Frontiers in Energy Research最新文献_第8页

Fault diagnosis of the distribution network based on the D-S evidence theory Bayesian network 基于 D-S 证据理论贝叶斯网络的配电网故障诊断

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-07 DOI: 10.3389/fenrg.2024.1422639

Xiaogang Wu, Hanying Zhao, Wentao Xu, Wulue Pan, Qingfeng Ji, Xiujuan Hua

{"title":"Fault diagnosis of the distribution network based on the D-S evidence theory Bayesian network","authors":"Xiaogang Wu, Hanying Zhao, Wentao Xu, Wulue Pan, Qingfeng Ji, Xiujuan Hua","doi":"10.3389/fenrg.2024.1422639","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1422639","url":null,"abstract":"Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents a fault diagnosis method for the distribution network based on the D-S evidence theory Bayesian network. First, the collected relay protection information is divided into two categories, protection information and circuit breaker information; the corresponding Bayesian network model is established based on their respective action logic, and the corresponding component failure probability is obtained by Bayesian backward inference. Second, the fault probabilities obtained from the two Bayesian networks are fused by the D-S evidence theory, and the obtained fault probabilities are used to diagnose the faulty component. Then, using the Bayesian network corresponding to the faulty component to perform Bayesian forward inference, the protection devices and circuit breakers are identified for misoperation or rejection to achieve the fault diagnosis of the distribution network. Finally, the correctness and reliability of the proposed diagnosis method are verified through the analysis of arithmetic cases.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on standardization of power transformer monitoring and early warning based on multi-source data 基于多源数据的电力变压器监测与预警标准化研究

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-06 DOI: 10.3389/fenrg.2024.1442299

Wang Wenhua, Cui Rui, Chen Yu, Zhao Xu, Xue Yongbing

{"title":"Research on standardization of power transformer monitoring and early warning based on multi-source data","authors":"Wang Wenhua, Cui Rui, Chen Yu, Zhao Xu, Xue Yongbing","doi":"10.3389/fenrg.2024.1442299","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1442299","url":null,"abstract":"To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction 基于改进型 SVSF 的配电网络动态状态估计新方法（考虑光伏功率预测

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-06 DOI: 10.3389/fenrg.2024.1421555

Huiqiang Zhi, Xiao Chang, Jinhao Wang, Rui Mao, Rui Fan, Tengxin Wang, Jinge Song, Guisheng Xiao

{"title":"A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction","authors":"Huiqiang Zhi, Xiao Chang, Jinhao Wang, Rui Mao, Rui Fan, Tengxin Wang, Jinge Song, Guisheng Xiao","doi":"10.3389/fenrg.2024.1421555","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1421555","url":null,"abstract":"The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates 评估潮湿气候下绿化墙降温性能的实验分析和深度学习模型

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1447655

Abdollah Baghaei Daemei, Tomasz Bradecki, Alina Pancewicz, Amirali Razzaghipour, Asma Jamali, Seyedeh Maryam Abbaszadegan, Reza Askarizad, Mostafa Kazemi, Ayyoob Sharifi

{"title":"An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates","authors":"Abdollah Baghaei Daemei, Tomasz Bradecki, Alina Pancewicz, Amirali Razzaghipour, Asma Jamali, Seyedeh Maryam Abbaszadegan, Reza Askarizad, Mostafa Kazemi, Ayyoob Sharifi","doi":"10.3389/fenrg.2024.1447655","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1447655","url":null,"abstract":"Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driving towards sustainability: exploring risk perceptions of fossil fuels, e-fuels, and electric drives in individual transport 迈向可持续发展：探索个人交通工具中化石燃料、电子燃料和电力驱动的风险认知

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1415430

Eva Rößler, Tim Schmeckel, Ute Kesselheim, Katrin Arning

{"title":"Driving towards sustainability: exploring risk perceptions of fossil fuels, e-fuels, and electric drives in individual transport","authors":"Eva Rößler, Tim Schmeckel, Ute Kesselheim, Katrin Arning","doi":"10.3389/fenrg.2024.1415430","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1415430","url":null,"abstract":"The transportation sector is a significant contributor to CO2 emissions, necessitating the adoption of alternative drive technologies to achieve decarbonization. This study investigates public perceptions of fossil fuels, e-fuels, and electric drives, with the aim of identifying factors influencing risk perceptions, perceived efficacy in combating climate change, and readiness to use or purchase cars with these technologies. Therefore, a quantitative study using a questionnaire (N = 141) was conducted. The results indicate that e-fuels and electric drives are perceived more positively than fossil fuels. E-fuels were found to have the lowest risk perceptions. Differences in cognitive and affective risk perceptions, as well as in financial, environmental, and health-related risks, were observed across drive types. Car affinity was found to correlate positively with risk perceptions of e-fuels and fossil fuels, but negatively with electric drives. The risk perception of global warming showed an inverse relationship. Regarding the prediction of readiness, differences were found between e-fuels and electric drives in terms of the influencing factors on readiness. The study contributes to the understanding of public perceptions by providing a comparison between different drive technologies and offers valuable insights for developing targeted communication strategies.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on prediction method of photovoltaic power generation based on transformer model 基于变压器模型的光伏发电预测方法研究

