Static Security Risk Assessment for Islanded Hybrid AC/DC Microgrid

The increasingly complex power grid structure and the volatility caused by the high proportion of renewable energy have brought great challenges to the traditional static security assessment of power grids. To solve this problem, a multi-source data-driven power grid static security risk assessment method is proposed in this paper. Firstly, the historical operation data of the power grid, including the grid structure of the system, load power, generator power and weather information, are used to build a multi-source data set. Then, a static security assessment model is constructed using long-term and short-term memory neural network and deep neural networks, and the multi-source data sets are used for off-line training. According to the output results of the assessment model, a three-level static security risk assessment index system is then developed. Finally, the proposed method is tested through a provincial 500kV power grid. The example results show that the method proposed in this paper can effectively realize the assessment of power grid static security risks, which can be used to assist the system operators for future intelligent dispatch and control.

Due to the high probability of power flow violation during the N-1 static safety check under maintenance, and the risk assessment indicators are single and subjective, a method of static security risk assessment based on multi-source heterogeneous information for planned maintenance is proposed. By regularizing dispatcher experience, the static security factors are analyzed from equipment failure rate, electrical characteristics and grid topology, etc. Then, the selected indicators are preprocessed. Finally, Deep Belief Neural Network (DBNN) is used to evaluate risk. Through deep mining of the membership and mapping relationship between multiple indicators, the risk self-assessment is realized, and dispatcher is given the assistant decision when orderly adjusting equipment that causes limit. Simulation results of IEEE 39-bus show that the method proposed in this paper can quickly assess risk level without analysis of dispatcher; compared with DNN algorithm, DBNN has higher accuracy. The validity and feasibility of this research are proved.

Static risk factors have been viewed as relatively fixed aspects of offenders’ histories, such as age and the extent of previous offending, which increase the risk of reoffending but do not change and cannot be changed through intervention. Given that they are “fixed” historical characteristics, static risk factors are not typically seen as meaningful targets for intervention. In contrast, dynamic risk factors (DRFs) have been viewed as possible psychological or behavioral features of the offender that raise the risk of reoffending but maybe potentially changeable. The notion that some dynamic risk factors may be relatively stable for sexual offenders in that change occurs over extended periods of time, while other DRFs are less stable and may change acutely based on immediate contexts or affective experiences. Some authors have suggested that both proposed static and dynamic risk factors each are markers of underlying psychological vulnerabilities to commit sexual offenses and others have argued that such vulnerabilities or needs reflect propensities to act out sexually. In this chapter, two efforts to identify and quantify dynamic risk factors are reviewed that have culminated in two risk assessment instruments, the Stable-2007 and the SRA-FV. The results of the research efforts suggest that each of these instruments provides a useful measure of characteristics of sexual offenders that are related to sexual offense recidivism; they have been shown to have incremental validity relative to so-called static risk assessments. However, instead of being truly “dynamic” and changeable, such risk factors may simply reflect a more general or broader dispositional characterization of sexual offenders and one that surprisingly captures less variance than the more concrete manifestation of underlying psychological vulnerabilities as expressed in static, historical behaviors.

Wind farms commonly cluster in regions rich in wind resources. Thus, correlation of wind speeds from different wind farms should not be ignored when modeling a power system with large wind energy penetration. This paper proposes a probabilistic optimal power flow (POPF) technique based on the quasi-Monte Carlo simulation (QMCS) considering the correlation of wind speeds using copula functions. In this paper, a copula function is used to model the dependent structure of random wind speeds and their forecast errors. QMCS is employed in the sampling procedure to reduce computation burden. The proposed method is applied in probabilistic power flow (PPF). Furthermore, the PPF is used in the POPF problem that aims at minimizing the expectation and downside risk of fuel cost simultaneously. Simulation studies are conducted on a modified IEEE 118-bus power system with wind farms integrated in two areas, and the results show that the accuracy and efficiency are improved by the proposed method.

To evaluate the impact of the randomness and correlation of photovoltaic (PV) and load on AC/DC systems with a multiport current flow controller (M-CFC), this paper proposes a probabilistic optimal power flow calculation for AC/DC systems based on a nonparametric kernel density estimation. First, according to the M-CFC model, the DC power flow calculation method with M-CFC was deduced, and its influence on line loss was analyzed. Second, a nonparametric kernel density estimation with an adaptive bandwidth was used to accurately describe the probability distribution of the PV and load, and correlation samples of the PV and load were obtained by the mixed copula function. Then an optimization model that considers system loss and static security was established, and a fast nondominated sorting genetic algorithm based on the elite strategy (NSGA-II) was used to calculate the multi-objective probability optimal power flow of the AC/DC system. Finally, a case study was performed on the modified IEEE39 bus system using measured PV and load data. We verified the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty, and M-CFC can improve the static security of the system.

