Agents for Fuzzy Indices of Reliability Power System with Uncertainty Using Monte Carlo Algorithm
The standard deviation of load level uncertainty in power system reliability assessment has a different value for each load level leading to complexity iterations required in the convergence of Monte Carlo algorithm. In this present work, the fuzzy system agent perspective would be used to control...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5804/4/Agents_for_fuzzy_indices_of_reliability_power_system_with_uncertainty_using_Monte_Carlo_algorithm.pdf http://umpir.ump.edu.my/id/eprint/5804/ http://dx.doi.org/10.1109/PEOCO.2014.6814436 |
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| Summary: | The standard deviation of load level uncertainty in
power system reliability assessment has a different value for each load level leading to complexity iterations required in the convergence of Monte Carlo algorithm. In this present work, the fuzzy system agent perspective would be used to control such convergence. Two agents are developed based on fuzzy parameters of Monte Carlo i.e. current with its means and variances; the other agent is the probability of outage capacity for each state. These agents shall be applied in terms of the loss of load probability (LOLP) and loss of load expectation (LOLE) which when implemented and compared based on a Malaysian distribution network (DISCO-Net). The obtained outcomes showed that the fuzzy parameters of Monte Carlo provided a better limitation for variance techniques in uncertainty load levels. |
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