<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study presented a new hybrid algorithm to improve the state estimation (SE) of radial distribution power systems (PSs). The proposed particle swarm optimization–neural network (PSO–NN) algorithm constructed an independent and fast monitoring system with high accuracy that can detect abnormal conditions or failures in a PS. In this study, PSO was adopted to determine the appropriate weights of the NN model. The speed and accuracy of PSO with the NN model were evaluated in the SE of power system networks (PSNs). The information obtained through SE was used to enhance the operations and customer service delivery in terms of energy consumption and power quality in PSNs. Capacitor banks were installed to reduce the losses and improve the voltage profiles. The PSO–NN algorithm was assessed on IEEE (9, 33, and 69) bus standards. Simulation results proved that the new technique can be tested on any distribution network because of its accurate and efficient SE. Results indicated that the PSO–NN algorithm had better performance than the phasor measurement units.</p>
</abstract>

This study presented a new hybrid algorithm to improve the state estimation (SE) of radial distribution power systems (PSs). The proposed particle swarm optimization–neural network (PSO–NN) algorithm constructed an independent and fast monitoring system with high accuracy that can detect abnormal conditions or failures in a PS. In this study, PSO was adopted to determine the appropriate weights of the NN model. The speed and accuracy of PSO with the NN model were evaluated in the SE of power system networks (PSNs). The information obtained through SE was used to enhance the operations and customer service delivery in terms of energy consumption and power quality in PSNs. Capacitor banks were installed to reduce the losses and improve the voltage profiles. The PSO–NN algorithm was assessed on IEEE (9, 33, and 69) bus standards. Simulation results proved that the new technique can be tested on any distribution network because of its accurate and efficient SE. Results indicated that the PSO–NN algorithm had better performance than the phasor measurement units.

Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Employment of PSO Algorithm to Improve the Neural Network Technique for Radial Distribution System State Estimation

This study presented a new hybrid algorithm to improve the state estimation (SE) of radial distribution power systems (PSs). The proposed particle swarm...

Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

Article Category: research-article

Online veröffentlicht: 05. Sept. 2019

Seitenbereich: 1 - 10

Eingereicht: 17. Jan. 2019

DOI: https://doi.org/10.21307/ijssis-2019-005

Schlüsselwörter
RDPS, RDPS, PSO, Neural network, Hybrid algorithm, State estimation, Monitoring system, Optimization methods, PMU

© 2019 Husham Idan Hussein et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

Article Category: research-article

Online veröffentlicht: 05. Sept. 2019

Seitenbereich: 1 - 10

Eingereicht: 17. Jan. 2019

DOI: https://doi.org/10.21307/ijssis-2019-005

SchlüsselwörterRDPS, RDPS, PSO, Neural network, Hybrid algorithm, State estimation, Monitoring system, Optimization methods, PMU

© 2019 Husham Idan Hussein et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
RDPS, RDPS, PSO, Neural network, Hybrid algorithm, State estimation, Monitoring system, Optimization methods, PMU