In an islanded hybrid AC/DC microgrid, the existing intermittent distributed generations (DGs) and local loads variation frequently cause power fluctuation. And the DC bus voltage is sensitive to it. A virtual inertia control strategy of interlinking converters (ILCs) in an islanded hybrid microgrid is proposed in this paper, which restrains the DC bus voltage fluctuation and enhances the inertia of the hybrid microgrid. Traditional droop control methods of ILCs mostly only focus on power sharing. The proposed control strategy can not only maintain proportional power distribution between DC and AC subgrids, but also regulate the DC bus voltage directly. It can improve microgrid stability during DC-side loading transitions or distributed energy fluctuations. Moreover, no additional energy storage or inverters are required in a cost-effective manner. The validity of the proposed control method is verified by offline time-domain simulation in MATLAB/Simulink and real-time experiment in OPAL-RT digital platform.

Methodologically-sound security assessments are crucial for understanding a system in fulfilling the requirements, realizing its behavior, and identifying implications. A system is made resilient if and only if there is enduring confidence that it will function as expected. It is cyber secure if it displays this property in the face of an adversary. This chapter provides an explanation of various static security risk assessment methodologies (SSRAM) having long epochs for assessing and revisiting the risks and explains their strengths and weaknesses. The SSRAM will form the basis for elaborating on dynamic security risk assessment methodologies that must have very short epochs depending on the emergent threats and vulnerabilities to combat new or evolving threats. This is essential for handling cyber security of dynamic complex systems including future networks having a number of self-managing properties including self-protection for defending against malicious attacks.

This study proposes a new paradigm for real-time, predictive, and multidimensional risk assessment in industrial environments by leveraging multimodal data (video, audio, environmental sensors). Existing risk assessment systems typically rely on single data sources or subjective judgment, making it difficult to adapt swiftly to complex workplace changes and achieve real-time responsiveness. To address these limitations, we constructed a large-scale multimodal dataset of approximately 10TB, employing real-time streaming-based preprocessing and data synchronization. This approach integrates various data sources—high-resolution cameras, high-sensitivity microphones, and environmental sensors (temperature, humidity, vibration)—and applies data augmentation techniques such as AutoAugment and MixUp to build robust models capable of handling diverse environmental conditions. We adopted a hybrid analytical algorithm combining Vision Transformer (ViT) and YOLOv8, achieving high accuracy (over 95%) and real-time processing (average response time under one second). Additionally, we utilized machine learning algorithms such as SVM, Random Forest, and K-Means to detect anomalies in audio and environmental sensor data, thus identifying latent risk factors. Experimental results demonstrate multifaceted performance improvements compared to conventional approaches, including over a 15% increase in accuracy, approximately 30% reduction in response time, about 20% reduction in power consumption, and user satisfaction exceeding 90%. These achievements were verified across various industrial settings—chemical, manufacturing, logistics—highlighting the system’s capacity to detect complex risk factors and respond proactively. By seamlessly integrating multimodal data analysis, state-of-the-art deep learning models (ViT, YOLOv8), and reinforcement learning-based response strategies, we have demonstrated a transition from traditional, static, and retrospective risk assessment to an intelligent, real-time, and predictive safety management framework.

All nuclear power plants must consider external flooding hazards such as local intense precipitation, riverine flooding, flooding due to upstream dam failure, and coastal flooding due to storm surge or tsunami. While external flooding events could potentially interrupt off-site power, threaten plant structures, systems and components important to safety, or limit plant access, they have often been qualitatively assessed as risk insignificant and screened out from detailed evaluation and quantification. Recent lessons learned from the Fukushima seismic/tsunami initiated nuclear accident (2011), the Fort Calhoun (2011), Vermont Yankee (2013), Arkansas Nuclear One (2013), and St. Lucie (2014) flooding events have highlighted the need for more detailed risk analysis. However, incorporating an external flood model into the traditional event tree/fault tree approach used in static probabilistic risk assessment (PRA) models is challenging because it is difficult to accurately represent plant system and component behavior and reliability of manual actions during an ever-progressing flood event. The plant response to external flood may be highly spatial and time dependent, subject to the hydrological and hydraulic characteristics of the flood event. Such unique challenges prompt the investigation of using simulation-based dynamic analysis approaches for external flood risk assessment. Simulation methods can better model the performance of structures, systems, and components during an external flooding event. A general framework to perform a simulation-based dynamic flooding analysis is presented in this paper with the subtasks of flood hazard analysis, flood fragility analysis, plant response modeling, safety margin analysis or PRA quantification. A new type of PRA technique, State-based PRA Modeling, is introduced to incorporate time-related interactions such as those from both 3D physical simulations and random failures into traditional PRA logic models.