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1452173

Ning Zhou, Bo-wen Shang, Jin-shuai Zhang, Ming-ming Xu

{"title":"Research on prediction method of photovoltaic power generation based on transformer model","authors":"Ning Zhou, Bo-wen Shang, Jin-shuai Zhang, Ming-ming Xu","doi":"10.3389/fenrg.2024.1452173","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1452173","url":null,"abstract":"Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways 多范式微电网系统建模与控制，提高洛克威地区的电力稳定性

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1404811

Ayman Aljarbouh, Dmytro Zubov, Issam A. R. Moghrabi

{"title":"Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways","authors":"Ayman Aljarbouh, Dmytro Zubov, Issam A. R. Moghrabi","doi":"10.3389/fenrg.2024.1404811","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1404811","url":null,"abstract":"The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An operating mode control method for photovoltaic (PV) battery hybrid systems 光伏电池混合动力系统的运行模式控制方法

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1435310

Wenping Zhang, Yiming Wang, Po Xu, Donghui Li, Baosong Liu

引用次数: 0

Sliding mode control based on maximum power point tracking for dynamics of wind turbine system 基于最大功率点跟踪的滑模控制，用于风力涡轮机系统的动态控制

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-05 DOI: 10.3389/fenrg.2024.1434695

Borhen Torchani, Ahmad Taher Azar, Saim Ahmed, Ahmed Redha Mahlous, Ibraheem Kasim Ibraheem

引用次数: 0

Mixing nuclear and conventional fossil fuel units within the baseload of PP using the CPLEX Optimizer 使用 CPLEX 优化器在 PP 的基荷内混合使用核电机组和常规化石燃料机组

IF 3.4 4区工程技术

Frontiers in Energy Research Pub Date : 2024-08-02 DOI: 10.3389/fenrg.2024.1400905

Abdullah M. Al-Qahtani, Abdullah M. Al-Shaalan, Waheed A. Al-Masry, Hassan M. Hussein Farh

{"title":"Mixing nuclear and conventional fossil fuel units within the baseload of PP using the CPLEX Optimizer","authors":"Abdullah M. Al-Qahtani, Abdullah M. Al-Shaalan, Waheed A. Al-Masry, Hassan M. Hussein Farh","doi":"10.3389/fenrg.2024.1400905","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1400905","url":null,"abstract":"The future electric loads in the Kingdom of Saudi Arabia (KSA) are increasing significantly, particularly in the Eastern Province of the KSA. These high-rise loads are primarily driven by the operational needs of the Saudi Arabian oil company Aramco, including oil refineries, and the infrastructures of the Saudi Basic Industries Corporation (SABIC) factories. This study aims to construct a nuclear power plant in that area to supplement and support the baseload currently covered by conventional generation units powered by fossil fuels within the Saudi Electricity Company (SEC) operations. The objective function is to minimize the operational costs of the power systems to the greatest extent possible. This paper describes a case study conducted using the IBM CPLEX Optimizer software to compare the operational costs of KSA’s power systems for a 24-h period. Two scenarios were considered and addressed: the first scenario without the inclusion of a nuclear power plant (NPP) and the second scenario with the inclusion of the NPP. The unit commitment problem was modeled for both scenarios. The obtained results revealed that the second scenario, which involved the penetration of the NPP, offered an optimal economic solution for operating KSA’s power systems. By employing the CPLEX Optimizer software and analyzing the unit commitment problem, this study provides valuable insights into the economic advantages of integrating the NPP into the power systems of the Kingdom of Saudi Arabia. The NPP shows viability in terms of minimizing the operational costs to 32.10 $/MWh compared to the first scenario where the operational costs were 42.10 $/MWh and resulted in almost 24% reduction in operational costs. In addition, the NPP is deemed as an optimal technology to contribute to the net zero goal by 2060, where it can reduce the reliance on fossil fuel power plants and contribute to the reduction of CO2 emissions.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0