Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (12)
  • Open Access

    ARTICLE

    Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

    Jiachang Liu1,*, Zhengwei Huang2, Junfeng Xiang1, Lu Liu1, Manlin Hu1

    Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

    Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

  • Open Access

    ARTICLE

    Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

    Tao Bao*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou

    Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150 - 21 May 2024

    Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able More >

  • Open Access

    ARTICLE

    Reliability-Based Model for Incomplete Preventive Replacement Maintenance of Photovoltaic Power Systems

    Wei Chen, Ming Li*, Tingting Pei, Cunyu Sun, Huan Lei

    Energy Engineering, Vol.121, No.1, pp. 125-144, 2024, DOI:10.32604/ee.2023.042812 - 27 December 2023

    Abstract At present, the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance, breakdown maintenance, and condition-based maintenance, which is very likely to lead to over- or under-repair of equipment. Therefore, a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed. First, a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment, and the equipment is replaced when its reliability drops to the replacement threshold in the… More >

  • Open Access

    ARTICLE

    A Data Driven Security Correction Method for Power Systems with UPFC

    Qun Li, Ningyu Zhang*, Jianhua Zhou, Xinyao Zhu, Peng Li

    Energy Engineering, Vol.120, No.6, pp. 1485-1502, 2023, DOI:10.32604/ee.2023.022856 - 03 April 2023

    Abstract The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training… More >

  • Open Access

    ARTICLE

    Research on Evaluation of Multi-Timescale Flexibility and Energy Storage Deployment for the High-Penetration Renewable Energy of Power Systems

    Hongliang Wang1, Jiahua Hu1, Danhuang Dong1, Cenfeng Wang1, Feixia Tang2, Yizheng Wang1, Changsen Feng2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1137-1158, 2023, DOI:10.32604/cmes.2022.021965 - 31 August 2022

    Abstract With the rapid and wide deployment of renewable energy, the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance. The output power of renewable energy is uncertain, and thus flexible regulation for the power balance is highly demanded. Considering the multi-timescale output characteristics of renewable energy, a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper. Through the multi-timescale decomposition algorithm on the basis of mathematical morphology, the multi-timescale components are separated More >

  • Open Access

    ARTICLE

    Frequency Control Approach and Load Forecasting Assessment for Wind Systems

    K. Sukanya*, P. Vijayakumar

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 971-982, 2023, DOI:10.32604/iasc.2023.028047 - 06 June 2022

    Abstract Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller… More >

  • Open Access

    ARTICLE

    State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

    Ziwei Jiang1, Shuaibing Li1,*, Xiping Ma2, Xingmin Li2, Yongqiang Kang1, Hongwei Li3, Haiying Dong1

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 295-317, 2022, DOI:10.32604/cmes.2022.019996 - 02 June 2022

    Abstract

    The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid. To improve the prediction accuracy of power systems with source-load two-terminal uncertainties, an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper. In the algorithm, the Q0 is used to offset the modeling error, and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.

    More >

  • Open Access

    ARTICLE

    Disturbance Evaluation in Power System Based on Machine Learning

    Emad M. Ahmed1,*, Mohamed A. Ahmed1, Ziad M. Ali2,3, Imran Khan4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 231-254, 2022, DOI:10.32604/cmc.2022.022005 - 03 November 2021

    Abstract The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances.… More >

  • Open Access

    ARTICLE

    Behaviours of Multi-Stakeholders under China’s Renewable Portfolio Standards: A Game Theory-Based Analysis

    Bing Wang1,2, Kailei Deng1, Liting He1, Zhenming Sun1,*

    Energy Engineering, Vol.118, No.5, pp. 1333-1351, 2021, DOI:10.32604/EE.2021.014258 - 16 July 2021

    Abstract China has implemented both quantitative and policy incentives for renewable energy development since 2019 and is currently in the policy transition stage. The implementation of renewable portfolio standards (RPSs) is difficult due to the interests of multiple stakeholders, including power generation enterprises, power grid companies, power users, local governments, and the central government. Based on China’s RPS policy and power system reform documents, this research sorted out the core game decision problems of China’s renewable energy industry and established a conceptual game decision model of the renewable energy industry from the perspective of local governments,… More >

  • Open Access

    ARTICLE

    Conjoint Knowledge Discovery Utilizing Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems

    Tharam S. Dillon1,2,∗, Yi-Ping Phoebe Chen1,†, Elizabeth Chang2,‡, Mukesh Mohania3,§, Vish Ramakonar4

    Computer Systems Science and Engineering, Vol.35, No.5, pp. 321-334, 2020, DOI:10.32604/csse.2020.35.321

    Abstract In Digital Enterprises Structured Data and Semi/Unstructured Content are normally stored in two different repositories, with the first often being stored in relational Databases and the second in a content manager which is frequently at an external outsourcer. This storage of complementary information in two different silos has led to the information being processed and data mined separately which is undesirable. Effective knowledge and information use requires seamless access and intelligent analysis of information in its totality to allow enterprises to gain enhanced insights. In this paper, we develop techniques to carry out correlation of More >

Displaying 1-10 on page 1 of 12. Per Page