Risk Estimation Method Research Articles

Airspace Safety Assessment and Risk Estimation based on the Fuzzy Petri Net Model with Multi-Risk Factor Analysis

In this paper an airspace safety assessment and risk estimation method based on the fuzzy Petri net (FPN) model is presented by analyzing 44 related multi-risk factors. In the proposed method, two kinds of real air accident data from 2010 until the present are used to statistically deduce the causes of the corresponding air accidents, namely accidents between manned aircraft, as well as the accidents between manned and unmanned aircraft. All of the data needed are drawn from the authoritative aviation safety network (ASN). These 44 related multi-risk factors are derived from four main aspects that include man, machine, medium, and management. From this, a multi-level evaluation index system for airspace safety is built. Furthermore, the determination of the risk index is based on the official reports which could make the proposed method more objective and accurate. Correspondingly, the airspace safety assessment and risk estimation model based on FPN can be generated by defining multi-risk factors and calculating the weight coefficients. To overcome the deficiency of many network nodes, a risk threshold is introduced to simplify the risk estimation model. The Zhuhai terminal area is taken as an example to assess and estimate its safety. The effectiveness and accuracy of the proposed method are also verified. Results indicate that management is a key factor in airspace safety, with a lack of training being a significant cause of a 126.59% rise in air risk.

Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation

AbstractAccurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of Value‐at‐Risk and Conditional Value‐at‐Risk compared to original methods.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

A risk estimation method for depression based on the dysbiosis of intestinal microbiota in Japanese patients.

Early detection of depression is important for preventing depression-related suicides and reducing the risk of recurrence. This study explored the association between depression and intestinal microbiota and developed a depression risk-estimation method based on this. The intestinal microbiota of Japanese patients with depression (33 males and 35 females) and disease-free controls (246 males and 384 females) in their 20's to 60's were compared by sex using 16S rRNA gene amplicon sequencing. A depression-risk estimation method was developed using structural equation modeling. Intestinal bacteria taxa that differed between depression and control groups were identified based on effect size (absolute value greater than 0.2). Neglecta was more abundant, while Coprobacter, Butyricimonas, Clostridium_XlVb, and Romboutsia were less abundant in the male depression group compared to the male control group. In the female depression group, Massilimicrobiota, Merdimonas, and Sellimonas were more abundant, whereas Dorea and Agathobacter were less abundant compared to the female control group. Several of the intestinal bacterial taxa that were less abundant in depression were associated with butyrate or hydrogen production. Using these depression-associated intestinal bacteria as indicators, risk-estimation models using structural equation modeling for depression were developed. In the risk-estimation models for males and females, the areas under the receiver operating characteristic curve were 0.72 and 0.70, respectively, indicating that these models can distinguish between individuals with and without depression. This study provides insights into depression etiology and aids in its early detection and treatment.

Whole value at risk for flood damage estimates through spatial data analysis

Effective disaster risk reduction (DRR) for flooding requires a comprehensive estimate of the whole value at risk (WVAR) to inform appropriate and proportionate mitigation expenditure. Conventional flood risk estimation methods focus on the direct effects of inundation on community value and generally ignore collateral effects on assets and populations outside the flooded area. Consequently, conventional methods tend to underestimate the cost of flooding, leading to an underestimate of the return on DRR investment. Using spatial data analysis in an urban case study for Toronto, Canada, we identify and capture the collateral value at risk (ColVaR) to estimate the WVAR more comprehensively. In our case study, ColVaR (mean estimate) amounts to 70% of direct losses (ColVar = $344 M; direct losses = $475 M CAD), ranging from 20%–150% (ColVar $100–$740 M) when spanning the 90% confidence intervals of our Monte Carlo simulations. Thus, we demonstrate that if the collateral value at risk is ignored, WVAR can be significantly underestimated, potentially leading to reduced disaster risk reduction resource allocations and thereby adding risk exposure for communities. We present an accessible, seven-step process using existing spatial analysis tools and techniques that infrastructure stakeholders and planners can use to estimate ColVaR and better formulate DRR measures for their communities.

