Predicting Voltage Stability Indices of Nigerian 330kV 30 Bus Power Network Using an Auditory Machine Intelligence Technique

Wokoma, Biobele A.; Osegi, Emmanuel N.; Idachaba, Alex. O.

doi:10.1109/africon46755.2019.9133915

Cited by 1 publication

(1 citation statement)

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“…In other words, the proposed method provides the best performance regarding prediction interval coverage probability (PICP) and Winkler scores for all test cases. [83] predicts the voltage stability of Nigeria's (30) 330 kV bus using Auditory Machine intelligence techniques (AMI) and the method was compared with the Group Method of Data Handling (GMDH). The results of simulation studies show that the AMI technique is competitive with the GMDH time-series technique for several experimental simulation runs.…”

Section: Vi) Voltage and Reactive Power Controlmentioning

confidence: 99%

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Makanju,

Shongwe,

Famoriji

2024

IEEE Access

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Prediction in the power system network is very crucial as expansion is needed in the network. Several methods have been used to predict the load on a network, from short to long time load prediction, to ensure adequate planning for future use. Since the power system network is dynamic, other parameters, such as voltage and frequency prediction, are necessary for effective planning against contingencies. Also, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This review analyzed different machine learning approaches used for load, frequency, and voltage prediction in power systems and proposed a machine learning predictive approach using network topology behavior as input variables to the model. The analysis of the proposed model was tested using a regression model, Decision tree regressor, and long short-term memory. The analysis results indicate that with network topology behavior as input to the model, the prediction will be more accurate than when isolated variables of a particular Bus in a network are used for prediction. This work suggests that network topology behavior data should be used for prediction in a power system network rather than the use of isolated data of a particular bus or exogenous data for prediction in a power system. Therefore, this research recommends that the accuracy of different predictive models be tested on power system parameters by hybridizing the network topology behavior dataset and the exogenous dataset.

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Section: Vi) Voltage and Reactive Power Controlmentioning

confidence: 99%