Analisa Perhitungan dan Pengaturan Relai Arus Lebih dan Relai Gangguan Tanah pada Kubikel Cakra 20 KV di PT XYZ

Gangguan hubung singkat fasa ke tanah dan fasa-fasa merupakan salah satu permasalahan yang mungkin timbul dalam pengoperasian transformator daya dalam sebuah Gardu Induk. Gangguan yang disebabkan oleh adanya hubung singkat menimbulkan banyak kerugian, kerugian pada sistem transmisi kelistrikan maupun kerugian di pihak konsumen energi listrik. Salah satu cara untuk mengatasi gangguan ini adalah dengan memasang peralatan pengaman pada transformator. Relai arus lebih merupakan relai proteksi yang bekerja dengan Pemutus Tenaga (Circuit Breaker). Pada tulisan ini diberikan perhitungan setting relay arus lebih dan relay tanah pada pada penyulang keluar dari kubikel Cakra 20 kV di sebuah Perusahaan, yang karena alasan privacy disebutkan sebagai PT XYZ. Analisa yang dilakukan menunjukkan bahwa setting relay arus lebih dan relay tanah eksisting telah sesuai dengan standar yang berlaku

Adaptive feature selection using v-shaped binary particle swarm optimization

<div><p>Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.</p></div

Comparison of classification accuracy for three classifiers on full set.

<p>Comparison of classification accuracy for three classifiers on full set.</p

Comparison of compression ratio.

<p>Comparison of compression ratio.</p

Run times on UCI datasets.

<p>Run times on UCI datasets.</p

Minimization results for fitness function 1 using different transfer functions.

<p>Minimization results for fitness function 1 using different transfer functions.</p

S-shaped and V-shaped families of transfer functions.

<p>S-shaped and V-shaped families of transfer functions.</p

Descriptions of UCI benchmark datasets.

<p>Descriptions of UCI benchmark datasets.</p

Comparison of classification accuracy for the naïve bayes classifier.

<p>Comparison of classification accuracy for the naïve bayes classifier.</p