Power Network Parameter Estimation Method Based on Data Mining Technology

The parameter values which actually change with the circumstances, weather and load level etc. produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition （SCADA） database as several types. The data processing technology was implied to treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.

The parameter values which actually change with the circumstances, weather and load level etc. produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was implied to treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coeffcient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.