Situational Awareness Tool Research Articles

Exploring the Influence of Human System Interfaces: Introducing Support Tools and an Experimental Study

Situational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed.

Using a long‐range autonomous underwater vehicle to find and sample harmful algal blooms in Lake Erie

AbstractCyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

Graph Neural Network-Based Distribution System State Estimators

State estimation is an essential tool for situational awareness and control to ensure safe operation. While current state-of-the-art techniques, such as model-based sparsity-aware state estimators, provide superior performance over conventional approaches, they have poor scalability and require large computational times. These limitations can be overcome by utilizing deep learning models such as deep neural networks (DNNs). However, DNNs are prone to over-fitting and cannot incorporate structural information of networks. Furthermore, current model-based approaches require detailed knowledge of network parameters that may be unavailable in large systems. Therefore, new models with comparable performance are desired that either do not require network parameters or that can work using partial knowledge of these parameters. Recently, graph neural networks (GNNs) have become popular deep learning models that extend neural models to graph structures and incorporate structural information of the networks through graph structures. Therefore, this article proposes GNN-based state estimators by modeling the state estimation problem in distribution systems as node-level prediction problems on their graph representations with state measurement matrices and tensors as input features. Feature scaling and pseudo-measurement generation phases are introduced to enhance their performance. These approaches are evaluated on the IEEE 33, 37-node systems, and an unbalanced three-phase 559-node system. The proposed approaches provide comparable performance to sparsity-aware state estimators while using significantly lower computational times. The GNN-based approaches produce state estimates conforming to the power flow constraints without prior knowledge of the network parameters, thus suggesting that the proposed models can learn the system's underlying physical flows.

Use of design thinking and human factors approach to improve situation awareness in the pediatric intensive care unit.

Optimal design of healthcare spaces can enhance patient care. We applied design thinking and human factors principles to optimize communication and signage on high risk patients to improve situation awareness in a new clinical space for the pediatric ICU. To assess the impact of these tools in mitigating situation awareness concerns within the new clinical space. We hypothesized that implementing these design-informed tools would either maintain or improve situation awareness. A 15-week design thinking process was employed, involving research, ideation, and refinement to develop and implement new situation awareness tools. The process included engagement with interprofessional clinical teams, scenario planning, workflow mapping, iterative feedback collection, and collaboration with an industry partner for signage development and implementation. Improved and updated communication devices and bedside mitigation plans. Process metrics included individual and shared situation awareness of PICU care teams and our patient outcome metric was the rate of cardiopulmonary resuscitation (CPR) events pre- and post-transition. When evaluating all patients, shared situation awareness for accurate high-risk status improved from 81% pre-transition to 92% post-transition (p = .006). When assessing individual care team roles, accuracy of patient high-risk status improved from 88% to 95% (p = .05) for RNs, 85% to 96% (p = .003) for residents, and 88% to 95% (p = .03) for RTs. There was no change in the rate of CPR events following the transition.

Toward Probabilistic Post-Fire Debris-Flow Hazard Decision Support

Abstract Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., ≥24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to “messaging fatigue” if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest ∼10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards.

Safer this way: Identifying flooded roads for facilitating mobility during floods

Severe storms and associated flooding pose a significant risk to urban mobility. Consequently, 40 to 63% of flood-related deaths are linked to roadway-related incidents in developed countries. The dynamic nature of flooding and the lack of real-time information make it challenging to sense flooding and its impact on roadways. Hence, existing state-of-the-art methods fall short of providing a robust, reliable, and affordable tool to facilitate situational awareness during storms. Such a tool is indispensable to aid emergency response, especially considering the potential increase in risk to flood exposure due to climate change and other factors. This study addresses this need by providing an open-source framework that couples real-time rainfall data, a physics-based flood model, and network and spatial analyses to sense real-time flood impact on the road transportation system. Case studies using three recent storms in Houston, Texas demonstrate the framework’s ability to provide vehicle-class specific roadway conditions for even minor roads and residential streets—a problem existing approaches struggle with. Aside from road-link conditions, the framework can also estimate network-level flood impacts, such as identifying regions without access to critical facilities like hospitals, giving decision-makers a more holistic view of network performance. Further, the framework is interoperable with existing situational awareness tools and could augment their ability to sense road conditions during flooding. Finally, the proposed framework can equip flood-prone communities and emergency responders with reliable and accessible situational awareness content using open-source tools and data to promote safer mobility during flooding—a key goal of intelligent transportation systems.

