Applications of Genetic Algorithm with Integrated Machine Learning

Raj, Arman; Kumar, Avneesh; Sharma, Vandana; Rani, Seema; Shanu, Ankit Kumar; Singh, Tanya

doi:10.1109/iciptm57143.2023.10118328

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Genetic Algorithms (GA) are known as function optimization algorithms, based on Charles Darwin's theory of evolution and operating on principles similar to the mechanisms of species evolution through genetic combination and alteration [31]. They were introduced by Holland [32] and popularized by Goldberg [33], offering a simplified approach to formulating and solving complex problems with multiple variables [34].…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Optimizing Instrument Transformer Performance through Adaptive Blind Equalization and Genetic Algorithms

Resende,

Silva,

Nepomuceno

et al. 2023

Energies

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In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equalization technique offers a potential solution to improve the frequency response of a large number of instrumentation transformers already installed in substations. These transformers were designed to accurately measure only the fundamental phasor component. Therefore, in order to use them for harmonic phasor measurement, methodologies for reducing frequency distortion must be applied. In this work, we propose a novel approach to improve the frequency response of the instrument transformer using adaptive blind equalization. The blind technique can compensate for distortions caused by voltage and current transducers without requiring prior knowledge of input signals or circuit characteristics. The proposed methodology uses a Linear Prediction Filter to convert the colored noise present at the channel output into white noise. Furthermore, a genetic algorithm is used to find a pole to cancel possible zeroes present in the frequency response of some transducers. The main advantage of blind equalization with the genetic algorithm is its independence, operating without clear information about the channel or the input signal. Through extensive experimentation, we demonstrate the effectiveness of the proposed methodology in significantly reducing the absolute error in ratio and phase caused by current and voltage transformers. Simulated and laboratory experiments are presented in this paper.

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Section: Genetic Algorithmmentioning

confidence: 99%

“…The objective of GA is to choose the most adapted individuals in the population, considering all their characteristics, to achieve the best possible combinations. Although the evolutionary process of the algorithm is random, it follows a determined selection structure based on the adaptation of the individuals [34].…”

Section: Genetic Algorithmmentioning

confidence: 99%