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Guowei Cai
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

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Journal article
Published: 23 December 2019 in Energies
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This study focuses on the dynamics of a grid-tied voltage source converter (GVSC) during electromechanical oscillations. A small-signal model with GVSC port variables (DC voltage and AC power) as the outputs and a terminal voltage vector as the input is derived to reveal the passive response of the GVSC on the basis of the power equation in the d–q coordinate system. An input–output transfer function matrix is constructed according to the proposed model. The frequency response of this matrix in the electromechanical bandwidth is described to reflect the dynamic behavior of the GVSC. The effects of the operation parameters, i.e., the grid strength, reference value of the control system, and grid voltage, on the dynamic behavior of the GVSC in the electromechanical bandwidth, are investigated using frequency domain sensitivity. Analysis results show that the GVSC generates responses with respect to the electromechanical mode. These responses have different sensitivities to the operation parameters. The IEEE 10-machine power system simulation is performed, and the power hardware-in-the-loop platform with the GVSC was applied to validate the analysis.

ACS Style

Bo Wang; Guowei Cai; Deyou Yang; Lixin Wang; Zhiye Yu; Wang. Investigation on Dynamic Response of Grid-Tied VSC During Electromechanical Oscillations of Power Systems. Energies 2019, 13, 94 .

AMA Style

Bo Wang, Guowei Cai, Deyou Yang, Lixin Wang, Zhiye Yu, Wang. Investigation on Dynamic Response of Grid-Tied VSC During Electromechanical Oscillations of Power Systems. Energies. 2019; 13 (1):94.

Chicago/Turabian Style

Bo Wang; Guowei Cai; Deyou Yang; Lixin Wang; Zhiye Yu; Wang. 2019. "Investigation on Dynamic Response of Grid-Tied VSC During Electromechanical Oscillations of Power Systems." Energies 13, no. 1: 94.

Journal article
Published: 03 August 2019 in Energies
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In the direct current (DC) microgrid composed of multiple distributed generations, due to the different distances between various converters and the DC bus in the system, the difference of the line resistance will reduce the current sharing accuracy of the system. The droop control was widely used in the operation control of the DC microgrid. It was necessary to select a large droop coefficient to improve the current sharing accuracy, but a too large droop coefficient will lead to a serious bus voltage drop and affect the power quality. In view of the contradiction between the voltage regulation and load current sharing in the traditional droop control, a hierarchical control algorithm based on the improved droop control of the fuzzy logic was proposed in this paper. By improving the droop curve, the problems of voltage regulation and current sharing were solved simultaneously. The effectiveness of the algorithm was verified by simulation.

ACS Style

Liang Zhang; Kang Chen; Shengbin Chi; Ling Lyu; Guowei Cai. The Hierarchical Control Algorithm for DC Microgrid Based on the Improved Droop Control of Fuzzy Logic. Energies 2019, 12, 2995 .

AMA Style

Liang Zhang, Kang Chen, Shengbin Chi, Ling Lyu, Guowei Cai. The Hierarchical Control Algorithm for DC Microgrid Based on the Improved Droop Control of Fuzzy Logic. Energies. 2019; 12 (15):2995.

Chicago/Turabian Style

Liang Zhang; Kang Chen; Shengbin Chi; Ling Lyu; Guowei Cai. 2019. "The Hierarchical Control Algorithm for DC Microgrid Based on the Improved Droop Control of Fuzzy Logic." Energies 12, no. 15: 2995.

Journal article
Published: 25 March 2019 in Energies
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Low-voltage direct current (DC) microgrid based on distributed generation (DG), the problems of load mutation affecting the DC bus under island mode, and the security problems that may arise when the DC microgrid is switched from island mode to grid-connected mode are considered. Firstly, a DC bus control algorithm based on disturbance observer (DOB) was proposed to suppress the impact of system load mutation on DC bus in island mode. Then, in a grid-connected mode, a pre-synchronization control algorithm based on a neural network adaptive control was proposed, and the droop controller was improved to ensure better control accuracy. Through this pre-synchronization control, the microgrid inverters output voltage could quickly track the power grid’s voltage and achieve an accurate grid-connected operation. The effectiveness of the algorithms was verified by simulation.

ACS Style

Liang Zhang; Kang Chen; Ling Lyu; Guowei Cai. Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm. Energies 2019, 12, 1162 .

