Uncertainties of renewable energy sources like wind power are one of the considerable challenges of prosumer microgrids. To meet the grid codes requirements regarding the voltage stability of wind farm integration, finding the balance between providing the demanding dynamic performance of the voltage and reactive power, and at the same time decreasing the investment on centralized reactive power compensation device, becomes an important research topic. This article compares the effects of the static synchronous compensator (STATCOM) and static VAR compensator (SVC) on transient voltage stability at the point of common coupling (PCC) of a doubly fed induction generator (DFIG)-based wind farm. And a new fast coordinated control scheme of STATCOM and DFIG is proposed for minimizing the capacity of centralized reactive power compensation device and making the best use of the reactive power control capability of DFIG. The simulation results based on test system show that STATCOM, even with less capacity, can contribute more reactive power than SVC for voltage stability, especially during the serious voltage drop transient stage, and perform a faster voltage recovery time after fault than SVC, proved to be a more economic choice; The proposed coordinated control scheme can not only improve the transient voltage stability, but also help reducing the capacity of STATCOM, so that the cost of investments in wind farms would be reduced.

The topology identification of the distribution system is the foundation of state estimation and system analyses. However, the lack of sufficient measurement devices in practice makes full-scale identification of distributed grids hardly achievable. On the other hand, the high-scale distributed renewable energy grid-connected aggravates the complex associations between nodal injection power and voltage. Taking full advantage of it, this article proposes a novel data-driven approach to identify the topology by backtracking the Jacobian matrix from the voltage and power data from grid terminal units. Specifically, a data-driven power-to-voltage mapping model is proposed. It uses the adaptive online deep learning algorithm based on hedge backpropagation to model the mapping relationship of terminal active power, reactive power, and voltage. Then, backtracking the mapping model, a Jacobian matrix consisting of the partial derivative of voltage to the active and reactive power is obtained, which reflects the connectivity between nodes. Finally, through the Jacobian matrix, a maximum connectivity screening algorithm is developed to generate the topology of medium distribution systems. The effectiveness of the proposed method is verified on IEEE 33-node, IEEE 123-node, and actual distribution systems.

This paper presents a small-signal analysis of different grid side controllers for full power converter wind turbines with inertia response capability. In real wind turbines, the DC link controller, the drivetrain damping controller and the inertial response might present contradictory control actions in a close bandwidth range. This situation might lead to reduced control performance, increased component stress and non-compliance of connection agreements. The paper presents an analysis of the internal wind turbine dynamics by considering different grid-side converter control topologies: standard current control used in the wind industry, standard current control with inertia emulation capabilities and virtual synchronous machines. Comments are made on the similarities between each topology and the negative effects and limits, and possible remedies are discussed. Finally, the conclusion poses that the inclusion of a DC link voltage controller reduces the ability of a converter to respond to external frequency events without energy storage. The degradation increases with the DC link voltage control speed.

Nonintrusive load monitoring (NILM), also known as energy disaggregation, infers the energy consumption of individual appliances from household metered electricity data. Recently, NILM has garnered significant attention as it can assist households in reducing energy usage and improving their electricity behaviors. In this paper, we propose a two‐subnetwork model consisting of a regression subnetwork and a seq2point‐based classification subnetwork for NILM. In the regression subnetwork, stacked dilated convolutions are utilized to extract multiscale features. Subsequently, a self‐attention mechanism is applied to the multiscale features to obtain their contextual representations. The proposed model, compared to existing load disaggregation models, has a larger receptive field and can capture crucial information within the data. The study utilizes the low‐frequency UK‐DALE dataset, released in 2015, containing timestamps, power of various appliances, and device state labels. House1 and House5 are employed as the training set, while House2 data is reserved for testing. The proposed model achieves lower errors for all appliances compared to other algorithms. Specifically, the proposed model shows a 13.85% improvement in mean absolute error (MAE), a 21.27% improvement in signal aggregate error (SAE), and a 26.15% improvement in F1 score over existing algorithms. Our proposed approach evidently exhibits superior disaggregation accuracy compared to existing methods.

The accurate determination of the annual lightning flash number to a wind turbine is essential for designing proper lightning protection measures. However, the method to assess the lightning risk of wind turbines recommended by the IEC is not associated with the actual lightning attachment process. Additionally, there is little research on positive cloud-to-ground (+CG) lightning. In this study, a lightning risk assessment method correlated with wind turbines on the basis of an improved stochastic lightning model is proposed. Based on the model, the influence of the lightning current amplitude, wind turbine heights and blade rotations on lightning strike risk are quantitatively analyzed. Moreover, the development and distribution characteristics of negative cloud-to-ground (−CG) lightning and +CG lightning are discussed. Finally, a more accurate calculation method of lightning strike risk is proposed considering the polarity effect. The results show that the effect of blade rotations on lightning risk cannot be ignored when the lightning current is large or when the wind turbine is tall. The −CG lightning has more bifurcation, but the dispersity of its development path and the lightning strike point are both small. The +CG lightning has scattered lightning strike points, and the development paths are tortuous, and. According to the verification calculation, the results calculated in this paper are in better agreement with the observation data than the results calculated by the IEC (International Electrotechnical Commission) recommended method.