Salp swarm algorithm and phasor measurement unit based hybrid robust neural network model for online monitoring of voltage stability
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Incessant assessment of voltage stability is lively aspect to safeguard the electrical power system operation. The traditional methods for online assessment in terms of voltage stability analysis are extremely time consuming and also difficult for supervising it in online. In connection to this a novel model based on Salp swarm algorithm and artificial neural network (SSA–ANN) is considered for online monitoring of voltage stability in this manuscript. As we know that ANN is an influential and promising predictive tool to attain the efficacy and accuracy in terms of training and testing time. The input for model is the PMU data and the output for the model is voltage stability margin index which is used for voltage stability monitoring. SSA is used for tuning the Meta parameters such as the activation functions and number of nodes along with the learning rate. The solution method opted for this stability monitoring utilises the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the neural network model. The efficiency of the proposed model is verified by means of various test cases and compared with the same data set to attest it’s pre-eminence.
KeywordsArtificial neural network (ANN) Salp swarm algorithm (SSA) Phasor measurement units (PMU) Voltage stability monitoring index (VSMI)
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Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
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