Wind Hazard Research Articles

Structural response assessment of ultra‐high performance concrete shear walls under cyclic lateral loads

AbstractUltra‐high performance concrete (UHPC) is an emerging class of cementitious materials with the strength and durability characteristics that far exceed those of normal‐strength concrete (NSC). While the use of UHPC in bridge structures has significantly advanced, the same cannot be stated for building structures. Among possible building applications, this study focuses on the use of UHPC in shear walls, where a significant loading demand is anticipated from the lateral loads due to wind and/or earthquake events. Upon validating a set of finite‐element models with the experimental test results, a holistic investigation has been performed in the current study to evaluate a variety of UHPC shear walls. In particular, the main structural response measures, including initial stiffness, force‐displacement relationship, and strength loss under cyclic lateral loads, have been extracted and compared. The study has been then extended to systematically investigate the effects of key design variables, such as wall thickness, aspect ratio, axial force, steel reinforcement, presence of boundary elements, and strength of embedded steel bars. The outcome of this study provides the building sector with firsthand information regarding the possibility of replacing NSC with UHPC in shear walls. This will be an important step forward to improve the safety and performance of building structures, especially where they are exposed to a high risk of wind and/or earthquake hazards.

New Zealand Country Report 2021

This paper summarises the activities that have taken place in wind engineering in New Zealand since 2012 when the previous APEC-WW took place in Hanoi, Vietnam. Since that date there have been considerable activities in wind engineering in New Zealand, ranging from an increase in government funding for wind hazard research, an analysis of historical wind data throughout New Zealand which has been used to update the regional wind speeds and directional multipliers for the 2021 version of the wind loading standard, and the construction of a large boundary layer wind tunnel with a computer-controlled three dimensional traversing rig at the University of Auckland that has allowed extensive wind tunnel investigations to be carried out. In addition, this period has seen a strong link to Chinese universities develop through CSC-funded research students that have come to study in New Zealand. Finally, the very successful 9th Asia-Pacific Conference on Wind Engineering (APEC-WW) was held at the University of Auckland in December 2017.

Modeling and estimation of hurricane wind hazard affecting Mexican coastal regions

In the present study, we develop a stochastic track model that can be used to sample the synthetic tropical cyclone (TC) (i.e., hurricane) tracks affecting Mexico with their genesis from the Atlantic basin and the northeastern Pacific basin. The developed model is validated based on the comparison of the statistics of the main characteristics of the historical and synthetic landfalling TC events. We estimate the TC wind hazard for the coastal regions of Mexico by using the historical or synthetic TC tracks in combination with a gradient wind field based model or a vertically averaged boundary layer slab model. Such a TC wind hazard assessment for Mexico is the first of its kind. The results indicate that the use of a short period of the historical tracks for TC wind hazard is inadequate, and the use of the simple gradient wind field based model with well-calibrated scaling factors is efficient and effective for estimating TC wind hazard. The comparison of the probability distributions for the extreme TC winds obtained using the developed stochastic track model to those based on surface wind observations for a few meteorological stations confirms the adequacy of the model.

Data-driven framework for automated simulation of wind and windborne debris effects for hurricane regional loss estimation

Hurricanes are a major driver of disaster losses in the United States; these losses can be minimized through the development of open-source scientific workflows that conduct site-specific, building-specific and component-level loss assessments across entire regions. While today's open-source workflows provide parcel-level granularity, common use of non-specific building characterizations limits the fidelity of these representations, particularly with respect to site-specific effects of wind and windborne debris (WBD) hazards. In response, this paper presents a new framework that utilizes publicly available data to enable modeling and analysis capabilities necessary to (1) capture site-specific, building-specific exposure to wind and WBD hazards and (2) propagate these demands through the building envelope's load path. The proposed data-driven framework is applied to an office building located in Florida's Bay County, the landfall site of Hurricane Michael. Leveraging a new building data model for regional loss estimation, this case study demonstrates the framework's use of open data to automate population of site models, WBD missile environment, wind pressure distributions, and component capacities to enable fault tree analyses for target and source buildings. The proposed approach can be utilized to identify hazard- and building-specific, component-level damage consistent with actual damage observed in Hurricane Michael.

