Abstract
One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.
Highlights
In recent years, due to economic competition and deregulation, power systems are being operated closer and closer to their limits
This paper presents the artificial neural network (ANN) application for estimation of peak and duration overvoltages under switching transients during transformer energization
Values in column TPSB are the values of overvoltage duration calculated by power system blockset (PSB) program in seconds and TRBFNN values are the values simulated by trained neural network
Summary
Due to economic competition and deregulation, power systems are being operated closer and closer to their limits. This paper presents the artificial neural network (ANN) application for estimation of peak and duration overvoltages under switching transients during transformer energization. In the proposed ANN we have considered the most important aspects, which influence the transient overvoltages such as voltage at transformer bus before switching, equivalent resistance, equivalent inductance, equivalent capacitance, line length, switching angle, saturation curve slope, and remanent flux. This information will help the operator to select the proper sequence of transformer to be energized safely with transients appearing safe within the limits. Results of the studies are presented for a partial of 39-bus New England test system to illustrate the proposed approach
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