Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Srivastava, K. Anurag

Committee Member

Ginn III, Herbert

Committee Member

Schulz N. Noel

Date of Degree

12-13-2008

Document Type

Graduate Thesis - Open Access

Major

Electrical Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

The importance of the electric power infrastructure has been exposed by several blackouts throughout the world in the last decade. These blackouts were caused mainly by physical vulnerabilities, human errors and natural disasters. The power grid is becoming more and more prone to outages which affect not only the power system network, but also other infrastructures and the society in several ways. Utilities generally operate with an (N-1) security level (no violations for single outage), and blackouts are generally caused by higher order contingencies. There is lack of effective methods and analysis tools to deal with higher order contingencies. Higher order contingences include multiple line outages, multiple generator outages or a combination of both. This research work focuses on developing tools to take corrective actions based on sensitivity for these multiple outages. Algorithms developed are Multiple Line Outage Bus Sensitivity Factor (MLOBSF), Multiple Line Outage Voltage Sensitivity (MLOVS), Multiple Generator Outage Bus Sensitivity Factor (MGOBSF) and Multiple Generator Outage Voltage Sensitivity (MGOVS) algorithms based on DC and AC load flow models. These developed algorithms provide the impact on the system due to multiple contingencies and help the operator at the control center to take corrective actions in a quick and effective way. These developed algorithms were tested on three test systems; the six buses, thirty seven buses and the 137 buses actual utility test case. The test results demonstrate that given situational awareness the algorithms provide additional decision support that can be used for remedial actions and/or for recovery after an outage. Integrating these into a power system energy management system (EMS) will provide a tool for operators to have a better understanding of the system before and during an extreme condition.

URI

https://hdl.handle.net/11668/15608

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