A Very Short-Term Load forecasting using Kalman filter for Load Frequency Control with Economic Load Dispatch

نویسندگان

  • R. Shankar
  • K. Chatterjee
  • T. K. Chatterjee
چکیده

In this paper we have proposed a control technique for the automatic generation control of multi generating power unit of the interconnected power system. This technique established the relationship between the economic load dispatch and load forecasting mechanism to the classical concepts of the load frequency control (LFC). The LFC system monitors to keep the power system frequency at nominal value, generator output according to the load demand and net interchange scheduled tie line power flows within prescribed limit among the different control area of the power system. Due to relatively fast area load demand fluctuations and accordingly slow response of instantaneous estimate of area control error (ACE), we need some load forecasting technique for better dynamic system response as well as improved & effective load frequency control to the power system. Load prediction technique has been accomplished using the klaman filter prediction recursive algorithms and a bank of hourly predicted load data is obtained and then the concepts of 5 minute look ahead forecasting technique is applied and finally total load is shared among the different generating units according to the calculation of economic load dispatch via participation factor’s. Results and Discussion section of this paper of simulated interconnected system’s graphs support this new technique wisely.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

متن کامل

Short term load forecast by using Locally Linear Embedding manifold learning and a hybrid RBF-Fuzzy network

The aim of the short term load forecasting is to forecast the electric power load for unit commitment, evaluating the reliability of the system, economic dispatch, and so on. Short term load forecasting obviously plays an important role in traditional non-cooperative power systems. Moreover, in a restructured power system a generator company (GENCO) should predict the system demand and its corr...

متن کامل

Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression

The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...

متن کامل

Short term load forecasting using fuzzy logic

Load forecasting is essential for planning and operation in energy management. It enhances the Energy efficient and reliable operation of a power system. The energy supplied by utilities meets the load plus the energy lost in the system is ensured by this tool. Since in power system the next day’s power generation must be scheduled every day. The dayahead short term load forecasting (STLF) is a...

متن کامل

Fuzzy logic based Load Forecasting

The dayahead short term load forecasting (STLF) is a necessary daily task for power dispatch. Short term load forecasting is essential for unit commitment, economic allocation of generation, maintenance schedules. This paper presents a solution methodology using fuzzy logic for short term load forecasting. Fuzzy logic approach is implemented on weather sensitive data and historical load data fo...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012