An extended Kalman particle filter for power system dynamic state estimation

Yu, Yang; Wang, Zhongjie; Lu, Chengchao

doi:10.1108/compel-11-2017-0493

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…Gams [16] primarily depended on this strategy, which they further improved, in order to generate neural network ensembles [19,20]. To make matters even better, Domingos [17] employed cross-validation to speed up the development of his proposed rule induction system, known as RISE [21]. In this study, the training sets were split into equal-sized divisions depending on the number of people that took part in it.…”

Section: X-validationmentioning

confidence: 99%

“…In essence, a weighted vote is nothing more than the weight obtained by the When a correct prediction is produced, the confidence of the base classifier is calculated, which is generally computed by the related meta classifier when a successful prediction is made. Moreover, in his work, Yu et al [17] proposes grading as a generalization method for cross-validation selection, in which the training set is divided into n subsets and an n-1 classifier is constructed, therefore removing one split at a time to investigate the speed of misclassification [24]. Finally, this strategy chooses the learning classifier with the lowest rate of misclassification.…”

Section: Gradingmentioning

confidence: 99%

“…When various classifiers are hybridized together, a large number of outputs are formed by voting among the participants. According to Du et al [13], an uneven learning process is required in order to generate statistically meaningful findings [17]. The basic classifier is also trained repeatedly on additional samples of the original dataset utilizing random sampling with a replacement technique in this method, which is a significant improvement over previous methods.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble and Filtering-Based System for Predicting Educational Data Mining

Hananto¹

2021

J. Appl. Data Sci.

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When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.

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Section: X-validationmentioning

confidence: 99%

Section: Gradingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Ensemble and Filtering-Based System for Predicting Educational Data Mining

Hananto¹

2021

J. Appl. Data Sci.

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“…Dynamic state estimation has been previously used to estimate the state variables of PMSG, DFIG, synchronous machine and induction motor ((Shahriari et al , 2016); (Shahriari et al , 2016); (Ghahremani and Kamwa, 2011); (Yu et al , 2018); (Bogosyan et al , 2007)). Recently, in Shahriari et al (2018), dynamic state estimation has been used to improve LVRT capability of DFIG; in this paper, LVRT capability of PMSG is enhanced by applying the dynamic state estimation approach.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of low-voltage ride-through capability of permanent magnet synchronous generator wind turbine by applying state-estimation technique

Shahriari

Mohammadi

Raoofat

2020

COMPEL

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Purpose The purpose of this study is to propose a control scheme based on state estimation algorithm to improve zero or low-voltage ride-through capability of permanent magnet synchronous generator (PMSG) wind turbine. Design/methodology/approach Based on the updated grid codes, during and after faults, it is necessary to ensure wind energy generation in the network. PMSG is a type of wind energy technology that is growing rapidly in the network. The control scheme based on extended Kalman filter (EKF) is proposed to improve the low voltage ride-through (LVRT) capability of the PMSG. In the control scheme, because the state estimation algorithm is applied, the requirement of DC link voltage measurement device and generator speed sensor is removed. Furthermore, by applying this technique, the extent of possible noise on measurement tools is reduced. Findings In the proposed control scheme, zero or low-voltage ride-through capability of PMSG is enhanced. Furthermore, the requirement of DC link voltage measurement device and generator speed sensor is removed and the amount of possible noise on the measurement tools is minimized. To evaluate the ability of the proposed method, four different cases, including short and long duration short circuit fault close to PMSG in the presence and absence of measurement noise are studied. The results confirm the superiority of the proposed method. Originality/value This study introduces EKF to enhance LVRT capability of a PMSG wind turbine.

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“…measured and non-measured state variables, and to minimize the amount of measurement noise level, an extended Kalman filter (EKF) is used. State variables of a synchronous machine and induction motor are estimated in Yu et al (2018), Bogosyan et al (2007). Shahriari et al (2016a) present a nonlinear model for permanent magnet synchronous generator (PMSG) wind turbine and estimate the state variables of PMSG.…”

Section: Introductionmentioning

confidence: 99%

Modelling and dynamic state estimation of a doubly fed induction generator wind turbine

Shahriari

2020

COMPEL

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Purpose This paper aims to propose an 18th-order nonlinear model for doubly fed induction generator (DFIG) wind turbines. Based on the proposed model, which is more complete than the models previously developed, an extended Kalman filter (EKF) is used to estimate the DFIG state variables. Design/methodology/approach State estimation is a popular approach in power system control and monitoring because of minimizing measurement noise level and obtaining non-measured state variables. To estimate all state variables of DFIG wind turbine, it is necessary to develop a model that considers all state variables. So, an 18th-order nonlinear model is proposed for DFIG wind turbines. EKF is used to estimate the DFIG state variables based on the proposed model. Findings An 18th-order nonlinear model is proposed for DFIG wind turbines. Furthermore, based on the proposed model, its state variables are estimated. Simulation studies are done in four cases to verify the ability of the proposed model in the estimation of state variables under noisy, wind speed variation and fault condition. The results demonstrate priority of the proposed model in the estimation of DFIG state variables. Originality/value Evaluating DFIG model to estimate its state variables precisely.

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An extended Kalman particle filter for power system dynamic state estimation

Cited by 4 publications

References 28 publications

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

Enhancement of low-voltage ride-through capability of permanent magnet synchronous generator wind turbine by applying state-estimation technique

Modelling and dynamic state estimation of a doubly fed induction generator wind turbine

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