Wind-induced failure analysis of a transmission tower-line system with long-term measured data and orientation effect

This paper proposes a comprehensive wind-induced performance evaluation framework for transmission tower-line systems (TTLSs) from both structural safety and normal operation dimensions, incorporating the joint effect of wind speed and direction, as well as the line orientation. Based on the field wind data recorded in Weifang, China during 1971–2020, the actual failure probability of a local TTLS is calculated considering the joint probability distribution (JPD) of wind speed and direction, and the optimal layout of the transmission line is provided. The results show that the developed QS copula model can not only account for the continuity and cyclicity of wind direction but also have a fine goodness-of-fit. The wind direction with a relatively lower occurrence probability does not necessarily yield smaller failure probability of the TTLS, thereby the JPD of wind speed and direction should be incorporated to achieve reliable performance assessment. For the failure analysis with respect to structural damage, the most favorable layout of the exemplar TTLS is the orientation of 265°; while the most advantageous layout is the orientation of 38° in terms of flashover risk. The presented framework is conducive to performance assessment and optimization design of TTLSs subjected to wind hazards.

Ensemble of long short-term memory recurrent neural network for semi-active control of tuned liquid wall damper

A critical challenge in structural control is the design of controllers capable of rapid adaptation to uncertainties, including changes in local dynamics and unknown parameters. A solution is the use of data-based controllers, but these typically require pre-training and may be computationally expensive to utilize. In this paper, the authors proposed a fast neuro-controller capable of pure online learning. The controller is built using an ensemble of recurrent neural networks (ERNNs) based on long short-term memory (LSTM) cells running in parallel. A multi-rate sampler is used to sequentially construct the individual RNN input vectors using local measurements at sampling rates pre-determined from the structure’s natural frequencies. Each RNN performs an individual estimation with the associated sampling rate, and these estimations are assembled into an attention and a dense layer to produce an estimated control force. Critical advantages of the proposed architecture are (1) rapid online adaptation and processing; (2) capability to adapt to local dynamics and non-stationarities; and (3) pure online learning architecture minimizing reliance on pre-training. First, the performance of the ERNNs control algorithm is validated on an actively controlled single-degree-of-freedom system, and stability of the controller is demonstrated. Second, the ERNNs is simulated on a full-scale 20-story building equipped with a semi-active tuned liquid wall damper (TLWD) system that consisted of distributed semi-active tuned liquid multiple column dampers. The ERNNs controller effectively improved the passive performance of the control strategy and performed similarly to that of a full state-feedback sliding mode controller under multi-hazard scenario. The evolution of attention weights under both the wind and seismic hazards demonstrated that the importance of each RNN follows the importance of the structural frequency responses, showing that the controller was capable of capturing and adapting fast-changing dynamics.

Lifetime multi-hazard fragility analysis of transmission towers under earthquake and wind considering wind-induced fatigue effect

Wind-induced fatigue damage is a predominant factor eventuating in structural performance degradation of transmission towers exposed to long-term environmental wind. Moreover, transmission towers may suffer from multiple extreme natural hazards (such as earthquake and strong wind) during their service lives. However, these phenomena have not been well accounted for in current design specifications and previous studies for transmission towers. To this end, this paper proposes a comprehensive probabilistic analysis framework for achieving the lifetime multi-hazard fragility evaluation by considering the wind-induced fatigue, which is applied in a 1000 kV steel tubular transmission tower. Firstly, based on the field wind data measured at the meteorological station of Qingdao city, China, from Jan. 1, 1971 to Dec. 31, 2020, a fatigue analysis is performed on an actual transmission tower constructed in this region to compute the annual mean fatigue damage. Secondly, a series of time-dependent uncertain numerical models of the tower are established by integrating the accumulated fatigue damage and material deterioration model, and nonlinear time history analyses are imposed on these models to examine the effect of fatigue damage on lifetime responses under combined earthquake and wind loads. Analogously, the time-dependent probabilistic multi-hazard demand models are derived via the dynamic analyses and statistical regression, and these demand models are employed to further develop the lifetime multi-hazard fragility surfaces of the tower. Finally, a fast lifetime safety assessment method, i.e. the critical safety surface, is proposed to efficiently judge the collapse state of the transmission tower at various wind speeds, PGAs and service times. Numerical results indicate that the structural capacity of the transmission tower will experience significant deterioration due to wind-induced fatigue damage; the multi-hazard induced structural responses and fragilities will considerably increase with the service life although the tower is initially well-designed. The proposed analysis framework will be beneficial for the design and maintenance of transmission towers subjected to both earthquake and wind hazards for designers and decision-makers.