BACKGROUND Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients’ conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are often limited by their inability to handle irregular sampling and missing values, and many lack rigorous external validation across institutions. OBJECTIVE We aimed to develop a real-time, interpretable risk prediction model that continuously assesses ICU patient mortality using irregular, longitudinal EMR data, with improved performance and generalizability over traditional static scoring systems. METHODS A time-aware bidirectional attention-based long short-term memory (TBAL) model was developed using EMR data from the MIMIC-IV (Medical Information Mart for Intensive Care) and eICU Collaborative Research Database (eICU-CRD) databases, comprising 176,344 ICU stays. The model incorporated dynamic variables, including vital signs, laboratory results, and medication data, updated hourly, to perform static and continuous mortality risk assessments. External cross-validation and subgroup sensitivity analyses were conducted to evaluate robustness and fairness. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, and <i>F</i><sub>1</sub>-score. Interpretability was enhanced using integrated gradients to identify key predictors. RESULTS For the static 12-hour to 1-day mortality prediction task, the TBAL model achieved AUROCs of 95.9 (95% CI 94.2-97.5) and 93.3 (95% CI 91.5-95.3) and AUPRCs of 48.5 and 21.6 in MIMIC-IV and eICU-CRD, respectively. Accuracy and <i>F</i><sub>1</sub>-scores reached 94.1 and 46.7 in MIMIC-IV and 92.2 and 28.1 in eICU-CRD. In dynamic prediction tasks, AUROCs reached 93.6 (95% CI 93.2-93.9) and 91.9 (95% CI 91.6-92.1), with AUPRCs of 41.3 and 50, respectively. The model maintained high recall for positive cases (82.6% and 79.1% in MIMIC-IV and eICU-CRD). Cross-database validation yielded AUROCs of 81.3 and 76.1, confirming generalizability. Subgroup analysis showed stable performance across age, sex, and severity strata, with top predictors including lactate, vasopressor use, and Glasgow Coma Scale score. CONCLUSIONS The TBAL model offers a robust, interpretable, and generalizable solution for dynamic real-time mortality risk prediction in ICU patients. Its ability to adapt to irregular temporal patterns and to provide hourly updated predictions positions it as a promising decision-support tool. Future work should validate its utility in prospective clinical trials and investigate its integration into real-world ICU workflows to enhance patient outcomes. CLINICALTRIAL

Unmanned Aerial Vehicles (UAVs, commonly re-ferred to as drones) are increasingly used in applications such as facade inspection, aerial photography, last mile delivery, and public safety. Such applications demand that the drone operates in close proximity to buildings and people, therefore posing a safety hazard. Drone flight safety currently rests on a static, one-off risk assessment prior to the flight, and safe flight execution with strict compliance to prevailing drone regulations. The static risk assessment can effectively identify risks and mitigating control measures prior to the flight, but needs to be performed regularly to ensure safety. Safe flight execution involves ensuring airspace clearance, correct drone safety settings, piloting the drone with high situational awareness, and frequently, concurrently controlling the drone payload (such as a camera) effectively. Performing the safety risk assessment is a one-off process and does not scale well, while it can be challenging to ensure all aspects of safe drone operation are covered during flight. In order to address these two pain points, we propose sensor-based runtime risk assessment. All relevant raw drone sensor readings, such as inertial measurement unit (IMU) and microelectromechanical (MEMS), are streamed into a rules engine during runtime. These readings are then used to calculate the drone's state over a historical sliding window, which is then used to compute a risk level that is comparable with prevailing drone standards. Our proposal digitises and automates risk assessment during runtime, can be customised to the risk assessment policy in different jurisdictions, has the potential to be inter-operable with different drone models, and is highly scalable. Most importantly, drone flight safety is significantly enhanced as the drone pilot will have the runtime risk level of the drone flight as a reference during flight We illustrate our proposal using actual data obtained from a DJI Matrice 300 RTK drone.

‘What on earth can this possibly mean’? French reentry courts and experts' risk assessment

The permanent monitoring of structural behavior of monuments is a crucial target in the framework of cultural heritage preservation. The paper deals with the monitoring and static risk assessment using the technique of dynamic parameter measurement in a broad Wireless Sensors Network (WSN) system. The historical buildings exposed to the traffic vibration, which can induce stresses inside the material, as well as small seismic actions, are vulnerable. Then a permanent monitoring of the micro-shocks/vibrations is suggested to be applied to the most vulnerable monuments. The use of a WSN requires to provide a robust infrastructure to support the data collecting and decision making before, during, and after a crisis event. When a crisis occurs, through the use of such information collection methods an appropriate plan is dynamically instantiated with the specific details of the crisis.

Personality difficulties provide challenges to offender management, particularly in regard to risk and diagnosis. No provisions exist for identifying personality disturbance in probation services, despite a political emphasis on the need for specialist knowledge of this group in the criminal justice system. This article reports on a pilot project where mental health professionals collaborated with probation services to develop methods of identifying personality disturbance in a London sample. There was a disproportionately large ratio of Black/Black British individuals identified in this study. There was a high prevalence of personality problems in probation caseloads. Different sampling methods identified separate groups of high risk personality disturbed offenders. All individuals identified carried a high risk of re-offending, based on static risk assessments. Emphasis was given to the importance of using developmental variables to help identify adult personality disturbance. The study discovered a potential gap in the method by which data is collected by the national offender management service, omitting lifer custody cases in their assessment of dangerous and severe personality disorder individuals. Ideas for future research were provided.

A multivariate dimension-reduction method for probabilistic power flow calculation