Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers

Abstract

When a trip occurs, the utility of company-type 115/22 kV loading transformer trips out of the electrical system, cutting off power to the distribution of a company customer. The outage damage is valuable at 8.5 US/kWh. A 12-step load transfer procedure at the utility control center takes the operator 433 seconds to complete when restoring power to a customer experiencing an outage. Problem number 1 is that many Supervisor Control and Data Acquisition (SCADA) message alarms will appear during the event, which confuses the operator and possibly leads to an incorrect analysis of the trip event details. Problem number 2 occurs in the process where the operator incorrectly predicts the MW-load data of the Loading Transformer will cause a power outage, causing the neighboring loading transformer to overload and cause damage after the operator completes the LTR process. In this study, Pyauto2, an application developed with the Python platform that connects to the utility control center’s SCADA and runs automatically, is introduced Pyauto2 serves two purposes: to work as an operator to reduce person-hours in the utility company’s LTR process and to analyze find accurate answers to trip events of 68 Loading Transformers installed in the electrical system in the central region of Thailand. The last purpose is to use Pyauto2 to reduce the LTR time. Pyauto2 can analyze the SCADA message alarm via fault tree analysis. To help plan the transfer load, it forecasts MW load data on the day of the loading transformer trip using two-time series forecasting techniques. Holt-Winters exponential smoothing (HWS) method is the second technique, and the triple exponential moving average (TEMA) is the first HWS method. In this study, the distorted data are filtered out via the exponential moving average (EMA) technique before being sent to TEMA and HWS for forecasting. The data gathered between 2017 and 2020 revealed distortion in the MW load data, which may be brought on using SCADA equipment or brief communication failures. Temporary outage, reduced traffic on holidays, and arrangement of the distribution grid route. In this study, the grid search method is compared with limited-memory Broyden-Fletcher–Goldfarb-Shanno (L-BFGS) to modify the alpha-gamma–beta value used in this HWS. The prediction error values of the L-BFGS calculations are lower than those of the grid search method, with a mean absolute error of 0.4576, a mean square error of 0.3996, and a root mean square error of 0.6084. After Pyauto2 is introduced, the average LTR time decreases from 433 s to only 64.88 s, and Pyauto2 works as a substitute for the operator and accurately diagnoses the SCADA alarm, preventing the occurrence of neighbor loading transformer supplying power overload after LTR.INDEX TERMS Fault section diagnosis, transformer restoration, fault tree analysis, exponential moving average, Holt-Winters method, Python language.

Keywords

• Fault section diagnosis
• Transformer restoration
• Fault tree analysis
• Exponential moving average
• Holt-Winters method
• Python language