Simulating Wind Disturbances over Rubber Trees with Phenotypic Trait Analysis Using Terrestrial Laser Scanning

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a lifting steel truss carrying a large forced draft fan near a rubber plantation on Hainan Island, and we aligned three selected small rubber trees in a row in front of the fan (with separation distances from the forced draft fan outlet of approximately 1.3, 3.3, and 5.3 m) to explore the susceptibility of rubber trees to the mechanical loading of hurricane-level winds. By adjusting the power of the forced draft fan, four wind speeds were emitted: 0 m/s, 10.5 m/s, 13.5 m/s, and 17.5 m/s. Meanwhile, point clouds of the three rubber trees under different continuous wind speeds were acquired using two terrestrial laser scanners. Computer algorithms were applied to derive the key parameters of the three rubber trees, namely, the zenith and azimuth angles of each leaf, effective leaf area index (LAI), windward area of each tree, volume of the tree canopy, and trunk tilt angle, from these point clouds under all four wind speeds. The results show that by increasing the wind speed from 0 m/s to 17.5 m/s, the leaf zenith angles of the three rubber trees were unimodally distributed with the peak concentrated at 0°, while the leaf azimuth angles were bimodally distributed with the peaks concentrated at 0° and 360°. The effective LAI values of the three trees increased from 2.97, 4.77, and 3.63 (no wind) to 3.84, 5.9, and 4.29 (wind speed of 17.5 m/s), respectively, due to a decrease in the vertical crown projection area caused by the compression of the tree canopy. We also found that the effective LAI, windward area, and canopy volume of the third rubber tree (the tree farthest from the forced draft fan) varied less than those of the other two trees, reflecting the attenuation of the wind speed by the crowns of the two trees closer to the fan. The experimental results also indicate that the joint use of light detection and ranging (LiDAR) data with computer graphics algorithms to analyse the dynamic changes in tree phenotypic characteristics during the passage of a hurricane is promising, enabling the development of a novel strategy for mitigating wind hazards. The proposed method with the designed device capable of producing an adjustable wind speed also has the potential to study the impacts of wind damage under various forest conditions by further modifying the tree spacing and tree species.

Tropical cyclone full track simulation in the western North Pacific based on random forests

The tropical cyclone (TC) full track simulation method pioneered by Vickery has been widely used in the estimation of hurricane/typhoon wind hazards. The linear auto-regression used in the Vickery model may be inadequate to capture the complex patterns of storm tracks in the western North Pacific Ocean, which accommodates almost one-third of the world's annual TCs. In this study, a novel TC simulation method is developed based on the random forests method (RF) in conjunction with cluster analysis of TC tracks. Specifically, TC movement models with probabilistic termination classifiers, and the TC intensity model are newly developed based on the RF technique for typhoon simulation in the western North Pacific. Historical typhoon tracks are classified into four clusters in light of their geographical characteristics for facilitating the development of the TC movement model corresponding to each cluster. Thousand-year typhoon simulations for 47 coastal sites of China are performed by the proposed RF-based full track simulation method, which achieves more accurate results in reproducing historical statistics of key typhoon parameters than those of the original Vickery full track simulation approach.

Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018

Tropical cyclones (TCs) may cause severe impacts on the activities of coastal fishing vessels. Due to the unavailability or unacceptability of detailed Automatic Identification System (AIS) data that are capable of differentiating fishing activity from navigation, as well as the lack of detailed models and observation data of TC winds, few studies have provided quantitative and reliable assessment of the impacts of TCs on fishing activities. In this study, we modeled snapshots for the TC winds of 52 TCs over the Northwest Pacific (NWP) basin from 2013 to 2018, as well as daily fishing hours and daily hours of presence (hereafter “vessel hours”) of fishing vessels. Based on these data, the spatiotemporal pattern of fishing vessel activity over offshore China was first analyzed and mapped. Then, a TC wind hazard index and absolute and relative impact indices were proposed to assess the impact of the 52 TCs on fishing and vessel hours. Their relationship was then fitted with the cumulative distribution function (CDF) of the log-normal distribution. The results show that in the 2013−2018 period, the most active fishing areas were located in the South China Sea. In each instance, an increase was first observed in the initial several years; then a decrease followed in the yearly total fishing hours per vessel in the remaining years. The relative impact index was significantly correlated to the TC wind hazard index proposed in this study. Based on the quantitative relationship between the specified TC hazard index and the impact indices, it is possible to implement a pre-cyclone rapid loss assessment due to TC avoidance in the future.

Risk assessment of rehabilitation strategies for steel lattice telecommunication towers of Greece under extreme wind hazard

The risk and losses associated with the wind-induced failure of existing steel lattice telecommunication towers are assessed for a number of upgrade/replace/redesign schemes. Specifically, a performance-based wind engineering framework is employed for assessing a typical tower topology used by EU telecommunication network operators over four different cases: a conventional design, its corroded version after 60 years, a strengthened version of the corroded tower by applying fibre-reinforced polymer plates, and a redesign with high-strength steel. Multiple potential sites of installation were considered throughout coastal and mainland Greece, comprising two different groups of design wind speed. Mischaracterization of the site-specific wind distribution is by far the most important risk factor, with corrosion coming right behind. Still, selecting a rehabilitation approach does not depend only on site and tower characteristics, but also on the projected direct and indirect losses. By considering service to different populations, even after 60 years of corrosion, the “Do Nothing” approach may still be competitive when serving few residents and for short projected lifetime, while an upgrade is considered optimal for larger towns, or wherever higher revenue is on the line.

Explainable deep learning powered building risk assessment model for proactive hurricane response.

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities' preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings' risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.

Next generation PBWE: Extension of the SAC-FEMA method to high-rise buildings under wind hazards

This paper proposes a method for the Performance-Based Wind Engineering (PBWE) of buildings by extending the well-established SAC-FEMA method originally implemented in seismic engineering for the evaluation of the mean annual frequency (MAF) of pertinent structural Limit States (LSs) in frame buildings. The development of such a method, that we will call the “SAC-FEMA WIND” method, implies the consideration of specific wind engineering peculiarities, like the classification of the across-wind response in two distinct regimes due to buffeting and vortex shedding phenomena, with the consequent adaption of the analysis procedures already defined for earthquakes to these two regimes. The method is then applied for the MAF evaluation of the occupants’ comfort LS for a high-rise steel building: the SAC-FEMA WIND is calibrated by comparing the obtained results with those coming from a Monte Carlo numerical analysis. This allows the definition of appropriate analysis procedures and shows the reliability of the SAC-FEMA WIND in evaluating MAFs. The SAC-FEMA WIND can be viewed as a “next generation” method for PBWE, in the sense that, after years of developments of the PBWE in research, now simplified methods like the SAC-FEMA WIND are needed for the implementation in Standards and in the design practice of explicit probabilistic PBWE approaches.

Machine-learning-based prediction of vortex-induced vibration in long-span bridges using limited information

