Search

Larger-scale stochastic power systems urge the development of real-time alternating current optimal power flow, artificial intelligence (AI) thus becomes an alternative. However, traditional AI only imitates experiences, and cannot follow in-depth physics. This may cause an undesired nongeneralizability and topology intractability. To address this issue, a physics-guided graph neutral network (PG-GNN) is proposed. The PG-GNN firstly capture the physical constraints by a dual Lagrangian. Besides, the branch features of power grids are fully exploited to allow the PG-GNN to master tremendous topological patterns. To further manage the out-of-distribution topology, stability property of the PG-GNN is proved, then upon this evidence, an online transfer learning is proposed to allow the PG-GNN to fast master the unexpected topology. Numerical tests on benchmarks show that, the proposed method holds well topology-transferability, enables near or even better solutions than conventional optimizer, but merits much more than 100 times efficiency.

This letter proposes a physics-informed action network (PIAN) for power system transient stability preventive control (TSPC). The network firstly renders deep learning to reduce the TSPC complexity. Unlike common data-driven methods that superficially imitate control experience, TSPC is then analytically embedded into the proposed PIAN network, so that to enforce the network to learn in-depth physical patterns. The well-learned PIAN enables highly generalized real-time decisions. Comparisons with one model-based and two data-driven baselines on the IEEE 39-bus system and the IEEE 145-bus system highlight that, the proposed method enables highly reliable control decisions, and beats the others in terms of decision efficiency and generalizability.

The voltage stability problem has strong nonlinearity and sensitive to dimensionality, which makes it hard to balance accuracy and efficiency in its control. To enhance voltage stability margin (VSM) precisely and rapidly, a surrogate control algorithm with back propagation neural network (BPNN) embedded is carried out. Firstly, BPNN is adopted to parameterize a nonlinear voltage stability boundary mapped by operational variables. Further, benefited from the speed and precision ability of BPNN calculation, BPNN is embedded into preventive control model to replace the time-consuming calculation process of VSM. In doing so, an efficient and nonlinear simplified control model oriented to voltage stability can be constructed to enable efficiency for load margin enhancement. Besides, non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the VSM control model with the advantages of fast running speed and good convergence of solution set. At last, numerical studies on 118-bus systems testify that BPNN can achieve VSM evaluation with high accuracy and rapidly assist in NSGA-II improving the static voltage stability margin.

Southwest China power grid (SCPG) and central China power grid are planned to be unsynchronized by using back-to-back voltage source converter based high voltage direct current transmission between Chongqing and Hubei power grids. After asynchronous interconnection, the proportion of hydro in installed capacity is about 70%. In other words, the SCPG is a typical hydrodominant power system. Due to the negative damping effect of governors of hydrogenerators, there is a serious risk of ultralow-frequency oscillation (ULFO) in the range of 0.05 Hz. In this paper, optimization strategies of governor for hydropower unit are presented to eliminate ULFOs. First, the control mode is selected based on comparing damping characteristic of different control modes of governor. Then, a relevant coefficient index is proposed to select sensitive generators of ULFO. Afterward, PID parameters of governor are optimized based on a new performance criteria considering the damping characteristic of governor. Finally, optimization strategies are presented and applied to SCPG. Simulation results show effectiveness and practicability of the proposed measures, which have been adopted and will be implemented before the asynchronous interconnection.

As a flexible resource, electric vehicles (EVs) can realize various auxiliary services such as peak shaving, reactive power optimization, fault recovery, and emergency power supply. It is essential to quickly and accurately evaluate the EV schedulable capacity to realize auxiliary services. In this context, this paper first simulates EV charging demand using trip chain and Monte Carlo (MC) methods based on the 2017 National Household Travel Survey (NHTS2017) results to provide a multi-time scale estimation of EV charging station schedulable capacity to provide a data foundation. Then, considering incentive price, price sensitivity, and user credit, a demand response model is established to describe the uncertainty of user response and constraints such as differentiated demand on both sides of the grid user, battery charging and discharging state, battery loss, and response uncertainty are integrated to establish a multi-time scale evaluation model of the schedulable capacity of EV charging stations. Finally, the effectiveness of the proposed evaluation model is verified by simulation, and the effects of site area, incentive price, and dispatching time scale on the maximum schedulable capacity of EV charging stations are analyzed.

This paper presents a measurement-based solution for low frequency oscillation (LFO) analysis in both real time monitoring and off-line case study. An online LFO property discrimination method is developed first, which alternately uses empirical mode decomposition (EMD)/Hilbert transform (HT) and square calculation to process the measurement data. The method magnifies the variation trend of oscillating variables to accurately discriminate the property of the oscillation. Subsequently, an oscillation source locating method for the forced oscillation (FO) and a strongly correlated generator identification method for the weak damping oscillation (WDO) are proposed. Finally, numerical study results on a test system of the isolated Changdu grid in Tibet validate the proposed methods.

This paper proposes a new Fault Ride Through (FRT) method of Permanent Magnetic Synchronous Generator (PMSG) wind energy system based on the improved machine-side generator (MSC) control and superconducting magnetic energy storage (SMES) under grid symmetric fault condition. The system topology and basic principle of the proposed scheme are investigated in detail. The control strategies of improved MSC control and SMES are also presented. Furthermore, the simulation of PMSG integrated with the proposed scheme is conducted in PSCAD/EMTDC. Simulations show that the proposed FRT method could maintain key parameters of PMSG within the acceptable range and the capital cost of SMES is also reduced during grid fault, which demonstrates its feasibility and practicability.

Voltage sag, as an unavoidable power quality problem, has been attracted wide attention. Voltage sag risk assessment and early warning technology research have great significance for power supply companies and power users. This paper conducts the quantitative assessment of voltage sag risk based on the fault intensity and probability distribution of each component in power grids. In addition, a voltage sag automatic assessment and early warning platform is developed, which can visualize the failure probability of power grid components.

Voltage sag, as an unavoidable power quality problem, has been attracted wide attention. Voltage sag risk assessment and early warning technology research have great significance for power supply companies and power users. This paper conducts the quantitative assessment of voltage sag risk based on the fault intensity and probability distribution of each component in power grids. In addition, a voltage sag automatic assessment and early warning platform is developed, which can visualize the failure probability of power grid components.

This paper proposes a new Fault Ride Through (FRT) method of Permanent Magnetic Synchronous Generator (PMSG) wind energy system based on the improved machine-side generator (MSC) control and superconducting magnetic energy storage (SMES) under grid symmetric fault condition. The system topology and basic principle of the proposed scheme are investigated in detail. The control strategies of improved MSC control and SMES are also presented. Furthermore, the simulation of PMSG integrated with the proposed scheme is conducted in PSCAD/EMTDC. Simulations show that the proposed FRT method could maintain key parameters of PMSG within the acceptable range and the capital cost of SMES is also reduced during grid fault, which demonstrates its feasibility and practicability.

A novel low voltage ride-through (LVRT) scheme for PMSG-based wind turbines based on the Resistor Superconducting Fault Current Limiter (R-SFCL) is proposed in this paper. The LVRT scheme is mainly formed by R-SFCL in series between the transformer and the Grid Side Converter (GSC), and basic modelling has been discussed in detail. The proposed LVRT scheme is implemented to interact with PMSG model in PSCAD/EMTDC under three phase short circuit fault condition, which proves that the proposed scheme based on R-SFCL can improve the transient performance and LVRT capability to consolidate grid connection with wind turbines.