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Simulation of Electrical Load Forecasting in Substation Transformers Using ANFIS

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Author(s): Vaibhav Telrandhe | V. R. Ingle

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: ncipet;
Issue: 5;
Date: 2012;
Original page

ABSTRACT
Forecasting models for a daily load curve using ANFIS (Adaptive Neuro-Fuzzy Inference System) data such as power, current, winding temperature, oil temperature and atmospheric temperature etc. After training networks using actual historical load and data properly processed, results indicate that ANFIS forecasting model presented clear superiority with features of simple algorithm, high accuracy and high stability and is more adaptable to the applications in design of load forecasting at substation transformer. The proposed method of a electrical load forecasting with forecasted load management with the work presents a methodology for estimating the maximum power that can be extracted from distribution substation transformers based on Estimated values of future load, current temperature values measured at various locations within the transformer, and transformer reliability requirements.
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