The high penetration of distributed generators and ubiquitous inverter-based resources plays a critical role in the optimal operation of distribution systems. This paper presents a data-driven stochastic optimization framework for optimal operation of soft-of-point (SOP) integrated distribution networks with active management schemes. Firstly, the optimal operation of SOPs in distribution networks is formulated as a network-constrained energy management problem; Subsequently, Gaussian process is integrated to enable an accurate representation of potential distribution of uncertainties. Ultimately, a stochastic optimization approach is introduced to address this uncertainty-aware network-constrained energy management. The numerical simulation results in 33-bus distribution networks demonstrate that the proposed approach has 1.06% improvement compared to the scenarios without SOPs. These results underscore the effectiveness and economic benefits of the proposed approach.

Microgrid has been extensively applied in the modern power system as a supplementary mode for the distributed energy resources. The microgrid with wind energy is usually vulnerable to the intermittence and uncertainty of the wind energy. To increase the robustness of the microgrid, the energy storage system (ESS) is necessary to compensate the power imbalance between the power supply and the load. To further maximize the economic efficiency of the system, the system level control for the microgrid is desired to be optimized when it is integrated with the utility grid. Aiming at the aforementioned problem, this paper comprehensively analyzes the power flow of a typical loop microgrid. A transformer-based wind power prediction (WPP) algorithm is proposed and compared with recurrent neural networks algorithm. With the historical weather data, it can accurately predict the 24 h average wind energy. Based on the predicted wind energy and the time-of-use (TOU) electricity price, a day-ahead daily cycling profile of the ESS with particle swarm optimization algorithm is introduced. It comprehensively considers the system capacity constraints and the battery degree of health. The functionality of the proposed energy management strategy is validated from three levels. First, WPP is conducted with the proposed algorithm and the true historical weather data. It has validated the accuracy of the transformer algorithm in prediction of the hourly level wind energy. Second, with the predicted wind energy, a case study is given to validate the day-ahead daily cycling profile. A typical 1 MVA microgrid is utilized as the simulation model to validate performance of the daily cycling optimization algorithm. The case study results show that the ESS daily cycling can effectively reduce the daily energy expense and help to shave the peak power demand in the grid.

Accurate fault location for distribution networks is essential for service restoration and enhancement of system resilience. However, the widespread integration of distributed energy resources brings a challenge to fault location techniques in distribution networks. This paper presents a novel fault location method for active distribution networks based on multi-terminal traveling wave. The proposed method utilizes VMD-TEO to capture the time of arrival of the initial fault traveling wave. The first stage is to identify the preliminary fault section. According to the double-ended traveling wave method and the arrival time of traveling wave at all recorders, a fault section location matrix (FSLM) is built. The second stage is accurate location in the fault section. Considering the uncertainty wave velocity in distribution line, the least squares method is employed for ac-curate fault location. Through extensive simulation studies conducted in PSCAD/EMTDC, the high accuracy effectiveness of the proposed method is verified under various fault locations, and fault types.

Due to the influence of geographical environment, climatic conditions, and load levels on AC transmission lines, there will inevitably be errors between transmission line parameters and real ones during modeling, which may bring wrong guidance to power flow calculation and reactive voltage control. However, the existing transmission line parameter measurement methods have some limitations, which cannot quickly and accurately measure the actual line parameters under different conditions in real time. In this article, a real-time measuring method of AC line parameters on the basis of digital twin is proposed, an accurate AC line digital twin model is established, the electrical quantity at both ends of the actual line is collected, and the data interaction method with the virtual simulation model is present, which comprehensively improves the rapidity and accuracy of AC transmission line measurement. The simulation result shows that measurement method mentioned above can quickly and reliably measure AC transmission line parameters that vary under different conditions, and the error is controlled within a reasonable range.

