POWER SYSTEM STATE ESTIMATION - IIT Mandi

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Probability Distribution of Measurement Errors20Actualdistributionf(x)Gaussiandistibution03x
Weighted least Squares-State Estimator• The problem of state estimation is to determine theestimate that best fits the measurement model .• The static-state of an M bus electric power network isdenoted by x, a vector of dimension n=2M-1, comprised ofM bus voltages and M-1 bus voltage angles (slack bus istaken as reference).• The state estimation problem can be formulated as aminimization of the weighted least-squares (WLS)function problem.m2•(zihi(x))min J(x)=i 12i
Page 1 and 2: POWER SYSTEMSTATE ESTIMATIONPresent
Page 3 and 4: Introduction• Transmission system
Page 5 and 6: Objectives of State Estimation• O
Page 7 and 8: Power System State Estimation (Cont
Page 9 and 10: Power System State Estimation (Cont
Page 11 and 12: Consider a Simple DC Load Flow Exam
Page 13 and 14: Case with all meters have small err
Page 15 and 16: State Estimation: determining our b
Page 17 and 18: (Cont.,)‣Least Absolute Value(LAV
Page 19: (Cont.,)•12 2PDF( ) exp( / 2 )2
Page 23 and 24: (Cont.,)• The type of Newton‟s
Page 25 and 26: (Cont.,)•J( x)xT 1H R (e H x) 0T
Page 27 and 28: (Cont.,)• To derive the [H] matri
Page 29 and 30: (Cont.,)•est1est25 2.5 0- 5 0 -40
Page 31 and 32: Solution of the weighted least squa
Page 33 and 34: (Cont.,)• Critical measurement: A
Page 35 and 36: Bad Data Detection and Identificati
Page 37 and 38: Chi-square probability density func
Page 39 and 40: (Cont.,)• The degrees of freedom
Page 41 and 42: Bad Data Identification•'(zi zi)
Page 43 and 44: Bad Data Analysis-Example•
Page 45 and 46: (Cont.,)• Forming the H matrix we
Page 47 and 48: (Const.,)•z1z23.01544Azz349.00123
Page 49 and 50: (Cont.,)•4f e / 100(0.00877) 100(
Page 51 and 52: (Cont.,)•' 1 T 1Rii(I HG H R )ReN
Page 53 and 54: Conclusion• Real time monitoring

POWER SYSTEM STATE ESTIMATION - IIT Mandi

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