Institutions, Arizona State University, Salt River Project power and water. Tokyo Institute of Technology, Smart grid cyber physical system. Power state estimation for the smart grid, Cyber security for the smart grid. application to the state estimation, Future research directions. Smart grid cyber physical system, NISTR National Institute of Standards and Technology Report. Smart grid trends and challenges, Increased renewable and distributed generation solar and. wind storage and load management, More competitive and free electricity markets and loads. responding to the price smart meters, Increased communication and integration of IT information. technologies and ICT information and communication. technologies sensed data smart meters and need for. The grid should operate efficiently reliably securely. Confidentiality should be insured as well, Power state estimation for the smart grid. State estimation SE evaluates the bus voltage magnitudes and. phase angles exploiting measurements communicated to the. control center with SCADA Supervisory Control and Data. Acquisition, SE is useful for real time operation i e contingency analysis. control in power markets, Static SE typically occurs in 30 s intervals AZ USA. possibly more frequently in future, The financial and real life consequences of bad SE can be. tremendous 2003 blackout Important research topic, Power State estimation example. State estimation SE evaluates the bus voltage magnitudes and. phase angles using redundant power injection measurements. line power flows at different locations voltage magnitudes. Injection IEEE 14 bus system, Static state estimation in the smart grid. Estimate the vector x from the measurements vector z. voltage magnitudes and phase angle, power flows power injections and voltage magnitudes. gaussian noise null mean and covariance R, is linked to the topology of the grid. The practical iterative algorithm, weighted least squares. H is the Jacobian of h i e, Stop the algorithm if, Bad data detection in state estimation. Analyze the residual vector after convergence Gaussian. Check the normalized, residuals RA, The c2 test is used. Reject all observations outside the 99 7 confidence interval as outliers. Hybrid state estimation in the smart grid, The phasor measurement units PMUs measure the. voltage magnitudes and phase angles directly, The measurements from PMUs are synchronized thanks to. the use of GPS, Different reporting rates of conventional measurements a. new measurement every 2 5 s and PMU measurements, 30 120 measurements per second. PMUs are costly and still limited i e SE combines both. regular measurements SCADAs and PMUs Hybrid SE, PMU measurements characteristics. The PMUs clocks are synchronized using the 1 ps per. second signal from the GPS Errors are present in the. subintervals, Faulty synchronization of PMUs is called by time skew. Jumps are present every second in, Jumps the PMU phase angle measurements. Voltage Angle Degree, 289 Solution are proposed for example. Zhang et all 2012, 0 1 2 3 4 5 6 7 8 9 10, PMU measurements characteristics. If the PMU noise is Gaussian the averaging the PMU recorded. measurements would reduce the noise in the static case. Objective insure the best possible tradeoff between. reducing noise uncertainty and tracking system changes. Uncertainty of data, Variation of data, Estimation. Solution A simple hypothesis testing based Nbl, method is proposed in Zhang et al 2013 Buffer length. Application on a real life system, Part of the Western Electricity Coordinating Council system WECC 1310. buses 1820 branches 200 generators and 5000 SCADA measurements. Evaluation at five different load conditions, Application on a real life system. Active power injection residuals Level 1, Level 1 PMU buses and buses. directly connected to them, 0 2 BL Second, Set 1 88 Set 2 89 Set 3 89 Set 4 89 Set 5 90. Dataset number Buses count, No BL Case without any PMU measurements. BL First Case with algorithm from project 1, BL Second Case with algorithm from project 2. Murugessen et al 2015 14, Application on a real life system. Murugessen et al 2015, Reactive power injection residuals Level 1. The improvement was, obtained for up to level 3, 1 BL Second. Set 1 84 Set 2 84 Set 3 85 Set 4 86 Set 5 86, Dataset number Buses count. Enhancing power state estimation accuracy and cyber security in the smart grid 1 Yacine Chakhchoukh ychakhch asu edu ECE 421 2017

Classical estimation theory ignored model selection out of necessity Armed with modern computational equipment statisticians can now deal with model selection problems more realistically The limited but useful goal of this paper is to provide a general tool for the assessment of standard errors in such situations

physical education teacher who interacts with 400 students a week confronts a different challenge than that of the kinder garten teacher with a class of 19 children It is only reasonable that the extent of their knowledge of students is correspond ingly different However even the teacher with 400 students

RACK FORCE ESTIMATION FOR ELECTRIC POWER STEERING Thomas Weiskircher Applied Dynamics amp Control Research Group International Center for Automotive Research Clemson University Greenville South Carolina 29607 Email tweiski g clemson edu Steve Fankem Institute for Mechatronics in Mechanical and Automotive Engineering TU Kaiserslautern 67663 Kaiserslautern Germany Email steve fankem mv

To address the control problems for nite and in nite dimensional systems the full state information is usually necessary In this thesis an optimal state estimation method is devel oped for spectral distributed parameter systems to account for full state estimation problems with state constraints due to physical limitations In particular a

Distributed Parameter State Estimation and Optimal Feedback Control An Experimental Study in Two Space Dimensions GERHARD K LAUSTERER AND W HARMON RAY Abstract Both optimal and suboptimal distributed parameter state estimation om were applied in real time to a heated cylindrical ingot

Mathematical Preliminaries 3 The problem of parameter estimation for nonlinear state space models is estimates of parameters that correspond to meaningful

every line outage for IEEE 14 bus test system to implement the module for power system static security assessment The security classification contingency selection and ranking are done based on the performance index which is capable of accurately differentiating the secure and non secure cases Here

Real Time Radar Based Tracking and State Estimation of Multiple Non Conformant Aircraft Brandon Cook1 Timothy Arnett2 Owen Macmann3 and Manish Kumar4 University of Cincinnati Cincinnati OH 45220 In this study a novel solution for automated tracking of multiple unknown aircraft is proposed Many current methods use transponders to self report state information and augment track

www rericjournal ait ac th 49 The smart sensors and actuators are deployed for monitoring automatic control and two way communication functionalities In other words the multiple smart sensors detect the voltage fluctuation at the PCCs and they will transmit the information to the nearby intelligent control center Based on the received signal the control center estimate the system state

Event Based Input and State Estimation for Linear Discrete Time Varying Systems Liang Hu a Zidong Wang Qing Long Hanb and Xiaohui Liua aDepartment of Computer Science Brunel University London Uxbridge Middlesex UB8 3PH U K bSchool of Software and Electrical Engineering Swinburne University of Technology Hawthorn

Cost Behavior and Cost Estimation 1 Types of Cost Behavior Patterns Summary of VC and FC Behavior Cost In Total Per Unit Total VC is VC per unit remains VC proportional to the activity the same over wide ranges level within the RR of activity Total FC remains the same even when the activity FC per unit goes FC level changes within the down as activity level goes up RR 2 The Activity Base

then tested on a UAV simulator and a radar imaging simulator Keywords UAV State Estimation Kalman Filter Extended Kalman Filter Synthetic Aperture Radar I INTRODUCTION Since they are lighter cheaper and easier to deploy than the traditional radar platforms such as planes and satellites 1 unmanned aerial vehicles UAV 2 are a new center of interest for radar applications To this