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.

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.

This paper explain the application of hardware-software co-design methodology to the distribution of the controller task between the processors of a electronic control systems for a multilevel power converter, in order to increase the controller reliability. This framework operates with a component library that provides multiple alternatives for a given task, each of which is potentially different from the others in terms of reliability, performance, and area metrics. The experimental evaluation demonstrates the importance of including co-design techniques to achieve better system reliability