AMA Style

Liang Zhang, Kang Chen, Ling Lyu, Guowei Cai. Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm. Energies. 2019; 12 (6):1162.

Chicago/Turabian Style

Liang Zhang; Kang Chen; Ling Lyu; Guowei Cai. 2019. "Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm." Energies 12, no. 6: 1162.

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: 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: 25 November 2016 in Energies
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In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.

ACS Style

Guowei Cai; Wenjin Wang; Junhai Lu. A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction. Energies 2016, 9, 994 .

AMA Style

Guowei Cai, Wenjin Wang, Junhai Lu. A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction. Energies. 2016; 9 (12):994.

Chicago/Turabian Style

Guowei Cai; Wenjin Wang; Junhai Lu. 2016. "A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction." Energies 9, no. 12: 994.

Journal article
Published: 25 November 2016 in Energies
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Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

ACS Style

Nantian Huang; Chong Yuan; Guowei Cai; Enkai Xing. Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine. Energies 2016, 9, 989 .

AMA Style

Nantian Huang, Chong Yuan, Guowei Cai, Enkai Xing. Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine. Energies. 2016; 9 (12):989.

Chicago/Turabian Style

Nantian Huang; Chong Yuan; Guowei Cai; Enkai Xing. 2016. "Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine." Energies 9, no. 12: 989.

Journal article
Published: 10 November 2016 in Sensors
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Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

ACS Style

Nantian Huang; Huaijin Chen; Guowei Cai; Lihua Fang; Yuqiang Wang. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier. Sensors 2016, 16, 1887 .

AMA Style

Nantian Huang, Huaijin Chen, Guowei Cai, Lihua Fang, Yuqiang Wang. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier. Sensors. 2016; 16 (11):1887.

Chicago/Turabian Style

Nantian Huang; Huaijin Chen; Guowei Cai; Lihua Fang; Yuqiang Wang. 2016. "Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier." Sensors 16, no. 11: 1887.

Journal article
Published: 09 November 2016 in Energies
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In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine.

ACS Style

Nantian Huang; Hua Peng; Guowei Cai; Jikai Chen. Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm. Energies 2016, 9, 927 .

AMA Style

Nantian Huang, Hua Peng, Guowei Cai, Jikai Chen. Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm. Energies. 2016; 9 (11):927.

Chicago/Turabian Style

Nantian Huang; Hua Peng; Guowei Cai; Jikai Chen. 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm." Energies 9, no. 11: 927.

Journal article
Published: 08 September 2016 in Entropy
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A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected by statistical experiments is used to balance the relevance and redundancy of features when using the G-mRMR. Third, each feature is ranked in a descending order according to its relevance and redundancy as computed by G-mRMR. A sequential forward selection method is utilized for choosing the optimal subset. Finally, a STLF predictor is constructed based on random forest with the obtained optimal subset. The effectiveness and improvement of the proposed method was tested with actual load data.

ACS Style

Nantian Huang; Zhiqiang Hu; Guowei Cai; Dongfeng Yang. Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria. Entropy 2016, 18, 330 .

AMA Style

Nantian Huang, Zhiqiang Hu, Guowei Cai, Dongfeng Yang. Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria. Entropy. 2016; 18 (9):330.

Chicago/Turabian Style

Nantian Huang; Zhiqiang Hu; Guowei Cai; Dongfeng Yang. 2016. "Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria." Entropy 18, no. 9: 330.

Journal article
Published: 03 September 2016 in Entropy
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In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

ACS Style

Nantian Huang; Lihua Fang; Guowei Cai; Dianguo Xu; Huaijin Chen; Yonghui Nie. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy. Entropy 2016, 18, 322 .

AMA Style

Nantian Huang, Lihua Fang, Guowei Cai, Dianguo Xu, Huaijin Chen, Yonghui Nie. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy. Entropy. 2016; 18 (9):322.

Chicago/Turabian Style

Nantian Huang; Lihua Fang; Guowei Cai; Dianguo Xu; Huaijin Chen; Yonghui Nie. 2016. "Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy." Entropy 18, no. 9: 322.

