• Namangan Institute of Engineering and Technology nammti.uz
  • Key words: machine learning; explainable; grid frequency; stability




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    Key words: machine learning; explainable; grid frequency; stability 
     
    Introduction. The functionality of a power system depends on the supply and demand for 
    electricity being balanced [1]. Significant power disparities put the supply security at risk, which in 
    severe circumstances may result in widespread blackouts [2]. As though Electricity markets are 
    utilised to balance supply and demand, usually every hour; hence, extra control techniques are 
    required. to achieve power system balancing over shorter time horizons. The grid of power the main 
    objective of these control strategies is frequency [3]. That reflects power disparities resulting from 
    departures from its 50-point reference value (or 60) Hz, which need to stay modest to provide 
    steady grid functioning. As we shift to renewable energy sources, there are more and more different 
    causes for grid frequency variations. Regular deterministic frequency fluctuations are caused by the 
    market-based dispatch of generators [4], and other influences on the grid frequency may come from 
    major social gatherings [5], local laws [6, 7], and other factors. Modern power networks face 
    significant obstacles when integrating wind and solar power generation on a broad scale because 
    these energy sources lack inherent inertia [8] and because of their fluctuating nature [9, 10], which 
    can result in extra power imbalances and frequency variations. Because electricity system stability 


    Namangan Institute of Engineering and Technology 
    nammti.uz 
    10.25.2023
    Pg.291 
    is so important, it is critical to comprehend and separate these various effects on the grid frequency. 
    Moreover, control measures are required to stabilise the frequency [1]. Research on the 
    relationship between these control requirements and outside influences is still ongoing [11]. 
    In this article, we present how eXplainable Artificial Intelligence (XAI) may be used to better 
    comprehend the factors that influence frequency stability and control, as well as the hazards 
    involved. We demonstrate applications of our technique to frequency stability indicators [1], 
    deterministic frequency deviations [6], and frequency control activation in Germany [11] 
    throughout Europe using operational data from key European power systems. 

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    Key words: machine learning; explainable; grid frequency; stability

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