Wind power is one of the main forms of renewable energy generation. Safe operation of wind farms is of significant importance to the reliable operation of modern electrical systems. China has successively established multiple large-scale wind power bases in regions such as Inner Mongolia, Hebei, and southeastern coastal areas. The protection of collector lines in wind farms commonly employs traditional overcurrent element. However, with this method, a fault at any point along the collector lines could result in the entire line being disconnected, resulting in the interruption of renewable energy absorption, power outage for un-faulted areas and long outage recovery duration. This paper analyzes the characteristics of collector line faults, and then proposes a new collector line protection scheme based on localized protection principle. Electromagnetic transient program is used to verify the effectiveness of the proposed protection scheme.

A converter valve is the core equipment of HVDC transmission system, whose operating temperature threshold is strictly constrained. This paper proposes a novel prediction method for outlet water temperature of converter valve based on F-BP network, which aims to accurately predict the outlet water temperature and assists the operation and maintenance personnel to take measures in time so that the temperature of the converter valve will not exceed its preset threshold when the operation condition has changed. Firstly, the principle and method of the construction of typical operation databases of converter valve is stated, including data standardization, the calculation of the optimal clustering category number and the final clustering process. Then, the steps of using the typical operation databases and BP neural network to make predictions are presented. Using MATLAB, we predicted the outlet water temperature of a converter valve in Chuxiong Converter Station with F-BP method and two other existing methods in comparison. The results indicate that the proposed approach’s prediction accuracy increases by 0.9141 °C and 0.9938 °C respectively compared with the simple BP neural network and linear regression, which contributes to the prediction application of the outlet water of a converter valve.

To improve the power flow regulation ability and power supply reliability of the distribution network, flexible interconnection device(FID) is introduced into the distribution network, and a self-synchronous voltage source (SSVS)-based FID control method is proposed. At first, a back-to-back FID mathematical model is established, and three operating modes of the system are analyzed. Traditional control methods (TCM) and SSVS control methods of FID are introduced and compared. This proposed strategy formulates power dispatch instructions based on the power flow and load condition data of the interconnected substations, achieving load balancing and stable control of load transfer. It avoids the shortcomings of traditional PI parameter control methods, which require switching control strategies for multi-mode operation and have difficulties in tuning. Furthermore, proposed SSVS strategy has the ability to participate in power grid frequency modulation. Finally, a FID simulation model is constructed in the MATLAB/Simulink, and the effectiveness of the proposed control strategy is verified by simulation under typical operation conditions.

Commutation failure is one of the most common faults in UHVDC stations, which seriously endangers power transmission and safe operation. Therefore, this paper proposes a commutation failure diagnosis method based on wavelet energy entropy and long short-term memory (LSTM) neural network to diagnose and trace fault recording data at UHVDC station. Firstly, a ±1100kV UHVDC simulation model is established in this paper. In the case of commutation failure and short circuit fault, the model is compared with the recorded data to improve the accuracy of the model and provide sufficient data basis for LSTM training. By analyzing the accumulated commutation failure condition data in the field, the simulation model can be set to obtain a more suitable fault training data set for the actual UHVDC system, and improve the training accuracy of LSTM neural network. Then 11 field recording data were selected for wavelet energy entropy calculation to extract fault features and construct LSTM neural network fault algorithm, which was deployed in the field fault recording server to complete diagnostic function verification and accuracy test. The test results show that the proposed method can achieve 100% simulation diagnosis accuracy and 86% field diagnosis accuracy in tracing commutation failure.

With the wide application of ultra-high voltage direct current (UHVDC) transmission technology, the stability problems of DC system represented by DC blocking have become increasingly prominent. Based on the recorded data of a ±1100kV UHVDC converter station in China, the operation of the converter station before and after the commutation failure is demonstrated. The process and causes of the accident are theoretically analyzed. Through the modeling and simulation of the UHVDC transmission system, the correctness of the theoretical analysis is verified. The negative impact of the fault on semiconductor devices is analyzed combined with the simulation waveforms. The improved strategy is put forward according to the signal flow and mechanism. Finally, a digital signal processing device controlled single-phase half wave rectifier circuit is built, and the improved strategy is confirmed to be able to avoid such false triggering by the thyristor triggering experiment.

