An optimized algorithm for optimal power flow based on deep learning

Su, Qinggang; Khan, Habib Ullah; Khan, Imran; Choi, Bong Jun; Wu, Falin; Aly, Ayman A.

doi:10.1016/j.egyr.2021.04.022

Cited by 11 publications

(4 citation statements)

References 48 publications

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“…are algorithms that also fall into this category. DL models have shown that they have more accuracy and efficiency in prediction based on unstructured data compared to ML algorithms [145,146].…”

Section: Algorithmsmentioning

confidence: 99%

Machine Learning and Deep Learning in Energy Systems: A Review

et al. 2022

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With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

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Section: Algorithmsmentioning

confidence: 99%

Machine Learning and Deep Learning in Energy Systems: A Review

et al. 2022

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“…The theory of ML is mainly to design some algorithms that allow computers to "learn" automatically. An ML algorithm is a kind of algorithm that automatically analyzes and obtains laws from data, and uses the laws to predict unknown data [42,43]. Because a large number of statistical theories are involved in the ML algorithm, ML is particularly closely related to inferential statistics, also known as statistical learning theory [44][45][46][47][48][49][50].…”

Section: Theory Literature Review Of Prediction Methodsmentioning

confidence: 99%

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

Chen

2023

Sustainability

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In the era when product design must meet the needs of consumers, the products preferred by consumers are an important source of design creativity and design reference for product designers to design products. Therefore, how to effectively grasp the products that consumers prefer has become an important issue for product designers. In order to allow designers to have more convenient and accurate consumer preference product prediction tools, this study proposed machine learning (ML) to analyze and predict sustainable patterns in consumer product preferences and conducted a feasibility study on the use of ML for predicting sustainable patterns in consumer product preferences. A total of three experiments were carried out in this study: the KJ method to predict consumer product preference experiment, the AHP method to predict consumer product preference experiment, and ML to predict consumer product preference experiment. This study uses the three experiments to discuss and compare the prediction ability of ML and the current commonly used forecasting tools, namely the KJ method and AHP method. The research results show that no matter what kind of consumer product attribute preference is predicted, the accuracy rate of consumer product preference prediction by ML is much higher than that of the KJ method and AHP method. These research results show that no matter the product attribute dimension, ML has the ability to predict consumer preferences, and ML has a better ability to predict consumer preferences than traditional tools. Therefore, this study believes that ML can be used to analyze and predict sustainable patterns in consumer product preferences. Therefore, this study suggests that product designers can use ML technology to assist in the analysis and prediction of consumer product preferences, so as to improve the grasp of consumer preference products.

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“…In the formula, δ is the neighborhood threshold, which is a real number between 0 and 1; 2 is the distance function which represents the similarity between sample x i and sample x j in feature space B. The larger the B (x i , x j ), the lower the similarity between x i and x j in feature space B. Eq.…”

Section: Rough Neighborhood Reductionmentioning

confidence: 99%

“…The development of grid interconnection and intelligence makes today's power grids increasingly complex and open; while the scale of power grids continues to expand, uncertain risk factors are also increasing [1][2][3][4][5]. This puts forward higher requirements for the safety and stability analysis of the power grid.…”

Section: Introductionmentioning

confidence: 99%

An Optimal DF Based Method for Transient Stability Analysis

Zaki¹,

Ahmed²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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The effect of energy on the natural environment has become increasingly severe as human consumption of fossil energy has increased. The capacity of the synchronous generators to keep working without losing synchronization when the system is exposed to severe faults such as short circuits is referred to as the power system's transient stability. As the power system's safe and stable operation and mechanism of action become more complicated, higher demands for accurate and rapid power system transient stability analysis are made. Current methods for analyzing transient stability are less accurate because they do not account for misclassification of unstable samples. As a result, this paper proposes a novel approach for analyzing transient stability. The key concept is to use deep forest (DF) and a neighborhood rough reduction approach together. Using the neighborhood rough sets, the original feature space is obtained by creating many optimal feature subsets at various granularity levels. Then, by deploying the DF cascade structure, the mapping connection between the transient stability state and the features is reinforced. The weighted voting technique is used in the learning process to increase the classification accuracy of unstable samples. When contrasted to current methods, simulation results indicate that the proposed approach outperforms them.

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An optimized algorithm for optimal power flow based on deep learning

Cited by 11 publications

References 48 publications

Machine Learning and Deep Learning in Energy Systems: A Review

Machine Learning and Deep Learning in Energy Systems: A Review

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

An Optimal DF Based Method for Transient Stability Analysis

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