Failure Probability Of Lines Research Articles

MCS-ML based line vulnerability for infrastructural resilience assessment with multi-wind speed cyclonic zones

The resilience of critical infrastructure is paramount for power systems prone to cyclonic activities characterized by varying wind speeds. This study conducts a thorough examination of the impact of cyclone lasting for a week on transmission lines for each passing day. Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) is applied to extract hourly wind speed data for the cyclone. Sequential Monte Carlo Simulation (SMCS) is used to model the day-wise hourly transmission line fragility and determination of line vulnerabilities across high, medium, and low wind speed zones. Fragility curves are developed to depict the correlation between wind speeds and line failure probabilities in each zone. The line outage scenarios are generated based on the critical wind speeds and collapse wind speeds for each zone. Line Vulnerability Index (LVI) is proposed to determine the vulnerability and the resilience status of the transmission lines. Machine Learning (ML) based models, FFNN (Feed Forward Neural Network) and RFC (Random Forest Classifier), are proposed for assessment of infrastructure resilience by vulnerability ranking of the lines. Hourly wind speed and hourly line failure probability are the inputs to train the ML models and, LVI and ranking are predicted for the IEEE 30-bus system.

Renewable Energy Real-Time Carrying Capacity Assessment Method and Response Strategy under Typhoon Weather

Currently, the integration of distributed power supply into the power grid is steadily increasing. The grid’s carrying capacity serves as a crucial metric for evaluating the grid’s resilience following the widespread integration of distributed power supply. During typhoon conditions, if the power grid experiences line breakage and load loss faults, the grid’s framework is altered, rendering the conventional carrying capacity assessment method obsolete. This study introduces a method for assessing the risk of line carrying capacity and an index for line overload probability under typhoon conditions, integrating line and transformer capacity constraints to evaluate the grid’s carrying capacity risk. The probability of line failure is modeled during typhoon events, and a modified IEEE39 node example is employed to simulate a high-penetration grid in a typhoon scenario. Addressing the issue of inadequate intraday dispatch capability under insufficient carrying capacity, we propose a multi-timescale dispatch method and derive the optimal grid dispatch strategy using the viscous bacteria algorithm. The efficacy of the multi-timescale dispatch strategy in addressing the grid’s carrying capacity risk is validated through simulation, while the economic cost of mitigating the grid’s carrying capacity risk and the line overload probability is assessed across varying parameter values.

Optimal decision‐making method of transmission lines' maintenance sequence under extreme ice disasters

AbstractProlonged exposure of power transmission lines to extreme ice disasters in the atmosphere disrupts transmission. First, this study establishes a comprehensive failure probability model for the impact of extreme ice disasters on the transmission lines to better understand and improve the transmission lines' ability to withstand such disasters. It predicts the line failure probability based on the initial design data of the lines. Second, the system's weak points are identified, and the fault scenario set is established using the Monte Carlo state sampling method. Next, the system's state is calculated using the resilience assessment index and the DC optimal load reduction model. Finally, an optimal decision‐making method for the transmission line maintenance sequence is proposed from the post‐disaster maintenance scheduling perspective. Considering the travel time and maintenance effect, this method can effectively restore the system state in response to extreme ice disasters. The IEEE 30‐bus power system is taken as an example of simulation verification. The results show that this method can effectively complete the system load state restoration and improve the transmission system's resilience. It also has certain practicality and good practical application values.

Proactive Security-Constrained Unit Commitment Against Typhoon Disasters: An Approximate Dynamic Programming Approach

Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This paper proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process (MDP), and a piecewise linear function (PLF) based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.

Deep Learning Based Hurricane Resilient Coplanning of Transmission Lines, Battery Energy Storages, and Wind Farms

In this article, a multistage model for expansion coplanning of transmission lines, battery energy storages, and wind farms (WFs) is presented considering resilience against extreme weather events. In addition to high-voltage alternating current lines, multiterminal voltage source converter based high-voltage direct current lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed scenarios are generated using Monte Carlo simulation. The fragility curve concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable portfolio standard policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a chronological time-period clustering algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on bidirectional long short-term memory networks is presented to forecast the yearly peak loads. The mixed-integer linear programming formulation of the proposed model is solved using a Benders decomposition algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.

Distributionally Robust Microgrid Formation Approach for Service Restoration Under Random Contingency

When a major outage occurs in a distribution system due to extreme events, service restoration (SR) strategies pick up critical loads after isolating the faults that have occurred. However, in extreme conditions, traditional SR strategies could pose potential security risks to restored services due to subsequent contingencies in succeeding events. To address this challenge, we propose a microgrid-based SR methodology that aims to enhance the preparedness of microgrids during unfolding extreme events. The proposed SR strategy comprises microgrid formation (MF) and sequential service restoration (SSR) steps. The MF makes the benefit of topology switching, generator allocation, and load demand response. Additionally, the uncertainty of line failure probability is considered, and a distributionally robust optimization model is proposed to maximize the expected load restoration with regard to the worst-case distribution of contingencies. Then, the SSR is formulated as a mixed-integer linear program model to yield proper load switching sequences and generation of power sources for sequentially restoring the outage system. The proposed SR measure enhances the system resilience by the proactive formation of microgrids, reducing the impact of cascading phenomenon when lines with high failure probability are tripped. The effectiveness of the proposed method is validated by numerical simulations.

