An Integrated Model-Driven and Data-Driven Method for On-Line Prediction of Transient Stability of Power System With Wind Power Generation

The increase of wind power permeability in modern power grid has turned rapid and accurate transient stability (TS) prediction into a more challenging issue. To accurately and promptly perform online TS prediction for power system with doubly fed induction generator (DFIG)-based wind farms, an integrated model-driven and data-driven method is proposed in this paper. The influence of DFIGs is considered in the transformation to guarantee the accuracy of the equivalent one machine infinite bus (OMIB) model transformed from the target system. The P- $\delta $ trajectory of the OMIB is fitted with the generator-terminal information to predict TS. To improve the prediction speed, an extreme learning machine (ELM)-based method is utilized to process the other DFIG and system information and evaluate the system status immediately after failure. The simulation results verify that the proposed method can reduce the dependence of the data-driven method on the data sample size and improve the speed and accuracy of online prediction.

[1]  Dheeman Chatterjee,et al.  Active Power Control of DFIG-Based Wind Farm for Improvement of Transient Stability of Power Systems , 2016, IEEE Transactions on Power Systems.

[2]  Jiabing Hu,et al.  Inertia Characteristic of DFIG-Based WT Under Transient Control and its Impact on the First-Swing Stability of SGs , 2017, IEEE Transactions on Energy Conversion.

[3]  Qunying Liu,et al.  XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System , 2019, IEEE Access.

[4]  Yuchen Zhang,et al.  Robust Dispatch of High Wind Power-Penetrated Power Systems Against Transient Instability , 2018, IEEE Transactions on Power Systems.

[5]  Pratyasa Bhui,et al.  Real-Time Prediction and Control of Transient Stability Using Transient Energy Function , 2017, IEEE Transactions on Power Systems.

[6]  Federico Milano,et al.  Semi-Implicit Formulation of Differential-Algebraic Equations for Transient Stability Analysis , 2016, IEEE Transactions on Power Systems.

[7]  Gengyin Li,et al.  Power system transient stability analysis with integration of DFIGs based on center of inertia , 2016 .

[8]  Ali Reza Seifi,et al.  Transient Stability of Power Grids Comprising Wind Turbines: New Formulation, Implementation, and Application in Real-Time Assessment , 2019, IEEE Systems Journal.

[9]  Shuai Lu,et al.  Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction , 2014, IEEE Transactions on Power Systems.

[10]  Federico Milano,et al.  Semi-implicit formulation of differential-algebraic equations for transient stability analysis , 2017, 2017 IEEE Power & Energy Society General Meeting.

[11]  Rui Zhang,et al.  Real-time transient stability assessment model using extreme learning machine , 2011 .

[12]  U. D. Annakkage,et al.  Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements , 2011, IEEE Transactions on Power Systems.

[13]  Xue Feng,et al.  Transient power angle stability analysis of emergency wind turbine tripping , 2015 .

[14]  Shahram Montaser Kouhsari,et al.  A novel recursive approach for real-time transient stability assessment based on corrected kinetic energy , 2016, Appl. Soft Comput..

[15]  Dong Yue,et al.  An efficient and robust case sorting algorithm for transient stability assessment , 2015, 2015 IEEE Power & Energy Society General Meeting.

[16]  C. Jensen,et al.  Power System Security Assessment Using Neural Networks: Feature Selection Using Fisher Discrimination , 2001, IEEE Power Engineering Review.

[17]  Rojan Bhattarai,et al.  Transient Stability Enhancement of Power Grid With Integrated Wide Area Control of Wind Farms and Synchronous Generators , 2017, IEEE Transactions on Power Systems.

[18]  M. Ribbens-Pavella,et al.  Extended Equal Area Criterion Justifications, Generalizations, Applications , 1989, IEEE Power Engineering Review.

[19]  Ali Reza Seifi,et al.  Sensitivity-based approach for real-time evaluation of transient stability of wind turbines interconnected to power grids , 2018 .

[20]  Rong Ye,et al.  Artificial Neural Network Classifier of Transient Stability Based on Time-Domain Simulation , 2018, 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC).

[21]  Ali Amiri,et al.  A Learning Framework for Size and Type Independent Transient Stability Prediction of Power System Using Twin Convolutional Support Vector Machine , 2018, IEEE Access.

[22]  Yuchen Zhang,et al.  Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff , 2018 .

[23]  Kenneth A. Loparo,et al.  Transient Stability Analysis for Offshore Wind Power Plant Integration Planning Studies—Part I: Short-Term Faults , 2019, IEEE Transactions on Industry Applications.

[24]  Feng Li,et al.  Hybrid method for power system transient stability prediction based on two-stage computing resources , 2017 .

[25]  Yuchen Zhang,et al.  Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff , 2017, IEEE Transactions on Industrial Informatics.

[26]  Kenneth A. Loparo,et al.  Transient Stability Analysis for Offshore Wind Power Plant Integration Planning Studies—Part II: Long-Term Faults , 2019, IEEE Transactions on Industry Applications.

[27]  Gengyin Li,et al.  Coordinated Control of DFIG Based Wind Farms and SGs for Improving Transient Stability , 2018, IEEE Access.