Long-span bridges are susceptible to wind-induced vibration due to their high flexibility, low-frequency dominance, and light damping capacity. Vortex-induced vibrations (VIVs), which usually occur under in-service conditions, can result in discomfort to users and detrimental effects on the fatigue capacity of structural elements; therefore, accurate VIV assessments are essential in ensuring the vibrational serviceability of bridges. Despite the research efforts of data-driven VIV prediction, the robustness and general applicability of the proposed methods remains challenging, in that each method requires different conditions for the datasets in order to develop machine-learning (ML) models. Furthermore, collecting sufficient VIV datasets (anomaly state) from various operational conditions is impractical, time-consuming, and even impossible in some situations compared with non-VIV datasets (normal state). This imbalance in the dataset could degrade the model performance. To address this issue, this paper focuses on developing a general framework for introducing ML algorithms to predict VIVs with a limited amount of information. To properly replicate the practical cases, two different scenarios are assumed along with the amount of VIV data: (1) no VIV data are available, or (2) only a small number of VIV data can be obtained. A variety of ML-assisted methods are introduced for each scenario to predict VIVs in order to demonstrate the versatility of the proposed framework. The effectiveness and applicability of the proposed framework are demonstrated using actual monitoring data. Different methods are prepared to provide further insight into the ML algorithms used for VIV prediction. The proposed framework in this paper is expected to advance our knowledge and understanding of the application of ML algorithms to bridge systems, which are essential in enhancing resilience against wind hazards.

Wind Gusts Associated with Tornado-Scale Vortices in the Tropical Cyclone Boundary Layer: A Numerical Simulation

It has been demonstrated that the tornado-scale vortex (TSV) is one of the fine-scale structures associated with extreme updrafts in the tropical cyclone boundary layer (TCBL), but the relationship between surface wind gusts and TSVs is still unclear. In this study, the one-second model output simulated in the Weather Research and Forecast (WRF) model with the large eddy simulation (WRF-LES) is used to investigate the relationships between TSVs and surface wind gusts. Results show that surface wind gust factors in the regions where TSVs are prevalent are significantly larger than those in other regions. 88% of the maximum gust factors associated with TSVs (vertical velocity larger than 20 m s−1) are larger than 1.25 (gust factors larger than 1.25 account for only 1% of the 1-min gust factors in the TC inner core), and the mean maximum 1-min gust factor associated with a TSV is larger than 1.3, while the mean 1-min gust factor in the TC inner core is only 1.1. The surface gust factors associated with TSVs in tropical cyclone eyewall can reach about 1.8, which can cause severe surface wind hazards. This study suggests that potential risk will increase in the regions where TSVs are prevalent because of the large wind gusts and gust factors. Finer real-time observations are needed to monitor the evolution of TSVs for improving the operational TC-related surface gust warnings.

A refined model of a typhoon near-surface wind field based on CFD

The simulation of near-surface typhoon wind fields is crucial for high-precision typhoon hazard assessments and thus of great significance for disaster forecasting, loss risk assessment and emergency management. The terrain correction method for simulating regional large-scale wind fields has a single correction method that cannot satisfy the requirements of refined risk assessment. This paper aims to use the advantage with regard to accuracy of the fluid dynamics mechanism model (CFD, computational fluid dynamics) in small-scale wind speed simulations and obtain a terrain correction method suitable for simulating regional large-scale wind fields by extracting the spatial variation of the wind speed over complex terrain. Specifically, typical mountains with different cross-sectional shapes and slopes are used to characterize the undulating terrain, and the CFD model is used to simulate and analyze the wind speed changes on the upwind and leeward slopes, at the mountain top, and in the downwind area under different initial wind speeds. The wind speed at these locations has a good quantitative relationship with the initial wind speed. Combined with the typical building wind load codes in China, the wind speed correction algorithm suitable for large-scale complex terrain is supplemented and improved. This paper presents the simulation results of three typhoons, and taking Typhoon No. 0713 as an example, a near-surface typhoon wind field simulation is performed. Compared with that of other models, the accuracy of the terrain-corrected simulation results by the method provided in this article is increased by 8.8–16.89%. Such CFD-based typhoon disaster near-surface wind fields can more accurately reflect the spatial distribution and intensity of typhoon wind hazards over large-scale complex terrain and can provide technical support for the loss risk prediction and assessment of forest resources, mountain forestry economies, crops and other vulnerable bodies during typhoon disasters.