Interactive SARS-CoV-2 dashboard for real-time geospatial visualisation of sewage and clinical surveillance data from Dhaka, Bangladesh: a tool for public health situational awareness.

Throughout the COVID-19 pandemic, many dashboards were created to visualise clinical case incidence. Other dashboards have displayed SARS-CoV-2 sewage data, largely from countries with formal sewage networks. However, very few dashboards from low-income and lower-middle-income countries integrated both clinical and sewage data sets. We created a dashboard to track in real-time both COVID-19 clinical cases and the level of SARS-CoV-2 virus in sewage in Dhaka, Bangladesh. The development of this dashboard was a collaborative iterative process with Bangladesh public health stakeholders to include specific features to address their needs. The final dashboard product provides spatiotemporal visualisations of COVID-19 cases and SARS-CoV-2 viral load at 51 sewage collection sites in 21 wards in Dhaka since 24 March 2020. Our dashboard was updated weekly for the Bangladesh COVID-19 national task force to provide supplemental data for public health stakeholders making public policy decisions on mitigation efforts. Here, we highlight the importance of working closely with public health stakeholders to create a COVID-19 dashboard for public health impact. In the future, the dashboard can be expanded to track trends of other infectious diseases as sewage surveillance is increased for other pathogens.

An Integrated Situational Awareness Tool for Resilience-Driven Restoration With Sustainable Energy Resources

Integrating sustainable energy resources transforms the distribution grid into an active system with higher variations observed in load and generation. Estimating distributed generation, gross load, and cold load pick-up (CLPU) become more challenging with behind-the-meter (BTM) distributed energy resources (DERs), especially in case of outages caused by extreme events. This work proposes a resilience-driven restoration scheme using the most updated information from an integrated and enhanced situational awareness tool (ESAT) using kernelized Bayesian state-space inference (KBSI) with Markov Chain Monte Carlo (MCMC) and multiple optimization algorithms. ESAT consists of the BTM load/ DER estimation and disaggregation, CLPU estimation, and network topology estimation with de-energized islands. The proposed work provides solutions to establish informed restoration schemes considering resilience criteria for a quick recovery of high-priority loads. A resilience metric is utilized after outages to measure the effectiveness of ESAT-driven restoration for the supposed threats. The performance of developed ESAT is demonstrated using actual field datasets and validated using the emulated real scenarios on a benchmark model.

Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage

Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.

First responders’ innovative methodology and curriculum definition for advanced virtual reality training

Within the ASSISTANCE Project (Adapted Situation Awareness Tools and Tailored Training Scenarios for Increasing the Capabilities and Enhancing the Protection of First Responders) the development of a novel and advanced training programme is currently underway to adapt to the needs and characteristics of first responders. Once the methodology was ready, a complete training curriculum was prepared in order to probe the concept of the European training network based on online Virtual Reality platforms. The curriculum is composed of subjects, which have been developed with a stepwise approach, taking into consideration the pre-requisites needed foreach subject and a gradual increase in difficulty and complexity. These subjects can be divided into two different types: theoretical subjects, which will be taught through the Moodle server and practical subjects, which will be taught through the performance of different virtual scenarios with the available Virtual Reality platforms. Each subject definition includes a short summary of the subject, their main, general and specific objectives as well as their preferred evaluation method and the pre-requisites needed. This paper presents the ASSISTANCE training methodology and the curriculum developed including the preliminary scheduling needed to complete all the proposed subjects.

Near real time monitoring and forecasting for COVID-19 situational awareness

In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.

Collection of End User Requirements and Use Cases during a Pandemic—Towards a Framework for Applied Research Projects

Research projects in the security domain often aim to develop innovative technology-based solutions for end users (e.g., situational awareness tools, crisis management tools). The pandemic crisis hit hard and without warning, not only influencing our everyday life but also the scientific community. To continue applied research projects during a pandemic, work structures needed to be adapted (e.g., user requirements collection, use case development), as face-to-face events were impossible but crucial to collect high quality requirements with a variety of different stakeholders. To ensure continued multi-stakeholder engagement we developed an overarching framework for collecting user requirements and use cases in an online setting and applied the framework within two research projects. The framework consists of four steps with the aim to assure high quality user requirements and use case collection (first analysis, stakeholder consultation, evaluation and prioritization, technical evaluation). The two projects presented in this paper provide insight on the potential of the framework. The framework offers a structured approach that fits for many different security research projects in terms of the easy application and its transferability. The main advantages (e.g., easily adaptable, reduced workshop time, no need to travel, suitability for different contexts and project types, etc.) and drawbacks (e.g., organization of online events, feedback collection time, etc.) of the framework are presented and discussed in this paper to offer increased stakeholder engagement. Empirical testing of the framework is proposed.

