Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

This paper proposes a novel equivalence model of the smart grid. The smart grid can be equivalent to a three-layer model, namely power supply, power distribution, and power layer. For equivalent model, the system voltage collapses path are analyzed, the characteristics of the N structure complex smart grid model are derived. The weighted graph model of the brittle relationship of complex smart grid systems is analyzed. The model has the characteristics of logical clarity, hierarchical representation, qualitative analysis. By finding the crash path through different equivalent modes, the crash state path between subsystems can be analyzed. The voltage collapse of one node in the operation of the smart grid system will be transmitted to the entire system through a chain reaction, which will cause the entire system to collapse. The maximum voltage collapse path for different equivalent modes is used to analyze the most likely collapse path. The method of predicting and controlling the brittle voltage collapse path of the smart grid can effectively optimize the performance of the entire system, which has important guiding significance for the design and control of the smart grid system.

In view of the high-quality requirements of the power supply quality of the user-side sensitive equipment, the system for voltage sag in the grid operation is stable and efficient. This paper proposes a new virtual voltage comparison compensation dynamic voltage recovery method. The algorithm that uses the invariant constant signal as the input quantity, and the real-side input load side signal reduces the influence of the input quantity uncertainty change on the generated compensation signal and improves the simplicity of the system operation control and the system stability. The flexibility of the proposed control strategy, as well as the effectiveness of the optimal design method, is verified by both simulation and experimental results.

A conditionally constrained compound (CCC) sub‐gradient method typically involves solutions by multi‐agents, which have important applications in distributed energy resources (DER). The proposed DER system structure model uses operating state switching to analyze the characteristics of the model transformation. The work proposes that energy storage (ES) transformation is the main factor that influences the transformation in the DER model. For solving this optimization problem, this study uses a compound sub‐gradient method that is distributed among the agents. Compared with the conventional analysis methods, a system configuration and equivalent model of distributed power is obtained using the sub‐gradient method, and the power distribution characteristics of the DER in the off‐grid state are further given. Using a time‐based representation, this method can be applied to complex distributed new energy access applications and uses an iterative compound step ladder algorithm with a specific step size for switching. The compound distributed gradient algorithm further determines the selected range of algorithm parameters. Finally, the results of the convergence rate explicitly characterize the effectiveness of the method.

This paper proposes a novel torque feedback equivalent structure oriented to the diversity of torque feedback in multi‐layer and multi‐axis complex systems. First, in view of the existing synchronisation control problem, this paper presents master reference synchronous model and master–slave synchronous model in a multi‐layer and multi‐axis synchronous control system. Second, the change of the direct feedback to front layer axis is lower than that of the direct feedback to master axis, and the adjustment difference of the axis output is low. Moreover, the stability and superiority of the proposed controller are theoretically analysed. By two methods, it can be seen that the control system can converge at a faster speed and run stably with good dynamic performance and synchronisation performance. The results verify the effectiveness of the adaptive control model in which the two equivalent torque feedback structures converge to zero.