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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Frimpong, Emmanuel Asuming
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Electrical & Electronics Engineering

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A Robust Scheme for Coherency Detection in Power Systems Adom-Bamfi, Gideon; Frimpong, Emmanuel Asuming
JURNAL NASIONAL TEKNIK ELEKTRO Vol 10, No 2: July 2021
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (964.568 KB) | DOI: 10.25077/jnte.v10n2.908.2021

Abstract

This paper presents an approach for online generator coherency identification based on windowed dynamic time warping (DTW). Generator rotor speed deviations measured by phasor measurement units (PMUs) are used as input data to compute a DTW dissimilarity matrix. Using the dissimilarity matrix together with Agglomerative Hierarchical Clustering (AHC) and Hubert-Levin index (C-index), generators are optimally grouped into coherent clusters. In addition to the clustering of generators, an index for characterizing the transmission delay of a Wide Area Measurement System (WAMS) is presented. A data delay factor that can indicate whether there is an inconsistent PMU data transmission delay is also proposed. The coherency identification technique and indices were tested using simulations carried out on the IEEE 39-bus system. The test results indicate that the proposed scheme accurately clusters generators into coherent groups. The suggested indices were also found to be valid. Keywords : coherence identification, dynamic time warping, speed deviation, phasor measurement unit, dissimilarity matrix
ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems Kwarteng, Monister Yaw; Effah, Francis Boafo; Kwegyir, Daniel; Frimpong, Emmanuel Asuming
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1072.2023

Abstract

Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.
Co-Authors Adom-Bamfi, Gideon Effah, Francis Boafo Kwarteng, Monister Yaw Kwegyir, Daniel