Load State Identification Method for Wet Ball Mills Based on the MEEMD Singular Value Entropy and PNN Classification

Cai, Guoqing; Liu, Xin; Dai, Congcong; Zong, Lu; Luo, Xiaoyan

doi:10.1007/s42461-019-00166-9

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…A feature extraction STLF model based on EMD and improved generalized regression neural network(GRNN) with minimizing redundancy and maximizing correlation(mRMR) was presented in [3]. A load identification method based on modified ensemble empirical mode decomposition (MEEMD) for load state identification was proposed in [4]. But there are two technical problems with the EMD-like decomposition algorithm.…”

Section: Introductionmentioning

confidence: 99%

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Zhang

et al. 2022

IEEE Trans. Ind. Inf.

141

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As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Variational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

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Section: Introductionmentioning

confidence: 99%

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Zhang

et al. 2022

IEEE Trans. Ind. Inf.

141

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“…However, the EEMD and CEEMD algorithms are time-consuming, the number of iterations has a great impact on the decomposition effect. Therefore, this paper uses a modified ensemble empirical mode decomposition (MEEMD) [20,21] algorithm to extract fault features of the three-phase PWM rectifier, which not only suppress the mode confusion in the decomposition process, but also reduce the calculation amount.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Zhang

2020

Electronics

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Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy.

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Load State Identification Method for Wet Ball Mills Based on the MEEMD Singular Value Entropy and PNN Classification

Cited by 2 publications

References 24 publications

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

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