Abstract-This paper describes the basic design, refinement, and verification using finite-element analysis, and operational simulation using the Virtual Test Bed, of a linear machine for an electromagnetic aircraft launcher, for the aircraft carrier of the future. Choices of basic machine format and procedures for determining basic dimensions are presented. A detailed design for a permanent-magnet version is presented, and wound-field coil and induction machine versions are briefly discussed. The long armature-short field geometry is justified, and in particular the impact of this geometry on the scale of the power electronic drive system is examined.Index Terms-Aircraft launcher, linear machine, linear permanent-magnet (PM) synchronous machine, PM machine, power electronic drive, system simulation, track sectioning.

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This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper focuses on FDI attacks, which are modeled as bad data. State of the art strategies for FDI detection in real time monitoring rely on physics model-based weighted least squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modeling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this paper presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118 bus system.

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Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters.