Online Identification of Distribution Line Parameters by PMUs under Accuracy, Positive Sequence, and Noise Considerations.
Mustafa M. Al KhabbazMohammad A. AbidoPublished in: J. Electr. Comput. Eng. (2018)
Keyphrases
- input data
- bias variance
- high accuracy
- computational cost
- parameter identification
- online learning
- figure of merit
- normal distribution
- random noise
- identification rate
- error rate
- maximum likelihood
- probability distribution
- training and testing data
- parameter values
- window size
- parameter settings
- noise sensitivity
- estimated parameters
- optimal parameters
- closely spaced
- noise level
- probability density function
- expectation maximization
- classification accuracy
- real time
- prior distribution
- root mean square error
- noisy data
- extreme values
- line segments
- maximum likelihood estimator
- missing data
- prediction accuracy
- parameter estimation
- learning algorithm