Application of CS-PWM rectifier for the operation and control of wind-driven generators

Akbarali, Mahaboob Subahani; Subramaniam, Sreeramanan; Kumaresan, N.

doi:10.1080/15567036.2020.1778140

Cited by 4 publications

(1 citation statement)

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“…Load forecasting in power systems is a challenging but significant task for essential functions such as generation scheduling, economic dispatch, fuel scheduling and unit maintenance. Further, the grid integration with renewable energy sources such as wind and solar has gained more attention for the reduction of carbon emission (Akbarali, et al, 2020;Al-Shetwi et al, 2020). So, the load curve of any power system network becomes very non-linear.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network

Mayilsamy

Akbarali

et al. 2021

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Purpose The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

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Section: Introductionmentioning

confidence: 99%