Lifecycle cost forecast of 110 kV power transformers based on support vector regression and gray wolf optimization

Du, Mingming; Zhao, Yuqi; Liu, Chaojun; Zhu, Zhu

doi:10.1016/j.aej.2021.04.019

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Compared to other popular algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) [24], the GWO algorithm offers higher convergence speed and superior search capabilities. Consequently, it is frequently employed in model parameter optimization and adjustment [25,26].…”

Section: Related Workmentioning

confidence: 99%

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Yang,

Yan,

Zhou

2024

Applied Sciences

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Accurate prediction of the heat load in district heating systems is challenging due to various influencing factors, substantial transmission lag in the pipe network, frequent fluctuations, and significant peak-to-valley differences. An autoencoder—grey wolf optimization—gated recurrent unit (AE-GWO-GRU)-based heat load prediction method for district heating systems is proposed, employing techniques such as data augmentation, lag feature extraction, and input feature extraction, which contribute to improvements in the model’s prediction accuracy and heat load control stability. By using the AE approach to augment the data, the issue of the training model’s accuracy being compromised due to a shortage of data is effectively resolved. The study discusses the influencing factors and lag time of heat load, applies the partial autocorrelation function (PACF) principle to downsample the sequence, reduces the interference of lag and instantaneous changes, and improves the stationary characteristics of the heat load time series. To increase prediction accuracy, the GWO algorithm is used to tune the parameters of the GRU prediction model. The prediction error, measured by RMSE and MAPE, dropped from 56.69 and 2.45% to 47.90 and 2.17%, respectively, compared to the single GRU prediction approach. The findings demonstrate greater accuracy and stability in heat load prediction, underscoring the practical value of the proposed method.

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Section: Related Workmentioning

confidence: 99%

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Yang,

Yan,

Zhou

2024

Applied Sciences

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“…Its effectiveness was verified. (Du et al, 2021) predicted the LC of power transformer in intelligent building, and finally found that the Mean Absolute Percentage Error (MAPE) of the model was 5.20% on the test set, showing higher accuracy.…”

Section: Intelligent Application Of Iot System In Intelligent Buildingmentioning

confidence: 99%

“…Its effectiveness was verified. (Du et al, 2021) forecasted the LC of power transformer in intelligent transportation, and finally found that MAPE of the model was only 5.20%, suggesting that the accuracy is extremely high. (Francisco et al, 2020) used smart meters to adjust building energy in the DTs of SC, making smart energy management within the geographical scope of medium and large buildings in SC a key step in SC construction.…”

Section: Prospect Analysis Of Esermentioning

confidence: 99%

Practical Application of Internet of Things in the Creation of Intelligent Services and Environments

2022

Front. Internet Things

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The information era is coming. As a popular technology in the information age, Internet of Things (IoT) can collect various types of information in real time, realize the ubiquitous connection between things and people, and process the intelligent perception, identification, and management of things, processes, and information, and environment protection through various network access methods. Applying IoT to various fields is like wrapping the Earth with a layer of "digital skin" (Gubbi et al., 2013;Bauwens et al., 2020;Li and Da Xu, 2020). Moreover, under the trend of global warming, the emergence of the theme of Energy Saving and Emission Reduction (ESER) undoubtedly poses new challenges to the development of IoT.When IoT is widely used in various life scenarios, its main purpose is to provide intelligent services and environments. IoT applications can connect any sensing device to the Internet for data transmission, so as to realize intelligent identification, tracking, positioning, and monitoring of sensing devices. In addition, IoT contains many types of Wireless Sensor Networks (WSN), which, as a part of IoT sensing layer, can meet the actual needs of people to obtain reliable data in special environments (Sadowski and Spachos, 2020). WSN is a multi-hop network which is self-organized by many sensor nodes. It has the characteristics of flexibility, fault tolerance, high awareness, low cost, strong survivability, and fast layout. Therefore, IoT has a wide range of applications, such as environmental monitoring, agriculture, military, and medical care, and can collect, process, and disseminate collected data deployed in various environments (Aman et al., 2020;Fortino et al., 2020). However, for some special scenes, many sensor nodes are often placed in areas that cannot be accessed by human beings. While enjoying the convenience brought by WSN, people are also faced with the problems that illegal personnel obtain illegitimate interests by intercepting the monitoring data transmitted in the public channel. Therefore, the attention to security issues in IoT cannot be ignored.With the increasing commercial scope of 5th Generation Mobile Communication Technology (5G) communication technology in cities, the application of IoT in power, construction, industry, intelligent transportation, agriculture, logistics, intelligence, high efficiency, and ESER is being promoted (Verma et al., 2020). Of course, applying IoT is also inseparable from the combination of 5G communication, Cloud Computing (CC), Edge Computing (EC), blockchain, AI, and other technologies. IoT can collect many data from the environment, effectively monitor, analyze, and manage energy consumption, and reasonably improve IoT by identifying and analyzing opportunities of energy efficiency improvement (Khan et al., 2020;Liao et al., 2020;Saračević et al., 2020).In summary, in today's high-speed 5G communication technology, IoT is widely applied, and guaranteeing its performance is of high significance. Therefore, taking the practical application of IoT...

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“…The investment decision on power transformers directly bears on the investment cost, social benefits, and competitiveness of power companies in the face of market-oriented reform. As the power sector invests in the construction of substations, the financial aspect becomes a crucial factor alongside safety considerations [4]. Consequently, there is a collective effort from engineering personnel, scholars, and experts to tirelessly explore and research optimal solutions for substation equipment management.…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

Zhao,

Gui,

Chen

et al. 2024

Applied Sciences

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Transformers, as the hub equipment of the power system, are highly valued by engineering and scientific researchers in production practice and scientific research. The goal of transformer research is to ensure the safe operation of transformers while considering their economic benefits, arrange transformer inspections reasonably to reduce inspection costs, and save labor and maintenance costs reasonably. This study first provides a brief analysis of the life cycle cost of transformers. The life cycle of transformers is divided into initial cost, operating cost, maintenance cost, fault cost, and scrap cost. Based on the distribution of transformer failure rate, the corresponding life cycle cost of transformers is calculated. Taking 110 kV transformers as an example, the differences in the impact of key factors on the full life cycle cost are analyzed to achieve high quality, high reliability, economically optimal equipment procurement.

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Lifecycle cost forecast of 110 kV power transformers based on support vector regression and gray wolf optimization

Cited by 16 publications

References 12 publications

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Practical Application of Internet of Things in the Creation of Intelligent Services and Environments

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

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Lifecycle cost forecast of 110 kV power transformers based on support vector regression and gray wolf optimization

Cited by 16 publications

References 12 publications

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Practical Application of Internet of Things in the Creation of Intelligent Services and Environments

Life Cycle Cost Estimation and Analysis of Transformers Based on Failure Rate

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Product

Resources

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Lifecycle cost forecast of 110 kV power transformers based on support vector regression and gray wolf optimization