Ensemble methods of classification for power systems security assessment

Жуков, А. В.; Tomin, Nikita; Kurbatsky, Victor; Sidorov, Denis; Panasetsky, Daniil; Foley, Aoife

doi:10.1016/j.aci.2017.09.007

Cited by 35 publications

(23 citation statements)

References 36 publications

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“…So, the method becomes suitable for real‐time applications 49 . RF, an ensemble structure of DTs, is used in Reference 50 for power system security assessment. Ensemble methods of classification try to improve the accuracy of classifications by using multi‐classifiers based on the principle of collective wisdom.…”

Section: Categorization Based On Power System Implementationmentioning

confidence: 99%

Static security assessment of power systems: A review

Gholami

Sanjari

Safari

et al. 2020

Int Trans Electr Energ Syst

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Summary The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.

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Section: Categorization Based On Power System Implementationmentioning

confidence: 99%

Static security assessment of power systems: A review

Gholami

Sanjari

Safari

et al. 2020

Int Trans Electr Energ Syst

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“…Menurut [6] mengembangkan suatu metode untuk meningkatkan akurasi prediksi dari classifier yang tidak stabil, yaitu metode Ensemble. Metode Ensemble merupakan metode kombinasi banyak classifier tunggal dimana hasil prediksi masing-masing classifier digabungkan menjadi prediksi akhir melalui proses voting mayoritas untuk klasifikasi atau voting rata-rata untuk kasus regresi [8]. Penelitian sebelumnya menunjukkan bahwa metode Ensemble seringkali menghasilkan prediksi yang lebih akurat dibandingkan dengan classifier tunggal [13].…”

Section: Pendahuluanunclassified

Analisis Random Forest Pada Klasifikasi Cart Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka

Suwardika

Suniantara²

2019

BAREKENG

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Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.

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“…Despite the fact that perhaps non-natural, extra irregular calculations (like arbitrary call trees) might be acclimated fabricate a more grounded gathering than terrible intentional calculations (like entropylessening choice trees). General theory of ensemble's algorithm was first proposed in an approach in the form of algebraic by Zhuravlev et al [16]. Based on Zhuravlev et al [16], the N basic algorithm's composition h t = C(a t (x)), t = 1,...., N is taken to mean a superposition of recursive operators at a t : X → R, of a correction operation F : R N → R and call rule C : R → Y like H(x) = C (F(a 1 (x),…., a N (x))), where x X, X may be a house of objects, Ymight be a lot of answers, and R might be a place of appraisals.…”

Section: Ensemble Classificationmentioning

confidence: 99%

“…This method was referred to as boosting. Schapire et al [16] developed the primary obvious polynomial-time boosting of polynomial-time algorithmic program. It totally should change over powerless models into robust model by building associate degree ensemble of classifiers.…”

Section: Ensemble Classificationmentioning

confidence: 99%

An Ensemble Classifier based Power Quality Disturbances Classification

Janahiraman¹,

Bala²

2019

IJEAT

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Evolution of the current modern era demands a huge and good power quality supply day by day. Power utility suppliers and power exchange specialist organizations face a noteworthy test in recognizing the kind of Power Quality Disturbances (PQD). Our research illustrates the technique of PQD classification by utilizing wavelet signal decomposition and Ensemble classification. A normal wave without disturbance and waves with PQD events of single-type and hybrid-type were generated using MATLAB using the mathematical model as per the definition and parameters outlined by IEEE 1159 and IEC61000 customary. Discrete Wavelet Transform (DWT) is pertained to decompose the signal form the generated PQD to get the illustration in time and frequency domain. In this research work, our database consists of 14000 generated signals of a normal wave and the PQDs, which were divided into 80% for the train set and 20% for the test set for each PQDs. An ensemble methodology for multiclass order was chosen as the classifier of the component vector for the PQD. Examinations were conjointly made with elective sorts of classifiers and different kinds of mother wavelet channel capacities to observe and investigate the exhibition qualification. The outcomes demonstrated that the blend of DWT and Ensemble Classifier delivers an optimal solution to recognize the class of PQD with a precision of 100% for each train and test set.

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Ensemble methods of classification for power systems security assessment

Cited by 35 publications

References 36 publications

Static security assessment of power systems: A review

Static security assessment of power systems: A review

Analisis Random Forest Pada Klasifikasi Cart Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka

An Ensemble Classifier based Power Quality Disturbances Classification

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