The "N-1" contingency analysis is an indispensable tool in maintaining the integrity of power systems, particularly as we navigate the integration of renewable energy. While the task grows more complex, the industry''s commitment to innovation and collaboration ensures that grid operators are well-equipped to manage the challenges ahead.
A new N-1 static security assessment method based on deep convolutional neural network (DCNN) for the power grid with a high penetration rate of renewable energy generation is proposed in this paper.
This paper explores a computationally efficient method to analyze the severity and the ranking of N-1-1 contingencies for large power system SSA. The performance of the FDLF based SSA method is demonstrated on two standard IEEE 14 and 118 bus systems.
N-1-1 Contingency: A sequence of events consisting of the initial loss of a single generator or transmission component (Primary Contingency), followed by system adjustments, followed by another loss of a single generator or transmission component (Secondary Contingency).
Specifically, (i) limits on rate-of-change-of-frequency and maximum frequency derivation are introduced to provide sufficient governor reserve from multiple sources for guaranteeing dynamic frequency performance against N-1 contingency; (ii) The de-loaded mode of variable speed wind turbines is proposed for leveraging economics of system operati...
This paper considers the expansion of an electric power system to achieve the N-1-1 reliability criterion with operation compliance check on unit commitment, economic dispatch, and power flow under contingency states. The resulting problem yields a very large
Modeling N − 1 security in power system capacity expansion problems introduces many extra constraints if all possible outages are accounted for, which leads to a high computational burden. Typical approaches to avoid this burden consider only a subset of possible outages relevant to a given dispatch situation.
The traditional N − 1 security assessment approaches give a limited perspective on the degree of power system security. This study proposes a novel security assessment approach using the index of post-contingency steady-state security distance (PCSSD).
Deep Learning for Power System Applications. Fangxing Li & Yan Du. Part of the book series: Power Electronics and Power Systems ( (PEPS)) 311 Accesses. Abstract. In this chapter, a data-driven method is proposed for fast N-1 contingency screening and further cascading outage screening in power systems.
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