A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

• Main Authors: Javier Moriano, Francisco Javier Rodríguez, Pedro Martín, Jose Antonio Jiménez, Branislav Vuksanovic
• Format: Article
• Language: English
• Published: MDPI AG 2016-01-01
• Series: Sensors
• Subjects: Short Term Load Forecasting (STLF); Artificial Neural Network (ANN); measurement error detection; secondary substation (SS)
• Online Access: http://www.mdpi.com/1424-8220/16/1/85

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.