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This paper presents a novel control strategy for the operation of a direct-drive permanent-magnet synchronous-generator-based stand-alone variable-speed wind turbine. The control strategy for the generatorside converter with maximum power extraction is presented. The stand-alone control is featured with output voltage and frequency controller that is capable of handling variable load. The potential excess of power is dissipated in the dump-load resistor with the chopper control, and the dc-link voltage is maintained. Dynamic representation of dc bus and small-signal analysis are presented. Simulation results show that the controllers can extract maximum power and regulate the voltage and frequency under varying wind and load conditions. The controller shows very good dynamic and steady-state performance. Disciplines Physical Sciences and Mathematics

A high penetration of rooftop solar photovoltaic (PV) resources into low-voltage (LV) distribution networks creates reverse power-flow and voltage-rise problems. This generally occurs when the generation from PV resources substantially exceeds the load demand during high insolation period. This paper has investigated the solar PV impacts and developed a mitigation strategy by an effective use of distributed energy storage systems integrated with solar PV units in LV networks. The storage is used to consume surplus solar PV power locally during PV peak, and the stored energy is utilized in the evening for the peak-load support. A charging/ discharging control strategy is developed taking into account the current state of charge (SoC) of the storage and the intended length of charging/discharging period to effectively utilize the available capacity of the storage. The proposed strategy can also mitigate the impact of sudden changes in PV output, due to unstable weather conditions, by putting the storage into a short-term discharge mode. The charging rate is adjusted dynamically to recover the charge drained during the short-term discharge to ensure that the level of SoC is as close to the desired SoC as possible. A comprehensive battery model is used to capture the realistic behavior of the distributed energy storage units in a distribution feeder. The proposed PV impact mitigation strategy is tested on a practical distribution network in Australia and validated through simulations. 2013 IEEE.

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Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. Abstract: Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy 10 of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying 11 geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate 12 variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is 13 employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear 14 relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression 15 analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this 16paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic 17 variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South 18Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are 19 dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using 20 the proposed model are found to be closely matched with the actual electricity demands highlighting the fact tha...