Geometric field model of driver’s perceived risk for safe and human-like trajectory planning

Autonomous driving systems must provide safe, predictable, and consistent behaviors across diverse scenarios to enhance user experience. The geometric driver risk field (GDRF) method is proposed for human-like, interpretable, and fast risk estimations. The method models the driver’s subjectively perceived risk with fields formed by sets of geometric shapes. Unlike current safety measures adopted in trajectory planning algorithms, the proposed method aligns with driver risk perception properties. It provides risk estimations based on probable future vehicle states and the risk consequences at different locations. Compared with existing potential field methods for risk estimations, the mathematical form of the method has a high calculation efficiency while keeping the ability to model the details of the risk information. Influences of different obstacle types, multiple regions with different risk levels, and the relative motions between vehicles are modeled in a unified manner. The graph-search and optimization trajectory planning algorithms with a hierarchy framework are also designed to attain desired risk objectives in the planned trajectories. The framework mitigates the possible suboptimality problems in previous hierarchy trajectory planning algorithms. The proposed GDRF method and planning algorithms are examined in driving scenarios with different traffic states and road geometries. Results showed that the proposed risk estimation method outperforms previous risk-oriented potential field methods by more human-like and safer trajectories with evidence from existing studies and safety measures like time headway (THW), time to collision (TTC), and time to lane crossing (TLC). It also provides more consistent behaviors while maintaining a short computation time. The integration of the proposed risk estimation method and trajectory planning algorithms holds great potential for improving the safety and user experience of autonomous driving systems in real-world scenarios.

Determination of priority influencing factors for risk assessment of carcinogenic toxic elements in resuspendable surface dust of urban communities

Urban communities are important places for residents' daily life and activities. This study determined the contamination, eco-health hazards, and influencing factors of certain carcinogenic toxic elements (CTEs) in resuspendable surface dust (RSD) of urban communities in Xi'an. The results indicated that the mean contents of As (12.7 mg kg−1), Cr (132.9 mg kg−1), Ni (33.5 mg kg−1), and Pb (112.6 mg kg−1) in the study areas were higher than their background values. The contamination level and eco-health risk were quantified based on the enrichment factor, Nemerow integrated enrichment factor, modified Nemerow integrated ecological risk index, and health risk estimation method, combined with Monte Carlo simulation. The overall pollution and ecological risk of CTEs in the RSD were moderate and low, respectively. CTEs in RSD have a high cancer risk to all residents (over-standard rate > 80%) and non-cancer risk to children (over-standard rate = 26.9%) in Xi'an. The major factors affecting the environmental risks of CTEs were traffic and residential activities. Vegetation distribution in the city reduced the environmental risks of CTEs in the RSD. Therefore, future management strategies should focus more on protecting children's health, increasing green spaces and public transportation, and maintaining the hygiene of urban communities.

Design of Fire Risk Estimation Method Based on Facility Data for Thermal Power Plants.

In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states-Steady, Transient, and Anomaly-categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities.

Coordinated multi-stage and multi-objective optimization approach for ship collision avoidance decision-making

Appropriate decision-making is essential to prevent ship collisions and further ensure navigational safety at sea. Current research on collision avoidance decision-making (CADM) primarily aims to find limited feasible solutions based on a designated safe distance under the strong assumption that ships take no or little maneuvering actions. To overcome such limitations, a multi-stage and multi-objective CADM optimization approach is proposed to satisfy the requirements of safety and economy, while considering the ship path uncertainties and risk preferences of ship officers. First, a comprehensive collision risk estimation method is formulated by quantifying degree of domain violation and probability of domain violation (PDV) in dynamic and uncertain situations. Then, the CADM for each individual ship is treated as a multi-objective optimization problem, and a three-stage optimization approach outlines the process to find optimal collision avoidance path. Preliminary non-dominated solutions are obtained and filtered to enhance the robustness by an accepted threshold of the PDV. Optimal solutions are selected for ship officers with different risk preferences. Finally, the coordinated collision avoidance path for multi-ship encounter is determined based on the exchange of individual collision avoidance path through several rounds of coordination. The effectiveness of CADM method is validated through extensive case studies. The results show that it can provide feasible solutions for multi-objectives and adaptively determine the most appropriate scheme for ship officers with different risk preferences to make coordinated decisions.

