Open AccessTransient stability‐oriented assessment and application of preventive control action for power system
Author(s) -
Soni Bhanu P.,
Saxena Akash,
Gupta Vikas,
Surana Simrath L.
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9353
Subject(s) - electric power system , phasor , transient (computer programming) , computer science , stability (learning theory) , control engineering , reliability engineering , control theory (sociology) , contingency , engineering , power (physics) , control (management) , physics , quantum mechanics , machine learning , artificial intelligence , operating system , linguistics , philosophy
The modern power system is becoming more complex and dynamic because of increasing penetration of renewable energy resources, operating closer to system capacity for economic benefits. In order to maintain the system stability, the system operator is required to initiate appropriate preventive control action under severe contingencies while satisfying the system operating constraints. Real time transient stability assessment (TSA) of power system is proposed in this paper by using wide area measurement system (WAMS) and phasor measurement units (PMUs). An architecture based on least square support vector machine (LS‐SVM) has been developed to identify the system stability state in real time. Also, this paper proposes a coherency based selection method to identify the appropriate members for generation rescheduling as preventive operation in insecure operating contingency. Rotor angle trajectories based transient stability index (TSI) is employed to classify the generators as either critical or non‐critical generators in a power network. Accordingly preventive control action in the form of generation rescheduling can be initiated to achieve stability. The method has been demonstrated on the IEEE 10‐machines, 39‐bus system. The proposed methodology is effective and capable of handling complex power system models with multiple contingencies.