Doubly fed induction generators (DFIGs) are vulnerable to grid related electrical faults. Standards require DFIGs to be disconnected from the grid unless augmented with a fault ride through (FRT) capability. A fault current limiter (FCL) can enhance the overall stability of wind farms and allow them to maintain grid-code requirements. In this paper, a neuro fuzzy logic controlled parallel resonance type fault current limiter (NFLC-PRFCL) is proposed to enhance the FRT capability of the DFIG based wind farm. Theoretical and graphical analysis of the proposed method are carried out by MATLAB/Simulink software. The performance of the NFLC-PRFCL is compared with other documented FCL devices, e.g., the bridge type fault current limiter (BFCL) and the series dynamic braking resistor (SDBR). The performance of the NFLC-PRFCL is also compared with that of the existing fuzzy logic controlled parallel resonance fault current limiter (FLC-PRFCL). From the simulation results, it is found that the NFLC-PRFCL outperforms its competitors and enables the DFIG to maintain a near-seamless performance during various fault events.

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems.

High penetration of Doubly Fed Induction Generator (DFIG) into existing power grid can attribute complex issues as they are very sensitive to the grid faults. In addition, Fault Ride Through (FRT) is one of the main requirements of the grid code for integrating Wind Farms (WFs) into the power grid. In this work, to enhance the FRT capability of the DFIG based WFs, a Bridge-Type Flux Coupling Non-Superconducting Fault Current Limiter (BFC-NSFCL) is proposed. The effectiveness of the proposed BFC-NSFCL is evaluated through performance comparison with that of the Bridge-Type Fault Current Limiter (BFCL) and Series Dynamic Braking Resistor (SDBR). Moreover, a dynamic nonlinear controller is also proposed for controlling the operation of the BFC-NSFCL. Extensive simulations are carried out in the MATLAB/SIMULINK environment for both symmetrical and unsymmetrical temporary as well as permanent faults. Based on the simulation results and different numerical analysis, it is found that the proposed nonlinear controller based BFC-NSFCL is very effective in enhancing the FRT capability of the WF. Also, the BFC-NSFCL outperforms the conventional BFCL and SDBR by maintaining a near-seamless performance during various grid fault situations.

Any fault related to grid is a matter of great concern for doubly fed induction generator (DFIG) based power system as DFIGs stator windings are connected to the grid directly. To augment the transient performance of the DFIGs, superconducting fault current limiter (SFCL) is a certified device. To boost the performance of a flux-coupling-type SFCL (FC-SFCL) by ensuing the adaptive use of fault current limiting impedance based on fault severity, rather involving the full impedance unnecessarily, a nonlinear controller (NC) for FC-SFCL (NC-FC-SFCL) is presented in this paper. Reason behind choosing a straightforward NC for this work is to have simple implementation capability with the full flavor of a nonlinear controller. Effectiveness of the NC-FC-SFCL is compared with conventionally controlled FC-SFCL for various fault scenarios. Simulation results suggest that, NC-FC-SFCL can improve the overall fault ride through (FRT) capability which is verified both by graphically and numerically. Additionally, this effective use of the fault limiting impedance guarantees better transient sub-synchronous resonance (SSR) performance, and exhibits better total harmonic distortion responses.