Visual Attention of Anesthesia Providers in Simulated Anesthesia Emergencies Using Conventional Number-Based and Avatar-Based Patient Monitoring: Prospective Eye-Tracking Study.

BackgroundInadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making.ObjectiveThe spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality.MethodsThis prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results.ResultsFifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5-317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9-51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient –1.39; 95% CI –2.44 to –0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient –0.23; 95% CI: –0.4 to –0.06; P=.01).ConclusionsUsing eye tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness.

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.

DDoS Cyber-Incident Detection in Smart Grids

The smart grid (SG) offers potential benefits for utilities, electric generators, and customers alike. However, the prevalence of cyber-attacks targeting the SG emphasizes its dark side. In particular, distributed denial-of-service (DDoS) attacks can affect the communication of different devices, interrupting the SG’s operation. This could have profound implications for the power system, including area blackouts. The problem is that few operational technology tools provide reflective DDoS protection. Furthermore, such tools often fail to classify the types of attacks that have occurred. Defensive capabilities are necessary to identify the footprints of attacks in a timely manner, as they occur, and to make these systems sustainable for delivery of the services as expected. To meet this need for defensive capabilities, we developed a situational awareness tool to detect system compromise by monitoring the indicators of compromise (IOCs) of amplification DDoS attacks. We achieved this aim by finding IOCs and exploring attack footprints to understand the nature of such attacks and their cyber behavior. Finally, an evaluation of our approach against a real dataset of DDoS attack instances indicated that our tool can distinguish and detect different types of amplification DDoS attacks.

Digital Strategies to Improve Food Assistance in Disasters: A Scoping Review.

Modern digital strategies, including Internet of Things, machine learning, and mobile applications, have revolutionized situational awareness during disaster management. Despite their importance, no review of digital strategies to support emergency food security efforts has been conducted. This scoping review fills that gap. Keywords were defined within the concepts of food assistance, digital technology, and disasters. After the database searches, PRISMA guidelines were followed to perform a partnered, 2-round scoping literature review. The search identified 3201 articles, and 26 articles met criteria and were included in the analysis. The data types used to describe the tools were text/opinion (42.3%), qualitative (23.1%), system architecture (19.2%), quantitative and qualitative (11.5 %), and quantitative (3.8%). The tools' main functions were Resource Allocation (41.7%), Data Collection and Management (33%), Interagency Communications (15.4 %), Beneficiary Communications (11.5%), and Fundraising (7.7%). The platforms used to achieve these goals were Mobile Application (36%), Internet of Things (20%), Website (20%), and Mobile Survey (8%); 92% covered the disaster response phase. Digital tools for planning, situational awareness, client choice, and recovery are needed to support emergency food assistance, but there is a lack of these tools and research on their effectiveness across all disaster phases.

Optimizing Situation Awareness to Reduce Emergency Transfers in Hospitalized Children.

Interventions to improve care team situation awareness (SA) are associated with reduced rates of unrecognized clinical deterioration in hospitalized children. By addressing themes from recent safety events and emerging corruptors to SA in our system, we aimed to decrease emergency transfers (ETs) to the ICU by 50% over 10 months. An interdisciplinary team of physicians, nurses, respiratory therapists, and families convened to improve the original SA model for clinical deterioration and address communication inadequacies and evolving technology in our inpatient system. The key drivers included the establishment of a shared mental model, psychologically safe escalation, and efficient and effective SA tools. Novel interventions including the intentional inclusion of families and the interdisciplinary team in huddles, a mental model checklist, door signage, and an electronic health record SA navigator were evaluated via a time series analysis. Sequential inpatient-wide testing of the model allowed for iteration and consensus building across care teams and families. The primary outcome measure was ETs, defined as any ICU transfer in which the patient receives intubation, inotropes, or ≥3 fluid boluses within 1 hour. The rate of ETs per 10 000 patient-days decreased from 1.34 to 0.41 during the study period. This coincided with special cause improvement in process measures, including risk recognition before medical response team activation and the use of tools to facilitate shared SA. An innovative, proactive, and reliable process to predict, prevent, and respond to clinical deterioration was associated with a nearly 70% reduction in ETs.

