On-line ammonia nitrogen measurement using generalized additive model and stochastic configuration networks

Wang, Wei; Jia, Yang; Yu, Wen; Pang, Hongshuai; Cai, Kewei

doi:10.1016/j.measurement.2020.108743

Cited by 17 publications

(2 citation statements)

References 27 publications

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“…It has the advantages of low computational cost, high efficiency and not easily falling into the local optimal solution [ 48 , 49 ]. It has a wide range of application in chemical medicine [ 50 ], wind speed prediction [ 51 ] and the aviation industry [ 52 ]. In this paper, the features extracted by TSMBE are inputted into the SCN network to complete the pattern recognition of different state signals of the transformer.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Chen

Tian

Zhang

et al. 2022

Entropy

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In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN). Firstly, bubble entropy is introduced to overcome the shortcomings of traditional entropy models that rely too heavily on hyperparameters. Secondly, on the basis of bubble entropy, a tool for measuring signal complexity, TSMBE, is proposed. Then, the TSMBE of the transformer vibration signal is extracted as a fault feature. Finally, the fault feature is inputted into the stochastic configuration network model to achieve an accurate identification of different transformer state signals. The proposed method was applied to real power transformer fault cases, and the research results showed that TSMBE-SCN achieved 99.01%, 99.1%, 99.11%, 99.11%, 99.14% and 99.02% of the diagnostic rates under different folding numbers, respectively, compared with conventional diagnostic models MBE-SCN, TSMSE-SCN, MSE-SCN, TSMDE-SCN and MDE-SCN. This comparison shows that TSMBE-SCN has a strong competitive advantage, which verifies that the proposed method has a good diagnostic effect. This study provides a new method for power transformer fault diagnosis, which has good reference value.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Chen

Tian

Zhang

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…The advantages of perfect generalization and fast learning (Wang & Li, 2017b) make SCN suitable for all types of complex predictions. SCN is widely applied in regression and classification tasks, including predicting the thermal response of avionics equipment (Pang et al, 2020), measuring ammonia nitrogen in seawater (Wang et al, 2020), predicting the carbon residual content of crude oil (Lu & Ding, 2019), grading fruits and vegetables (Li et al, 2019), and classifying targets in synthetic aperture radar images (Wang et al, 2019). Although SCN has good performance, in some cases, the prediction effect is not ideal.…”

Section: Introductionmentioning

confidence: 99%

Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

Chen

Xiao

Wang

et al. 2022

Journal of Forecasting

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The stochastic configuration network (SCN), a type of randomized learning algorithm, can solve the infeasible problem in random vector functional link (RVFL) by establishing a supervisory mechanism. The advantages of fast learning, convergence and not easily falling into local optima make SCN popular. However, the prediction effect of SCN is affected by the parameter settings and the nonstationarity of input data. In this paper, a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and SCN is proposed. The SCN will predict relatively stable data after decomposition by VMD, and parameters of SCN are optimized by IWOA. The IWOA diversifies the initial population by employing logistic chaotic map based on bit reversal and improves the search ability by using Lévy flight. The exploration and exploitation of IWOA are superior to those of other optimization algorithms in multiple benchmark functions and CEC2020. Moreover, the proposed model is applied to the prediction of the nonstationary wind speeds in four seasons. We evaluate the performance of the proposed model using four evaluation indicators. The results show that the R 2 of the proposed model under four seasons are more than 0.999, and the root mean square error, mean absolute error, and symmetric mean absolute percentage error are less than 0.3, 0.17, and 13%, respectively, which are almost 1/10, 1/10, and 1/4 those of SCN, respectively.

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An AdaBoost Based - Deep Stochastic Configuration Network

Zhang

Ding

2022

IFIP Advances in Information and Communication Technology

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On-line ammonia nitrogen measurement using generalized additive model and stochastic configuration networks

Cited by 17 publications

References 27 publications

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

An AdaBoost Based - Deep Stochastic Configuration Network

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