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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Effah, Francis Boafo
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Electrical & Electronics Engineering

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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.
Natural Exponential Inertia Weight and Acceleration Coefficient Particle Swarm Optimization Algorithm tuned PID Controller for DC Motor Speed Control. Adu-Buabeng , Dominic; Sekyere, Yaw Opoku Mensah; Effah, Francis Boafo
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1401.2025

Abstract

This paper presents a novel optimization algorithm, the NExIWAC (Natural Exponential Inertia Weight and Acceleration Coefficient) variant of Particle Swarm Optimization (PSO), for tuning PID controllers in DC motor speed control systems. The proposed NExIWAC algorithm improves control performance by dynamically adjusting the inertia weight and acceleration coefficients during optimization. To evaluate its effectiveness, the NExIWAC-tuned PID controller was compared against five established metaheuristic algorithms: Atomic Search Optimization (ASO), Sand Cat Swarm Optimization (SCSO), Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Stochastic Fractal Search (SFS). The system's step response was analyzed under a reference speed demand of 1 p.u., with performance metrics including steady-state error, rise time, settling time, overshoot, and Integral of Time-weighted Absolute Error (ITAE). The NExIWAC algorithm demonstrated superior performance, achieving the fastest rise and settling times, zero steady-state error, and the lowest ITAE value among the tested algorithms. A robustness analysis was conducted by varying motor parameters, such as armature resistance and motor constant, by ±50%. The NExIWAC-PID controller exhibited stable and reliable performance under all conditions. Stability analysis through Bode plots and pole-zero mapping further confirmed the system's robust behavior, with a high phase margin and poles located in the left half of the complex plane. The results indicate that the NExIWAC algorithm is a powerful and reliable optimization tool for tuning PID controllers in DC motor applications, offering significant advantages in terms of precision, stability, and adaptability.
Co-Authors Adu-Buabeng , Dominic Frimpong, Emmanuel Asuming Kwarteng, Monister Yaw Kwegyir, Daniel Sekyere, Yaw Opoku Mensah