Power thyristor is one of core components of ultra-high voltage direct current (UHVDC) transmission system. The operating junction temperature of thyristor is an important parameter to evaluate the operation state and health for UHVDC station. Thermal sensitive parameter method is the most widely used for junction temperature estimation and monitoring of semiconductor devices. The data source is mainly experiments. UHVDC thyristor have the characteristics of large volume and high voltage and current levels, which leads to the high cost of extracting thermal sensitive parameters. This paper presents a method for extracting thermal sensitive parameters based on technology computer aided design (TCAD) simulation. By modeling and calibrating the thyristor used in ±1100kV converter stations in China, the main thermal and electrical characteristics of the thyristor meet the data manual indicators. The simulation of thermal sensitive parameters in the whole working condition range is realized by setting the combined working conditions. In addition, the automatic extraction of thermal sensitive parameters in simulation results is realized through tools command programming.

SiC-MOSFET has shown up prominently as one of the most promising power electronics devices owing to its comprehensive splendid performance. In spite of that, the challenge of electric and thermal stress still restricts further technical application of SiC-MOSFET. The internal characteristics of SiC-MOSFET is closely related to its internal structural parameters and doping concentration together with its material physical parameters. In order to develop further explore on the stress boundary of SiC-MOSFET, a physicalbased numerical model of a specific SiC-MOSFET device is established based on TCAD (Technology Computer Aided Design) in this work. To verify the accuracy of the model, a simple but effective parameters calibration procedure which only needs overt datasheet provided by producers is developed. Short circuit simulation is also performed to verify the accuracy of the model. With credible physical-based numerical model of SiC-MOSFET, it is possible to improve its performance and broaden its application scenarios.

In the scheduling of wind farms, the fluctuation of system's power demand is required to track within a certain time period. It benefits to reasonably arrange division of wind turbines and distribution of active power output. This paper proposes a stochastic optimal scheduling method based on scenario analysis for wind farms. While tracking the power demand on the system side, the uncertainty of wind speed and the effect of wake flow in the wind farm are also considered in the model in order to minimize the operating cost. The method of scenario analysis can determine uncertain variables, and the nonuniform segmentation piecewise linearization method is used to linearize the model. The mixed-integer linear programming method is used to figure out the optimal scheduling strategy for the wind farm, thus, greatly increasing the economical operation of the wind farm. This proposed method is proved to be effective on an actual wind farm in East Inner Mongolia of China.

With large scale wind power penetration,the active power optimization scheduling is of great importance to the scheduling,control and safe operation of power grid.On the basis of wind speed distribution and wind power evaluation,this paper studies the wind farm scheduling model using the output and status of wind turbine generators as decision variables considering the wind curtailment and start-up and cut-off of units,and aiming at the minimization of wind farm costs.The piecewise linear wind power coefficient is firstly presented,and then the wind farm scheduling model based on mixed-integer linear programming(MILP)is presented.The simulation is done based on the real wind farm parameters and the results verify the feasibility and effectiveness of the proposed model and method.With the scheduling model,a reasonable wind turbine generator commitment can be presented and the wind farm costs can be reduced.

This paper proposes a methodology to model the external characteristics of a wind farm in service or in planning stage. For system operators, the interested external characteristics of a wind farm are mainly the aggregated available output that the wind farm can feed into power system. In order to accurately and efficiently model the wind production, the wind speed spatiotemporal distribution is characterized. The proposed model takes wake effect and time delay into account, which can improve the spatiotemporal resolutions of the input data provided by numerical weather prediction (NWP). In addition, the layouts and the mechanical characteristics of wind turbine generators (WTGs) are considered in the wind farm output model. How the operational statuses of WTGs influence the wind farm output is discussed. The proposed methodology is validated using the measurements and NWP data of an actual wind farm in China. The results demonstrate the effectiveness of the proposed method for modeling the external characteristics of a wind farm. The wind farm outputs provided by the proposed model can be used as the references for the generation scheduling or for planning of new wind farms. Copyright (c) 2015 John Wiley & Sons, Ltd.