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<p>Power system state estimation is the backbone of any energy management applications. Hence, ensuring the data authenticity on power systems state estimation (PSSE) is extremely vital. False data injection attack (FDIA) has the ability to perturb the measurements derived from both supervisory control and data acquisition (SCADA) system and phasor measurement units (PMUs) which eventually degrades the estimation of the state variables in power system. In this paper, a cosine dissimilarity index-based approach has been proposed for detection of intelligently manipulated data in cyber-physical power systems equipped with PMUs. The proposed framework considers the changes in estimated states at current time instant of present day with day before. The predicted value of the power system states at present time sample of current day is also compared with forecasted state variables of the prior day. Thereafter, the variation of estimated states is compared with changes of forecasted state variables through a novel cosine dissimilarity index (NCDI)-based approach. In case of FDIA in power systems, the NCDI value will become higher, and will remain low during normal condition. The efficacy of the suggested detection methodology is assessed by implementing it on IEEE 14 and IEEE 118 bus test systems.</p>

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A novel cosine dissimilarity index-based approach for detection of false data in cyber-physical power systems

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • S. Kundu,
  • Ark Dev,
  • J. Banerjee,
  • V. Verma,
  • S. Gupta,
  • B. K. Saha Roy,
  • S. S. Thakur

摘要

Power system state estimation is the backbone of any energy management applications. Hence, ensuring the data authenticity on power systems state estimation (PSSE) is extremely vital. False data injection attack (FDIA) has the ability to perturb the measurements derived from both supervisory control and data acquisition (SCADA) system and phasor measurement units (PMUs) which eventually degrades the estimation of the state variables in power system. In this paper, a cosine dissimilarity index-based approach has been proposed for detection of intelligently manipulated data in cyber-physical power systems equipped with PMUs. The proposed framework considers the changes in estimated states at current time instant of present day with day before. The predicted value of the power system states at present time sample of current day is also compared with forecasted state variables of the prior day. Thereafter, the variation of estimated states is compared with changes of forecasted state variables through a novel cosine dissimilarity index (NCDI)-based approach. In case of FDIA in power systems, the NCDI value will become higher, and will remain low during normal condition. The efficacy of the suggested detection methodology is assessed by implementing it on IEEE 14 and IEEE 118 bus test systems.