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Distribution System Network Resilience Enhancement Against Predicted Hurricane Events Using Statistical Probabilistic System Line Damage Prediction Model | IEEE Conference Publication | IEEE Xplore

Distribution System Network Resilience Enhancement Against Predicted Hurricane Events Using Statistical Probabilistic System Line Damage Prediction Model


Abstract:

To enhance the resilience of the distribution power system network against hurricane events, quality proactive operational planning is required. The first action is to le...Show More

Abstract:

To enhance the resilience of the distribution power system network against hurricane events, quality proactive operational planning is required. The first action is to leverage the oncoming predicted hurricane events data for accurate proactive prediction of system line outage under hurricane events. In this article, a hybrid system line outage prediction method based on a statistical probabilistic system components fragility curve (FC), Monte-Carlo simulation (MCS), and scenario reduction algorithm (SCENRED) is proposed. The proposed hybrid model is meant to consider the system network configuration and the hurricane dynamic as the two main causes of grid topology failure during contingencies. Hurricane historical data are used to develop the system line outage prediction model. This model is investigated on a standard IEEE 15-bus test system. The system line outage prediction results validate the effectiveness of the proposed system component's FC-MCS-SCENRED model. The system line outage prediction provides further insights into the proactive system network optimal reconfiguration against hurricane events hence enhances grid resilience against hurricane event via operational planning.
Date of Conference: 23-27 August 2021
Date Added to IEEE Xplore: 28 September 2021
ISBN Information:
Conference Location: Nairobi, Kenya

I. Introduction

Reliable power supply is the backbone of any industrialized and developing nation around the world. The problems caused by natural events on the critical infrastructures such as the electricity grid globally are increasing daily. This is understandable as the world is passing through a serious time of global warming. One of the natural events that usually cause damages to the electric grid is a hurricane event. Hurricane events can be classified as low probability, high impact (LPHI) events [1]. The impact of natural events such as hurricanes on electricity networks is huge if quantified in terms of energy loss economically. The extreme weather condition significantly impacts the aging power distribution network negatively all around the globe [2]. Many nations around the globe are faced with the reality of the need to enhance their aging power grid networks against the LPHI extreme weather attack resulting in large power blackouts. For instance, an incident of severe windy snowfall threw the Eastern Cape province of South Africa into blackout with about 80 interruptions of supply events on the 8th of August, 2012. In that incident, a total number of 150,000 customers were affected, coupled with an extended period of power supply loss due to extensive damage to the Eskom transmission and distribution electricity infrastructure [3]. Similarly, on the 26th of September 2017, a category 5 storm from hurricanes Harvey, Irma, and Maria struck the United States of America destroying several power infrastructures. In that incident around 7.5 million customers in Texas, Florida, and Puerto Rico were left without electricity for several days [4]. Unfortunately, the power distribution systems globally were not primarily designed to withstand the impact of hurricane events. Nowadays, enhancing the power systems network against hurricane events has become an important research topic to both the utility companies and the researchers globally [5]. This is because, loss of power due to natural events will surely lead to loss of service of other critical interdependent infrastructures such as the health sector, telecommunication, transport systems, security, finance sectors, etc.

References

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