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This chapter provides an overview of the theory of controlled sensing, and its application to the sequential design of data-acquisition and decision-making processes. Based on the theory, it provides an overview of the applications to the quickest detection and localization of anomalies in power systems. This application is motivated by the fact that agile localization of anomalous events plays a pivotal role in enhancing the overall reliability of the grid and avoiding cascading failures. This is especially of paramount significance in the large-scale grids due to their geographical expansions and the large volume of data generated. Built on the theory of controlled sensing, the chapter discusses a stochastic graphical framework for localizing the anomalies with the minimum amount of data. This framework capitalizes on the strong correlation structures observed among the measurements collected from different buses. This framework, at its core, collects the measurements sequentially and progressively updates its decision about the location of the anomaly.
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
The present-day electricity grid is rapidly evolving towards a complex interconnection of distributed modules equipped with a broad range of heterogenous sensing and decision-making functionalities. The proliferation of highly intermittent small energy resources and changing customer needs highlight more adaptive and responsive grid operability in terms of decision making and control. Conventional grid-controlling techniques are unable to cope with such dynamics, as manifested by the increasing reliance on faster time-scale control (such as FACTs devices which enable power electronics-based switching ) and system sampling techniques (such as PMUs ) to mitigate rapid system fluctutions. It is hard to overemphasize the role of reliable system state estimation on the efficient operability of current and future grid-control techniques.
The complexity in estimator design stems mainly from the fact that, unlike conventional scenarios, the smart grid state estimator needs to possess the attributes of being distributed, adaptive, and accurate over relatively short time intervals. The design of distributed inference and decision-making tasks is indeed key to sustaining the evolving demands and functionalities of the grid [3–9]. Due to the sheer size of the network (at both the transmission and distribution levels), it will no longer be feasible to communicate the entire raw measurement data from all points at all times to a centralized SCADA for state estimation and control; rather, the various substations or regional transmission organizations (RTOs) should use the cyber or information processing/exchange layer efficiently to compute estimates and controls in a distributed manner.
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