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Enhancing power state estimation accuracy and cyber
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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

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