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All Journal International Journal of Applied Power Engineering (IJAPE) Buletin Ilmiah Sarjana Teknik Elektro
Neda, Omar Muhammed
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Electrical & Electronics Engineering

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A Review on Smart Distribution Systems and the Role of Deep Learning-based Automation in Enhancing Grid Reliability and Efficiency Neda, Omar Muhammed
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13152

Abstract

The increasing complexity of modern power distribution systems has accelerated the need for advanced automation solutions to maintain grid reliability and efficiency. smart distribution systems (SDS), integrating distributed energy resources (DERs), internet of things (IoT) technologies, and advanced data analytics, are reshaping the conventional grid into a flexible and intelligent network. This review focuses on the application of deep learning (DL) techniques in enhancing automation within SDS, highlighting their role in key tasks such as anomaly detection, fault location, load forecasting, outage estimation, and customer clustering. Five DL models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, deep neural networks (DNNs), autoencoders, and hybrid models, are evaluated using synthetic datasets that approximate real world grid behavior. Acknowledging the limitations of synthetic data, this review emphasizes the need for future validation using empirical datasets and adaptive learning techniques. Performance trends are qualitatively compared across models and tasks, with observations such as suitability of LSTMs for time series forecasting and CNNs for localized event detection. Challenges including data quality, computational costs, and implementation constraints are discussed, along with potential mitigation strategies such as lightweight model architectures and explainable artificial intelligence. A comparative perspective with traditional machine learning and physiscs-based models is also provided to highlight the unique advantages and tradeoffs of DL methods. The findings undescore the potential of DL in SDS automation while outlining key areas further research and real-world deployment.
A novel WSSA technique for multi-objective optimal capacitors placement and rating in radial distribution networks Neda, Omar Muhammed
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i4.pp934-950

Abstract

Minimizing power loss while keeping the voltage profile within acceptable limits is a great challenge for the distribution system operators. Properly sized and optimally placed shunt capacitors (SCs) in radial distribution networks (RDNs) can enhance system efficiency and offer both technical and economic benefits. This paper presents a novel meta-heuristic technique, the weight salp swarm algorithm (WSSA) as a modified version of the original SSA algorithm by incorporating an inertia weight parameter to improve precision, speed, and consistency in solving the optimal capacitor placement (OCP) problem. The proposed method minimizes power loss, annual total costs, and improves the voltage profile of RDNs, ensuring practical applicability. Two RDNs, IEEE 33-bus and a real Iraqi 65-bus in Sadat Al-Hindiya, Babel Governorate, Iraq, were used to evaluate WSSA's performance. Comparative analysis with recently published approaches demonstrates WSSA’s superiority in reducing power loss, lowering costs, and improving voltage profiles. For the IEEE 33-bus, power loss is decreased by 34.81%, and the total cost is lessened by 29.08% (savings of $30,965.33). For the Iraqi 65-bus, WSSA reduces power loss by 32.03% and decreases the total cost by 29.51% (savings of $69,201.57). These results confirm WSSA’s effectiveness in achieving OCP with enhanced technical and economic benefits.
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