Load Frequency Control for Interconnected Power System Using Different Controllers
Issue:
Volume 1, Issue 4, August 2013
Pages:
85-89
Received:
10 July 2013
Published:
10 August 2013
Abstract: This paper explores the potential of using soft computing methodologies in controllers and their advantages over conventional methods. PID controller, being the most widely used controller in industrial applications, needs efficient methods to control the different parameters of the plant. As reported by several researchers, the conventional approach of PID controller is not very efficient due to the presence of non-linearity in the system of the plant. Also, the output of the conventional PID system has a quite high overshoot and settling time. The main focus of this work is on the controller to obtain good output frequency responses. The tuning of PID controller is necessary to get an output with better dynamic and static performance. The application of PID controller imparts it the ability of tuning itself automatically in an on-line process while the application. The output response of PID-tuning is compared with I, PI and conventional PID controller and found reasonably good over these conventional controllers.
Abstract: This paper explores the potential of using soft computing methodologies in controllers and their advantages over conventional methods. PID controller, being the most widely used controller in industrial applications, needs efficient methods to control the different parameters of the plant. As reported by several researchers, the conventional approa...
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The Intelligent Forecasting Model of Time Series
Sonja Pravilović,
Annalisa Appice
Issue:
Volume 1, Issue 4, August 2013
Pages:
90-98
Received:
16 July 2013
Published:
10 August 2013
Abstract: Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.
Abstract: Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA model...
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