This paper presents a measurement-based analysis of a 1.5 Hz forced oscillation triggered during a reactive power capability test conducted at a power plant in Dominion Energy's power system. Owing to the slow evolving nature of the critical mode, it is demonstrated how time-frequency analysis of the period leading to the oscillation holds crucial information for finding the oscillation's source. Furthermore, it is shown how the use of wavelets enables to more granular analysis of the evolution and impact of the forced oscillation - a capability that will help Dominion better monitor and regulate the dynamic components of an evolving grid.

This paper proposes a practical method to monitor power system inertia using Pumped Storage Hydropower (PSH) switching-off events. This approach offers real-time system-level inertia estimation with minimal expenses, no disruption, and the inclusion of behind-the-meter inertia. First, accurate inertia estimation is achieved through improved RoCoF calculation that accounts for pre-event RoCoF, reducing common random frequency fluctuations in practice. Second, PSH field data is analyzed, highlighting the benefits of using switching-off events for grid inertia estimation. Third, an event detection trigger is designed to capture pump switching-off events based on local and system features. Fourth, the method is validated on the U.S. Eastern Interconnection model with over 60,000 buses, demonstrating very high accuracy (3 with field validation showing a 9.9 challenges in practical power system inertia estimation, this method enhances decision-making for power grid reliability and efficiency, addressing challenges posed by renewable energy integration.

The transition from conventional to modern power systems is causing an increase in integration of inverter-based resources (IBRs). This generally leads to a decrease in total system inertia, which in-turn increases the system’s rate-of-change-of-frequency (RoCoF) during disturbances. This poses a threat to the frequency stability of the system and may falsely trigger protective devices. To monitor system status and plan for integrating renewable energy sources like photovoltaic, wind, and energy storage systems, a realistic study of inertia estimation and analysis in the United States (US) over the past decade is needed. This paper uses field-measured phasor measurement unit (PMU) data collected throughout the US from 2013 to 2023 via the Frequency Monitoring Network (FNET/GridEye) operated by the University of Tennessee, Knoxville (UTK) and Oak Ridge National Laboratory (ORNL). The collected PMU frequency data is utilized to estimate the system inertia of the three US interconnections: Eastern, Western, and Texas. Various RoCoF time windows are investigated for estimating the inertia of each interconnection by maximizing the correlation coefficient between the measured RoCoF and power mismatch. The resulting inertia trends over the past decade show approximately a 6% decline in inertia in the Eastern interconnection, a 15% decline in inertia in the Western interconnection, and a 16% increase in inertia in Texas. Key insights into how inertia is changing amidst the complex energy landscape are extracted using the fuel mix trend data. This provides valuable information for future energy strategies and planning.

This paper analyzes pumped storage plant data for grid inertia estimation amid rising renewable energy use. Traditional methods use sudden MW changes to estimate inertia using the swing equation. Pumped storage plants offer multiple MW change events for inertia estimation due to their various operation modes. Our analysis shows that pump turn-off events have distinct sudden MW changes ideal for inertia estimation. These events consistently display MW changes, negating the need for continuous MW data streaming. We also study the plant’s operation patterns and their link to low inertia times. Our findings suggest the potential of using pump events for more informed decisions on grid reliability amidst renewable energy challenges.

Rising deployment of inverter-based resources (IBRs), characterized by a lack of rotating mass, is decreasing the total inertia of the system. This can lead to an increased Rate of Change of Frequency (RoCoF) during the disturbance and false activation of protective devices. There is a need to assess the inertia over the past decade amidst the evolving landscape of renewable energy sources to develop strategies for integrating energy storage, enhancing resilience measures, and ensuring the stable and reliable operation of the grid. Therefore, a realistic assessment of the inertia trend using a measurement-based approach that addresses the limitations of existing models is proposed. An inertia study of the Western Interconnection in the United States is performed utilizing the data from 2013 to 2022, obtained from FNET/ GridEye network. The three-second RoCoF time window is chosen for the study as it showed an optimum balance between inclusion of primary response from governor. The obtained inertia trend result shows a small percentage declination of inertia over the decade. By examining the result alongside a generation mix graph, insights are gained into the dynamic interplay between shifting energy landscape and system inertia.

Primary frequency response is a quintessential component that stabilizes power systems amid active power disturbances. However, the strength of primary frequency response, as calculated with disturbance events, shows a low consistency from event to event. Thus, its effectiveness as a system strength measure is questionable. This work shows that consistency and improved accuracy can be achieved on a statistical basis via event filtering. Event MW estimation and primary frequency response trending analyses are the applications presented in this paper. Synchronized field measurements in the Eastern Interconnection, powered by FNET/GridEye, are used for validation of the analyses and applications.

