Artificial Intelligence Techniques for Power System Transient Stability Assessment

Sarajčev, Petar; Kunac, Antonijo; Petrović, Goran; Despalatović, Marin

doi:10.3390/en15020507

Cited by 22 publications

(15 citation statements)

References 85 publications

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“…ML is further divided into supervised learning, unsupervised learning, and reinforced learning for use in power electronics. Some of the research trends and an overview of artificial intelligence in power electronics can be found in [22][23][24][25][26][27][28][29][30].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Kareem

Hur

2022

Electronics

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The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.

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Section: Motivation and Related Workmentioning

confidence: 99%

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Kareem

Hur

2022

Electronics

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“…The scientific area that examines this effort is called "Artificial Intelligence" (AI) and can be considered as a computer science branch which is divided into many subareas due to the recent surge in research on this field. Figure 1 illustrates the sub-branches of AI [20][21][22][23][24]. The basic concept of AI is the description and the design of agents that receive stimuli from the environment and perform actions.…”

Section: Artificial Intelligencementioning

confidence: 99%

Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning

et al. 2022

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Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network.

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“…A review paper [4] discusses broader aspects of the artificial intelligence applications to the power system transient stability assessment. The review covers data generation processes (from wide-area measurements as well as from simulations), data processing pipelines (features engineering, splitting strategies, dimensionality reduction, embedding, encoding), model building and training (including ensembles and hyperparameters optimization techniques), deployment, and management (with monitoring for detecting bias and data drift).…”

Section: A Short Review Of the Contributions In This Issuementioning

confidence: 99%

Machine Learning in Power System Dynamic Security Assessment

Sarajčev

2022

Energies

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Artificial Intelligence Techniques for Power System Transient Stability Assessment

Cited by 22 publications

References 85 publications

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning

Machine Learning in Power System Dynamic Security Assessment

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