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Nantian Huang
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, China

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Journal article
Published: 12 November 2020 in Journal of Energy Storage
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The reused batteries have become a practical alternative to household energy storage system, which is conducive to the effective utilization of excessive roof photovoltaic power generation and the sustainable development of energy. Economic incentives are the driving force for residential consumers to develop photovoltaic and energy storage. This study combines a solar-load uncertainty model and economic analysis to assess the financial impact of adding a reused-battery energy storage system to a photovoltaic assemblage in the context of multi-tariff policies and photovoltaic resource regions in China. First, we classify the types of residents based on the correlation between the users’ electricity consumption behavior and solar radiation. Secondly, to characterize the solar-load uncertainty, a deep scenario generation method based on an improved variational autoencoder is proposed to generate solar-load scenarios. Then, a mixed-integer linear programming model is developed which takes solar-load uncertainty into account. Finally, the operating cost of photovoltaic with a reused-battery energy storage system for each type of residential user under multi-tariff policies in China considering solar load uncertainty is obtained. The results demonstrate that the generated scenarios can effectively describe the uncertainty of the photovoltaic output and residential load. And, the correlation between the users’ electricity consumption behavior and solar radiation can guide the residential customer to install the reused-battery energy storage system. Moreover, economic feasibility and sustainable development of photovoltaic with reused-battery energy storage system depending on the regulation of market tariff policy.

ACS Style

Nantian Huang; Wenting Wang; Guowei Cai; Jiajin Qi; Yijun Jiang. Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China. Journal of Energy Storage 2020, 33, 102081 .

AMA Style

Nantian Huang, Wenting Wang, Guowei Cai, Jiajin Qi, Yijun Jiang. Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China. Journal of Energy Storage. 2020; 33 ():102081.

Chicago/Turabian Style

Nantian Huang; Wenting Wang; Guowei Cai; Jiajin Qi; Yijun Jiang. 2020. "Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China." Journal of Energy Storage 33, no. : 102081.

Research article
Published: 28 March 2019 in IET Renewable Power Generation
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Microgrid systems, such as solar photovoltaic (PV) and wind turbine (WT), integrated with diesel generator can provide adequate energy to supply increased demands and are economically feasible for current and future use considering depletion of conventional sources. It is, thus, important to determine the appropriate sizes of PV, WT, diesel generator, and associated energy storage system (ESS) for efficient, economic, and reliable operation of electric power system in microgrid. Stochastic nature of intermittent renewable energy (RE) resources complicate their planning, integration, and operation of electric power system. Therefore, it is critical to generate typical scenarios of wind speed, irradiation, and load time series to reflect their stochastic characteristic for microgrid system planning and operation. In this study, a wind-irradiation-load typical scenarios generation method is proposed for optimal sizing RE resources of microgrid. The teaching-learning-based optimisation (TLBO) method is used to find the best configuration of the microgrid system. Simulation results show that scenarios generated by the proposed model have ability to approximate the original scenarios and reduce planning data effectively.

ACS Style

Dongfeng Yang; Chao Jiang; Guowei Cai; Nantian Huang. Optimal sizing of a wind/solar/battery/diesel hybrid microgrid based on typical scenarios considering meteorological variability. IET Renewable Power Generation 2019, 13, 1446 -1455.

AMA Style

Dongfeng Yang, Chao Jiang, Guowei Cai, Nantian Huang. Optimal sizing of a wind/solar/battery/diesel hybrid microgrid based on typical scenarios considering meteorological variability. IET Renewable Power Generation. 2019; 13 (9):1446-1455.

Chicago/Turabian Style

Dongfeng Yang; Chao Jiang; Guowei Cai; Nantian Huang. 2019. "Optimal sizing of a wind/solar/battery/diesel hybrid microgrid based on typical scenarios considering meteorological variability." IET Renewable Power Generation 13, no. 9: 1446-1455.

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: 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: 22 September 2016 in Energies
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The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

ACS Style

Nantian Huang; Guobo Lu; Dianguo Xu. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest. Energies 2016, 9, 767 .

AMA Style

Nantian Huang, Guobo Lu, Dianguo Xu. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest. Energies. 2016; 9 (10):767.

Chicago/Turabian Style

Nantian Huang; Guobo Lu; Dianguo Xu. 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest." Energies 9, no. 10: 767.

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: 30 September 2012 in International Journal of Advancements in Computing Technology
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ACS Style

Lin Lin -; Huang Nantian -; Zhang Yingjun -; Qi Jiajin -. Power Quality Disturbance Recognition Utilizing Modified Fourier Neural Network and S-transform. International Journal of Advancements in Computing Technology 2012, 4, 170 -179.

AMA Style

Lin Lin -, Huang Nantian -, Zhang Yingjun -, Qi Jiajin -. Power Quality Disturbance Recognition Utilizing Modified Fourier Neural Network and S-transform. International Journal of Advancements in Computing Technology. 2012; 4 (17):170-179.

Chicago/Turabian Style

Lin Lin -; Huang Nantian -; Zhang Yingjun -; Qi Jiajin -. 2012. "Power Quality Disturbance Recognition Utilizing Modified Fourier Neural Network and S-transform." International Journal of Advancements in Computing Technology 4, no. 17: 170-179.

Journal article
Published: 05 June 2012 in Neurocomputing
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Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time–frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.

ACS Style

Nantian Huang; Dianguo Xu; Xiaosheng Liu; Lin Lin. Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing 2012, 98, 12 -23.

AMA Style

Nantian Huang, Dianguo Xu, Xiaosheng Liu, Lin Lin. Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing. 2012; 98 ():12-23.

Chicago/Turabian Style

Nantian Huang; Dianguo Xu; Xiaosheng Liu; Lin Lin. 2012. "Power quality disturbances classification based on S-transform and probabilistic neural network." Neurocomputing 98, no. : 12-23.

Journal article
Published: 10 December 2009 in JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT
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ACS Style

Nantian Huang; Dianguo Xu; Xiaosheng Liu. Electric power quality disturbance classification based on modified multilayer feedforward neural network. JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 2009, 2009, 62 -66.

AMA Style

Nantian Huang, Dianguo Xu, Xiaosheng Liu. Electric power quality disturbance classification based on modified multilayer feedforward neural network. JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT. 2009; 2009 (10):62-66.

Chicago/Turabian Style

Nantian Huang; Dianguo Xu; Xiaosheng Liu. 2009. "Electric power quality disturbance classification based on modified multilayer feedforward neural network." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 2009, no. 10: 62-66.