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Fuchao Liu
State Grid Gansu Electric Power Company, Gansu Electric Power Research Institute, Lanzhou 730050, China

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Journal article
Published: 19 February 2020 in Applied Sciences
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Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

ACS Style

Gang Zhang; Benben Xu; Kaoshe Zhang; Jinwang Hou; Tuo Xie; Xin Li; Fuchao Liu. Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition. Applied Sciences 2020, 10, 1409 .

AMA Style

Gang Zhang, Benben Xu, Kaoshe Zhang, Jinwang Hou, Tuo Xie, Xin Li, Fuchao Liu. Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition. Applied Sciences. 2020; 10 (4):1409.

Chicago/Turabian Style

Gang Zhang; Benben Xu; Kaoshe Zhang; Jinwang Hou; Tuo Xie; Xin Li; Fuchao Liu. 2020. "Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition." Applied Sciences 10, no. 4: 1409.

Review article
Published: 05 July 2019 in Journal of Hydrology
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Accurate and reliable short-term runoff prediction is of great significance to the management of water resources optimization and reservoir flood operation. In order to improve the accuracy of short-term runoff forecasting, a hybrid model -based “feature decomposition-learning reconstruction” named VMD-DBN-IPSO was proposed. In this paper, variational mode decomposition (VMD) is first used to decompose the original daily runoff series into a set of sub-sequence for improving the frequency resolution. Partial autocorrelation function (PACF) is then applied to determine the input variables of each sub- sequence. The improved particle swarm optimization (IPSO) algorithm is combined with the deep belief network (DBN) model to predict each sub-sequences and finally reconstruct the ensemble forecasting result. Three quantitative evaluation indicators, mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSE), were used to evaluate and compare the established models using the historical daily runoff data (1/1/1988-31/12/2017) at Yangxian and Ankang hydrological station in the Han River Basin of China. Meanwhile, a comparative analysis of the performance of VMD-DBN-IPSO model under different forecast periods (1-, 3-, 5- and 7-day lead time) was performed. In addition, the prediction ability of peak runoff of the VMD-DBN-IPSO model is further verified by analyzing the 10 peak flows during the testing data-series. The results indicate that the VMD-DBN-IPSO model can always achieve the best performance in the training and testing stage, and has good stability and representativeness, the NSE coefficient remains above 0.8, and the prediction error of peak flow is within 20%. it is a preferred data-driven tool for forecasting daily runoff.

ACS Style

Tuo Xie; Gang Zhang; Jinwang Hou; Jiancang Xie; Meng Lv; Fuchao Liu. Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China. Journal of Hydrology 2019, 577, 123915 .

AMA Style

Tuo Xie, Gang Zhang, Jinwang Hou, Jiancang Xie, Meng Lv, Fuchao Liu. Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China. Journal of Hydrology. 2019; 577 ():123915.

Chicago/Turabian Style

Tuo Xie; Gang Zhang; Jinwang Hou; Jiancang Xie; Meng Lv; Fuchao Liu. 2019. "Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China." Journal of Hydrology 577, no. : 123915.

Journal article
Published: 06 March 2019 in Applied Sciences
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Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

ACS Style

Gang Zhang; Zhixuan Li; Jinwang Hou; Kaoshe Zhang; Fuchao Liu; Xin Zhang. Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function. Applied Sciences 2019, 9, 955 .

AMA Style

Gang Zhang, Zhixuan Li, Jinwang Hou, Kaoshe Zhang, Fuchao Liu, Xin Zhang. Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function. Applied Sciences. 2019; 9 (5):955.

Chicago/Turabian Style

Gang Zhang; Zhixuan Li; Jinwang Hou; Kaoshe Zhang; Fuchao Liu; Xin Zhang. 2019. "Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function." Applied Sciences 9, no. 5: 955.