On adaptive confidence sets for the Wasserstein distances

In the density estimation model, we investigate the problem of constructing adaptive honest confidence sets with radius measured in Wasserstein distance $W_p$, $p\geq1$, and for densities with unknown regularity measured on a Besov scale. As sampling domains, we focus on the $d-$dimensional torus $\mathbb{T}^d$, in which case $1\leq p\leq 2$, and $\mathbb{R}^d$, for which $p=1$. We identify necessary and sufficient conditions for the existence of adaptive confidence sets with diameters of the order of the regularity-dependent $W_p$-minimax estimation rate. Interestingly, it appears that the possibility of such adaptation of the diameter depends on the dimension of the underlying space. In low dimensions, $d\leq 4$, adaptation to any regularity is possible. In higher dimensions, adaptation is possible if and only if the underlying regularities belong to some interval of width at least $d/(d-4)$. This contrasts with the usual $L_p-$theory where, independently of the dimension, adaptation requires regularities to lie in a small fixed-width window. For configurations allowing these adaptive sets to exist, we explicitly construct confidence regions via the method of risk estimation, centred at adaptive estimators. Those are the first results in a statistical approach to adaptive uncertainty quantification with Wasserstein distances. Our analysis and methods extend more globally to weak losses such as Sobolev norm distances with negative smoothness indices.

Comprehensive frailty assessment with multidimensional frailty domains as a predictor ofmortality among vascular and cardiac surgicalpatients.

The frailty concept has become a fundamental part of daily clinical practice. In this study our purpose was to create a risk estimation method with a comprehensive aspect of patients' preoperative frailty. In our prospective, observational study, patients were enrolled between September 2014 and August 2017 in the Department of Cardiac Surgery and Department of Vascular Surgery at Semmelweis University, Budapest, Hungary. A comprehensive frailty score was built from four main domains: biological, functional-nutritional, cognitive-psychological and sociological. Each domain contained numerous indicators. In addition, the EUROSCORE for cardiac patients and the Vascular POSSUM for vascular patients were calculated and adjusted for mortality. Data from 228 participants were included for statistical analysis. A total of 161 patients underwent vascular surgery, and 67 underwent cardiac surgery. The preoperatively estimated mortality was not significantly different (median: 2.700, IQR (interquartile range): 2.000-4.900 vs. 3.000, IQR: 1.140-6.000, P = 0.266). The comprehensive frailty index was significantly different (0.400 (0.358-0.467) vs. 0.348 (0.303-0.460), P = 0.001). Indeceased patients had elevated comprehensive frailty index (0.371 (0.316-0.445) vs. 0.423 (0.365-0.500), P < 0.001). In the multivariate Cox model an increased risk for mortality in quartiles 2, 3 and 4 compared with quartile 1 as a reference was found (AHR (95% CI): 1.974 (0.982-3.969), 2.306 (1.155-4.603), and 3.058 (1.556-6.010), respectively). The comprehensive frailty index developed in this study could be an important predictor of long-term mortality after vascular or cardiac surgery. Accurate frailty estimation could make the traditional risk scoring systems more accurate and reliable.

Worst-Case Integrity Risk Sensitivity for RAIM with Constellation Modernization

The integrity improvement of receiver autonomous integrity monitoring (RAIM) can benefit from a combination of constellations. With the rapid development of constellation modernization, integrity parameters, including the probability of satellite fault (Psat) and user range accuracy (URA), have improved. The integrity loss of RAIM needs to be accurately characterized to control the effect of the improved integrity parameters. To reveal the sensitivity of integrity risk with respect to Psat and URA, a conservative integrity risk estimation method is proposed based on the worst-case protection concept. Acceptable Psat and URA were derived by comparing the estimated worst-case integrity risk with the required integrity risk. The simulation results showed that RAIM can meet the integrity risk requirement of LPV-200 when Psat was 10−4 and URA was smaller than 0.88 m.

