based coupling method among the state variables, in which we add a novel set of linear differential power flow equations to the DSSE problem formulation in forms of virtual measurements. Third, we study the joint estimation of sensitivity distribution factors and power flows in low-observable power distribution systems by developing a novel physics-aware measurement-based approach that takes into account the sparsity features of the problem extracted for radial power distribution systems.
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