The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system security. This paper presents a comprehensive assessment of the influence of different parameters in artificial neural networks, such as the amount of historic data, batch size, number of hidden layers, number of neurons per hidden layer, and the amount of training data on the short term forecast accuracy. In order to identify the parameters which are most influential with respect to forecast accuracy, a sensitivity study isolating the various factors on a one-At-A-Time basis has been performed. To minimize the forecast error across the investigated forecast horizons, the developed neural networks use the feed forward back propagation algorithm. From the investigated cases it is concluded that a neural network with two hidden layers is most suitable for wind forecasting on the timeframes considered. Furthermore, with increasing forecast horizons (from 5 to 60 minutes ahead), better performance is achieved when neural networks contain increased neurons in the hidden layers and have enlarged training data sets.@en

Participation of wind energy in the generation portfolio of power systems is increasing, making it more challenging for system operators to adequately maintain system security. It therefore becomes increasingly crucial to accurately predict the wind generation. This work investigates how different parameters influence the performance of forecasting algorithms. Firstly, this work analyzes the combined influence of the input data, batch size, number of neurons and hidden layers, and the training data on the forecast accuracy across forecast horizons of 5, 15, 30 and 60 min. It was found that increasing look ahead times require among others more hidden layers and lower batch sizes. Next, the optimizer and loss function leading to the most accurate forecasts were identified. It was concluded that the Adadelta optimizer and Mean Absolute Error loss function consistently result in the best performing forecasting algorithm. Finally, it was investigated if the most accurate optimizer-loss function combination is influenced by the choice of the performance metric. Whereas the Adadelta-Mean Absolute Error pair remains the most accurate combination irrespective of the evaluation metric, a strong relation was observed between the Root Mean Square Error performance metric and Mean Square Error loss function. Analyses were performed on 12 wind farms.@en

PV and QV analyses have been widely used in industry. It has already been proven that these steady state methods can be used to assess power system's load ability from voltage stability perspective and that their use in terms of accuracy is justified when compared to time domain simulations. However, this prior validation was carried out for conventional synchronous generator dominated power systems. With increasing levels of power electronics interfaced generation (PEIG) being integrated in power systems, the accuracy of the PV and QV methods for these 'green' power systems can be challenged. This paper investigates to what extend the use of these methods is justified when the power system faces a displacement of conventional generation with PEIG. To this end, assessments with the IEEE 9 bus system and full converter wind turbine generators have been performed in this study. It is shown that, when compared to time domain simulations, the traditional PV and QV analyses do not always accurately predict the saddle-node bifurcation point. Steady state PV analyses show inaccuracies between 1.8% and 16.8% (when compared to time domain simulations) in identification of the instability point. The mismatch between steady state and time domain QV analyses is between 6.1% and 22.9%. Based on the achieved results, QV analysis is shown to be typically less accurate than PV analysis for PEIG rich systems.@en