شماره ركورد كنفرانس :
2727
عنوان مقاله :
Dynamic State Estimation Based on Time Series and Exponential Smoothing
عنوان به زبان ديگر :
Dynamic State Estimation Based on Time Series and Exponential Smoothing
پديدآورندگان :
Davoudi Mehdi نويسنده Buein Zahra Technical University - Department of Electrical and Computer Engineering , Salimi Beni Arman نويسنده Iran Grid Management Co - Electrical Engineering Research Department
كليدواژه :
Dynamic State Estimation , Time Series , Exponential Smoothing , Weighted Least Squares (WLS) , Combinational Prediction
عنوان كنفرانس :
اولين كنفرانس بين المللي دستاوردهاي نوين پژوهشي در مهندسي برق و كامپيوتر
چكيده لاتين :
Abstract— Dynamic State Estimation (DSE) is one of the most important parts of Wide Area Monitoring and Control Systems
because other functions are based on the results of the state estimator. DSE is based on a statistical predictive method. For
this reason, different statistical methods such as time series, Kalman filter, exponential smoothing, regression, and etc. are
used and each of which has benefits and drawbacks. This paper proposes a method for DSE on the basis of linear combination of time series and exponential smoothing. The predicted state
estimates for different methods are compared using mean square errors. The important advantage of the proposed combinational method is the reduction of error comparing to each method.
شماره مدرك كنفرانس :
4240260
