Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Wang, Qingsong; Xi, Haoyu; Deng, Fujin; Cheng, Ming; Buja, Giuseppe

doi:10.1049/rpg2.12320

Cited by 25 publications

(13 citation statements)

References 23 publications

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“…BP neural network is a multilayer feedforward network structure and is trained by an error backpropagation algorithm [19][20][21]. It can better establish the two-dimensional surface relationship between the output of the voltage transformer and its electronic voltage transformer input and temperature and has strong non-linear processing capability.…”

Section: Temperature Compensation Based On Bp Neural Networkmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

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Section: Temperature Compensation Based On Bp Neural Networkmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…Qingsong, and et al [11] developed a novel control for the Boost converter which combines GA and PID control from a back propagation (BP) neural network to enhance the converter's dynamic response and stability performance. Where, BP neural network coupled with a genetic algorithm by combining the global optimization capabilities of genetic algorithms with the adaptive adjustment properties of BP neural networks, PID control (GA-BPPID) was presented to enhance the dynamic and anti-interference performances of the Boost circuit.…”

Section: Intelligent Controllermentioning

confidence: 99%

A Review in Applications of Control Engineering Based on Genetic Algorithm

najm¹

2022

AJES

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The most popular evolutionary search techniques are genetic algorithms (GAs). Even though they are frequently used to solve control engineering problems, they are currently not a common tool in the control engineer's toolbox. This may be due in part to the fact that there are currently few general overviews of the employment of GAs for control engineering problems, and that they are often reported on at computer science conferences rather than conferences for control engineers. This review study is intended to assist researchers and practitioners in identifying prospective research issues, potential solutions, as well as advantages and disadvantages of each technique. This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems. Additionally, offers a number of control techniques used with the GA that have undergone extensive research. The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn't used till the time of preparing this review.

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“…The BP method is a member of the family of  -learning rules, which are frequently transformed into error-prone gradient descent algorithms [16][17].…”

Section: Introductionmentioning

confidence: 99%

Innovative Research on the Quality Evaluation Method of English Translation Teaching in Colleges and Universities Based on Neural Network Modeling

Dou,

Yang

2024

Applied Mathematics and Nonlinear Sciences

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This paper presents BP neural network as an evaluation tool for English translation instruction in colleges and universities. It does this by analyzing and combing the structure of BP neural networks, combining the five first-level indices with the assessment of indexes data preprocessing method, and building an evaluation system based on BP neural networks. We get sample data for the teaching quality assessment indexes based on the example teaching, evaluate it, and confirm that the BP neural network is feasible to use in the evaluation model. Two randomly selected college English language teaching classrooms are used as an example for the class, and the teaching quality rating method is utilized and analyzed. The teaching quality evaluation system’s findings are used to investigate the influencing factors that affect the quality of English translation teaching in higher education institutions. The teaching effect is also discussed about the impact of course time and content arrangements. According to the analysis, the English translation teaching evaluation index system built around the BP neural network model has a substantial amount of credibility because Kendall’s harmony coefficient W = 0.756 χ 2 = 7.687 χ 2 (p − 0.025) = 7.249 indicates that the consistency credibility of the instructional quality evaluation system goes to the test at a significant level of P = 0.025 and the opinions are very uniform.

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Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Cited by 25 publications

References 23 publications

Voltage transformer metering error state prediction method based on GA-BP algorithm

Voltage transformer metering error state prediction method based on GA-BP algorithm

A Review in Applications of Control Engineering Based on Genetic Algorithm

Innovative Research on the Quality Evaluation Method of English Translation Teaching in Colleges and Universities Based on Neural Network Modeling

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