Sharman Sundarajoo, Sharman Sundarajoo and Dur Muhammad Soomro, Dur Muhammad Soomro (2023) A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding. A PARTICLE SWARM OPTIMIZATION TRAINED FEEDFORWARD NEURAL NETWORK FOR UNDER-VOLTAGE LOAD SHEDDING, 21 (2). pp. 1-16.
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Abstract
This paper suggests an under-voltage load shedding (UVLS) approach to avoid voltage collapse in stressed distribution systems. Prior to a blackout, a failing system reaches an emergency state, and UVLS is executed as the final option to prevent voltage collapse. Hence, this article introduces an optimal UVLS method using a feedforward artificial neural network (ANN) model trained with the particle swarm optimization (PSO) algorithm to obtain the optimal load shedding amount for a distribution system. PSO is used to obtain the best topology and optimum initial weights of the ANN model to enhance the precision of the ANN model. Thus, the dispute between the optimum fitting regression of the allocation of ANN nodes and computational time was disclosed, while the MSE of the ANN model was minimized. Moreover, the proposed method uses the stability index (SI) to identify the weak buses in the system following an emergency state. Different overload scenarios are examined on the IEEE 33-bus distribution network to validate the efficacy of the suggested UVLS scheme. A comparative study is performed to further assess the performance of the proposed technique. The comparison indicates that the recommended method is effective in terms of voltage stability and remaining load.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Under-Voltage Load Shedding, Artificial Neural Network, Particle Swarm Optimization, Stability Index, Voltage Stability, Voltage Collapse |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering |
| Depositing User: | Mr. Mohamad Zulkhibri Rahmad |
| Date Deposited: | 21 Nov 2023 01:12 |
| Last Modified: | 21 Nov 2023 01:12 |
| URI: | http://eprints.uthm.edu.my/id/eprint/10376 |
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