Use and Scalability of OpenFOAM for Wind Fields and Pollution Dispersion with Building- and Ground-Resolving Topography

Complex flow and pollutant dispersion simulations in real urban settings were investigated by using computational fluid dynamics (CFD) simulations with the SST k−ω Reynolds-averaged Navier–Stokes (RANS) equation with OpenFOAM. The model was validated with a wind-tunnel experiment using two surface-mounted cubes in tandem, and the flow features were reproduced with the correct qualitative behaviour. The real urban geometry of the Parade Square in Warsaw, Poland was represented with both laser-scanning data for the ground geometry and the CityGML standard to describe the buildings as an example. The Eulerian dispersion of a passive scalar and the flow behaviour could be resolved within minutes over a computational domain with a size of 958 × 758 m2 and a height of 300 m with over 2 M cells due to the good and strong parallel scalability in OpenFOAM. This implies that RANS modelling with parallel computing in OpenFOAM can potentially be used as a tool for situational awareness on a local urban scale; however, entire cities would be too large.

The implementation of a pediatric early warning system in the Sidra Pediatric Emergency Department, Qatar

Background: Establishing a paediatric early warning system in a paediatric Emergency Department (ED) is a complex process and more so with the COVID-19 pandemic. PUMA (PEWS Utilisation & Morality Avoidance) is a qualitative system assessment survey tool which assesses the strengths and weaknesses of the patient care safety processes and systems within a department. This model draws together evidence from two theoretically informed systematic reviews. Methods: The Sidra Medicine ED Quality group surveyed online 200 staff from the department of physicians/nursing team focussing on processes of monitoring patients and documentation, communication amongst the team and with parents, staff empowerment, situational awareness, escalation processes and response to the deteriorating child in the three broad domains of Detect, Prepare, and Act, with a further seven smaller domains (monitor, record, interpret, review, prepare, escalate, and evaluate) (). Survey analysis enabled to review current practice, identify areas that are working well and areas for improvement. Results and implications: The online survey helped achieve a 85% return rate and identify seven areas for improvement in the system. The spider diagram () illustrates the areas of strength and weakness in the seven domains with respect to Detect, Prepare, and Act. We collaborated with the Cerner team, created an automatic documentation of vital signs from triage and treatment areas to the patient’s Electronic Medical Record by associating patient’s cardiac monitors to reduce manual errors and for the timely monitoring of vital signs. A one-day “Back to Basics Training” refresher course for the nursing team was conducted. A senior nurse, as a watcher in the triage and treatment area, identified children at high risk of deterioration. A Pediatric ED Situational Awareness Tool (PEDSAT) was developed locally and is in trial to help manage sick children effectively. Conclusion: PUMA, a novel system assessment tool, empowered our ED to tailor a quality program with an aim to deliver effective and efficient patient care.

Dynamic Security and Stability Region Under Different Renewable Energy Permeability in IENGS System

The integration of renewable energy sources (RESs) remains growing rapidly in recent years. At the same time, security problems after contingencies were attracting great attention, due to deficiencies for the stable operation, even could induce the collapse of integrated energy systems with the deep interconnection of such RESs. To existing issues, the hyper-plane (HP) expression is proved to be an excellent tool for power system situational awareness and stability-constrained operation in practical dynamic security stability regions (PDSSR). In this study, a concept and model were proposed for a high renewable energy penetration of integrated electricity-natural gas system (HRE-IENGS) based on the N - 1 security guideline. The scheme instruction and system evaluation processes as follows: in order to improve the accuracy of the IENGS system, simulation fitting solutions of PDSSR boundary were presented according to a hybrid algorithm, including dynamic self-adaptive differential evolution algorithm (DSADE), and a least square support vector machine (LSSVM) algorithm. The adopted algorithm approach was applied to rapid approximating of the PDSSR boundary by HP expression in power injection spaces. Simultaneously, different renewable energy permeabilities (DREPs) were set from 5 to 50 %, and active power injections of DREPs under short circuit fault on IEEE 118 bus system simulated by using DIgSLENT/power factory software. As the consequence, this mentioned method effectively described the security stability boundary of HRE-IENGS, and realized the three-dimensional visualization space of DREPs-PDSSR. In addition, the behavior of operation points and PDSSR under DREPs were carefully investigated, and unstable runs were accurately exposed. Through the PDSSR, the state analysis could be conducted rapidly on several parameters, including security and stability assessment with various energy supply capabilities. Meanwhile, these indexes were calculated offline and applied on-line. The results of this study verified the accuracy and effectiveness of the proposed modeling for the considered system, and thus could provide technical support for the stability of the HRE-IENGS system.