Research On Synthetic Control Of Substation Voltage/reactive Power Based On Load Forecasting

This paper mainly discusses a control method of substation voltage and reactive power .In order to get suitable decision for one day 24 hours tap-transformer's step switch and shunt capacitor switch, an approach of substation voltage and reactive power control on the basis of the combination of Artificial Neural Network (ANN) reactive power forecasting and evolutionary programming optimal decision-making is put forward. Firstly, Radial Basis Function Network(RBFN) is applied in short-term load forecasting ,it is abtained one-day 24 hours average load values based on nonlinear approximation capability of RBF neural network. In this paper ,subtractive clustering method is introduced for proper RBF centers, thus direct training the network, control the number of clustering by using automatical end-criterion, RBFN can obtain both the parameters of the neurons and the number of the hidden neurons, also can improve network inflection accuracy. The RBF network has a better performance, and better forecasting accuracy .Then mathematical model of substation Voltage/Var control is constructed, the squares minimization of Voltage differences as target, also considering requirement of Voltage and power balance, taking it into consideration that the magnitude constraint of transformer ratio and compensating capacitor, also that constraint of operation times of one day transformer tap and capacitor switch. Because of forecasting reactive load at first, it can detect the change of voltage at low-voltage bus from the change of reactive load or the change of voltage at high-voltage bus, then it can decide that adjusting transformer tap or capacitor switch, and avoid blindly and deficient adjusting. On the condition the reactive power is balanced and voltage qualified, it can realize the switching times of loaded taps and capacitors being efficiently decreased. Aimed at multiple-limit, multiple-object, non-linear, discrete of Voltage/Var optimization and control, on account of whole evolution of evolutionary programming, no demand for differentiability of optimal function, and random search, it can obtain global optimum with mayor probability, this paper solve optimal function with evolutionary programming.