A New Fast and Efficient Artificial Neural Network Based State Estimator Incorporated into a Linear Optimal Regulator for Power System Control Enhancement

Jazaeri, Mostafa; Nasrabadi, Masoud Taji

doi:10.1080/15325008.2014.997326

Cited by 4 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extended Kalman filtering is a non-linear filtering algorithm which has been found to be an efficient technique for the detection, localization, and classification of PQDs in the utility grid [51]. Its complex domain version named as an extended complex Kalman filter is designed along with an estimator based on a feed-forward NN structure to elaborate PQDs for accurate detection in [52]. Maiden application of a variant of KF algorithm known as local ensemble transform-based KF for power system harmonic estimation is presented in [53].…”

Section: Figure 2 Categorization Of Sp Based Detection Techniquesmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

View full text Add to dashboard Cite

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

show abstract

Section: Figure 2 Categorization Of Sp Based Detection Techniquesmentioning

confidence: 99%