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This study suggests a new algorithm based on a combination of fuzzy logic and genetic algorithm (GA) to improve voltage profile in a microgrid. The considered microgrid includes control variables such as onload tap changer (OLTC), active power output from distributed generators (DG) and reactive power output from feeder switched capacitors that are controlled in a microgrid controller (MGC) by communication links. The proposed method was used to obtain the optimum value of control variables to establish voltage stabilization in varying load condition as online. For establishing voltage stabilization at the microgrid, an objective function is defined and is tried to minimize it by control variables. The control variables were changed based on fuzzy logic and the GA was employed for finding the optimum shape of membership functions. In order to verify the proposed method, a 34 buses microgrid in varying load condition was analyzed and was compared with previous works.

This study suggests a new algorithm based on a combination of fuzzy logic and genetic algorithm (GA) to improve voltage profile and reduce loss in a microgrid. Considered microgrid includes control variables such as onload tap changer (OLTC), active power output of distributed generators (DG) and reactive power output of feeder switched capacitors that are controlled in a microgrid controller (MGC) through communication links. The proposed method was used to obtain the optimum value of control variables to reduce the loss and improve the voltage profile. The problem formulations consider three distinct objectives related to cost of loss, cost of injected active power by infinity bus and cost of injected active power by DG. The novel formulation is a multi-objective and non-differentiable optimization problem. The control variables were changed base on fuzzy logic and the GA was employed for finding the optimum shape of membership functions. In order to verify the proposed method, a 34 bus microgrid was analyzed in varying load condition and was compared with previous works. Finally it has been tested on 33-buses system to be investigated the control variables status, cost function status and voltage fluctuation during one day. control variables and applied genetic algorithm to finding the optimal values of real power output of DG to improve the voltage profile and reduce the loss. The value of loss depends on penetration of DG in distributed Systems [2]. If this penetration is greater than a particular value, the rate of loss in distribution systems with DG is greater than the rate of that in distributed system without DG. Also the effect of DG with different technology on loss was investigated and it was shown that wind turbine has the worst behavior on real loss. OLTC is an autotransformer that can regulate its secondary voltage by varying the transformer ratio in a specified range. In some reports [3,4] Control variables are DG and OLTC and it was shown that not only the loss is reduced, but also DG capacity is increased by using OLTC. Viawan and Karlsson [5] considered reactive power output from DG (active power output