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Lin Lin
Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

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Journal article
Published: 02 June 2020 in IEEE Access
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Multi-energy unified planning is difficult because of the complex conflicting relationship between the coupling and complementary interaction of multiple forms of energy in micro energy grids (MEGs). Conflicting relationships between the economy and the environment as well as the impact of uncertain energy prices must be considered during MEG planning. To address these problems, this paper proposes a two-level game with an environment–economic planning model that considers dynamic energy pricing strategies. This model consists of an upper environment–economic planning level based on a multi-strategy evolution game considering players’ bounded rationality and a lower dynamic energy pricing level, including the MEG operator-user leader-follower Stackelberg game. Simultaneously, based on the energy hub theory, a multi energy coupling matrix is established for a MEG and includes electricity, gas, heat, and cooling. The evolutionary stability strategy (ESS) of the planning results is analyzed using the replicator dynamic equation of the evolutionary game, and the existence of the Nash equilibrium is proven for the dynamic energy pricing of Stackelberg games. Finally, the effectiveness of the proposed environment–economic planning two-level game model considering dynamic energy pricing strategies is verified using simulations. Because dynamic energy pricing and the environment–economic planning are considered, the number of energy equipment required during peak hours is reasonably reduced, thereby reducing the total planning cost and improving the energy utilization efficiency. Simultaneously, greenhouse gas (CO2) and air pollutant (NOx) emissions are reduced to decrease environmental impact.

ACS Style

Lin Lin; Jiaruiqi Bao; Jian Zheng; Guilin Huang; Jiping Du; Nantian Huang. Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy. IEEE Access 2020, 8, 103924 -103940.

AMA Style

Lin Lin, Jiaruiqi Bao, Jian Zheng, Guilin Huang, Jiping Du, Nantian Huang. Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy. IEEE Access. 2020; 8 (99):103924-103940.

Chicago/Turabian Style

Lin Lin; Jiaruiqi Bao; Jian Zheng; Guilin Huang; Jiping Du; Nantian Huang. 2020. "Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy." IEEE Access 8, no. 99: 103924-103940.

Journal article
Published: 22 April 2020 in Entropy
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The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

ACS Style

Jiajin Qi; Xu Gao; Nantian Huang. Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features. Entropy 2020, 22, 478 .

AMA Style

Jiajin Qi, Xu Gao, Nantian Huang. Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features. Entropy. 2020; 22 (4):478.

Chicago/Turabian Style

Jiajin Qi; Xu Gao; Nantian Huang. 2020. "Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features." Entropy 22, no. 4: 478.

Journal article
Published: 13 June 2019 in IEEE Access
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Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of wind power output and effectively improve a system’s wind power accommodation. However, high-dimensional historical wind speed information should be taken into account in wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Next, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. Experiments show that it reduces the information loss. Thus, the model has higher forecast accuracy than the traditional model.

ACS Style

Nantian Huang; Yinyin Wu; Guowei Cai; Heyan Zhu; Changyong Yu; Li Jiang; Ye Zhang; Jiansen Zhang; Enkai Xing. Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD. IEEE Access 2019, 7, 81027 -81046.

AMA Style

Nantian Huang, Yinyin Wu, Guowei Cai, Heyan Zhu, Changyong Yu, Li Jiang, Ye Zhang, Jiansen Zhang, Enkai Xing. Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD. IEEE Access. 2019; 7 (99):81027-81046.

Chicago/Turabian Style

Nantian Huang; Yinyin Wu; Guowei Cai; Heyan Zhu; Changyong Yu; Li Jiang; Ye Zhang; Jiansen Zhang; Enkai Xing. 2019. "Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD." IEEE Access 7, no. 99: 81027-81046.

Journal article
Published: 20 May 2019 in IEEE Access
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In the existing research of power quality disturbance (PQD) identification, the efficiency of signal processing is low and cannot meet the needs of practical application analysis. Furthermore, due to the lack of effective analysis of features, the complexity of classifiers is increased and the efficiency of classification are reduced by the redundant features. In order to overcome these shortcomings, in this paper, a PQD recognition method based on image enhancement techniques and feature importance analysis is proposed. Firstly, PQD signals are converted into gray images, and three image enhancement techniques include gamma correction, edge detection and peaks and valley detection are used to enhance the disturbance features. Then, the disturbance features are extracted from the binary images and the original feature set is constructed, the classification ability of each feature is measured by Gini importance. Based on the descending order of the Gini importance, the sequence forward search (SFS) method is used for feature selection to determine the optimal feature subset. Finally, random forest (RF) classifier is constructed by the optimal feature subset to identify PQD signals. The results of the simulation and contrast experiments show that the new method can determine the optimal classification subset, and recognition the PQD signals effectively in different noise environments. Furthermore, the new method has higher signal processing efficiency compared with EMD and ST methods.

ACS Style

Lin Lin; Da Wang; Shuye Zhao; Lingling Chen; Nantian Huang. Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques. IEEE Access 2019, 7, 67889 -67904.

AMA Style

Lin Lin, Da Wang, Shuye Zhao, Lingling Chen, Nantian Huang. Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques. IEEE Access. 2019; 7 (99):67889-67904.

Chicago/Turabian Style

Lin Lin; Da Wang; Shuye Zhao; Lingling Chen; Nantian Huang. 2019. "Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques." IEEE Access 7, no. 99: 67889-67904.

