Research Article
The Use of Artificial Intelligence in Assessing the Reliability of Electric Power Systems and Networks
Issue:
Volume 13, Issue 1, February 2025
Pages:
15-23
Received:
27 November 2024
Accepted:
12 December 2024
Published:
17 January 2025
DOI:
10.11648/j.jeee.20251301.12
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Abstract: Improving the reliability of power networks is a critical challenge, especially with the rise of renewable energy sources and the continuous growth in electricity demand. This article explores the use of artificial intelligence, specifically dynamic Bayesian networks (DBNs), to evaluate the reliability of electric power systems and networks, focusing on the IEEE 9-bus and IEEE 14-bus networks as case studies. To achieve this, a comprehensive study was conducted by simulating various operating scenarios using these networks as models. These networks were modeled using the simulation and analysis software PyAgrum. Key system variables, including nodes, lines, generators, and transformers, were integrated into the analysis, enabling the construction of conditional probability tables (CPTs) for each component. These tables accounted for both interdependencies and state transitions to reflect real-world dynamics accurately. Simulations performed using MATLAB enabled an in-depth analysis of reliability levels, revealing critical information on the availability rates of nodes, transformers, and generators. The analysis identified specific vulnerabilities within the network, such as node 2 in the IEEE 9-bus network achieving an availability rate of 65%, which indicates robust performance. Conversely, nodes 7 and 9 exhibited significantly lower availability rates of 20% and 40%, respectively, highlighting critical areas requiring immediate attention. Similarly, transformer 1 displayed a relatively high availability rate of 70%, indicating strong performance, whereas transformer 3 showed a notably low availability rate of 30%, suggesting an urgent need for upgrades or replacements. For generators, generator 1 had the lowest availability at 25%, representing a critical vulnerability, while generator 2, with a 55% availability rate, stood out as the most efficient and could serve as a benchmark for performance improvement efforts.
Abstract: Improving the reliability of power networks is a critical challenge, especially with the rise of renewable energy sources and the continuous growth in electricity demand. This article explores the use of artificial intelligence, specifically dynamic Bayesian networks (DBNs), to evaluate the reliability of electric power systems and networks, focusi...
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