RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Yang, Xiao; Fang, Haoxu

doi:10.3390/en15051634

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the samples are input into the network through the input layer, they reach the hidden layer. The hidden layer maps the input to a new space through an activation function, and then the system will linearly weigh the output of the hidden layer neurons to obtain the output value of the network (Wang et al, 2021;Yang and Fang, 2022). The topology of the RBF neural network is shown in Figure 2.…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

et al. 2023

View full text Add to dashboard Cite

Risk assessment is critical to ensure the safe operation of oil and gas pipeline systems. The core content of such risk assessment is to determine the failure probability of the pipelines quantitatively and accurately. Hence, this study combines the MATLAB neural network toolbox and adopts an Radial Basis Functions (RBF) neural network with a strong non-linear mapping relationship to build a corrosion failure probability prediction model for buried oil and gas gathering and transmission pipelines. Based on the hazard identification of pipeline corrosion failure, the model summarizes the causes of corrosion failure and determines the input and output vectors of the neural network based on the fault tree. According to the selected learning samples, through the design and training of network parameters, the RBF neural network that can predict the system failure probability is finally obtained. Taking the failure probability of 30 groups of high-pressure gathering and transmission pipelines of gas storage as an example, the capability of inputting the probability of the bottom event and outputting the probability of the top event is demonstrated through training data. Our results show that the calculated failure probability based on the fault tree analysis model is consistent with the predicted failure probability based on the RBF neural network model. Hence, the RBF neural network model is shown to be reliable in predicting the corrosion failure probability of buried pipelines.

show abstract

Section: Rbf Neural Networkmentioning

confidence: 99%

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In Ref. (Yang and Fang, 2022), the sliding mode parameters were optimized by introducing the radial basis function RBF neural network algorithm, which does not require any circuit model and controller parameters, and does not affect the performance of the controller when the external environment changes, but the introduction of the RBF neural network algorithm will increase the complexity of the control system.…”

Section: Introductionmentioning

confidence: 99%

An improved two-beat deadbeat synchronous predictive current control strategy for MMC based on Newton interpolation method

Cheng,

Zhang,

Xing

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Modular Multilevel Converter (MMC) is widely used in applications such as High Voltage Direct Current (HVDC) transmission, AC/DC power conversion centers, and large-scale power quality management in electrical grids due to its highly modular structure, strong redundancy and low harmonic content in AC output. The application of the traditional deadbeat predictive current control to MMC enhances the fast tracking ability of the output current, but it still has the problems of low output current accuracy and high dependence on bridge arm inductor. Based on this, this paper proposes an improved two-beat deadbeat synchronous predictive current control based on Newton interpolation method. By improving the two-beat deadbeat synchronous predictive current control strategy, the accuracy of the output current can be further improved and its fast tracking ability can be enhanced. Newton interpolation is introduced to improve the accuracy of the output current while reducing the dependence on the bridge arm inductor. The results show that the control strategy proposed in this paper reduces the output current THD by 2.88% compared with the two-beat deadbeat predictive current control, thus improving the accuracy; the bridge arm inductor value is reduced by 1.28%, thus reducing the dependence; and under the transient environment, the output current can be tracked to its predicted reference value 843 μs in advance, which enhances the fast tracking capability.

show abstract

RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Cited by 2 publications

References 26 publications

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

An improved two-beat deadbeat synchronous predictive current control strategy for MMC based on Newton interpolation method

Contact Info

Product

Resources

About