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The coordinated operation of an integrated energy system (IES) and a distribution network is the inevitable development trend of the energy Internet of the future. The day-ahead optimal scheduling of the IES is an important way to improve new energy efficiency and the energy economy. When the IES and the distribution network exchange electrical energy, the voltage of the distribution network may be out of limit. This article presents a two-stage joint optimal scheduling method for a distribution network with IESs to improve the economy of the IESs and the safety of the distribution network. In the first stage, the user’s demand response and the electrical energy interaction between IESs are considered, and the schedulable potential of the systems is fully tapped. In the second stage, a bi-level scheduling model is adopted: the upper model takes the distribution network as the control object and reduces the power loss by adjusting the exchange power between the distribution network and the IESs. The lower model takes the IESs as the control objects and obtains the scheme with the lowest cost in each IES through multi-objective particle swarm optimization. Taking the IEEE 33-node distribution system as an example, simulation research is performed to show that the total network loss is 17.01% lower and the total cost is 5.36% lower than the method without two-stage optimal scheduling, which verifies the effectiveness of the proposed method.
Yuhan Jiang; Fei Mei; Jixiang Lu; Jinjun Lu. Two-Stage Joint Optimal Scheduling of a Distribution Network with Integrated Energy Systems. IEEE Access 2021, 9, 1 -1.
AMA StyleYuhan Jiang, Fei Mei, Jixiang Lu, Jinjun Lu. Two-Stage Joint Optimal Scheduling of a Distribution Network with Integrated Energy Systems. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleYuhan Jiang; Fei Mei; Jixiang Lu; Jinjun Lu. 2021. "Two-Stage Joint Optimal Scheduling of a Distribution Network with Integrated Energy Systems." IEEE Access 9, no. : 1-1.
Problems related to the uncertainties of the sources and loads in integrated energy systems (IESs) are becoming more prominent with the interconnection of large-scale renewable energy sources and multi-energy loads. Moreover, such scenarios pose great challenges for the optimal operation of IESs. A distributed IES in an industrial park is regarded as the research object, and a stochastic optimal operation model based on multiple-scenario simulations is proposed to consider the prediction uncertainties arising in the case of distributed power generation and multi-energy loads. Specifically, scenario analysis for stochastic optimization is applied to address these prediction uncertainties in a two-part approach: operation scenario generation based on Latin hypercube sampling (LHS) and the reduction of multiple scenarios into a smaller number of more general scenarios based on K-means. Afterwards, a day-ahead stochastic optimal operation model for a distributed IES with the total operating economy as the decision-making objective is proposed based on typical operation scenarios. Moreover, the overall energy efficiency and new energy consumption capacity are all considered. In this way, the safe and economical operation of the IES can be guaranteed even under the negative influence of uncertainties. The validity and rationality of the proposed model are verified by analysis of examples.
Fei Mei; Jiatang Zhang; Jixiang Lu; Jinjun Lu; Yuhan Jiang; Jiaqi Gu; Kun Yu; Lei Gan. Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations. Energy 2020, 219, 119629 .
AMA StyleFei Mei, Jiatang Zhang, Jixiang Lu, Jinjun Lu, Yuhan Jiang, Jiaqi Gu, Kun Yu, Lei Gan. Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations. Energy. 2020; 219 ():119629.
Chicago/Turabian StyleFei Mei; Jiatang Zhang; Jixiang Lu; Jinjun Lu; Yuhan Jiang; Jiaqi Gu; Kun Yu; Lei Gan. 2020. "Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations." Energy 219, no. : 119629.
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 rapid growth of photovoltaic (PV) power in recent years, the stability of system operation, the performance of system contingency analysis as well as the power quality of the power grid are threatened by the inherent uncertainty and fluctuation of PV output. It is necessary to have the knowledge of PV output characteristics for reliable power system dispatching. Day-ahead PV power forecasting is an effective support for achieving optimal dispatching. Probabilistic forecasting can describe the uncertainty that is difficult to depict by deterministic forecasting, and the forecasting results are more comprehensive. An ensemble nonparametric probabilistic forecasting model of PV output is proposed based on the traditional deterministic forecasting method. Quantile regression averaging (QRA) is used to ensemble a group of independent long short-term memory (LSTM) deterministic forecasting models for obtaining the probabilistic forecasting of PV output. Real measured data are used to verify the effectiveness of this nonparametric probabilistic forecasting model. Additionally, in comparison with the benchmark methods, LSTM-QRA has higher prediction performance due to the better forecasting accuracy of independent deterministic forecasts.
