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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.

Current time power distribution systems, especially in developing countries, are steadily approaching towards its maximum operating limits and voltage stability is a major concern. Voltage instability leads to blackouts and makes the system unreliable. It is important to have a reliable power distribution system, which maintains voltages within the permissible range and ensure a high quality of output.

The voltage instability can be addressed using the various techniques e.g. reconfiguration, addition of capacitor banks etc., however need an efficient simulation of load flow and a mathematical method which address the uncertainty efficiently especially the uncertainty associated with input parameters. Distribution system uncertainties are due to error in measurement of feeder parameters, variation in expected values of the demands with time etc. and are main causes of uncertain simulation outputs.

Uncertainty can be analyzed and addressed using several techniques. In past, many solution methods have been developed on Load Flow distribution networks using Fuzzy and probabilistic models.

D. M. Falcao describes the conceptual basis and preliminary results of a load estimation based on the application of neural network and fuzzy set techniques [

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 then a random variable. The fuzzy analysis is based on fuzzy logic and fuzzy set theory, which is widely used in representing uncertain knowledge. Uncertain model parameters are treated as fuzzy numbers with a membership function.

For simulation purpose this paper uses a load flow algorithm, based on concept described by R. Raina, M Thomas, R. Ranjan [

This simulation is run on a typical 19 bus distribution system from the D. Thukram, H. M W. Banda and J. Jerome [

Input connected load data for the feeder are given in