Cardiovascular disease risk assessment among adults attending HIV Clinic at Korle-bu Teaching Hospital

Background: The risk of developing chronic cardiovascular diseases (CVDs) is a significant public health concern for people living with HIV (PLWH). This recognition has been in place for over a decade. The lack of resources in some settings means that most older PLWH will receive limited care, requiring further research to identify CVD risk and accurate estimation methods. Such research enables the identification of optimal models of care, improving outcomes for this population. Objective: This study aimed to perform a CVD risk assessment (using three different assessment tools) on PLWH attending the HIV clinic at the Korle-bu Teaching Hospital (KBTH). Methods: A hospital-based cross-sectional study involving 311 PLWH was conducted at the HIV Clinic of the KBTH using a questionnaire adopted from the WHO STEP wise approach to chronic disease risk factor surveillance. Blood pressure, anthropometric measurements and fasting blood samples were taken for metabolic/biochemical parameters. A retrospective chart review of clinical folders for HIV and ART-related data was done. To determine the level of risk for cardiovascular disease (CVD), three estimation methods were used: the 10-year Framingham risk score(FRS), the 10-year WHO/International Society of Hypertension (ISH)risk prediction chart, and the 5-year Data Collection on Adverse Effects of Anti-HIV Drugs (D:A:D) cardiovascular risk score. Results: The estimated 10-year moderate to high risk of CVD was 20.6% using the FRS, 13.2% using the WHO/ISH risk score, and 52.4% using the D:A:D score. The majority of study participants were classified as having a low risk of cardiovascular disease (CVD) according to the FRS and WHO/ISH scoring systems. However, the D:A:D cardiovascular scoring system identified that over 50% of the participants were at a moderate to high risk of developing CVD. Conclusion: This study indicates that when using the D:A:D risk assessment system, over 50% of the individuals who participated were found to have moderate-to-high risks of CVD. This underscores the importance of conducting a cardiovascular risk assessment before initiation of antiretroviral therapy as well as regular assessments to promptly identify and manage these risk factors, thereby aiding in preventing the occurrence of cardiovascular events. Additionally, the findings highlight the need for CVD management to be included in the HIV clinic

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA.

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Elective surgery scheduling considering transfer risk in hierarchical diagnosis and treatment system

With the aggravation of the shortage of staffing hospital beds, elective surgeries have to be postponed or even cancelled, which directly affects hospital income and patient health. Therefore, we propose a fuzzy scheduling model based on a patient transfer strategy in the hierarchical diagnosis and treatment system to ensure timely surgery. We propose a risk estimation method based on health failure mode and effect analysis to reduce the transfer risk and represent the surgery duration and the length of stay as fuzzy numbers to deal with the uncertainty. Given that solving the fuzzy model is challenging, we propose an equivalent formulation to transform the fuzzy model into a two-stage mixed integer linear programming (TMIP) model, which can reduce the loss of decision-making information. Finally, the column and constraint generation algorithm is used to solve the TMIP model that is suited for the structure of the main problem and subproblem. The experiment shows that the patient transfer strategy can effectively relieve the hospital bed shortage, and its potential can be further released if the transfer risk can be properly assessed. The proposed method for solving the fuzzy model can reduce the information loss comparing traditional methods.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department.

Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting. Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units. This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses. A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively. This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.

Generalized additive-multiplicative risk estimation model based on fuzzy and interval initial data

