Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach.

When the large-scale wind power is sent out through the high voltage direct current (HVDC) transmission system and a DC commutation failure occurs, the voltage of AC bus at the sending end decreases first and then increases. Suppose the reactive power supported in the low voltage ride-through process by various reactive resources is not timely returned. In that case, it may aggravate the voltage rise caused by the commutation failure, and the off-grid risk of wind turbine under high-voltage will be aggravated. In order to reduce the off-grid risk of wind turbines caused by the DC commutation failure, a transient voltage control strategy of DC sending-end regulator based on the online sequential extreme learning machine (OS-ELM) voltage prediction model is proposed. Firstly, the influence factors of commutation failures are analyzed. Aiming at the key factors, the real-time voltage comprehensive prediction model based on OS-ELM is used to predict the voltage increase during the commutation failure process and uses the voltage prediction results to optimize the transient response of the synchronous condenser. A large-scale wind farm together with the HVDC system is established in PSCAD to verify the effectiveness of the proposed scheme. Simulation results show that the proposed scheme can reduce the risk of wind power off-grid risk under DC commutation failures and increase the speed of voltage recovery at the point of common coupling.

With the development of the power system automation and the smart grid, intelligent monitoring sensors are widely used in the high-voltage power transmission lines (HVPLs). To ensure the stability and effectiveness of the monitoring sensors, the method of power supply should be solved urgently. In this paper, a three-coil wireless power supply system is proposed by considering the insulation problem. By comparing the insulation characteristics of the HVPLs with configuring the relay coil or not, the feasibility of the three-coil wireless power supply system in the application is validated. On the basis, the impedance matching conditions to achieve maximum efficiency and receiving power are analyzed. Particularly, a method to design the relay coil, used as an impedance matching window, is introduced so that the efficiency and the receiving power can be optimized simultaneously in different application situations. The theoretical analysis is confirmed by both simulation and experimental results.

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

Connected automated vehicles are now moving towards reality. With the help of advanced artificial intelligence and vehicle-to-vehicle/ vehicle-to-infrastructure communication, connected automated vehicles can keep higher speed with shorter headway, and drive with neighboring vehicles to formulate platoons. The application of connected automated vehicles will change the relationship of traffic flow and vehicle density. In this paper, we first analyze the increase of traffic capacity regarding to the ratio of connected automated vehicles. Then, we use microscopic traffic model to verify our analysis conclusion.