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Open AccessArticle

A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Department of Electronics, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain
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Sensors 2018, 18(11), 3947; https://doi.org/10.3390/s18113947
Received: 26 September 2018 / Revised: 7 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
Bad data as a result of measurement errors in secondary substation (SS) monitoring equipment is difficult to detect and negatively affects power system state estimation performance by both increasing the computational burden and jeopardizing the state estimation accuracy. In this paper a short-term load forecasting (STLF) hybrid strategy based on singular spectrum analysis (SSA) in combination with artificial neural networks (ANN), is presented. This STLF approach is aimed at detecting, identifying and eliminating and/or correcting such bad data before it is provided to the state estimator. This approach is developed to improve the accuracy of the load forecasts and it is tested against real power load data provided by electricity suppliers. Depending on the week considered, mean absolute percentage error (MAPE) values which range from 1.6% to 3.4% are achieved for STLF. Different systematic errors, such as gain and offset error levels and outliers, are successfully detected with a hit rate of 98%, and the corresponding measurements are corrected before they are sent to the control center for state estimation purposes. View Full-Text
Keywords: measurement errors; singular spectrum analysis (SSA); artificial neural networks (ANN); bad data (BD) detection measurement errors; singular spectrum analysis (SSA); artificial neural networks (ANN); bad data (BD) detection
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Martín, P.; Moreno, G.; Rodríguez, F.J.; Jiménez, J.A.; Fernández, I. A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment. Sensors 2018, 18, 3947.

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