No abstract

Modern society is to a larger and larger extent dependant on electric energy, and hence the reliance on and utilization of the electric grid is increasing steadily. At the same time the production and consumption patterns are changing from large centralized generation of electric power and pure consumers to distributed generation (DG) and more complex consumers. This transition causes higher stress on an aging infrastructure and major investments are required over the coming years to maintain a reliable supply of electric energy. Better monitoring solutions and predictive methods can increase the possible utilization of the existing grid and reduce the fault frequency. This paper presents some current challenges in the grid and a possible monitoring solution and fault prediction method. This is exemplified with statistics and field-measurements from the Norwegian power grid.

We propose an empirical method for identifying low damped modes and corresponding mode shapes using frequency measurements from a Wide Area Monitoring System. The method consists of two main steps: Firstly, Complex Principal Component Analysis is used in combination with the Hilbert Transform and Empirical Mode Decomposition to provide estimates of modes and mode shapes. The estimates are stored as multidimensional points. Secondly, the points are grouped using a clustering algorithm, and new averaged estimates of modes and mode shapes are computed as the centroids of the clusters. Applying the method on data resulting from a non-linear power system simulator yields estimates of dominant modes and corresponding mode shapes that are similar to those resulting from modal analysis of the linearized system model. Encouraged by the results, the method is further tested with real PMU data at transmission grid level. Initial results indicate that the performance of the proposed method is promising.

An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance.The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows:First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.

We propose a method for estimating the activity of oscillatory modes in power systems. The frequencies and mode shapes of the modes of interest are assumed to be known beforehand, either from linear modal analysis or from empirical mode estimation methods, and are used in combination with measurements from Phasor Measurement Units to estimate the instantaneous mode excitation in terms of amplitude and phase. The estimation is carried out using non-linear least squares to fit a set of curves to the measured data. Combining mode shapes with measured data allows the activity to be estimated from only a low number of consecutive measurement snapshots, resulting in a problem of low computational complexity that can be solved fast enough for the method to run online.The purpose of estimating the mode activity is, firstly, to contribute to increased situational awareness and facilitate methods that build further upon this information, and secondly, to be able to synthesize signals that can serve as input to controllers for power oscillation damping. It is expected that using this excitation measure will result in a more robust controller that is less prone to disturbances and noise.Index Terms-Empirical modal analysis, non-linear least squares, power oscillations, wide area monitoring and control