TY - GEN
T1 - SDAE-Based Probabilistic Stability Analysis of Wind Integrated Power Systems
AU - Wang, Tong
AU - Yang, Jing
AU - Liu, Jiuliang
AU - Gupta, Pooja
AU - Pal, Anamitra
AU - Deng, Jun
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by Fundamental Research Funds for the Central Universities (2018MS006).
PY - 2018/12/19
Y1 - 2018/12/19
N2 - This paper proposes a method for investigating the stability of the power system in presence of significant amounts of wind generation using stochastic differential algebraic equations (SDAEs). Contrary to the traditional approach of modelling the stochasticity only in the initial conditions, the SDAE-based method (SDAEM) describes wind power variation as a continuous stochastic process. Next, the model of a doubly fed induction generator (DFIG) grid-connected power system is also expressed as a SDAE. Finally, the probabilistic small signal stability (PSSS) and the probabilistic transient stability (PTS) conditions are defined. A single machine infinite bus (SMIB) system and a 16-machine, 68-bus system are used as the test systems for this analysis. The results indicate that SDAEM is more accurate and reliable in describing the system conditions in presence of stochastic inputs, and thus can be used to decide the right course of controller action.
AB - This paper proposes a method for investigating the stability of the power system in presence of significant amounts of wind generation using stochastic differential algebraic equations (SDAEs). Contrary to the traditional approach of modelling the stochasticity only in the initial conditions, the SDAE-based method (SDAEM) describes wind power variation as a continuous stochastic process. Next, the model of a doubly fed induction generator (DFIG) grid-connected power system is also expressed as a SDAE. Finally, the probabilistic small signal stability (PSSS) and the probabilistic transient stability (PTS) conditions are defined. A single machine infinite bus (SMIB) system and a 16-machine, 68-bus system are used as the test systems for this analysis. The results indicate that SDAEM is more accurate and reliable in describing the system conditions in presence of stochastic inputs, and thus can be used to decide the right course of controller action.
KW - power system stability
KW - probabilistic stability
KW - stochastic differential algebraic equations
KW - wind power
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U2 - 10.1109/EI2.2018.8582603
DO - 10.1109/EI2.2018.8582603
M3 - Conference contribution
AN - SCOPUS:85060849866
T3 - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings
BT - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018
Y2 - 20 October 2018 through 22 October 2018
ER -