Parameter estimation in unbalanced three-phase distribution lines using general regression neural networks with inconsistent data handling capacity

Abstract

The feeder parameters of a power system play an important role in its operation and analysis. However, in many situations, the values used in the calculations differ from the actual parameters, resulting in large errors. Therefore, in this study, a highly accurate feeder parameter estimation method for a three-phase distribution line was proposed. The proposed method uses the measured node voltages and power quantities from the two ends of the distribution line. A precise electrical model of the three-phase distribution line was used. An improved type of neural network, namely general regression neural networks (GRNN), is used to solve the complex non-linear equation and then estimate the actual parameters. However, the proposed estimation process does not require synchronised phasors or phasor-measurement units installed at the point of measurement. Nonetheless, the estimation process is also capable of handling incorrect data information, noisy datasets, and measurement errors while maintaining accuracy at the desired level. The estimator process is examined and analysed using four different IEEE unbalanced three-phase systems. In addition, the proposed methodology is compared with standard algorithms of radial basis function networks, quasi-Newton, and multi-run optimisation methods on the basis of maximum absolute percentage error and other standard error functions. The hyperparameter-tuned GRNN model was also tested using several statistical significance tests. In all scenarios, the performance and accuracy of the proposed methodology and model exceeded those of the other methods.