Relay Protection and Automation Algorithms of Electrical Networks Based on Simulation and Machine Learning Methods

Kulikov, Aleksandr; Loskutov, Anton; Bezdushniy, D.I.

doi:10.3390/en15186525

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…On the other hand, it allows you to reduce the total number of neurons in the ANN and increase the speed of calculations. This is very important for processing information flows and signals [18][19] in real time using microprocessor technology [20][21].…”

Section: Discussionmentioning

confidence: 99%

Different numerical dimensions data flows: machine learning instead of correlation methods

Slavutskii,

Lazareva,

Portnov

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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System analysis of multidimensional random data is one of the most important tasks of modern information technologies. Traditional statistical methods for analyzing such data do not always allow one to obtain adequate results. In this article, the joint use of the decision tree method and the artificial neural networks is proposed for data classification. It is shown that this approach can be an alternative to multiple factorization of multidimensional data and has a number of significant advantages over traditional statistical methods. Even when the data has limited and different digital scales, the use of machine learning makes it possible to conduct a hierarchical classification of intra-system data relationships, to reduce the number of parameters significant for analysis, and significantly reduce the size of the learning sample required. For the technical applications, this allows for real-time data analysis using a microprocessor implementation of neural network algorithms.

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Section: Discussionmentioning

confidence: 99%

Different numerical dimensions data flows: machine learning instead of correlation methods

Slavutskii,

Lazareva,

Portnov

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

View full text Add to dashboard Cite

show abstract

“…Also, a feature of such a machine learning model as a decision tree is the absence of the need for preprocessing of the initial data, in contrast to, for example, logistic regression or the support vector machine [16], the use of which requires data normalization and elimination of correlation between various features. Therefore, as a training sample for the machine learning model, the real and imaginary parts of the complex values of the mode parameters obtained directly as a result of the simulation model were used.…”

Section: Application Of the Decision Tree Algorithm For Recognizing T...mentioning

confidence: 99%

“…Previously, the authors conducted research on the use of machine learning methods to improve the functioning of relay protection and automation [3][4][5]15,16]. For example, the article [16] presents a classification of machine learning methods and examples of the application of some machine learning methods: k-nearest neighbor method; logistic regression method; support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

et al. 2023

Self Cite

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The complication of the structure, topology and composition of the future electrical networks is characterized by difficult-to-recognize circuit-mode situations and requires modern methods for analyzing information parameters. The growing trend of digitizing signals in substations and the use of the IEC 61850 standard results in a huge amount of new data available at the nodes of the electrical network. The development and analysis of new methods for detecting and recognizing the modes of electrical networks (normal and emergency) are topical research issues. The article explores a new approach to recognizing a faulted section of an electrical network with branches by concurrently analyzing several information features and applying machine learning methods: decision tree, random forest, and gradient boosting. The application of this approach for decision-making by relay protection has not been previously implemented. Simulation modeling and the Monte Carlo method are at the heart of obtaining training samples. The results of testing the studied methods under review showed the required flexibility, the ability to use a large number of information parameters, as well as the best results of fault recognition in comparison with the distance protection relay.

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“…As distributed computing technology matures, the utilization of distributed computing platforms can effectively facilitate the global optimization of relay protection setting design [25,26]. Intelligent optimization algorithms based on distributed technology have been widely employed to calculate global protection setting values.…”

Section: Introductionmentioning

confidence: 99%

A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay

Luo,

Sun,

et al. 2024

Applied Sciences

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To ensure a stable and reliable power supply, the valid and timely response of protective relays are indispensable. Through the prevention of fault expansions, potential equipment damage or system collapse can be averted, where their setting is one vital prerequisite for such effective implementations. However, the increasing complexity of distribution power systems results in more challenges for protection tuning strategies. Ergo, this paper presents an ensemble that combines the independent factor evaluation (IFE) and quantum genetic optimization (QGO) models to further optimize the performance of relays according to their distributed tuning environment. In this ensemble, both near and far-end fault characteristics can be incorporated. In the first stage, the IFE dimensional reduction model is deployed for massive heterogeneous input data, where the statistical independence of input signals is calculated, the linear transformation matrix to decouple mixed signals is found, the linear combination of such signals is formed, and the non-Gaussian property to sort them is established. This can ameliorate the following calculation efficiency under those high-dimensional data scenarios. Subsequently, the QGO model is designed to further improve relay settings, where qubit representation is built to reduce required chromosomes, the linear superposition of the optimal solution probability in different states is implemented for a better diversity and convergence performance, and a self-adaption quantum gate is established to dynamically update the qubit chromosome groups and two-state solution combinations. Lastly, an empirical case study is presented, which validates the enhanced convergence, accuracy, and rapidity of the proposed ensemble.

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Relay Protection and Automation Algorithms of Electrical Networks Based on Simulation and Machine Learning Methods

Cited by 11 publications

References 27 publications

Different numerical dimensions data flows: machine learning instead of correlation methods

Different numerical dimensions data flows: machine learning instead of correlation methods

Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay

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