A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

Zeng, Jie; Zhang, Chi; Xie, Ning; Yang, Ping; Xu, Chenyu; Zhang, Zhen

doi:10.1051/matecconf/20167010002

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…In this paper, the MIV algorithm [14] is used to choose the significant input items from the temperature and pulling speed. Input variables that significantly affect the output are retained, and those that do not play a significant role are excluded.…”

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

“…The delaying of temperature and pulling speed needs to be analyzed independently. Taking the temperature delaying time as an example, we establish a three-layer multi-input and single-output temperature-diameter dynamic neural network model [14]. The model is shown in Figure 5, and can be expressed as Formula (2):…”

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

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A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Jiang,

Teng,

Zhao

2024

Applied Sciences

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In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process.

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Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Jiang,

Teng,

Zhao

2024

Applied Sciences

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“…where m is the number of outputs; y i is the ith predicted output of the ELM model; o i is the ith actual output of ELM model; although k is an application dependent constant, k = 1 is normally selected [27]. The steps of the optimal ELM incorporating a GA are described as follows:…”

Section: Ga Optimisationmentioning

confidence: 99%

“…When the internal weights and biases are initialised, the ELM model calculates a predicted output. The fitness value can be found by calculating the sum of the absolute errors of the expected output and actual output of the ELM

F = k ()(, \sum_{i = 1}^{m} |y_{i} - o_{i}|)

where m is the number of outputs; y i is the i th predicted output of the ELM model; o i is the i th actual output of ELM model; although k is an application dependent constant, k = 1 is normally selected [27].…”

Section: Ga Optimisationmentioning

confidence: 99%

Integrated data‐driven model‐based approach to condition monitoring of the wind turbine gearbox

Qian

Cross

2017

IET Renewable Power Generation

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Condition Monitoring (CM) is considered an effective method to improve the reliability of wind turbines and implement cost-effective maintenance. This paper presents a single hidden-layer feed forward neural network (SLFN), trained using an extreme learning machine (ELM) algorithm, for condition monitoring of wind turbines. Gradient-based algorithms are commonly used to train SLFNs; however, these algorithms are slow and may become trapped in local optima. The use of an ELM algorithm can dramatically reduce learning time and overcome issues associated with local optima. In this paper, the ELM model is optimized using a genetic algorithm. The residual signal obtained by comparing the model and actual output is analyzed using the Mahalanobis distance measure due to its ability to capture correlations among multiple variables. An accumulated Mahalanobis distance value, obtained from a range of components, is used to evaluate the heath of a gearbox, one of the critical subsystems of a wind turbine. Models have been identified from supervisory control and data acquisition (SCADA) data obtained from a working wind farm. The results show that the proposed training method is considerably faster than traditional techniques, and the proposed method can efficiently identify faults and the health condition of the gearbox in wind turbines. IET Review Copy Only IET Renewable Power Generation This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.

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A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

Cited by 2 publications

References 12 publications

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

Integrated data‐driven model‐based approach to condition monitoring of the wind turbine gearbox

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