Comparison of Voltage Stability Assessment using Different Machine Learning Algorithms

Adhikari, Ananta; Naetiladdanon, Sumate; Sagswang, Anawach; Gurung, Samundra

doi:10.1109/ei250167.2020.9346750

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…MLBTs have also been utilized for short-term system status prediction/assessment, system stability control and long-term system planning. As a type of supervised learning, tree-based methods such as decision trees were applied to transient stability [13] and voltage stability assessment [14]. Neural networks such as extreme learning machine (ELM) [15] and convolutional neural networks (CNNs) [16] were used for frequency stability assessment.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks such as extreme learning machine (ELM) [15] and convolutional neural networks (CNNs) [16] were used for frequency stability assessment. In [14] and [7] artificial neural networks (ANNs) were used for voltage stability and system inertia prediction. Support vector machines (SVMs) were applied to frequency and voltage stability assessment in [17] [14], and transient stability control in [18].…”

Section: Introductionmentioning

confidence: 99%

“…In [14] and [7] artificial neural networks (ANNs) were used for voltage stability and system inertia prediction. Support vector machines (SVMs) were applied to frequency and voltage stability assessment in [17] [14], and transient stability control in [18]. The long short term memory (LSTM) approach was applied to distributed energy resource (DER) sizing in [19].…”

Section: Introductionmentioning

confidence: 99%

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On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Wang

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Panteli

et al. 2023

IEEE Trans. Sustain. Energy

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“…The random forest algorithm was used as the machine learning technique. Some machine learning algorithms for voltage stability assessment were compared as presented in [16]. Gaussian Process Regression (GPR), ANN, Support Vector Machine (SVM), dan Decision Tree (DT) were compared in this research.…”

Section: Introductionmentioning

confidence: 99%

A Proposed PMU-based Voltage Stability and Critical Bus Detection Method using Artificial Neural Network

Putranto,

Azhar

2023

Preprint

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Voltage stability detection is currently still becoming the main issue in the modern power system which renewable energy integrated. Classical method to assess the voltage stability require complete model of power system, power system analysis and long computation time. In the data driven analysis and synchronized measurement era, the model can be proposed based on the historical data to deal with the complexity. For that reason, the voltage stability and critical bus detection based on ANN algorithm with the instantaneous PMU measurement is proposed. Seven hidden layers consisted of one normalization, four rectifier linear unit, one softmax and one sigmoid layer are formulated to do the detection. To measure the accuracy, the k-fold cross-validation is used. The proposed model is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and PV integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16–18 second per detection which make the scalability to the real time detection is reasonable.

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“…As demonstrated in [5], today, machine learning (ML) based techniques such as artificial neural networks (ANNs), decision trees (DTs), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and support vector machines (SVMs) have become attractive tools for solving nonlinear problems with desired speed and accuracy. In particular, deep learning is used in [6], for short-term voltage stability assessment of power systems to learn the dependencies from post-disturbance system dynamic trajectories.…”

Section: Introductionmentioning

confidence: 99%

Voltage Stability Margin Estimation Using Machine Learning Tools

Guañuna,

Chamba,

Granda

et al. 2023

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Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.

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Comparison of Voltage Stability Assessment using Different Machine Learning Algorithms

Cited by 8 publications

References 10 publications

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

A Proposed PMU-based Voltage Stability and Critical Bus Detection Method using Artificial Neural Network

Voltage Stability Margin Estimation Using Machine Learning Tools

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