From THD to Causality: Epistemology of Artificial Intelligence-Based Harmonic Analysis in Hybrid Microgrids
DOI:
https://doi.org/10.51747/energy.si2025.252Keywords:
epistemology, harmonics analysis, microgridAbstract
The increasing penetration of PV, wind turbines, battery storage (BESS), and electric vehicle charging stations (EVCS) in hybrid microgrids complicates the harmonic landscape. Common practices rely on FFT-based measurements and THD/TDD indices, but source attribution and causality assignment are often uncertain. We map how epistemological positions shape how we measure, explain, and justify technical claims about harmonics. We then propose an Epistemically-Informed Harmonic AI (EPI-HAI) framework that combines standardized measurements (IEC/IEEE), physics-constrained AI modeling (KCL/KVL, impedance), XAI (SHAP/Grad-CAM), and uncertainty management to strengthen epistemic trust. A vignette of a PV–BESS–EVCS microgrid demonstrates that triangulation of evidence (n-order patterns, operating logs, line impedance) is more valid than mere spectral correlation. The three main contributions of this article are, the compilation of a map of the relationship between epistemology and methodology in harmonic analysis, the formulation of transparent and accountable physics-based artificial intelligence (AI) design principles and a discussion of pedagogical implications that can be applied in the development of power engineering curricula.
References
[1] IEEE Standard 519-2014, Recommended Practice and Requirements for Harmonic Control in Electric Power Systems, IEEE, 2014.
[2] IEC 61000-4-30, Testing and Measurement Techniques—Power Quality Measurement Methods, International Electrotechnical Commission, 2015.
[3] IEC 61000-4-7, General Guide on Harmonics and Interharmonics Measurements and Instrumentation, International Electrotechnical Commission, 2002.
[4] J. Arrillaga and N. R. Watson, Power System Harmonics, 2nd ed. Hoboken, NJ, USA: Wiley, 2003.
[5] M. H. J. Bollen and I. Y. H. Gu, Signal Processing of Power Quality Disturbances. Hoboken, NJ, USA: Wiley-IEEE Press, 2006.
[6] K. P. Murphy, Probabilistic Machine Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2022.
[7] J. Pearl, Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2009.
[8] K. Popper, The Logic of Scientific Discovery. London, U.K.: Routledge, 1959.
[9] T. S. Kuhn, The Structure of Scientific Revolutions, 2nd ed. Chicago, IL, USA: University of Chicago Press, 1970.
[10] R. Bhaskar, A Realist Theory of Science. London, U.K.: Routledge, 2008.
[11] S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Proc. NeurIPS, 2017.
[12] M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks,” J. Comput. Phys., 2019.
[13] L. Augusto Horta Nogueira and R. Silva Capaz, “Biofuels in Brazil: Evolution, achievements and perspectives on food security,” Global Food Security, vol. 2, no. 2, pp. 117–125, 2013.
[14] A. Pradhan and C. Mbohwa, “Development of biofuels in South Africa: Challenges and opportunities,” Renewable and Sustainable Energy Reviews, vol. 39, no. 2014, pp. 1089–1100, 2014.
[15] M. Messagie, K. Lebeau, T. Coosemans, C. Macharis, and J. Van Mierlo, “Environmental and financial evaluation of passenger vehicle technologies in Belgium,” Sustainability, vol. 5, no. 12, pp. 5020–5033, 2013.
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