Journal article
Published: 12 October 2018 in Applied Sciences
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Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

ACS Style

Tuo Xie; Gang Zhang; Hongchi Liu; Fuchao Liu; Peidong Du. A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average. Applied Sciences 2018, 8, 1901 .

AMA Style

Tuo Xie, Gang Zhang, Hongchi Liu, Fuchao Liu, Peidong Du. A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average. Applied Sciences. 2018; 8 (10):1901.

Chicago/Turabian Style

Tuo Xie; Gang Zhang; Hongchi Liu; Fuchao Liu; Peidong Du. 2018. "A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average." Applied Sciences 8, no. 10: 1901.

Journal article
Published: 14 November 2017 in Energies
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The grid structures, load levels, and running states of distribution networks in different supply regions are known as the influencing factors of energy loss. In this paper, the case library of energy loss is constructed to differentiate the crucial factors of energy loss in the different supply regions. First of all, the characteristic state values are selected as the representation of the cases based on the analysis of energy loss under various voltage classes and in different types of regions. Then, the methods of Grey Relational Analysis and the K-Nearest Neighbor are utilized to implement the critical technologies of case library construction, including case representation, processing, analysis, and retrieval. Moreover, the analysis software of the case library is designed based on the case library construction technology. Some case studies show that there are many differences and similarities concerning the factors that influence the energy loss in different types of regions. In addition, the most relevant sample case can be retrieved from the case library. Compared with the traditional techniques, constructing a case library provides a new way to find out the characteristics of energy loss in different supply regions and constitutes differentiated loss-reducing programs.

ACS Style

Ze Yuan; Weizhou Wang; Jing Peng; Fuchao Liu; Jianhua Yang. Case Library Construction Technology of Energy Loss in Distribution Networks Considering Regional Differentiation Theory. Energies 2017, 10, 1861 .

AMA Style

Ze Yuan, Weizhou Wang, Jing Peng, Fuchao Liu, Jianhua Yang. Case Library Construction Technology of Energy Loss in Distribution Networks Considering Regional Differentiation Theory. Energies. 2017; 10 (11):1861.

Chicago/Turabian Style

Ze Yuan; Weizhou Wang; Jing Peng; Fuchao Liu; Jianhua Yang. 2017. "Case Library Construction Technology of Energy Loss in Distribution Networks Considering Regional Differentiation Theory." Energies 10, no. 11: 1861.

Journal article
Published: 07 July 2017 in Water
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The Sacramento model is widely utilized in hydrological forecast, of which the accuracy and performance are primarily determined by the model parameters, indicating the key role of parameter estimation. This paper presents a multi-step parameter estimation method, which divides the parameter estimation of Sacramento model into three steps and realizes optimization step by step. We firstly use the immune clonal selection algorithm (ICSA) to solve the non-liner objective function of parameter estimation, and compare the parameter calibration result of ideal artificial data with Shuffled Complex Evolution (SCE-UA), Parallel Genetic Algorithm (PGA), and Serial Master-slaver Swarms Shuffling Evolution Algorithm Based on Particle Swarms Optimization (SMSE-PSO). The comparison result shows that ICSA has the best convergence, efficiency and precision. Then we apply ICSA to the parameter estimation of single-step and multi-step Sacramento model and simulate 32 floods based on application examples of Dongyang and Tantou river basins for validation. It is clearly shown that the results of multi-step method based on ICSA show higher accuracy and 100% qualified rate, indicating its higher precision and reliability, which has great potential to improve Sacramento model and hydrological forecast.

ACS Style

Gang Zhang; Tuo Xie; Lei Zhang; Xia Hua; Fuchao Liu. Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model. Water 2017, 9, 495 .

AMA Style

Gang Zhang, Tuo Xie, Lei Zhang, Xia Hua, Fuchao Liu. Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model. Water. 2017; 9 (7):495.

Chicago/Turabian Style

Gang Zhang; Tuo Xie; Lei Zhang; Xia Hua; Fuchao Liu. 2017. "Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model." Water 9, no. 7: 495.