Optimal Restoration Strategy Based on Resilience Improvement for Power Transmission Systems Under Extreme Weather Events

In the past few years, extreme weather events have threatened operations of power systems. The improvement of resilience for power system becomes crucial. This paper proposes an optimal restoration strategy, considering the resilience index of power transmission systems in restoration processes. Firstly, a typhoon is taken as an example to establish an extreme weather event impact model to obtain the failure probability of lines. Then Monte Carlo method is used to generate fault sets. For each restoration time point, the optimal restoration model is used to acquire the maximum recovery loads. The resilience index of system can be obtained by the resilience assessment methodology during restoration. Finally, the proposed strategy is compared with distance-based strategy in IEEE 6-bus system, which demonstrates the superiority of the proposed strategy.

Quantification and visualisation of extreme wind effects on transmission network outage probability and wind generation output

An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at-risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather-related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.

Estimating BOP Kill-Line Failure Probability Combining High level and Experts Opinion Informations

Most deepwater oil prospection activities are carried out by dynamic positioning rigs. Due to the risks of such activities, these rigs are more often subject to emergency disconnections and, therefore, the blowout preventer (BOP), which is a safety equipment, must have high reliability level. Hence, BOP is used to ensure the safety of well drilling process and become one of the most important safety device available, and its reliability becomes even more critical, specifically its kill and choke line, which are especially important for well control. Considering the non-homogeneity of failure data available from BOP system, the main issue for reliability estimates is based on Bayesian inference. Due to the above-mentioned limitation, the present paper will present a simplified application considering a Bayesian fault tree approach for a typical BOP kill line failure probability. The results showed some advantages of considering such methodological approach that make possible to combine different information sources for reliability figures.

Determination of the power transmission line ageing failure probability due to the impact of forest fire

With increased occurrences of a forest fire, the short-term reliability of power transmission lines needs to be re-evaluated considering the impact of forest fire on the ageing of transmission lines. How to include the impacts from a forest fire on line ageing in a comprehensive way becomes an important issue for the study of line failure probability. In this study, a method is proposed to evaluate the impact of forest fire on line ageing degree based on dynamic heat balance equation and Weibull distribution. The method will help maintenance staffs to assess the damage level of transmission lines. The method is tested by a contrast experiment and a case study with real meteorological data. Simulation results show that forest fire can greatly threaten the reliability of power transmission lines, while during this process flame characteristics, wind and load ratio could be the major factors that affect the line reliability. With the results obtained, this study also provides the system operator with valuable instructions on how to reduce the damages caused by fire to transmission lines.

Probability Analysis Method of Power System Cascading Failures Caused by Extreme Hot Weather

In recent years, natural disasters happened frequently, which caused more and more failures of power system. To study the transmission lines failure under extreme hot weather, firstly the line's temperature changing with time is forecast based on the heat balance equation and the line's failure probability is calculated according to the historical statistics of failure rate. Then a test system to simulate the power grid under extreme hot weather is built and the curve of line's failure probability changing with time is obtained. The results provide a reference to power sector to guarantee the security and stability of the power grid.

Stochastic Optimal Dispatch of Power System under Extreme Weather Disaster

Several large scale failures of power system took place due to extreme weather disaster recent years, which aroused the consideration of power network security operation. Considering that the line failure events caused by natural disaster presented random characteristic, using Poisson distribution theory to depict the probability of line failure, a stochastic power system optimal dispatch model based on chance constraints theory was also proposed. We adopted the Differential Evolution algorithm to calculate the total loss based on Monte-Carlo simulation. The results of IEEE 9-bus case study imply that the dispatch model will give full consideration of weather effects, and provide a more reasonable dispatch plan for power system disaster prevention and reduction.

Capacity-use and reliability based transmission embedded cost allocation with temporal considerations

Transmission services have to be provided as a separate item in a de-regulated or vertically restructured electricity supply industry. Though the primary function of a transmission line is to transmit power from source point(s) to loading point(s), major lines also play an important role in security. Sometimes a particular transaction cannot take place if a particular line is unavailable and, moreover, a specific line may be more important for the security of a particular transaction than it is for others. Clearly reliability is a relevant consideration in equitable cost allocation. A method for transmission line embedded cost allocation among transmission transactions accounting for both line capacity-use and reliability benefit is presented. Line capacity-use is determined by the amount of power transmitted, while the reliability benefit is calculated as the increment of the total probability of system failure, with the line out of service, compared to when the line is in service. Capacity-use is determined using a full AC power flow and hence the effects of reactive power can also be investigated. The cost allocation to each transaction according to the capacity-use and reliability benefit patterns throughout an accounting period during which system loading and line failure probability are varying are studied.