Fei. Mei; Jiaqi. Gu; Jixiang. Lu; Jinjun. Lu; Jiatang. Zhang; Yuhan. Jiang; Tian. Shi; Jianyong. Zheng. Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble Model. IEEE Access 2020, 8, 166138 -166149.
AMA StyleFei. Mei, Jiaqi. Gu, Jixiang. Lu, Jinjun. Lu, Jiatang. Zhang, Yuhan. Jiang, Tian. Shi, Jianyong. Zheng. Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble Model. IEEE Access. 2020; 8 (99):166138-166149.
Chicago/Turabian StyleFei. Mei; Jiaqi. Gu; Jixiang. Lu; Jinjun. Lu; Jiatang. Zhang; Yuhan. Jiang; Tian. Shi; Jianyong. Zheng. 2020. "Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble Model." IEEE Access 8, no. 99: 166138-166149.
The safety and stability of a distribution network will be affected by high photovoltaic (PV) penetration. Therefore, it is of great significance to evaluate the PV accommodation capacity of a distribution network and to select an appropriate PV accommodation scheme. This paper assesses the PV accommodation capacity of a distribution network with an improved algorithm and optimizes the accommodation scheme with a comprehensive index. First, the PSO (particle swarm optimization)–Monte Carlo algorithm is used to evaluate the maximum accommodation capacity of a distribution network with PV integration. Second, a year-round voltage timing simulation is performed to analyze the node voltage that exceeds the limit under the planned PV capacity, which is higher than the previously evaluated maximum accommodation capacity. Finally, the staged control strategy of the PV inverter and energy storage is carried out to select the scheme for the sizing and siting of energy storage. The simulation tests use a 10 kV standard distribution network as an example for PV evaluation and PV accommodation scheme selection to verify the feasibility and effectiveness of the proposed model.
Jiaqi Gu; Fei Mei; Jixiang Lu; Jinjun Lu; Jingcheng Chen; Xinmin Zhang; Limin Li. Three-Stage Analysis of the Maximum Accommodation Capacity of a Distribution System with High Photovoltaic Penetration. Energies 2020, 13, 4325 .
AMA StyleJiaqi Gu, Fei Mei, Jixiang Lu, Jinjun Lu, Jingcheng Chen, Xinmin Zhang, Limin Li. Three-Stage Analysis of the Maximum Accommodation Capacity of a Distribution System with High Photovoltaic Penetration. Energies. 2020; 13 (17):4325.
Chicago/Turabian StyleJiaqi Gu; Fei Mei; Jixiang Lu; Jinjun Lu; Jingcheng Chen; Xinmin Zhang; Limin Li. 2020. "Three-Stage Analysis of the Maximum Accommodation Capacity of a Distribution System with High Photovoltaic Penetration." Energies 13, no. 17: 4325.
With the widespread attention and research of distributed Photovoltaic (PV) systems, the state evaluation of distributed PV system, especially the evaluation of ash-covered state, has become increasingly prominent. To this end, an ash-covered state evaluation method for the distributed PV systems, which considers the full lifetime degradation, is proposed. First, the PV lifetime degradation rate is calculated based on the historical data of the PV system. Second, the fuzzy C-means (FCM) is used to cluster PV measured AC output power data into four weather types: sunny, cloudless, cloudy, and rainy. According to weather conditions, the Levenberg-Marquardt backpropagation (LMBP) Deep Neural Network (DNN) is adopted to establish four distributed PV AC output power fitting models by using measured meteorological data and measured output power data. Afterward, distributed PV output power fitted value is obtained by reducing the DNN output proportionally according to the PV degradation rate. Moreover, by analyzing the difference between the PV output power fitted value and the measured output power data, the ash-covered state of the system is evaluated, and a state alarm mechanism is established based on the system state. Finally, the validity and rationality of the proposed method are verified by the analysis of examples.
