Predictive control strategies for wind turbine system based on permanent magnet synchronous generator

Self-filtering based on the fault ride-through technique using a robust model predictive control for wind turbine rotor current

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy.

Salp swarm algorithm-based TS-FLCs for MPPT and fault ride-through capability enhancement of wind generators

This article presents an optimum design of Takagi-Sugeno fuzzy logic controllers (TS-FLCs) to enhance capability of fault ride-through (FRT) and the maximal power point tracking (MPPT) of the grid-tied wind farms. To obtain the optimum TS-FLCs, salp-swarm-algorithm (SSA) applied to minimize the sum of integral squared-error (ISE) function, where the variables of the cost function are the factors of Gaussian membership functions and the control rules of eight TS-FLCs. After that, the optimized TS-FLCs are applied to control the grid-tied variable-speed wind generator system, which is simulated using PSCAD/EMTDC. Thus, the transient and dynamic responses revealed that the optimum TS-FLCs have better stability margins than the optimum proportional-integral (PI) controllers. Furthermore, a realistic variable wind speed measured data are implemented to test the robustness of the MPPT based on the optimal TS-FLCs to extract more power than the MPPT based on optimal PI controllers.

Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance

Control of Solid Oxide Fuel Cells (SOFCs) is a challenging task since they are nonlinear dynamic systems and it is essential to precisely satisfy the existing technological constraints which must be imposed on the manipulated variable (the fuel flow) and on fuel utilisation. This paper details a constrained computationally efficient nonlinear Model Predictive Control (MPC) algorithm for the SOFC process. The predicted voltage and fuel utilisation trajectories are successively linearised on-line which leads to a simple to solve quadratic optimisation MPC problem. The emphasis is put on three aspects: (a) selection of tuning parameters, (b) feasibility of the constrained MPC optimisation problem, (c) good control quality and low computational burden. At first, tuning is thoroughly described. It is demonstrated that soft fuel utilisation constraints lead to feasible MPC optimisation. It is shown that control accuracy and constraints' satisfaction ability of the algorithm are very similar to those of the "ideal" MPC strategy with nonlinear on-line optimisation, but its computational burden is much lower. Finally, it is shown that the algorithm is much more precise than the simple MPC algorithm with successive on-line model linearisation and the classical MPC algorithm based on a parameter-constant linear model.

An imbalance fault detection method based on data normalization and EMD for marine current turbines

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.