Fuju Zhou^*, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu

Energy Engineering, Vol., , DOI:10.32604/ee.2024.047680

Abstract When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, indicating that it is suitable… More >

Bo Yang^1,2, Rui Xie¹, Zhengxun Guo^3,4,*

Energy Engineering, Vol., , DOI:10.32604/ee.2024.049423

Abstract Maximum power point tracking (MPPT) technology plays a key role in improving the energy conversion efficiency of photovoltaic (PV) systems, especially when multiple local maximum power points (LMPPs) occur under partial shading conditions (PSC). It is necessary to modify the operating point efficiently and accurately with the help of MPPT technology to maximize the collected power. Even though a lot of research has been carried out and impressive progress achieved for MPPT technology, it still faces some challenges and dilemmas. Firstly, the mathematical model established for PV cells is not precise enough. Second, the existing algorithms are often optimized for… More >

Nugroho Agung Pambudi^1,2,4,*, Andrew Chapman, Alfan Sarifudin^1,3, Desita Kamila Ulfa⁴, Iksan Riva Nanda⁵

Energy Engineering, Vol., , DOI:10.32604/ee.2024.047555

Abstract One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan, reaching zero production in 2015. In response, the country started importing more fossil energy including coal, oil, and natural gas to fill the energy gap. However, this led to a significant increase in carbon emissions, hindering the efforts to reduce its carbon footprint. In the current situation, Japan is actively working to balance its energy requirements with environmental considerations, including the utilization of hydrogen fuel. Therefore, this paper aims to explore the feasibility and implications of using hydrogen power plants as a… More >

Sichen Li^1,2, Dehua Liu^1,2,*, Liang Cheng^1,2, Pan Ma^1,2

Energy Engineering, Vol., , DOI:10.32604/ee.2024.049906

Abstract Due to the depletion of conventional energy reserves, there has been a global shift towards non-conventional energy sources. Shale oil and gas have emerged as key alternatives. These resources have dense and heterogeneous reservoirs, which require hydraulic fracturing to extract. This process depends on identifying optimal fracturing layers, also known as ‘sweet spots’. However, there is currently no uniform standard for locating these sweet spots. This paper presents a new model for evaluating fracturability that aims to address the current gap in the field. The model utilizes a hierarchical analysis approach and a mutation model, and is distinct in its… More >

Xingliang Deng¹, Peng Cao^2,*, Yintao Zhang¹, Yuhui Zhou³, Xiao Luo¹, Liang Wang³

Energy Engineering, Vol., , DOI:10.32604/ee.2023.045870

Abstract The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich. The connectivity of carbonate reservoirs is complex, and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs. Thus, effective prediction of fractured-vuggy reservoirs is difficult. In view of this, this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir. To identify the complex connectivity among pores, fractures, and vugs, a simplified one-dimensional connectivity model is established by using the meshless connection element method (CEM). Considering… More >

Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol., , DOI:10.32604/ee.2024.028223

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances, an improved Joint Generalized Orthogonal… More >

Quan Zhou^*, Haiyang Li

Energy Engineering, Vol., , DOI:10.32604/ee.2024.049387

Abstract Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with… More >

Yan Zhang¹, Bowen Du^2,*, Benren Pan¹, Guannan Wang¹, Guoqiang Xie¹, Tong Jiang²

Energy Engineering, Vol., , DOI:10.32604/ee.2024.048718

Abstract During faults in a distribution network, the output power of a distributed generation (DG) may be uncertain. Moreover, the output currents of distributed power sources are also affected by the output power, resulting in uncertainties in the calculation of the short-circuit current at the time of a fault. Additionally, the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources. Thus, it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network. In this study, an affine arithmetic algorithm for calculating short-circuit current intervals… More >

Jicai Guo^1,2, Xiaowen Song^1,2,*, Chang Liu^1,2, Yanfeng Zhang^1,2, Shijie Guo^1,2, Jianxin Wu^1,2, Chang Cai³, Qing’an Li^3,*

Energy Engineering, Vol., , DOI:10.32604/ee.2024.048854

Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted moving average (EWMA) analysis. In… More >

Ying Qu¹, Jinhao Wang¹, Xueting Cheng¹, Jie Hao¹, Weiru Wang¹, Zhewen Niu², Yuxiang Wu^2,*

Energy Engineering, Vol., , DOI:10.32604/ee.2024.048300

Abstract The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of the XGBoost model, enhancing the… More >