Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost

Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid’s stability. To evaluate the stability of the power system quickly and accurately, a low frequency oscillation stability evaluation method based on an improved XGboost algorithm and power system random response data is proposed in this paper. Firstly, the original input feature set describing the dynamic characteristics of the power system is established by analyzing the substance of low frequency oscillation. Taking the random response data of power system including the disturbance end time feature and the dynamic feature of power system as the input sample set, the wavelet threshold is applied to improve its effectiveness. Secondly, using the eigenvalue analysis method, different damping ratios are selected as threshold values to judge the stability of the system low-frequency oscillation. Then, the supervised training with improved XGboost algorithm is performed on the characteristics of stability. On this basis, the training model is obtained and applied to online low frequency oscillation stability evaluation of a power system. Finally, the simulation results of the eight-machine 36-node test system and Hebei southern power grid show that the proposed low frequency oscillation online evaluation method has the features of high evaluation accuracy, fast evaluation speed, low error rate of unstable sample evaluation, and strong anti-noise ability.

[1]  Vaithianathan Mani Venkatasubramanian,et al.  Oscillation monitoring from ambient PMU measurements by Frequency Domain Decomposition , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[2]  Daniel J. Trudnowski,et al.  Initial results in electromechanical mode identification from ambient data , 1997 .

[3]  Adrien Deliège,et al.  A New Wavelet-Based Mode Decomposition for Oscillating Signals and Comparison with the Empirical Mode Decomposition , 2016 .

[4]  Yonggang Li,et al.  The Stochastic Response Surface Method for Small-Signal Stability Study of Power System With Probabilistic Uncertainties in Correlated Photovoltaic and Loads , 2017, IEEE Transactions on Power Systems.

[5]  S.P. Teeuwsen,et al.  Assessment of the small signal stability of the European interconnected electric power system using neural networks , 2001, LESCOPE 01. 2001 Large Engineering Systems Conference on Power Engineering. Conference Proceedings. Theme: Powering Beyond 2001 (Cat. No.01ex490).

[6]  A projection algorithm for partial eigenvalue assignment problem using implicitly restarted Arnoldi method , 2013 .

[7]  Sushil K. Soonee,et al.  Detecting Low Frequency Oscillations through PMU-based measurements for Indian National Grid , 2016, 2016 Power Systems Computation Conference (PSCC).

[8]  Glauco N. Taranto,et al.  Damping Nomogram Method for Small-Signal Security Assessment of Power Systems , 2017 .

[9]  Ljiljana Platisa,et al.  Performance of Four Subjective Video Quality Assessment Protocols and Impact of Different Rating Preprocessing and Analysis Methods , 2017, IEEE Journal of Selected Topics in Signal Processing.

[10]  S. Singh,et al.  A Modified TLS-ESPRIT-Based Method for Low-Frequency Mode Identification in Power Systems Utilizing Synchrophasor Measurements , 2011, IEEE Transactions on Power Systems.

[11]  Haoyong Chen,et al.  Quasi-Monte Carlo Based Probabilistic Small Signal Stability Analysis for Power Systems With Plug-In Electric Vehicle and Wind Power Integration , 2013, IEEE Transactions on Power Systems.

[12]  V. Vittal,et al.  Consequence and impact of electric utility industry restructuring on transient stability and small-signal stability analysis , 2000, Proceedings of the IEEE.

[13]  Z.Y. Dong,et al.  Probabilistic small signal analysis using Monte Carlo simulation , 2005, IEEE Power Engineering Society General Meeting, 2005.

[14]  Daniel J. Trudnowski,et al.  Mode shape estimation algorithms under ambient conditions: A comparative review , 2013, IEEE Transactions on Power Systems.

[15]  Fu Jiang,et al.  XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud , 2018, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).

[16]  Jukka Turunen,et al.  Bayesian approach in the modal analysis of electromechanical oscillations , 2017 .

[17]  D. G. Colomé,et al.  Probabilistic performance indexes for small signal stability enhancement in weak wind-hydro-thermal power systems , 2009 .

[18]  Joost Rommes,et al.  Computing Rightmost Eigenvalues for Small-Signal Stability Assessment of Large-Scale Power Systems , 2010, IEEE Transactions on Power Systems.

[19]  Jovica V. Milanovic,et al.  Risk-Based Small-Disturbance Security Assessment of Power Systems , 2015, IEEE Transactions on Power Delivery.

[20]  Vaithianathan Venkatasubramanian,et al.  Electromechanical Mode Estimation Using Recursive Adaptive Stochastic Subspace Identification , 2014, IEEE Transactions on Power Systems.

[21]  Nikolay Nikolaev,et al.  Application of Monte Carlo method for probabilistic assessment of electric power system small-Signal stability , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

[22]  Dahai Zhang,et al.  A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost , 2018, IEEE Access.

[23]  Panganamala Ramana Kumar,et al.  Persistent-homology-based detection of power system low-frequency oscillations using PMUs , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[24]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[25]  Xinwei Zheng,et al.  Radar emitter classification for large data set based on weighted-xgboost , 2017 .

[26]  Z.Y. Dong,et al.  Probabilistic analysis of power system small signal stability region , 2005, 2005 International Conference on Control and Automation.

[27]  Yasunori Mitani,et al.  Small-Signal Stability Assessment , 2014 .

[28]  Yasunori Mitani,et al.  Power System Monitoring and Control , 2014 .

[29]  A. Torres,et al.  Contingency Analysis and Risk Assessment of Small Signal Instability , 2007, 2007 IEEE Lausanne Power Tech.