Abstract
For Power distribution system the most important task for distribution engineer is to efficiently simulate the system and address the uncertainty using a suitable mathematical method. This paper presents a comparison of two methods used in analyzing uncertainties. The first method is Montecarlo simulation (MCS) that considers input parameters as random variables and second one is fuzzy alpha cut method (FAC) in which uncertain parameters are treated as fuzzy numbers with given membership functions. Both techniques are tested on a typical Load flow solution simulation, where connected loads are considered as uncertain. In order to provide a basis for comparison between above two approaches, the shapes of the membership function used in the fuzzy method is taken same as the shape of the probability density function used in the Monte Carlo simulations. For more than one uncertain input variable, simulation result indicates that MCS method provides better output results compared to FAC, however takes more time due to number of runs. FAC provides an alternate method to MCS when addressing single or limited input variables and is fast.
Highlights
Current time power distribution systems, especially in developing countries, are steadily approaching towards its maximum operating limits and voltage stability is a major concern
The first method is Montecarlo simulation (MCS) that considers input parameters as random variables and second one is fuzzy alpha cut method (FAC) in which uncertain parameters are treated as fuzzy numbers with given membership functions
This paper presents a comparison of “Monte-Carlo simulation method (MCS)” a technique based on probability and “Fuzzy alpha cut method (FAC)” a technique based on Fuzzy
Summary
Current time power distribution systems, especially in developing countries, are steadily approaching towards its maximum operating limits and voltage stability is a major concern. The voltage instability can be addressed using the various techniques e.g. reconfiguration, addition of capacitor banks etc., need an efficient simulation of load flow and a mathematical method which address the uncertainty efficiently especially the uncertainty associated with input parameters. Many solution methods have been developed on Load Flow distribution networks using Fuzzy and probabilistic models. A. J Abebe presents a comparison of two methods (fuzzy alpha cut and Monte Carlo simulation) of analysis of uncertainty arising from uncertain model parameters [5]. This paper presents a comparison of “Monte-Carlo simulation method (MCS)” a technique based on probability and “Fuzzy alpha cut method (FAC)” a technique based on Fuzzy. The MCS technique treats uncertain parameter as random variable that obeys a given probabilistic distribution and model output is a random variable. Uncertain model parameters are treated as fuzzy numbers with a membership function
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
