There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning methods. The event prediction models are trained and validated using high-resolution power quality data from measuring instruments in the Norwegian power grid. The recorded event categories in the study were voltage dips, ground faults, rapid voltage changes and interruptions. Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602. The results also indicated that rapid voltage changes (accuracy = 0.710) and voltage dips (accuracy = 0.601) are easiest to predict among the tested power quality events.

The power system is changing rapidly, and new tools for predicting unwanted events are needed to keep a high level of security of supply. Large volumes of data from the Norwegian power grid have been collected over several years, and unwanted events as interruptions, earth faults, voltage dips and rapid voltage changes have been logged. This paper demonstrates the application of clustering and dimensionality-reduction techniques for the purpose of predicting unwanted events. Several techniques have been applied to reduce the dimensionality of the datasets and to cluster events based on analytical features, to separate events containing faults from a normal situation. The paper shows that the developed predictive model has some predictive capability when using balanced datasets containing similar muber of fault events and non-fault events. One of the main findings, however, is that this predictive capability is significantly reduced when using unbalanced datasets. Thus, the development of an accurate predictive model based on normal power system conditions, i.e. an unbalanced dataset of events and non-events, is a topic for further research.

<p>There is an increasing need for flexibility in power systems worldwide, giving rise to European policy documents outlining how Distribution System Operators (DSOs) should procure flexibility and include flexibility in planning and operation of their electricity distribution grids. This implies a remarkable change from today's situation where DSOs rely on investments in grid assets that they have full control of, to a new regime where DSOs should rely on flexibility provided by third parties. The objective of this work has been to gain a better understanding of the feasibility of and barriers to wide-spread utilization of flexibility in planning and operation of electricity distribution grids. Building upon a previous literature review and a taxonomy for classifying and characterizing power system flexibility, we propose frameworks for i) classifying flexibility resources and flexibility enablers in grid operation and planning, and ii) classifying and understanding barriers to utilizing them in terms of a flexibility value chain. These theoretical frameworks are tested against empirical data collected in semi-structured in-depth interviews with a representative selection of Norwegian DSOs. Mapping the findings to the frameworks gives a systematic overview of the flexibility situation in Norway and presents both country-specific and general insights about barriers to utilization of flexibility. </p>

<p>There is an increasing need for flexibility in power systems worldwide, giving rise to European policy documents outlining how Distribution System Operators (DSOs) should procure flexibility and include flexibility in planning and operation of their electricity distribution grids. This implies a remarkable change from today's situation where DSOs rely on investments in grid assets that they have full control of, to a new regime where DSOs should rely on flexibility provided by third parties. The objective of this work has been to gain a better understanding of the feasibility of and barriers to wide-spread utilization of flexibility in planning and operation of electricity distribution grids. Building upon a previous literature review and a taxonomy for classifying and characterizing power system flexibility, we propose frameworks for i) classifying flexibility resources and flexibility enablers in grid operation and planning, and ii) classifying and understanding barriers to utilizing them in terms of a flexibility value chain. These theoretical frameworks are tested against empirical data collected in semi-structured in-depth interviews with a representative selection of Norwegian DSOs. Mapping the findings to the frameworks gives a systematic overview of the flexibility situation in Norway and presents both country-specific and general insights about barriers to utilization of flexibility. </p>

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