Evaluation of PV and QV based Voltage Stability Analyses in Converter Dominated Power Systems

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

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

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.Intelligent Electrical Power Grid

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

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.</p

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

The portfolio is created for the course STAT/Math380: Statistics and Applications using backward design. It is a service course on introduction statistics for undergraduate students who are not majored in statistics at University of Nebraska-Lincoln. In this portfolio, I’ll provide the detail plans on what I will teach, how it will be taught, and what is my expectation on students learning. Besides, I’ll collect data from students to analyze whether students have achieved the course expectations, from where I’ll adjust and improve the way I deliver the knowledge in the future. This portfolio will be used to help document my teaching, share with my colleagues, and prepare my promotion and tenure