Human Factors Evaluation of Advanced Electric Power Grid Visualization Tools

Greitzer, Frank L.; Dauenhauer, Peter; Wierks, Tamara G.; Podmore, Robin

doi:10.2172/986732

Cited by 4 publications

(7 citation statements)

References 4 publications

(3 reference statements)

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“…According to the Human Factors Engineering discipline, this liquidity requires that the information system: (1) employ a well-designed user interface to ensure that the user can efficiently assimilate information [16] and (2) provide reasoning and cognitive support to users through assistive functions such as searching, facilitation of changes in context (adaptive displays), and computational support [17]. In order to sustain data liquidity, human and computer information systems must be mutually supportive [18,19].…”

Section: Data Liquiditymentioning

confidence: 99%

Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine

Abernethy

Wheeler

Courtney

et al. 2011

Behav. Med. Pract. Policy Res.

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The advancement of translational behavioral medicine will require that we discover new methods of managing large volumes of data from disparate sources such as disease surveillance systems, public health systems, and health information systems containing patientcentered data informed by behavioral and social sciences. The term "liquidity," when applied to data, refers to its availability and free flow throughout human/computer interactions. In seeking to achieve liquidity, the focus is not on creating a single, comprehensive database or set of coordinated datasets, nor is it solely on developing the electronic health record as the "one-stop shopping" source of health-related data. Rather, attention is on ensuring the availability of secure data through the various methods of collecting and storing data currently existent or under development-so that these components of the health information infrastructure together support a liquid data system. The value of accessible, interoperable, high-volume, reliable, secure, and contextually appropriate data is becoming apparent in many areas of the healthcare system, and health information liquidity is currently viewed as an important component of a patient-centered healthcare system. The translation from research interventions to behavioral and psychosocial indicators challenges the designers of healthcare systems to include this new set of data in the correct context. With the intention of advancing translational behavioral medicine at the local level, "on the ground" in the clinical office and research institution, this commentary discusses data liquidity from the patient's and clinician's perspective, requirements for a liquid healthcare data system, and the ways in which data liquidity can support translational behavioral medicine. Keywords Data liquidity, Translational behavioral medicine, Health information systemsIn the era of translational medicine, behavioral scientists are obliged to move beyond conceptual agreement to consider the "how to" of translation. In Medicine generally, barriers to translation, or "translation blocks," (with "T" standing for "translation") have been described at three phases of the research discovery-to-implementation continuum [1]. These blocks have been redefined so as to encompass behavioral medicine components: "T1," traditionally defining impedance in moving from the "bench" (i.e., basic science laboratory) to the "bedside" (i.e., clinical application), can be redefined in behavioral medicine as the transition point at which new understandings gained through basic science are translated into new preventive or therapeutic behavioral interventions; "T2," traditionally defining challenges in advancing new clinical interventions into practice, can be construed as the bottleneck where the results from behavioral medicine studies are translated into changes in behavioral medicine practice and healthcare decision-making [2]; and "T3," traditionally referring to blocks in translating clinically effective interventions into changes...

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Section: Data Liquiditymentioning

confidence: 99%

Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine

Abernethy

Wheeler

Courtney

et al. 2011

Behav. Med. Pract. Policy Res.

View full text Add to dashboard Cite

show abstract

“…As described in Greitzer, Dauenhauer, Wierks, & Podmore (2009), the tools available to the experimental and control groups are shown in Table 1-2, which also lists applicable mental models and performance measures/criteria. …”

Section: The Pse Toolmentioning

confidence: 99%

“…Figure 2-1 is a depiction of the Operator Decision Model that has proven useful for analyzing how expert power grid operators make decisions (Greitzer, Podmore, Robinson & Ey, 2009;Greitzer, Dauenhauer, Wierks, & Podmore, 2009). It can be seen that this model is strongly influenced by insights of Weick (1995) on sense making concepts that have been applied to power grid operations (Greitzer et al, 2008), and largely based on the RPD model; it incorporates the meta-cognitive/critique portion of the R/M model by invoking additional Mental Models and Mental Simulations in the pattern recognition process.…”

Section: An Operator Decision Modelmentioning

confidence: 99%

“…The objective of the experiment was to evaluate the ability of new visualization tools, developed by PNNL, to improve the performance of real power grid Operators engaged in realistic scenarios, as well as to conduct a human factors analyses based on the Operator Decision Model (Greitzer, Podmore, Robinson & Ey, 2009;Greitzer, Dauenhauer, Wierks, & Podmore, 2009). …”

Section: Introductionmentioning

confidence: 99%

“…Greitzer, Dauenhauer, Wierks, & Podmore (2009) examined the GCA and the PSE and offered the following assessment of the potential impacts of these tools and the best approaches for empirically testing such impacts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Evaluation of Electric Power Grid Visualization Tools in the EIOC

Greitzer¹,

Dauenhauer²,

Wierks³

et al. 2009

Self Cite

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Contingency visualization for real-time decision support in grid operation

Huang

Chen

Greitzer

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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Contingency analysis is a key function in control centers to assess the impact of various combinations of power system component failures based on state estimates. Today's practice analyzes only a limited set of contingency cases and lacks of capabilities in presenting the results in a way that is easy to be understood by grid operators in a short time frame of seconds to minutes. This limits the ability to operate the power grid for better reliability and efficiency. Faster analysis of more cases is required to safely and reliably operate today's power grids which have a less margin and more intermittent renewable energy sources. This paper explores the advancements in high performance computing and visual analytics for improving the computational speed and the information representation in contingency analysis. A framework of advanced contingency analysis is proposed. Case studies using the Western Electricity Coordinating Council (WECC) system are presented to demonstrate the feasibility of the proposed framework. Comparative assessment by real power grid operators has been performed as part of a WECC operator training class. The assessment results demonstrate the validity of the proposed contingency analysis and visualization approach.

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Human Factors Evaluation of Advanced Electric Power Grid Visualization Tools

Cited by 4 publications

References 4 publications

Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine

Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine

Experimental Evaluation of Electric Power Grid Visualization Tools in the EIOC

Contingency visualization for real-time decision support in grid operation

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