Two-Stage Short-Term Load Forecasting for Power Transformers Under Different Substation Operating Conditions

Abstract

The load of transformers shows higher volatility and uncertainty than do the system-level and substation-level loads. This paper proposes a two-stage short-term load forecasting (STLF) model for power transformers. 1) Three state-of-the-art technologies are applied to predict the aggregated substation-level load by taking the historical load, weather, and calendar data as inputs. In this stage, no specific STLF model needs to be developed, which allows the forecasters to select the most accurate prediction results for transformer-level load forecasting. 2) The load distribution factor (LDF) is defined as the ratio of the transformer load to the substation load. The relationship between LDF and substation load is captured by nonlinear regression functions under different substation operating conditions, and the load of each parallel transformer is predicted using these nonlinear regression functions. Each nonlinear function can be accurately established even if the historical load data are scarce under some irregular operating conditions. Three application examples show the effectiveness and rationality of the proposed method. The third example demonstrates that STLF of transformers is necessary because it provides important information for optimizing substation operating schemes and equipment maintenance plans.