Deyang Yin; Fei Mei; Jianyong Zheng; Weiguo He. Ash-Covered State Evaluation and Alarm of Distributed Photovoltaic System Considering Full Lifetime Degradation. IEEE Access 2020, 8, 121398 -121408.
AMA StyleDeyang Yin, Fei Mei, Jianyong Zheng, Weiguo He. Ash-Covered State Evaluation and Alarm of Distributed Photovoltaic System Considering Full Lifetime Degradation. IEEE Access. 2020; 8 ():121398-121408.
Chicago/Turabian StyleDeyang Yin; Fei Mei; Jianyong Zheng; Weiguo He. 2020. "Ash-Covered State Evaluation and Alarm of Distributed Photovoltaic System Considering Full Lifetime Degradation." IEEE Access 8, no. : 121398-121408.
Fei Mei; Haoyuan Sha. Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network. IEEE Access 2019, 7, 170854 -170863.
AMA StyleFei Mei, Haoyuan Sha. Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network. IEEE Access. 2019; 7 ():170854-170863.
Chicago/Turabian StyleFei Mei; Haoyuan Sha. 2019. "Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network." IEEE Access 7, no. : 170854-170863.
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.
Multi-district integrated energy system (IES) can make full use of the complementary characteristics of district power and thermal system, and loads in different districts. It can improve the flexibility and economy of system operation, which has a good development prospect. Firstly, based on the general energy transfer model of the district heating network (DHN), the DHN system is described by the basic equations of the heating network and nodes considering the characteristics of the transmission time delay and heat loss in pipelines. A coupling model of DHN and multi-district IES is established. Secondly, the flexible demand response (FDR) model of electric and thermal loads is established. The load characteristics of each district in IES are studied. A shiftable load model based on the electric quantity balance is constructed. Considering the flexibility of the heat demand, a thermal load adjustment model based on the comfort constraint is constructed to make the thermal load elastic and controllable in time and space. Finally, a mixed integer linear programming (MILP) model for operation optimization of multi-district IES with the DHN considering the FDR of electric and thermal loads is established based on the supply and demand sides. The result shows that the proposed model makes full use of the complementary characteristics of electric and thermal loads in different districts. It realizes the coordinated distribution of thermal energy among different districts and improves the efficiency of thermal energy utilization through the DHN. FDR effectively reduces the peak-valley difference of loads. It further reduces the total operating cost by the coordinated operation of the DHN and multi-district IES.
Cheng Zhou; Jianyong Zheng; Sai Liu; Fei Mei; Yi Pan; Tian Shi; Jianzhang Wu. Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads. Energies 2019, 12, 3831 .
AMA StyleCheng Zhou, Jianyong Zheng, Sai Liu, Fei Mei, Yi Pan, Tian Shi, Jianzhang Wu. Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads. Energies. 2019; 12 (20):3831.
Chicago/Turabian StyleCheng Zhou; Jianyong Zheng; Sai Liu; Fei Mei; Yi Pan; Tian Shi; Jianzhang Wu. 2019. "Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads." Energies 12, no. 20: 3831.
The saturated-core fault current limiter (SCFCL) has been studied by several scholars in the past decades. However, these studies have mainly focused on the AC coils of the SCFCL, the DC excitation has seldom been mentioned. In this paper, the DC coil of SCFCL is studied as well as the influence of the novel structure of tightly-coupled SCFCL(TSCFCL) on the voltage of DC coil. A novel DC energy-released circuit is proposed, which overcomes the shortcoming of traditional single topology whose energy-released speed is limited by withstanding voltage of IGBT. In order to select a suitable structure of air gap for fault current limiter, the effects of different structures on the saturation of the core are studied.. A novel simulation method is presented in this paper which can overcome the drawback of traditional finite element analysis cannot simulate the switching characteristics of IGBT under high frequency. Finally, a prototype is manufactured to verify the theory of this paper.
Haocong Shen; Fei Mei; Hongfei Chen; Jianyong Zheng. Novel Topology of DC Energy-Released Circuit For Saturated-Core Fault Current Limiter. IEEE Access 2019, 7, 125939 -125951.
