To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.

Subsynchronous oscillation, caused by the interaction between the rotor side converter (RSC) control of the doubly fed induction generator (DFIG) and series-compensated transmission line, is an alleged subsynchronous control interaction (SSCI). SSCI can cause DFIGs to go offline and crowbar circuit breakdown, and then deteriorate power system stability. This paper proposes a novel adaptive super-twisting sliding mode SSCI mitigation method for series-compensated DFIG-based wind power systems. Rotor currents were constrained to track the reference values which are determined by maximum power point tracking (MPPT) and reactive power demand. Super-twisting control laws were designed to generate RSC control signals. True adaptive and non-overestimated control gains were conceived with the aid of barrier function, without need of upper bound of uncertainty derivatives. Stability proof of the studied closed-loop power system was demonstrated in detail with the help of the Lyapunov method. Time-domain simulation for 100 MW aggregated DFIG wind farm was executed on MATLAB/Simulink platform. Some comparative simulation results with conventional PI control, partial feedback linearization control, and first-order sliding mode were also obtained, which verify the validity, robustness, and superiority of the proposed control strategy.

A variable-gain higher-order sliding mode pitch control strategy is proposed for a strongly nonlinear and coupled floating offshore wind power system. The main goal of the proposed strategy is to suppress platform motion caused by random disturbances such as waves and wind speed and to reduce fatigue loads and power fluctuations. Feedback control and super-twisting second-order sliding mode algorithm were adopted to carry out collective pitch control and track the rated rotor speed, which involves the factor of platform pitch. To adaptively adjust the collective pitch control parameters according to random wave and wind speed disturbances, the barrier function method was used to conceive adaptive sliding mode control gains. For comparison purposes, the proposed control strategy and PI control were executed under different wind and wave conditions on a FAST and MATLAB/Simulink platform. Furthermore, the fatigue load was calculated by Mlife. The results demonstrate that the proposed scheme is effective and robust. Moreover, it has advantages in resisting external disturbances, especially in suppressing the platform pitch and roll, as well as reducing the power fluctuations and the fatigue load on the blade root.

Sub-synchronous control interaction (SSCI) is an oscillation phenomenon caused by the interaction of converter control and series-compensated transmission line. This paper proposes a novel adaptive higher-order sliding mode (AHOSM) control strategy for damping the SSCI of a series-compensated DFIG-based wind power system. On the basis of system modeling and oscillation mechanism analysis, SSCI suppression is converted to the current tracking control problem. Firstly, an auxiliary feedback control is employed for the nonlinear series-compensated system, then integral sliding mode functions are defined to design a second-order sliding mode control law for the equivalent system. Adaptive laws for the control gains are then conceived based on the Lyapunov function considering unknown upper bounds of uncertainty derivatives. System stability is also analyzed in detail along with adaptive laws’ design. The effectiveness of the proposed control scheme is verified under different series-compensated level, different wind speed, symmetric and asymmetric short circuit fault, and internal and external disturbances. The PI control and conventional first-order sliding mode control scheme are also executed to compare the damping effect.

The floating platform motion of an offshore wind turbine system can exacerbate output power fluctuations and increase fatigue loads. This paper proposes a new scheme based on a fast second-order sliding mode (SOSM) control and an adaptive super-twisting extended state observer to suppress the platform motion and power fluctuation. Firstly, an affine nonlinear model of the floating wind turbine pitch system is constructed. Then, a fast SOSM pitch control law is adopted to adjust the blade pitch angle, and a new adaptive super-twisting extended state observer is constructed to achieve total disturbance observation. Finally, simulations are conducted under two cases of wind and wave conditions based on FAST (fatigue, aerodynamics, structures, and turbulence) and MATLAB/Simulink. Compared with the traditional proportional integral (PI) control scheme and standard super-twisting control scheme, the platform roll under the proposed scheme is reduced by 13% and 4%, and pitch is reduced by 16% and 3% in Case 1. Correspondingly, the roll is reduced by 9% and 15%, and pitch is reduced by 7% and 1% in Case 2. For the tower top pitch and yaw moment, load reductions of 7% and 3% or more are achievable compared with those under the PI control scheme. It is indicated that the proposed scheme is more effective in suppressing floating platform motion, stabilizing output power of the wind turbine system, and reducing tower loads.