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With the continuous improvement of the voltage level of the power system, the electromagnetic interference problem of the converter station has become more and more serious. The thyristor control unit (TCU) is the core equipment of the converter valve, and its normal operation is related to the safe and stable operation of the entire converter valve. This paper starts with the actual electromagnetic environment in the converter valve hall, analyzes the failure principle of the TCU under electromagnetic disturbance, and observes the electromagnetic field distribution and sensitive components on the circuit board. Then, a TCU failure early warning method based on pattern matching and support vector regression (SVR) is proposed. The failure trend is deduced by constructing an abnormal information vector, and then the failure predictor is constructed using support vector regression optimized by grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO). Considering the failure type and warning time comprehensively, an early warning is issued when the failure mode probability increases to the threshold. When new failure modes appear, the failure mode library will continue to expand. The calculation example shows that this method can effectively warn the TCU failure in the electromagnetic environment, and its prediction accuracy can reach 89.2%, which is better than the traditional failure prediction method.
Xuan Li; Jianyong Zheng; Fei Mei; Haoyuan Sha; Danqi Li. An Early Warning Method of TCU Failure in Electromagnetic Environment Based on Pattern Matching and Support Vector Regression. Energies 2020, 13, 5537 .
AMA StyleXuan Li, Jianyong Zheng, Fei Mei, Haoyuan Sha, Danqi Li. An Early Warning Method of TCU Failure in Electromagnetic Environment Based on Pattern Matching and Support Vector Regression. Energies. 2020; 13 (21):5537.
Chicago/Turabian StyleXuan Li; Jianyong Zheng; Fei Mei; Haoyuan Sha; Danqi Li. 2020. "An Early Warning Method of TCU Failure in Electromagnetic Environment Based on Pattern Matching and Support Vector Regression." Energies 13, no. 21: 5537.
With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.
Tian Shi; Fei Mei; Jixiang Lu; Jinjun Lu; Yi Pan; Cheng Zhou; Jianzhang Wu; Jianyong Zheng. Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting. Energies 2019, 12, 4349 .
AMA StyleTian Shi, Fei Mei, Jixiang Lu, Jinjun Lu, Yi Pan, Cheng Zhou, Jianzhang Wu, Jianyong Zheng. Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting. Energies. 2019; 12 (22):4349.
Chicago/Turabian StyleTian Shi; Fei Mei; Jixiang Lu; Jinjun Lu; Yi Pan; Cheng Zhou; Jianzhang Wu; Jianyong Zheng. 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting." Energies 12, no. 22: 4349.
Saturated-core Fault Current Limiter (SFCL) is a new type of iron-core reactor which can regulate the output reactance of iron-core reactor by using DC bias. The traditional method of energy-release utilizes the energy-release resistance to consume the energy in DC coil. This method requires high voltage withstand of IGBT. This paper proposes a new topology to speed up the process of energy release by adding a reverse voltage on DC coil and reduce the voltage on IGBT. In this paper, a simulation model is established in Jmag whose data is imported into Matlab to be prepared for further analysis. Finally, two kinds of models are built in MATLAB to verify the superiority of the new topology.
Haocong Shen; Jianyong Zheng. A Novel Topology For Improving the Dynamic Performance of Saturated-Core Fault Current Limiter. IOP Conference Series: Earth and Environmental Science 2019, 252, 032177 .
AMA StyleHaocong Shen, Jianyong Zheng. A Novel Topology For Improving the Dynamic Performance of Saturated-Core Fault Current Limiter. IOP Conference Series: Earth and Environmental Science. 2019; 252 (3):032177.
Chicago/Turabian StyleHaocong Shen; Jianyong Zheng. 2019. "A Novel Topology For Improving the Dynamic Performance of Saturated-Core Fault Current Limiter." IOP Conference Series: Earth and Environmental Science 252, no. 3: 032177.
In this paper, we solve the day-ahead scheduling programming problem for commercial building-level consumers with combined time-of-use ($/kWh) and demand ($/kW) pricing plans. Aiming at minimizing the monthly charge, the problem formulation is proposed as a generic algorithm that produces day-ahead building operation schedules. It considers the influences of daily peak load during on-peak hours and daily energy consumption on the monthly charge. The aggregation model of building-level space conditionings is built for scheduling demo. To obtain the near global optimum, a multi sub-swarms particle swarm optimization (MSPSO) is proposed by introducing the ideas of mutation operation, work hierarchy and iterative regrouping into particle swarm optimization (PSO). It improves the population diversity for enhancing the global searching ability and the ability of escaping from local optimum. The PSO comparison shows that MSPSO has better convergence performance with higher stability compared with some classical PSOs. Furthermore, a commercial office-style building with space conditionings is simulated. Using TOU and demand pricing plan from Duke Energy, numerical results demonstrate that the proposed day-ahead scheduling algorithm and the improved MSPSO can reduce the monthly charge by 30% and 17%, respectively.
