Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation

2020 ◽  
Author(s):  
Yingqi Liang ◽  
Junbo Zhang ◽  
Dipti Srinivasan
Keyword(s):  
Power System ◽  
Least Squares ◽  
Sensitivity Matrix ◽  
Matrix Estimation ◽  
System Sensitivity ◽  
Data Saturation

scholarly journals Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation

2020 ◽  
Author(s):  
Yingqi Liang ◽  
Junbo Zhang ◽  
Dipti Srinivasan
Keyword(s):  
Power System ◽  
Least Squares ◽  
Measured Data ◽  
Data Driven ◽  
Voltage Sensitivity ◽  
Time Varying ◽  
Sensitivity Matrix ◽  
Matrix Estimation ◽  
System Sensitivity ◽  
Data Saturation

<div>To data-driven estimate power system sensitivity matrix considering mitigating data saturation, a series of multivariable least squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memoryfading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix estimation based on time-varying online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.</div>

scholarly journals Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation

2020 ◽  
Author(s):  
Yingqi Liang ◽  
Junbo Zhang ◽  
Dipti Srinivasan
Keyword(s):  
Power System ◽  
Least Squares ◽  
Measured Data ◽  
Data Driven ◽  
Voltage Sensitivity ◽  
Time Varying ◽  
Sensitivity Matrix ◽  
Matrix Estimation ◽  
System Sensitivity ◽  
Data Saturation

<div>To data-driven estimate power system sensitivity matrix considering mitigating data saturation, a series of multivariable least squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memoryfading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix estimation based on time-varying online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.</div>

Power System Early Warning Mechanism based on Least Squares Support Vector Machine

2013 ◽  
Vol 8 (9) ◽  
pp. 577-584
Author(s):  
Luo Zhikun ◽  
Liu Xiaoxiao ◽  
Guo Xinze ◽  
Qi Wenhui ◽  
Lian Guohai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power System ◽  
Least Squares ◽  
Early Warning ◽  
Support Vector ◽  
Early Warning Mechanism
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