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Title
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Estimating Synchronous Inertia in Power Systems Using Equal Area Criterion and Artificial Intelligence
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Type
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Presentation
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Keywords
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Artificial neural networks, Equal area criterion, Inertia estimation.
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Abstract
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In this paper, an advanced algorithm is presented that utilizes artificial neural networks (ANN) for estimating the inertia of synchronous generators (SGs). The algorithm is enhanced by integrating a modified equal area criterion method along with the kinetic energy concept to compute the maximum mechanical power (Pmax). This calculated Pmax is introduced as a novel input feature to the ANN model. Beyond the contributions already discussed, a supplementary proposal involves the development of a novel index designed to identify the most optimal fault location in power systems. The efficiency of the proposed algorithm was rigorously evaluated using simulations on the IEEE 39-bus New England test system. The simulation results demonstrated the algorithm's high accuracy, with an estimation error of less than 1% in determining the inertia of the generators. These findings underscore the algorithm's potential for enhancing the performance of power system stability analysis.
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Researchers
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Innocent Kamwa (Third Researcher), Hêmin Golpîra (Second Researcher), Shiva Amini (First Researcher)
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