Artificial Intelligence Techniques for Power System Transient Stability Assessment

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

doi:10.3390/en15020507

Cited by 19 publications

(2 citation statements)

References 85 publications

(129 reference statements)

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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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“…For the representative scenes, TTC rules were mined and a knowledge base was formed, which was better adapted to real-time monitoring of the safety of wind power transmission channels. Reference [6] uses the correlation classification method to extract the power grid stable operation rules. Since the proposed method introduces the time factor, the obtained rules can not only reveal the information of the strongly correlated influencing factors of the stable operation of the power grid, but also extract the influence factor changes on the system state change the internal relationship, thus providing support for scheduling operation decisions.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

Ali¹,

Galal²,

Alkhalaf³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional "wind powerload consumption" feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.

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The role of machine learning in improving power distribution systems resilience

Fahim,

Kalinaki,

De Silva

et al. 2024

Future Modern Distribution Networks Resilience

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Artificial Intelligence Techniques for Power System Transient Stability Assessment

Cited by 19 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

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The role of machine learning in improving power distribution systems resilience

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