Abstract
Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE 30-bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.
Highlights
The changes in power systems’ parameters such as loading, generator reactive power limits, action of tap changing transformers, load recovery dynamics and line or generator outages may cause a gradually and uncontrolled drop of voltages leading to voltage instability [1]
In order to evaluate the performance of the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) model, the difference between the predicted and the actual output values was assessed according to the correlation coefficient (R), the root mean square error (RMSE) and the mean absolute percentage error (MAPE)
For both case studies of IEEE 30-bus and IEEE 118-bus systems, that the ANFIS model acquired relatively lower values of RMSE and MAPE, this means that the trained ANFIS model has a superior performance compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks
Summary
The changes in power systems’ parameters such as loading, generator reactive power limits, action of tap changing transformers, load recovery dynamics and line or generator outages may cause a gradually and uncontrolled drop of voltages leading to voltage instability [1]. Further enhancement of ANN performance in an on-line monitoring of voltage stability has been achieved by reducing the input data into an optimal size using Z-score-based algorithm [9]. The application of ANN-based Radial Basis Function (RBF) for on-line voltage stability evaluation has been performed by several researchers [10,11,12,13]. ANFIS soft computing technique is applied with the aim of developing an on-line voltage stability evaluation model. No. an optimization problem considering the operating cost, the real power losses and the voltage stability index is formulated. The objective function, which has been handled by using meta-heuristic algorithms, includes the fuel cost, real power losses and voltage stability index. The equality constraints represent the real and reactive power equations, which are expressed as follows: Nb
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
