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

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.

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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.