Journal article
Published: 28 January 2016 in Entropy
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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

ACS Style

Nantian Huang; Guobo Lu; Guowei Cai; Dianguo Xu; Jiafeng Xu; Fuqing Li; Liying Zhang. Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest. Entropy 2016, 18, 44 .

AMA Style

Nantian Huang, Guobo Lu, Guowei Cai, Dianguo Xu, Jiafeng Xu, Fuqing Li, Liying Zhang. Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest. Entropy. 2016; 18 (2):44.

Chicago/Turabian Style

Nantian Huang; Guobo Lu; Guowei Cai; Dianguo Xu; Jiafeng Xu; Fuqing Li; Liying Zhang. 2016. "Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest." Entropy 18, no. 2: 44.

Journal article
Published: 26 December 2015 in Entropy
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Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.

ACS Style

Nantian Huang; Huaijin Chen; Shuxin Zhang; Guowei Cai; Weiguo Li; Dianguo Xu; Lihua Fang. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine. Entropy 2015, 18, 7 .

AMA Style

Nantian Huang, Huaijin Chen, Shuxin Zhang, Guowei Cai, Weiguo Li, Dianguo Xu, Lihua Fang. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine. Entropy. 2015; 18 (1):7.

Chicago/Turabian Style

Nantian Huang; Huaijin Chen; Shuxin Zhang; Guowei Cai; Weiguo Li; Dianguo Xu; Lihua Fang. 2015. "Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine." Entropy 18, no. 1: 7.

Journal article
Published: 15 January 2015 in Energies
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In a microgrid, the distributed generators (DG) can power the user loads directly. As a result, power quality (PQ) events are more likely to affect the users. This paper proposes a Multiresolution Generalized S-transform (MGST) approach to improve the ability of analyzing and monitoring the power quality in a microgrid. Firstly, the time-frequency distribution characteristics of different types of disturbances are analyzed. Based on the characteristics, the frequency domain is segmented into three frequency areas. After that, the width factor of the window function in the S-transform is set in different frequency areas. MGST has different time-frequency resolution in each frequency area to satisfy the recognition requirements of different disturbances in each frequency area. Then, a rule-based decision tree classifier is designed. In addition, particle swarm optimization (PSO) is applied to extract the applicable features. Finally, the proposed method is compared with some others. The simulation experiments show that the new approach has better accuracy and noise immunity.

ACS Style

Nantian Huang; Shuxin Zhang; Guowei Cai; Dianguo Xu. Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree. Energies 2015, 8, 549 -572.

AMA Style

Nantian Huang, Shuxin Zhang, Guowei Cai, Dianguo Xu. Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree. Energies. 2015; 8 (1):549-572.

Chicago/Turabian Style

Nantian Huang; Shuxin Zhang; Guowei Cai; Dianguo Xu. 2015. "Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree." Energies 8, no. 1: 549-572.

Journal article
Published: 13 September 2013 in Energies
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As an important part of the smart grid, a wide-area measurement system (WAMS) provides the key technical support for power system monitoring, protection and control. But 20 uncertainties in system parameters and signal transmission time delay could worsen the damping effect and deteriorate the system stability. In the presented study, the subspace system identification technique (SIT) is used to firstly derive a low-order linear model of a power system from the measurements. Then, a novel adaptive wide-area damping control scheme for online tuning of the wide-area damping controller (WADC) parameters using the residue method is proposed. In order to eliminate the effects of the time delay to the signal transmission, a simple and practical time delay compensation algorithm is proposed to compensate the time delay in each wide-area control signal. Detailed examples, inspired by the IEEE test system under various disturbance scenarios, have been used to verify the effectiveness of the proposed adaptive wide-area damping control scheme.

ACS Style

Guowei Cai; Deyou Yang; Cheng Liu. Adaptive Wide-Area Damping Control Scheme for Smart Grids with Consideration of Signal Time Delay. Energies 2013, 6, 4841 -4858.

AMA Style

Guowei Cai, Deyou Yang, Cheng Liu. Adaptive Wide-Area Damping Control Scheme for Smart Grids with Consideration of Signal Time Delay. Energies. 2013; 6 (9):4841-4858.

Chicago/Turabian Style

Guowei Cai; Deyou Yang; Cheng Liu. 2013. "Adaptive Wide-Area Damping Control Scheme for Smart Grids with Consideration of Signal Time Delay." Energies 6, no. 9: 4841-4858.