Among the mathematical models of risk research, an important place is occupied by additive-multiplicative models of risk estimation. The components of such models are: three-stage hierarchical risk systems (built for a specific applied situation); partial risk estimators (determined by experts for a specific project, product, etc.); indicators of the weight of specific types of partial risks (found on the basis of a survey of experts in a particular application area); algorithms for calculating group risk estimators based on partial risk estimators and general risk estimator based on group risk estimators. As examples, three-stage hierarchical risk systems are considered in the production of a new innovative product and in the implementation of projects for the development of rocket and space technology. An algorithm for an additive-multiplicative model for risk estimation of a general form is proposed. Estimates of partial risks are products of weighting indicators by severity indicators, which corresponds to the well-known method of risk estimation in the form of the product of average damage by the probability of an undesirable event. Group risk estimators are built additively from i partial risk estimators, and the final overall risk estimator is calculated multiplicatively from group risk estimators. In previous works of the author, a special case of an additive-multiplicative risk estimation model was considered, in which, in particular, the components of the model were interpreted in terms of probability theory. It is proposed to carry out estimators of partial risks and weight coefficients on the basis of interval mathematics and fuzzy theory. The rules of arithmetic operations on interval and triangular fuzzy numbers are given. The application of the algorithm of the additive-multiplicative risk estimation model based on triangular fuzzy numbers is demonstrated using the example of risk estimation for the implementation of innovative projects. Within the framework of interval mathematics, risk estimators are considered in the implementation of projects for the development of rocket and space technology. The approach developed in this research article corresponds to the main provisions of the theory of stability of mathematical models of real phenomena and processes and to the results of systemic fuzzy interval mathematics.

Diagnóstico e discussão sobre Planos Municipais de Redução de Riscos no Brasil

Abstract The disasters associated with landslides and floods forced the Brazilian Federal Government to launch, in 2003, a program aimed at encouraging the development of Local Disaster Risk Reduction and Management Plans (PMRRs - Planos Municipais de Redução de Riscos). Given the uniqueness of this action and the variety of institutions accounting for executing these plans, which involve different material and human resources, it is important explaining how PMRRs have been built in order to, likely, improve them. The aim of the current study is to gather information about all 33 PMRRs available on the website of the Ministry of Regional Development by focusing on the following items: composition of the technical team involved in them, popular participation, risk mapping, typologies and costs of proposed structural and non-structural actions. Among the herein reached conclusions, it is worth emphasizing the excessive asymmetry observed in PMRRs’ approach to physical aspects of this issue to the detriment of its social aspects, a fact that affects both the risk estimation method and the proposed mitigating measures.

Learning spectral windowing parameters for regularization using unbiased predictive risk and generalized cross validation techniques for multiple data sets

During the inversion of discrete linear systems noise in data can be amplified and result in meaningless solutions. To combat this effect, regularization is imposed during the inversion. The influence of provided prior information is controlled by non-negative regularization parameter(s). There are a number of methods used to select appropriate regularization parameters. New methods of unbiased risk estimation and generalized cross validation are derived for finding spectral windowing regularization parameters. These estimators are extended for finding the regularization parameters when multiple data sets with common system matrices are available. It is demonstrated that spectral windowing regularization parameters can be learned from these new estimators applied for multiple data and with multiple windows. The results demonstrate that these modified methods, which do not require the use of true data for learning regularization parameters, are effective and efficient, and perform comparably to a learning method based on estimating the parameters using true data. The theoretical developments are validated for the case of two dimensional image deblurring. The results verify that the obtained estimates of spectral windowing regularization parameters can be used effectively on validation data sets that are separate from the training data, and do not require known data.

Lightning risk estimation and preventive control method for power distribution networks referring to the indeterminacy of wind power and photovoltaic

• An estimation model based on the prediction of distributed power is put forward to calculate the operating risk of distribution network in lightning weather. • A two-step preventive protection strategy, namely the power flow transferring based on topological reconstruction, and the coordination of the controllable resource, is put forward. • Simulation shows topological reconstruction and the optimization to the distributed power source can improve the stability of distribution system operation, and cut down the load loss in extreme weather. The power distribution networks are deeply influenced by the lightning weather. Such impact includes not only the load loss caused by the outage of the overhead lines due to direct and/or indirect lightning strikes, but also the output fluctuation of the wind power and photovoltaic (PV) connected to the network. Since lightning storms can be forecasted to some extent, it would be useful to establish a model for lightning risk estimation and preventive control of the distribution network, in order to cut the economic loss in extreme weather. In this paper, the scene analysis method is used to simulate the indeterminacy of the wind and PV output based on the lightning storm pre-warning, and the lightning risk and operating state of the distribution networks can be estimated. Referring to the results, lightning preventive strategy of distribution network can be formulated by network topology reconstruction and resource synergy.