Stability of the power quality is one of the objectives that power companies always try to assure. With energy shortage and the increases of fuel cost over years, reduction of expenses in all areas is another effort of the power company. Dealing with the above problems, Taiwan Power Company sets up a standard voltage for secondary side of each primary substation. Standard voltage is a commitment of expected 69kV primary substation bus voltage. A proper setting of the standard voltage can reduce voltage variation, in the secondary substation, and reduce the operation frequencies of the on load tap changer. Besides, it can prolong the service life and the maintenance cycle, and it can also reduce maintenance cost of each main transformer. This study proposes a method to calculate the standard voltage to improve the shortcomings that the voltage used to be set up with experience rule. The load and voltage data were used to build a neural network model. Improved particle swarm optimizer was used to find the parameters of the radial basis function neural network in order to build an efficient network. This network uses improved particle swarm optimizer again to the standard voltage. The proposed approach has been verified by the comparison of winter and summer standard voltages on the Tainan primary substation of taipower with accurate results.