Journal article
Published: 10 April 2019 in Entropy
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To improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered. Furthermore, commonly used multi-classifiers could misidentify the type or severity of faults without using normal samples as training samples. Therefore, a novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample. First, the bearing vibration signals are decomposed into numerous intrinsic mode functions using empirical wavelet transform. Then, 284 features including multiple entropy are extracted from the original signal and intrinsic mode functions to construct the initial feature set. Lastly, a hybrid classifier based on one-class support vector machine trained by normal samples and a random forest trained by imbalanced fault data without some specific severities is set up to accurately identify the mechanical state and specific fault type of the bearings. The experimental results show that the proposed method can significantly improve the classification accuracy compared with traditional methods in different diagnostic target.

ACS Style

Ruiqing Li; Bin Wang; Jiajin Qi; Da Wang; Nantian Huang. Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample. Entropy 2019, 21, 386 .

AMA Style

Ruiqing Li, Bin Wang, Jiajin Qi, Da Wang, Nantian Huang. Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample. Entropy. 2019; 21 (4):386.

Chicago/Turabian Style

Ruiqing Li; Bin Wang; Jiajin Qi; Da Wang; Nantian Huang. 2019. "Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample." Entropy 21, no. 4: 386.

Journal article
Published: 12 January 2019 in Sensors
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The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified.

ACS Style

Lin Lin; Bin Wang; Jiajin Qi; Lingling Chen; Nantian Huang. A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing. Sensors 2019, 19, 288 .

AMA Style

Lin Lin, Bin Wang, Jiajin Qi, Lingling Chen, Nantian Huang. A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing. Sensors. 2019; 19 (2):288.

Chicago/Turabian Style

Lin Lin; Bin Wang; Jiajin Qi; Lingling Chen; Nantian Huang. 2019. "A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing." Sensors 19, no. 2: 288.

Journal article
Published: 15 August 2018 in Sustainability
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Solar irradiation is influenced by many meteorological features, which results in a complex structure meaning its prediction has low efficiency and accuracy. The existing prediction methods are focused on analyzing the correlation between features and irradiation to reduce model complexity but they do not account for redundant analysis in feature subset. In order to reduce the information redundancy in the feature set and improve prediction accuracy, a novel feature selection method for short-term irradiation prediction based on Conditional Mutual Information (CMI) and Gaussian Process Regression (GPR) is proposed. Firstly, the CMI values of different features are calculated to evaluate correlation and redundant information between features in the feature subsets. Secondly, GPR with a stable prediction performance and adaptively determined hyper parameters is used as the predictor. The optimal feature subset and the GPR covariance function can be selected using Sequential Forward Selection (SFS). Finally, an optimal predictor is determined by the minimum prediction error and the prediction of solar irradiation is carried out by the determined predictor. The experimental results show that CMI-GPRAEK has the highest prediction accuracy with the optimal feature set has low dimension, which is 4.33% lower in MAPE than the predictor without feature selection, although both of them have an optimal kernel function. The CMI-GPRAEK is less complicated for the predictor and there is less redundancy between features in the model with the dimension of the optimal feature set is only 14.

ACS Style

Nantian Huang; Ruiqing Li; Lin Lin; Zhiyong Yu; Guowei Cai. Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression. Sustainability 2018, 10, 2889 .

AMA Style

Nantian Huang, Ruiqing Li, Lin Lin, Zhiyong Yu, Guowei Cai. Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression. Sustainability. 2018; 10 (8):2889.

Chicago/Turabian Style

Nantian Huang; Ruiqing Li; Lin Lin; Zhiyong Yu; Guowei Cai. 2018. "Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression." Sustainability 10, no. 8: 2889.

Journal article
Published: 20 July 2018 in Energies
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To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.

ACS Style

Lin Lin; Lin Xue; Zhiqiang Hu; Nantian Huang. Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours. Energies 2018, 11, 1899 .

AMA Style

Lin Lin, Lin Xue, Zhiqiang Hu, Nantian Huang. Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours. Energies. 2018; 11 (7):1899.

Chicago/Turabian Style

Lin Lin; Lin Xue; Zhiqiang Hu; Nantian Huang. 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours." Energies 11, no. 7: 1899.

Journal article
Published: 22 June 2018 in Energies
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Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments.

ACS Style

Nantian Huang; Enkai Xing; Guowei Cai; Zhiyong Yu; Bin Qi; Lin Lin. Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection. Energies 2018, 11, 1638 .

AMA Style

Nantian Huang, Enkai Xing, Guowei Cai, Zhiyong Yu, Bin Qi, Lin Lin. Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection. Energies. 2018; 11 (7):1638.

Chicago/Turabian Style

Nantian Huang; Enkai Xing; Guowei Cai; Zhiyong Yu; Bin Qi; Lin Lin. 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection." Energies 11, no. 7: 1638.

Journal article
Published: 16 September 2017 in Sensors
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In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

ACS Style

Nantian Huang; Jiajin Qi; Fuqing Li; Dongfeng Yang; Guowei Cai; Guilin Huang; Jian Zheng; Zhenxin Li. Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line. Sensors 2017, 17, 2133 .

AMA Style

Nantian Huang, Jiajin Qi, Fuqing Li, Dongfeng Yang, Guowei Cai, Guilin Huang, Jian Zheng, Zhenxin Li. Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line. Sensors. 2017; 17 (9):2133.

Chicago/Turabian Style

Nantian Huang; Jiajin Qi; Fuqing Li; Dongfeng Yang; Guowei Cai; Guilin Huang; Jian Zheng; Zhenxin Li. 2017. "Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line." Sensors 17, no. 9: 2133.