Application of improved BP neural network based on LM algorithm in desulfurization system of thermal power plant

Cheng, Huanxin; Cui, Lijie; Li, Jing

doi:10.1109/cac.2017.8243841

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…The structure of the BP neural network is shown in Figure 8. Due to the gradient descent method, the training can fall into the local minimum, which leads to the inability of effective training [24]. Therefore, this paper uses the Levenbrg-Marquardt algorithm based on the combination of the gradient method and Newton method, in order to optimize the BP neural network.…”

Section: Principle and Algorithm Of Bp Neural Networkmentioning

confidence: 99%

Fault Diagnosis Method of Six-Phase Permanent Magnet Synchronous Motor Based on Vector Space Decoupling

et al. 2022

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Compared with the three-phase motor, the six-phase motor has lower torque ripple and higher fault tolerance performance, which makes it widely used in aviation, ships, industrial manufacturing, and other application fields. However, the probability of failure of the polyphase motor system increases with the increase in the number of phases. In order to deal with the open phase fault and power switch fault of the six-phase inverter, a fault diagnosis method for the six-phase inverter based on vector space decoupling (VSD) is proposed. The open phase fault index is first determined according to the VSD decoupling inverse transform and the current constraints. The fault index is then optimized from the perspective of preventing misdiagnosis and improving reliability, and the open phase fault can be diagnosed in one fundamental cycle. In addition, the current trajectory of harmonic plane after switch fault is analyzed, and the back propagation (BP) neural network is used to identify the harmonic plane current trajectory of different types of switch fault. Finally, the correctness and feasibility of the proposed fault diagnosis method are verified by simulations and experiments. The obtained results demonstrate that the proposed method can quickly and accurately locate the open phase fault and switch fault without additional hardware. The proposed method is simple, efficient, and robust.

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Section: Principle and Algorithm Of Bp Neural Networkmentioning

confidence: 99%

Fault Diagnosis Method of Six-Phase Permanent Magnet Synchronous Motor Based on Vector Space Decoupling

et al. 2022

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A novel prediction model of desulfurization efficiency based on improved FCM-PLS-LSSVM

Wang

Zhang

Hou

et al. 2022

Multimed Tools Appl

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Prediction and Optimization of a Desulphurization System Using Cmac Neural Network and Genetic Algorithm

Kong

Zhang

Wang

et al. 2020

Journal of Environmental Engineering and Landscape Management

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In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints.

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Application of improved BP neural network based on LM algorithm in desulfurization system of thermal power plant

Cited by 3 publications

References 2 publications

Fault Diagnosis Method of Six-Phase Permanent Magnet Synchronous Motor Based on Vector Space Decoupling

Fault Diagnosis Method of Six-Phase Permanent Magnet Synchronous Motor Based on Vector Space Decoupling

A novel prediction model of desulfurization efficiency based on improved FCM-PLS-LSSVM

Prediction and Optimization of a Desulphurization System Using Cmac Neural Network and Genetic Algorithm

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