2020 Georgia Tech Fault and Disturbance Analysis Conference

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Utilization of PMU data through effective angle prediction algorithm for the prevention of wide area blackouts

The proposed paper will show the work about the development of a phase angle prediction method in order to use the data from synchrophasors or Phasor Measurement Units (PMUs) effectively so that wide scale power outages are prevented. The PMUs provide the information about the system states such as phase angles, frequency, rate of change of angles, etc., at a sampling rate as high as 120 samples / second and can capture oscillatory events following any severe disturbances. In order to prevent the interconnected power systems from wide area blackouts, the system states should be continuously monitored and analyzed in real time. This work will present the way to utilize such PMU measured system states in a more useful and effective way.

This paper will introduce a very accurate way to utilize the information provided by PMUs with the use of a fast, online, robust, and intelligent angle prediction algorithm that is able to initiate the protective actions before the system goes unstable. By assisting in the detection and actions necessary to bring the bulk system swinging oscillation centers back to the stable operating range, the algorithm presented here can be utilized to prevent wide area blackouts, and therefore, provide important functionality by properly using the information in real time PMU data. This algorithm is based on two Kalman filters, guided by the measurement prediction algorithm based on Taylor series expansion and finite difference method. The presented algorithm can predict the evolving instability following the fault clearance under any extreme contingency conditions. According to the tests performed in various stable and unstable scenarios in large power system, it is also validated that the algorithm does not give a false alarm during the stable system conditions. The test results also show that this algorithm has a very fast response time and is very accurate in the prediction of angular instability. The algorithm also includes the simulation noise on to the PMU measurements. In this work, the two Kalman filter prediction algorithm is used to make a decision about the forthcoming system angular instability, and then, initiate the mitigation measures that include Controlled Systems Separation (CSS) with the protection options such as Under Frequency Load Shedding (UFLS), Out of Step Tripping (OST) or system wide generator tripping.

Sarina Adhikari
EnerNex, A CESI Company
United States

David Mueller
EnerNex, A CESI Company
United States

Bob Zavadil
EnerNex, A CESI Company
United States

 


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