A Dynamic State Estimation Method Based on Mixed Measurements for Power System

• Autorzy: Li Y. , Wang B. , Wang Y. , Wang X.

Warianty tytułu

PL

Estymacja stanów dynamicznych w sieci elektroenergetycznej

• Języki publikacji: EN

Abstrakty

EN

Dynamic State Estimation (DSE) techniques have the ability to foresee potential contingencies and security risks. Any improvement in its ability to estimate would definitely go a long way in reducing the security risks in the modern power system. One important factor affecting the quality of estimation is the measurement accuracy. Phasor Measurement Unit (PMU) has revolutionized the way state estimation is performed. The unique ability to measure the voltage and current phasors (magnitude and phase angle) with very high accuracy makes PMU extremely useful in modern Energy Management Systems (EMS). Due to the high price, technology level and communication capacity, the PMU can’t be equipped in all buses in the system nowadays. Therefore, this paper brings forward an improved method on dynamic state estimation that combines some buses measurements from PMU with measurements from SCADA. As Relevance Vector Machine (RVM) has a better performance on the regression, the state estimation algorithm is based on RVM in this article. Since the input data dimension is too large, pre-processing of data is needed. Autoencoder Network (Autoencoder) can be used for data dimensionality reduction. So this paper uses Autoencoder to reduce the data dimensionality, and then uses RVM to estimate the state of power system.

PL

W artykule przedstawiono metodę estymacji stanów dynamicznych w sieci elektroenergetycznej, wykorzystujący pomiary fazy i amplitudy napięcia i prądu oraz pomiary SCADA z poszczególnych punktów sieci. W algorytmie wykorzystano maszynę wektorów RVM. Ze względu na zbyt duży wymiar danych wejściowych, zastosowano pre-processing z wykorzystaniem sieci neuronowej auto-encoderowej.

Słowa kluczowe

EN

state estimation; power system; relevance vector machine

PL

estymacja stanu; system elektroenergetyczny; RVM

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 5
• Strony: 222--227
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Li Y.

• State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University

autor

Wang B.

• State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University

autor

Wang Y.

• State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University

autor

Wang X.

• State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University

Bibliografia

• [1] Yu Erkeng, Power system state estimation, Beijing: Hydraulic and Electric Power Press, 1985
• [2] Tripathy S. C., Chohan S., On line tracking state estimation in power system, Tournal of the Institution of Engineers India: Electrical Engineering Division, 74(1994), 162-164
• [3] Tong-Koo Park, The Concept and Design of Dynamic State Estimation, in Proceedings of the American Control Conference San Diego, Cali-forma, Tune, 1999
• [4] Al-Othman A. K., Irving M.R., Uncertainty modelling in power system state estimation, IEE Proceedings: Generation, Transmission and Distribution, 152(2005), 233-239
• [5] Sinha A.K., Mondal T.K., Dynamic State Estimation Using ANN Based Bus load Pre-diction, IEEE Transactions on Power Systems, 14(1999), 1219-1225
• [6] Amjady, Nima, Short-term bus load forecasting of power systems by a new hybrid method, IEEE Transactions on Power Systems, 22(2007), 333-334
• [7] Holbert, Keith E., Mishra, Intrusion detection through SCADA systems using fuzzy logic-based state estimation methods, International Tournal of Critical Infrastructures, 3(2007), 58-87
• [8] Knmar, Ashwani, Robust dynamic state estimation of power harmonics, International Energy Systems, 28(2006), 65-74
• [9] Watson, N.R., An adaptive Kahnan filter for dynamic harmonic state estimation and harmonic injection tracking, IEEE Transactions on Power Delivery, 20(2005), 1577-1584
• [10] Thakur S.S., Sinha A, K., A robust dynamic state estimator for electric power systems, Toumal of the Institution of Engineers (India): Electrical Engineering Division, 84(2003), 42-46,.
• [11] Huang S.T., Shih K.R., Dynamic state estimation scheme including nonlinear measurement-function considerations, IEE Proceedings: Generation, Transmission and Distribution, 149(2002), 673-678
• [12] Lee Tae-Won, Lee S., New data fusion method and its application to state estimation of nonlinear.namic systems, Proceedings-IEEE International Conference on Robotics and Automation, 4(2000), 3525-3530
• [13] Sinha A.K., Mandal T.K., Hierarchical dynamic state estimator using ANN-based dynamic load prediction IEE Proceedings: Generation, Transmission and Distribution, 146(1999), 541-549
• [14] Smolensky P., Parallel Distributed Processing:Foundations, D. E. Rumelhart J., McClelland L., MITPress, Cambridge, 1(1986), 194–281
• [15] Hinton G. E., Neural Comput. 14(2002)
• [16] Hopfield J. J., Proc. Natl. Acad. Sci. U.S.A. 79(1982), 2554
• [17] Nga-Viet Nguyen, Shin V., Shevlyakov G., Power system state estimation with fusion method Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on Volume: 5 (2010) 71-76
• [18] Bian X., Li X.R., Chen H., Gan D., Qiu J., Joint Estimation of State and Parameter With Synchrophasors—Part I: State Tracking Power Systems, (2011), 1–13
• [19] Okon T., Wilkosz K., Consideration of different operation modes of UPFC in power system state estimation Environment and Electrical Engineering (EEEIC), (2011) 1–4
• [20] Gao W., Wang S., On-line dynamic state estimation of power systems, North American Power Symposium (NAPS), 2010, 1–6
• [21] Tang P., Jiang R., Zhao M.m, Feature Selection and Design of Intrusion Detection System Based on k-Means and Triangle Area Support Vector Machine,Future Networks, 2010. ICFN'10. Second International Conference on (2010) 144–148
• [22] Rodriguez-Martinez E., Goulermas J.Y., Mu T., Ralph J.F., Automatic Induction of Projection Pursuit Indices, Neural Networks, IEEE Transactions on (2010 ) 1281–1295