AMA StyleHaocong Shen, Fei Mei, Hongfei Chen, Jianyong Zheng. Novel Topology of DC Energy-Released Circuit For Saturated-Core Fault Current Limiter. IEEE Access. 2019; 7 (99):125939-125951.
Chicago/Turabian StyleHaocong Shen; Fei Mei; Hongfei Chen; Jianyong Zheng. 2019. "Novel Topology of DC Energy-Released Circuit For Saturated-Core Fault Current Limiter." IEEE Access 7, no. 99: 125939-125951.
As the coupling and integration of multi-energy flow in the integrated energy system (IES) deepens increasingly, the cascading failure will develop across different energy systems more easily and widely through the energy hub (EH). And it brings great challenges to the security and reliability of IES. The defects of present cascading failure model of IES have been summarized and a novel search strategy of fault chains in IES combined heating and power network was proposed in this paper. Firstly, the initial risk assessment index of each energy branch is proposed to form the initial fault sets. Then combined heat and power control strategies (CHPC) is introduced to deal with the branch overload conditions during the cascading failure. What’s more, in order to reduce the workload and overcome the limitation of present methods, we analyzed the relevance of the branches to be predicted by using Kernel Fuzzy C-means (KFCM) clustering algorithm and selected the branches with the highest value of relevance as the subsequent failure. Based on the predicted fault chain, vulnerability analysis is presented to locate critical component and find out the correlation between cascading outages. Comprehensive evaluation index is also established to effectively evaluate the impact severity of the cascading failure. Finally, the case studies are carried out on the combined heating and power systems to demonstrate the effectiveness of the proposed method.
Yi Pan; Fei Mei; Cheng Zhou; Tian Shi; Jianyong Zheng. Analysis on Integrated Energy System Cascading Failures Considering Interaction of Coupled Heating and Power Networks. IEEE Access 2019, 7, 89752 -89765.
AMA StyleYi Pan, Fei Mei, Cheng Zhou, Tian Shi, Jianyong Zheng. Analysis on Integrated Energy System Cascading Failures Considering Interaction of Coupled Heating and Power Networks. IEEE Access. 2019; 7 (99):89752-89765.
Chicago/Turabian StyleYi Pan; Fei Mei; Cheng Zhou; Tian Shi; Jianyong Zheng. 2019. "Analysis on Integrated Energy System Cascading Failures Considering Interaction of Coupled Heating and Power Networks." IEEE Access 7, no. 99: 89752-89765.
Haoyuan Sha; Fei Mei; Chenyu Zhang; Yi Pan; Jianyong Zheng; Taoran Li. Multi-Harmonic Sources Harmonic Contribution Determination Based on Data Filtering and Cluster Analysis. IEEE Access 2019, 7, 85276 -85285.
AMA StyleHaoyuan Sha, Fei Mei, Chenyu Zhang, Yi Pan, Jianyong Zheng, Taoran Li. Multi-Harmonic Sources Harmonic Contribution Determination Based on Data Filtering and Cluster Analysis. IEEE Access. 2019; 7 ():85276-85285.
Chicago/Turabian StyleHaoyuan Sha; Fei Mei; Chenyu Zhang; Yi Pan; Jianyong Zheng; Taoran Li. 2019. "Multi-Harmonic Sources Harmonic Contribution Determination Based on Data Filtering and Cluster Analysis." IEEE Access 7, no. : 85276-85285.
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.
A virtual synchronous machine (VSM) is a converter which, compared to other types of converters, has more friendly interactions with the power grid because it is able to simulate the external characteristics of a synchronous machine, which can provide virtual inertia and damping. When the grid voltage is unbalanced, there will be negative sequence current and power oscillations. There will also be double-frequency ripples on the DC bus, which affect the normal operation of the DC power source or load. In order to solve these problems, a comprehensive control strategy is proposed in this paper. The principle of a VSM operated as a current source converter, also called VISMA, is used in the design. A complex coefficient filter is applied to separate the positive and negative sequence components of the grid voltage. By analyzing the reasons of power oscillations under unbalanced voltage, the electrical simulation part of the VSM is improved to achieve several objectives: to suppress negative sequence current and DC voltage ripples. Additionally, the rated voltage in the reactive control part is adaptively adjusted to stabilize the system. The validity of the proposed control strategy is verified by simulation and experiment.
