A configuration of a self-excited induction generator supplying DC loads with constant voltage through a diode bridge rectifier and a buck-type DC–DC converter is proposed. The rectifier input current is made continuous, by connecting a suitably designed series filter inductor. The method of binary search algorithm is used to solve for the operating parameters of the generator. The performance of the entire unit is predetermined for the various patterns of speed and power operations. For the dynamic analysis of the induction machine, an abc-dq modeling has been employed and the simulation results on the unit are obtained under both steady state and transient state using MATLAB/Simulink toolbox. A prototype model of the unit has been built in the laboratory and the experimental results have been presented, thereby validating the performance and usefulness of the system.

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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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