Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System

Abstract—This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN. Keywor ds —Dynamic voltage collapse, prediction, artificial neural network, support vector machines

Performance Evaluation of Fuel Cell and Microturbine as Distributed Generators in a Microgrid

This paper presents dynamic models of distributed generators (DG) and investigates dynamic behaviour of the DG units within a microgrid system. The DG units include micro turbine, fuel cell and the electronically interfaced sources. The voltage source converter is adopted as the electronic interface which is equipped with its controller to maintain stability of the microgrid during small signal dynamics. This paper also introduces power management strategies and implements the DG load sharing concept to maintain the microgrid operation in standalone, grid-connected and islanding modes of operation. The results demonstrate the operation and performance of the microturbine and SOFC as distributed generators in a microgrid. Keywords: Microgrid, Distributed Generation, Microturbine, Fuel Cel

Strategi Manajemen Pembelajaran Berbasis Teknologi untuk Meningkatkan Prestasi Siswa

Penelitian ini bertujuan untuk mengevaluasi strategi manajemen pembelajaran berbasis teknologi di MTsN 17 Jombang serta dampaknya terhadap prestasi siswa. Seiring dengan pesatnya perkembangan teknologi, penggunaan teknologi dalam pendidikan menjadi sangat penting untuk meningkatkan kualitas pembelajaran. Penelitian ini dilakukan dengan metode kualitatif, menggunakan teknik pengumpulan data melalui wawancara, observasi, dan dokumentasi. Informan dalam penelitian ini meliputi kepala sekolah, guru, siswa, dan staf IT. Data yang terkumpul dianalisis melalui proses reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa integrasi teknologi yang efektif dalam pembelajaran dapat meningkatkan hasil belajar siswa, namun memerlukan dukungan kebijakan yang lebih baik, peningkatan pelatihan bagi guru, serta pemeliharaan infrastruktur teknologi. Penelitian ini merekomendasikan peningkatan anggaran untuk teknologi, pelatihan berkelanjutan bagi guru, dan perbaikan infrastruktur sebagai langkah penting untuk memaksimalkan manfaat teknologi dalam pendidikan

Improvement of authorship invarianceness for individuality representation in writer identification

Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classi?cation task in order to identify the handwritten authorship. However, additional steps need to be per- formed in order to have a better representation of input prior to the classi?cation task. Features extracted from the feature extraction task for a writer are in vari- ous representations, which degrades the classi?cation performance. This paper will discuss this additional process that can transform the various representations into a better representation of individual features for Individuality of Handwriting, in order to improve the performance of identification in WI

Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances

This paper presents a novel approach using Support Vector Regression (SVR) based S-transform to predict the classes of single and multiple power quality disturbances in a three-phase industrial power system. Most of the power quality disturbances recorded in an industrial power system are non-stationary and comprise of multiple power quality disturbances that coexist together for only a short duration in time due to the contribution of the network impedances and types of customers’ connected loads. The ability to detect and predict all the types of power quality disturbances encrypted in a voltage signal is vital in the analyses on the causes of the power quality disturbances and in the identification of incipient fault in the networks. In this paper, the performances of two types of SVR based S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in making prediction for the classes of single and multiple power quality disturbances. The results for the analyses of 651 numbers of single and multiple voltage disturbances gave prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively. Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV

A Study of Why Some Learners are More Successful than Others at Acquiring a Second Language: The Roles of Personality, Attitude & Motivation

The purpose of this thesis is to convey and to support my belief that learners\u27 affective domain, which consist of their personalities, attitudes, and motivation are responsible for causing the variation in the levels of second language proficiency of second language learners. My concern is to point out or support others who believe that second language learners are not machines that are able and willing to be programmed; they have feelings and attitudes which in turn govern their personalities and motivation. I also believe that the main focus of second language teaching should be on the persons learning the language, instead of merely on the forms, rules, and structures of the second language itself. I hope that this study will provide insights to all second language teachers

Studi Analisis terhadap Mekanisme Survey Pembiayaan Murabahah pada KJKS Binama

KJKS BINAMA merupakan lembaga intermediasi penghimpun dana dari masyarakat yang memiliki kelebihan dana dan menyalurkan dana tersebut kepada masyarakat yang membutuhkan dana. Hal utama yang membedakan dengan koperasi lainya dalam cara penghimpunan dan menyalurkan dana dari masyarakat harus sesuai dengan prinsip-prinsip syariah. Dalam penyaluaran dana KJKS BINAMA memiliki berbagai macam produk dan akad yang digunakan sesuai dengan kebutuhan para anggotanya. Salah satunya prodak pembiayaan murabahah. Survey merupakan prosedur awal dalam pemeriksaan calon anggota sebelum melakukan pembiayaan, oleh kerena itu penulis memfokuskan tentang study analisi terhadap survey pembiayaan murabahah di KJKS BIANAMA. Metode penelitian yang digunakan dalam tugas akhir ini adalah metode diskriptif analisis. Adapun metode pengumpulan data diantaranya dilakukan dengan cara wawancara kepada karyawan KJKS BINAMA, observasi secara langsung terhadap objek tertentu yang menjadi titik penelitian serta mencatat segala sesuatu yang berhubungan dengan survey pembiayaan murabahaah dan dokumentasi yang berkaitan dengan penelitian ini. Hasil penelitian yang telah dilkukan oleh penulis di KJKS BINAMA bahwa pembiayaan dengan akad murabahah harus melalui tahap survey yang mana prosedurnya harus sesuai dengan ketentuan-ketentuan yang berlaku di KJKS BINAMA

Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Impact on soil chemistry of atmospheric sulfur fallout near Arnot coal-fired power station, in the Eastern Transvaal highveld region, South Africa

Includes bibliographical references.The objective of the study was to evaluate the potential impact of atmospheric sulfur deposition on the soils surrounding Arnot power station. In particular, the study focused on the relationship between sulfur and organic carbon (OC) and on the various pools of sulfur in the soil. A representative selection of collected soil samples was characterized both physically and chemically in the laboratory. In addition to the representative soils sampled in 2000, archived soils sampled in 1996 and 1999 from the same area were included in some of the chemical analyses