Probabilistic load flow with non-gaussian correlated random variables using gaussian mixture models PROJECT TITLE :Probabilistic load flow with non-gaussian correlated random variables using gaussian mixture modelsABSTRACT :This study proposes the use of Gaussian mixture models to represent non-Gaussian correlated input variables, like wind power output or aggregated load demands within the probabilistic load flow drawback. The algorithm calculates the marginal distribution of any bus voltage or power flow as a total of Gaussian elements obtained from multiple weighted least sq. runs. The amount of trials depends on the quantity of Gaussian elements used to model every input random variable. Monte Carlo simulations are used to match the approximations. The effect of correlation between variables is taken into consideration in both formulations. The most advantage of the Gaussian elements methodology is that the probability density functions of any variable is directly obtained. Take a look at ends up in the 14-bus system and also the 57-bus system give a broad explanation of the benefits and constraints of the approximations, significantly in presence of correlated variables. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Continuation power flow with adaptive stepsize control via convergence monitor Generator coherency and area detection in large power systems
