Data-driven Models to Anticipate Critical Voltage Events in Power Systems

This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal and offers insight into the strengths and weaknesses of several widely utilized prediction models for this application.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Advanced load modelling for power system studies

Although power system load modelling is a mature research area, there is a renewed interest in updating available load models and formulating improved load modelling methodologies. The main drivers of this interest are the introduction of new types of non-conventional (e.g. power electronic interfaced) loads, the requirement to operate power supply systems with increasing levels of renewable distributed generation and the implementation of various load control functionalities (e.g. demand side management). As the majority of existing load models do not allow for a full and precise analysis of these new operating conditions, it is essential to develop new load models and update load modelling techniques. This thesis presents a detailed study of modern loads, focussing on the requirements for their correct representation in power system analysis. The developed models of the individual loads are then combined using a new load aggregation methodology for developing aggregate load models, suitable for the analysis of both existing and future power supply systems (so called ’smart grids’). The methodology uses a circuit-based load modelling approach, as this allows reproduction of the instantaneous current waveforms of the modelled load for any given supply voltage. This approach retains all electrical characteristics of the loads and provides a more realistic representation of some important phenomena (e.g. harmonic cancellation and attenuation due to load and supply system interactions) which are often neglected in traditional load modelling procedures. Case studies of the UK residential and commercial load sectors are presented as illustrations of the load aggregation methodology. The results show significant short-term and long-term temporal variations in the load characteristics, which are not available or reported in the existing literature. This information allows for a more comprehensive assessment of demand-side management functionalities and correlation with locally connected distributed generation. Both of these effects are investigated in the thesis by quantifying the possible extent and range of changes in power system performance for some expected near future changes in load configurations and network operating conditions

Using Logs Data to Identify When Software Engineers Experience Flow or Focused Work

Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine learning and a comprehensive collection of logs data to identify periods of related actions (indicating focus), and validate this metric against self-reported time in focus or flow using diary data and quarterly survey data. Our results indicate that we can determine when software engineers at a large technology company experience focused work which includes instances of flow. This metric speaks to engineering work, but can be leveraged in other domains to non-disruptively measure when people experience focus. Future research can build upon this work to identify signals associated with other facets of flow

Semantic Inferentialism as (a Form of) Active Externalism

Within contemporary philosophy of mind, it is taken for granted that externalist accounts of meaning and mental content are, in principle, orthogonal to the matter of whether cognition itself is bound within the biological brain or whether it can constitutively include parts of the world. Accordingly, Clark and Chalmers (1998) distinguish these varieties of externalism as ‘passive’ and ‘active’ respectively. The aim here is to suggest that we should resist the received way of thinking about these dividing lines. With reference to Brandom’s (1994; 2000; 2008) broad semantic inferentialism, we show that a theory of meaning can be at the same time a variety of active externalism. While we grant that supporters of other varieties of content externalism (e.g., Putnam 1975 and Burge 1986) can deny active externalism, this is not an option for semantic inferentialists: On this latter view, the role of the environment (both in its social and natural form) is not ‘passive’ in the sense assumed by the alternative approaches to content externalism