A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019)

Abstract. Compound hazards refer to two or more different natural hazards occurring over the same time period and spatial area. Compound hazards can operate on different spatial and temporal scales than their component single hazards. This article proposes a definition of compound hazards in space and time, presents a methodology for the spatiotemporal identification of compound hazards (SI–CH), and compiles two compound-hazard-related open-access databases for extreme precipitation and wind in Great Britain over a 40-year period. The SI–CH methodology is applied to hourly precipitation and wind gust values for 1979–2019 from climate reanalysis (ERA5) within a region including Great Britain and the British Channel. Extreme values (above the 99 % quantile) of precipitation and wind gust are clustered with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, creating clusters for precipitation and wind gusts. Compound hazard clusters that correspond to the spatial overlap of single hazard clusters during the aggregated duration of the two hazards are then identified. We compile these clusters into a detailed and comprehensive ERA5 Hazard Clusters Database 1979–2019 (given in the Supplement), which consists of 18 086 precipitation clusters, 6190 wind clusters, and 4555 compound hazard clusters for 1979–2019 in Great Britain. The methodology's ability to identify extreme precipitation and wind events is assessed with a catalogue of 157 significant events (96 extreme precipitation and 61 extreme wind events) in Great Britain over the period 1979–2019 (also given in the Supplement). We find good agreement between the SI–CH outputs and the catalogue with an overall hit rate (ratio between the number of joint events and the total number of events) of 93.7 %. The spatial variation of hazard intensity within wind, precipitation, and compound hazard clusters is then visualised and analysed. The study finds that the SI–CH approach (given as R code in the Supplement) can accurately identify single and compound hazard events and represent spatial and temporal properties of these events. We find that compound wind and precipitation extremes, despite occurring on smaller scales than single extremes, can occur on large scales in Great Britain with a decreasing spatial scale when the combined intensity of the hazards increases.

New York State Hurricane Hazard: History and Future Projections

Abstract This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

Estimation of reduction factors for combined wind and ice loading of power transmission lines using multivariate scenario sampling

In this paper, a multivariate scenario sampling method was used for estimation of reduction factors for loading of structures in power transmission lines in the combined wind and ice load case. The values of wind speed and ice thickness that must be used in combined load cases are obtained through multiplying the reduction factors by the design extreme (reference) values of each variable. The reduction factors were estimated according to the criteria of IEC 60826 standard for the mean recurrence intervals (MRI) of 50, 150, and 500 years. The reduction factors have been established through bivariate (ice and concurrent wind) hazard curves that extracted via two approaches; one based on a statistical analysis of the wind force applied to an ice-covered conductor (force-based approach) and the other based on a joint probability distribution of wind speed and ice thickness (joint probability-based approaches). In order to extract the hazard curves, an annual scenario sampling approach was utilized based on historical data recorded in 12 selected meteorological stations located in the cold regions of western Iran. Due to the lack of historical data, the thickness of ice formed around the conductor of transmission lines has been evaluated via simulations of ice accretion using a numerical model for cylindrical growth of wet snow sleeves. The results obtained for most of the meteorological stations show that the combined wind and ice reduction factors presented in IEC 60826 are conservative. It was also shown that two main reduction factors, (which specify the reduced value of a variable that must be combined with the extreme value of another) can be calculated merely by the force-based approach, and are not sensitive to MRI. In the same way, the MRI effect on hazard curves constructed by joint probability-based approaches is not significant, except for the relatively smaller ice thickness.

Review of failure risk and outage prediction in power system under wind hazards✰

Given that wind hazards pose a considerable damage to power system in various countries, researchers have developed methods for disaster prevention to diminish the vulnerabilities. This paper attempts to review the failure analysis procedure under the impact of wind hazards, considering the following attributes: wind field generation method, wind-induced electrical component failure model in transmission system, and building outage prediction model from data-driven view in distribution system. First, we compare the calculation of circulation and translation wind speed, with geographical, temporal and uncertainty factors of wind speed considered. Then, we introduce the wind-induced component failure models derived by wind load calculation and fragility curves. The application of the series reliability model in multiple cluster failures is briefly described. Finally, we summarize the applications of data analysis in building outage prediction models. The reviewed literature contains the statistical learning methods and machine learning methods. It is concluded that the frameworks for component-level failure model and user-level outage prediction model are well established. However, most of the studies still develop two models, separately. Future researchers may target at the hybrid application of both models to better provide fault analysis, prediction and emergency in power system under wind hazards.