Kedong Zhu; Ning Lu; Jianyong Zheng; Guoqiang Sun; Fei Mei. Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan. Electric Power Systems Research 2019, 173, 240 -250.
AMA StyleKedong Zhu, Ning Lu, Jianyong Zheng, Guoqiang Sun, Fei Mei. Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan. Electric Power Systems Research. 2019; 173 ():240-250.
Chicago/Turabian StyleKedong Zhu; Ning Lu; Jianyong Zheng; Guoqiang Sun; Fei Mei. 2019. "Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan." Electric Power Systems Research 173, no. : 240-250.
Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.
Fei Mei; Qingliang Wu; Tian Shi; Jixiang Lu; Yi Pan; Jianyong Zheng. An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network. Applied Sciences 2019, 9, 1487 .
AMA StyleFei Mei, Qingliang Wu, Tian Shi, Jixiang Lu, Yi Pan, Jianyong Zheng. An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network. Applied Sciences. 2019; 9 (7):1487.
Chicago/Turabian StyleFei Mei; Qingliang Wu; Tian Shi; Jixiang Lu; Yi Pan; Jianyong Zheng. 2019. "An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network." Applied Sciences 9, no. 7: 1487.
Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.
Haoyuan Sha; Fei Mei; Chenyu Zhang; Yi Pan; Jianyong Zheng. Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine. Energies 2019, 12, 1137 .
AMA StyleHaoyuan Sha, Fei Mei, Chenyu Zhang, Yi Pan, Jianyong Zheng. Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine. Energies. 2019; 12 (6):1137.
Chicago/Turabian StyleHaoyuan Sha; Fei Mei; Chenyu Zhang; Yi Pan; Jianyong Zheng. 2019. "Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine." Energies 12, no. 6: 1137.
The planning problem of distributed generators (DG) accessing the AC/DC distribution network is a hot research topic at present. In this paper, a location and volume model of DG is established that considers DG operation and maintenance costs, DG investment costs, system network loss costs, fuel costs, pollution compensation costs, and environmental protection subsidies. Furthermore, voltage and power constraints are also considered in the model. To solve the proposed model, a hybrid algorithm called the GA-ACO algorithm is presented that combines the ant colony algorithm (ACO) and the genetic algorithm (GA). On one hand GA has good robustness, good adaptability, and quick global searching ability but it also has some disadvantages such as premature convergence and low convergence speed. On the other hand, ACO has the ability of parallel processing and global searching but its convergence speed is very low at the beginning. The IEEE-33 node distribution network is taken as a basic network to verify the rationale of the proposed model and the effectiveness of the proposed hybrid algorithm. Simulation results show that the proposed model is very in line with reality, the hybrid algorithm is very effective in solving the model and it has advantages in both convergence speed and convergence results compared to ACO and GA.
Deyang Yin; Fei Mei; Jianyong Zheng. An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach. Applied Sciences 2019, 9, 1212 .
AMA StyleDeyang Yin, Fei Mei, Jianyong Zheng. An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach. Applied Sciences. 2019; 9 (6):1212.
Chicago/Turabian StyleDeyang Yin; Fei Mei; Jianyong Zheng. 2019. "An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach." Applied Sciences 9, no. 6: 1212.
A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.
Danqi Li; Fei Mei; Chenyu Zhang; Haoyuan Sha; Jianyong Zheng. Self-Supervised Voltage Sag Source Identification Method Based on CNN. Energies 2019, 12, 1059 .
AMA StyleDanqi Li, Fei Mei, Chenyu Zhang, Haoyuan Sha, Jianyong Zheng. Self-Supervised Voltage Sag Source Identification Method Based on CNN. Energies. 2019; 12 (6):1059.
Chicago/Turabian StyleDanqi Li; Fei Mei; Chenyu Zhang; Haoyuan Sha; Jianyong Zheng. 2019. "Self-Supervised Voltage Sag Source Identification Method Based on CNN." Energies 12, no. 6: 1059.
Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.
Fei Mei; Yong Ren; Qingliang Wu; Chenyu Zhang; Yi Pan; Haoyuan Sha; Jianyong Zheng. Online Recognition Method for Voltage Sags Based on a Deep Belief Network. Energies 2018, 12, 43 .