Huiyu Miao; Fei Mei; Yun Yang; Hongfei Chen; Jianyong Zheng. A Comprehensive VSM Control Strategy Designed for Unbalanced Grids. Energies 2019, 12, 1169 .
AMA StyleHuiyu Miao, Fei Mei, Yun Yang, Hongfei Chen, Jianyong Zheng. A Comprehensive VSM Control Strategy Designed for Unbalanced Grids. Energies. 2019; 12 (6):1169.
Chicago/Turabian StyleHuiyu Miao; Fei Mei; Yun Yang; Hongfei Chen; Jianyong Zheng. 2019. "A Comprehensive VSM Control Strategy Designed for Unbalanced Grids." Energies 12, no. 6: 1169.
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.
In order to solve the problem of power supply shortage in certain region of Nanjing city, a high-voltage direct current (HVDC) flexible transmission project needs to be constructed. It could be the first project with ±380 kV DC and real bipolar connection in the world. Two new converter stations will be built with 1500 MW transmission capacity. The system design scheme has been proposed including the main connection mode of converter stations, the operation mode, the control strategy, parameters design for main circuit and determination of converter station's power operation range. This technical solution can provide complete design method for future HVDC flexible transmission project, which is of great significance to the development and engineering application of the HVDC flexible technology in China.
Mei Fei; Wang Li; Zha Shengsen; Zheng Jianyong. System design for Nanjing HVDC flexible transmission project. The Journal of Engineering 2019, 2019, 2224 -2227.
AMA StyleMei Fei, Wang Li, Zha Shengsen, Zheng Jianyong. System design for Nanjing HVDC flexible transmission project. The Journal of Engineering. 2019; 2019 (16):2224-2227.
Chicago/Turabian StyleMei Fei; Wang Li; Zha Shengsen; Zheng Jianyong. 2019. "System design for Nanjing HVDC flexible transmission project." The Journal of Engineering 2019, no. 16: 2224-2227.
Traditional fault diagnosis for a high-voltage circuit breaker (HVCB) encounters the following problems: the fault features extracted by traditional shallow models is of weak expression ability, and the accuracy of fault identification can be affected by the lack of labeled training samples. To overcome these problems, we present a new approach for HVCB mechanical fault diagnosis based on a deep belief network (DBN) and a transfer learning strategy. This approach uses a DBN to achieve the deep mining and adaptive extraction of the inherent features of sample data, and combines the transfer learning method to improve the accuracy of the fault diagnosis model, which uses a large amount of selective auxiliary data to augment the tiny amount of target data learning by adjusting the weight of training samples. The target sample data are obtained by collecting the coil current signal of the HVCB from fault simulation experiments, and the auxiliary sample data are obtained through simulation based on the electromagnetic system mathematical model of the HVCB spring mechanism. The experimental results show that compared with the traditional feature extraction and fault diagnosis method, the DBN approach combined with transfer learning can achieve stronger feature learning and generalization ability.
Yi Pan; Fei Mei; Huiyu Miao; Jianyong Zheng; Kedong Zhu; Haoyuan Sha. An Approach for HVCB Mechanical Fault Diagnosis Based on a Deep Belief Network and a Transfer Learning Strategy. Journal of Electrical Engineering & Technology 2019, 14, 407 -419.
AMA StyleYi Pan, Fei Mei, Huiyu Miao, Jianyong Zheng, Kedong Zhu, Haoyuan Sha. An Approach for HVCB Mechanical Fault Diagnosis Based on a Deep Belief Network and a Transfer Learning Strategy. Journal of Electrical Engineering & Technology. 2019; 14 (1):407-419.
Chicago/Turabian StyleYi Pan; Fei Mei; Huiyu Miao; Jianyong Zheng; Kedong Zhu; Haoyuan Sha. 2019. "An Approach for HVCB Mechanical Fault Diagnosis Based on a Deep Belief Network and a Transfer Learning Strategy." Journal of Electrical Engineering & Technology 14, no. 1: 407-419.