“You should know the words by the company they keep!” has been one of the most famous 10 slogans attribute to John Rubert Firth, 1957. This has ignited a whole school in linguistic research 11 known as the British empiricist contextualism. Sixty years later, many unor semi-supervised 12 machine learning algorithms have been successfully designed and implemented aiming at 13 extracting word meaning from within the context of a text corpus. These algorithms treat words, 14 more or less, as vectors of real numbers representing frequencies of word occurrences within context 15 and word meaning as positions of words in a high-dimensional vector space models. Word 16 associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning 17 (DL) and other artificial neural networks based architectures, learning the positioning of words and 18 extracting word associations as measured by their distances has further improved. In this paper, 19 however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross20 disciplinary evaluation framework to judge about the nature of the extracted word associations by 21 state-of-the-art machine learning algorithms. Our preliminary results, which are based on word 22 associations extracted from the application of DL framework on a Google News text corpus, and 23 comparisons with human created word association lists, provide some insights into the inherited 24 limitations in interpreting the type of word associations and underpinning relations between words 25 with inevitable consequences in other areas, such as extraction of knowledge graphs or image 26 understanding. 27

“You should know the words by the company they keep!” has been one of the most famous slogans attribute to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space models. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results, which are based on word associations extracted from the application of DL framework on a Google News text corpus, and comparisons with human created word association lists, provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

Contents: Introduction: the learning organization and reflective practice - the emergence of a concept, Nick Gould Supervision, learning and transformative practices, Martyn Jones Social work supervision - contributing to innovative knowledge production and open expertise, SynnA ve Karvinen-Niinikoski Critical reflection: opportunities and threats to professional learning and service development in social work organizations, Mark Baldwin Critical reflection and organizational learning and change: a case study, Jan Fook Multi professional teams and the learning organization, Imogen Taylor Sustaining reflective practice in the workplace, Hilary Sage and Mary Allan Using 'Critical Incident Analysis' to promote critical reflection and holistic assessment, Judith Thomas Evaluation for a learning organization? Ian Shaw Reflecting on practice: exploring individual and organizational learning through a reflective teaching model, Bairbre Redmond Living out histories and identities in organizations: a case study from three perspectives, Harjeet Badwall, Patricia O'Connor and Amy Rossiter Conclusions - optimism and the art of the possible, Mark Baldwin Index.

Name Index Italic page references indicate tables and figures. Aaker, David A., 93, 110, 114–115, 136, 138, 140, 240–241 Aaker, Jennifer L., 7, 17, 24, 65, 83–84, 94, 173294, 358–359, 368 Aarts, Henk, 46 Achenreiner, Gwen B., 242 Adamy, Janet, 237 Adelman, Mara B., 342 Advertising Age, 218 Aggarwal, Pankaj, 7, 10, 14, 24–41, 83–84, 93, 361, 369–370, 388–389 Agnew, Christopher R., 284–286 Ahearne, Michael, 85 Ahluwalia, Rohini, 85, 307, 382 Ahuvia, Aaron C., 65–66, 74271, 276, 342–355, 344, 381388 Aiken, LS, 156 Aiken, Michael T., 307 Ailawadi, Kusum L., 388 Ainsworth, Mary DS, 327, 360, 387 Ajzen, Icek, 47, 126–127 … 406 NAME INDEX Bayon, Tomas, 203 Beach, Steven RH, 69 Bearden, William O., 112, 116, 270–271 Beatty, Sharon E., 45, 85, 94332 Becker, Gary S., 59 Becker-Olsen, Karen L., 204 Beehr, Terry A., 207 Belk, Russell W., 107, 110, 124, 138, 142, 145, 221, 270 …

Community-based preventative programmes are increasing in demand as the UK seeks alternative ways of supporting the growing number of older adults. As the use and promotion of preventative programmes increase, so does the need for evidence supporting their effectiveness. Through the use of mixed methods, this study explored a singing community-arts programme, the Golden Oldies, to determine the extent to which the programme contributes to participants' (n = 120) sense of health, self-development and social connectedness. Quantitative analyses found that between 73.1 and 98.3 per cent of participants agreed or strongly agreed that the Golden Oldies contributed to their self-development, health and sense of community as well as revealing a statistically significant increase in self-reported health prior to participation in the programme to the time of the study. Qualitative analysis (n = 5) revealed three themes—the Golden Oldies as: (i) a reduction in social isolation and increase in social contact; (ii) a therapeutic source; and (iii) a new lease for life. The results provide evidence of the preventative nature of the Golden Oldies programme through self-reported improvements in health and social relationships where social connections appeared to be the important thread that contributed to the perceived benefits. Implications for policy, practice and research are discussed.

This chapter provides an example of practice in one form of action research - co-operative inquiry (Heron 1995; Heron and Reason 2000). It describes the process of and lessons that were learned from co-operative inquiries by two groups of social workers exploring the tensions between professional discretion and bureaucratic procedures in the implementation of a complex social policy in the United Kingdom. The chapter explores the reasons why this methodology was chosen, following misgivings about prior use of traditional qualitative research methodology. It is argued that co-operative inquiry facilitated ownership of learning by groups of social workers who were experiencing marginalisation within their organisation. This relieved their anxieties and provided lessons for policy implementation that could, if replicated, reduce the deficit effect of the unreflective use of discretion which has proved so undermining in other areas of policy (Lipsky 1980).

Social Work in the Community explores contemporary approaches to community social work, community development and community action within political, theoretical, methodological and ethical frameworks. It emphasises the importance of collective perspectives on social work problems both as a different perspective but also as a critique of individualism within social work theory and practice. Social Work in the Community revisits radical and political approaches to social work from a community and collectivist standpoints.