Reliability assessment of HVdc systems using Monte Carlo simulation technique
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Date
2018
Authors
Hou, Wentian
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Abstract
With the proliferation of HVdc systems around the world, the reliability performance of HVdc system and the impact of HVdc systems on the reliability performance of overall power systems is becoming an important issue. In the past, predominantly analytical techniques were used to assess the reliability of HVdc systems. One of the major disadvantages with analytical techniques is that the models used in the reliability evaluation of the HVdc systems are highly approximate. Also, analytical methods do not produce the probability distributions associated with the reliability indices, which can provide more detailed information on the reliability performance of power systems.
This thesis presents a method for the reliability evaluation of HVdc systems using the Monte Carlo technique, with the emphasis on the use of the reliability index distributions in HVdc system adequacy assessment to provide a complete picture of the reliability of power systems that contain HVdc links. The concept of developing distributions and the related analysis of reliability index distributions are illustrated in this thesis. Appropriate techniques are also developed to incorporate deterministic considerations into probabilistic evaluations to perform well-being analysis on power systems containing HVdc link. The well-being indices would provide additional and useful adequacy indices for system planning. The distribution of the wellbeing indices was also examined, and the comparison and analysis are presented for different deterministic criteria and different scenarios.
The models, techniques and results presented in this thesis would provide valuable methods and inputs for power system planners and operators.
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Keywords
Power System Reliability, Reliability Index Distributions, Monte Carlo Simulation, HVdc System
Citation
Hou, W., B. Bagen, and A. M. Gole. "Including reliability index distributions in HVdc system adequacy assessment using Monte Carlo simulation." (2017): 27-6.