A robust stochastic stability analysis approach for power system considering wind speed prediction error based on Markov model

Mi, 2018, Stochastic small disturbance stability analysis of nonlinear multi-machine system with Itô differential equation, Int. J. Electr. Power Energy Syst., 101, 439, 10.1016/j.ijepes.2018.03.029

Ju, 2018, Analytical assessment for transient stability under stochastic continuous disturbances, IEEE Trans. Power Syst., 33, 2004, 10.1109/TPWRS.2017.2720687

Jay, 2020, Stochastic neural networks for cryptocurrency price prediction, IEEE Access, 8, 82804, 10.1109/ACCESS.2020.2990659

Zhang, 2012, Responses and stability of power system under small Gauss type random excitation, Sci. China-Technol. Sci., 55, 1873, 10.1007/s11431-012-4893-7

Dong, 2012, Numerical simulation for stochastic transient stability assessment, IEEE Trans. Power Syst., 27, 1741, 10.1109/TPWRS.2012.2187466

Wang, 2011, Numerical Simulation of Stochastic Differential Algebraic Equations for Power System Transient Stability with Random Loads, 1

Qiu, 2008, 1

Wang, 2013, The Fokker-Planck equation for power system stability probability density function evolution, IEEE Trans. Power Syst., 28, 2994, 10.1109/TPWRS.2012.2232317

Milano, 2013, A systematic method to model power systems as stochastic differential algebraic equations, IEEE Trans. Power Syst., 28, 4537, 10.1109/TPWRS.2013.2266441

Wang, 2015, Long-term stability analysis of power systems with wind power based on stochastic differential equations: model development and foundations, IEEE Trans. Sustain. Energy, 6, 1534, 10.1109/TSTE.2015.2454333

Ju, 2018, Stochastic dynamic analysis for power systems under uncertain variability, IEEE Trans. Power Syst., 33, 3789, 10.1109/TPWRS.2017.2777783

Kumar, 2016, Energy-efficient multimedia data dissemination in vehicular clouds: stochastic-reward-nets-based coalition game approach, IEEE Syst. J., 10, 847, 10.1109/JSYST.2015.2409651

Li, 2019, Analytic analysis for dynamic system frequency in power systems under uncertain variability, IEEE Trans. Power Syst., 34, 982, 10.1109/TPWRS.2018.2873410

Yuan, 2015, Stochastic small-signal stability of power systems with wind power generation, IEEE Trans. Power Syst., 30, 1680, 10.1109/TPWRS.2014.2353014

Sun, 2015, Robust stochastic stability of power system with time-varying delay under Gaussian random perturbations, Neurocomputing, 162, 1, 10.1016/j.neucom.2015.03.073

Ma, 2017, Robust stochastic stability analysis method of DFIG integration on power system considering virtual inertia control, IEEE Trans. Power Syst., 32, 4069, 10.1109/TPWRS.2017.2657650

Lu, 2016, Operation risk assessment of islanded wind-pv-diesel-storage microgrid based on Markov chain Monte Carlo method, Power Syst. Technol., 41, 823

Ma, 2015, Angle stability analysis of multi-operation power system based on cascading failure, Proc. CSEE, 35, 1

Cao, 2016, Switching Markov Gaussian models for dynamic power system inertia estimation, IEEE Trans. Power Syst., 31, 3394, 10.1109/TPWRS.2015.2501458

Peng, 2010, Pattern analysis on characteristics of wind speed distribution in wind farms, Power Syst. Technol., 34, 206

Jiang, 2014, A wind speed time series model based on advanced first-order Markov chain approach, Autom. Electr. Power Syst., 38, 22

Ma, 2016, Stability analysis considering time-varying wind speed for power system with multiple operating conditions, Electr. Power Autom. Equipment, 36, 26

Wang, 2019, Stochastic modeling and small signal stability analysis of wind power system based on Markov theory, Power Syst. Technol., 43, 646

Jia, 2012, Evaluation on capability of wind power accommodation based on its day-ahead forecasting, Power Syst. Technol., 36, 69

Quan, 2017, Short-term load and wind power forecasting using neural network-based prediction intervals, IEEE Trans. Neural Netw. Learn. Syst., 25, 303, 10.1109/TNNLS.2013.2276053

Duman, 2020, Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach, Neural Comput. Appl., 32, 8463, 10.1007/s00521-019-04338-y

Shen, 2013, H∞ filtering of continuous Markov jump linear system with partly known Markov modes and transition probabilities, J. Franklin Inst., 350, 3384, 10.1016/j.jfranklin.2013.08.006

Xu, 2020, Big data analytics of crime prevention and control based on image processing upon cloud computing, J. Surveill. Secur. Saf., 1, 16

Shen, 2013, Improved fuzzy control design for nonlinear Markovian-jump systems with incomplete transition descriptions, Fuzzy Sets. Syst., 217, 80, 10.1016/j.fss.2012.11.014

Z. Xu, Z. Wu, H. Su, P. Shi, and H. Que, “Energy-to-peak filtering of semi-Markov jump systems with mismatched modes,” IEEE Trans. Autom. Control, DOI: 10.1109/TAC.2019.2955014.

Y. Xu, Z. Wu, and Y. Pan, “Event-based dissipative filtering of Markovian jump neural networks subject to incomplete measurements and stochastic cyber-attacks,” IEEE Trans. Cybern., DOI: 10.1109/TCYB.2019.2946838.

Mao, 2006