AMA StyleFei Mei, Yong Ren, Qingliang Wu, Chenyu Zhang, Yi Pan, Haoyuan Sha, Jianyong Zheng. Online Recognition Method for Voltage Sags Based on a Deep Belief Network. Energies. 2018; 12 (1):43.
Chicago/Turabian StyleFei Mei; Yong Ren; Qingliang Wu; Chenyu Zhang; Yi Pan; Haoyuan Sha; Jianyong Zheng. 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network." Energies 12, no. 1: 43.
The saturated-core fault current limiter (SFCL) is widely used to limit the fault current. However, in the conventional SFCL structure, alternating current (AC) and direct current (DC) coils are wound on different loosely coupled cores. Owing to the leakage inductance, the traditional structure demonstrates relatively large demand for DC excitation power and excessive impedance during saturation. In this study, a new structure for winding closely coupled DC and AC coils on the same core in three phases is proposed to reduce the influence of leakage reactance on the SFCL performance. The leakage magnetic flux generated by both structures is analyzed by performing finite element analysis simulations and utilizing a magnetic field division method. The impedance of the limiter is measured at different DC currents and air gaps to optimize its dynamic performance. A fabricated prototype of the proposed limiter exhibits smaller steady-state losses and high current-limiting capability.
Haocong Shen; Fei Mei; Jianyong Zheng; Haoyuan Sha; Changjia She. Three-Phase Saturated-Core Fault Current Limiter. Energies 2018, 11, 3471 .
AMA StyleHaocong Shen, Fei Mei, Jianyong Zheng, Haoyuan Sha, Changjia She. Three-Phase Saturated-Core Fault Current Limiter. Energies. 2018; 11 (12):3471.
Chicago/Turabian StyleHaocong Shen; Fei Mei; Jianyong Zheng; Haoyuan Sha; Changjia She. 2018. "Three-Phase Saturated-Core Fault Current Limiter." Energies 11, no. 12: 3471.
A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.
Fei Mei; Yi Pan; Kedong Zhu; Jianyong Zheng. A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation. Sustainability 2018, 10, 820 .
AMA StyleFei Mei, Yi Pan, Kedong Zhu, Jianyong Zheng. A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation. Sustainability. 2018; 10 (3):820.
Chicago/Turabian StyleFei Mei; Yi Pan; Kedong Zhu; Jianyong Zheng. 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation." Sustainability 10, no. 3: 820.
Kedong Zhu; Fei Mei; Jianyong Zheng. Adaptive fault diagnosis of HVCBs based on P-SVDD and P-KFCM. Neurocomputing 2017, 240, 127 -136.
AMA StyleKedong Zhu, Fei Mei, Jianyong Zheng. Adaptive fault diagnosis of HVCBs based on P-SVDD and P-KFCM. Neurocomputing. 2017; 240 ():127-136.
Chicago/Turabian StyleKedong Zhu; Fei Mei; Jianyong Zheng. 2017. "Adaptive fault diagnosis of HVCBs based on P-SVDD and P-KFCM." Neurocomputing 240, no. : 127-136.
Chenyu Zhang; Jianyong Zheng; Jun Mei; Kai Deng; Fuju Zhou. Control Method for Fault-Tolerant Active Power Filters. Journal of Power Electronics 2015, 15, 796 -805.
AMA StyleChenyu Zhang, Jianyong Zheng, Jun Mei, Kai Deng, Fuju Zhou. Control Method for Fault-Tolerant Active Power Filters. Journal of Power Electronics. 2015; 15 (3):796-805.
Chicago/Turabian StyleChenyu Zhang; Jianyong Zheng; Jun Mei; Kai Deng; Fuju Zhou. 2015. "Control Method for Fault-Tolerant Active Power Filters." Journal of Power Electronics 15, no. 3: 796-805.
Kai Deng; Fei Mei; Jun Mei; Jianyong Zheng; Guangxu Fu. An Extended Switched-inductor Quasi-Z-source Inverter. Journal of Electrical Engineering & Technology 2014, 9, 541 -549.
AMA StyleKai Deng, Fei Mei, Jun Mei, Jianyong Zheng, Guangxu Fu. An Extended Switched-inductor Quasi-Z-source Inverter. Journal of Electrical Engineering & Technology. 2014; 9 (2):541-549.
Chicago/Turabian StyleKai Deng; Fei Mei; Jun Mei; Jianyong Zheng; Guangxu Fu. 2014. "An Extended Switched-inductor Quasi-Z-source Inverter." Journal of Electrical Engineering & Technology 9, no. 2: 541-549.