Outage Model Research Articles

Proactive Recovery of Electric Power Assets for Resiliency Enhancement

This paper presents a significant change in current electric power grid response and recovery schemes by developing a framework for proactive recovery of electric power assets with the primary objective of resiliency enhancement. Within the proposed framework, which can potentially present the next generation decision-making tool for proactive recovery, several coordinated models will be developed including: 1) the outage models to indicate the impact of hurricanes on power system components; 2) a stochastic pre-hurricane crew mobilization model for managing resources before the event; and 3) a deterministic post-hurricane recovery model for managing resources after the event. Proposed models will be extended to ensure applicability to a variety of electric power grids with different technologies and regulatory issues. The theoretical and practical implications of the developed models will push the research frontier of proactive response and recovery schemes in electric power grids, while its flexibility will support application to a variety of infrastructures, in response to a wide range of extreme weather events and natural disasters.

Wind Turbine Failure Rate Calculation Method Considering Multi-Factor Influences

In the traditional outage model, the failure rate of the wind turbine is often given as a long-term statistics value on average, that is only one value for a wind turbine, this calculation method is not reflect the real reliability level of wind turbines. According to historical failure data, the factors that affect the wind turbine outage are divided into several categories. Based on Markov Process, the calculation methods for wind turbine failure rate under single factor and multi-factor are given respectively. On this basis, the calculation methods for wind turbine failure rate under different levels of single factor and multi-factor are given respectively based on statistical analysis method. Because different factors are taken into account in the failure rate calculation, the failure rate will change according to the actual factors and then it could reflect the real risk level of power systems when uses this failure rate. Simulation calculation is carried out on a real wind farm; the results show the effectiveness of this proposed method.

Reliability Evaluation Technique for Electrical Distribution Networks Considering Planned Outages

The reliability evaluation of the electrical distribution networks (EDN) requires sufficient consideration of the effects of planned outages. The planned outages of the EDN can be divided, by outage models and their effects on the reliability into two major categories: by equipment and by feeder. After studying the characteristics of different categories of planned outages, this paper expands the classification of load points by outage time from 4 types to 7 types and defines corresponding reliability parameters for the different types. By using the section algorithm, this paper proposes a reliability evaluation technique of EDN considering equipment random failures and two categories of planned outages. The proposed technique has been applied to the RBTS-BUS6 test system and some practical EDNs in China. The study results demonstrate that the proposed technique is of higher practical value and can be used for evaluating the reliability performance of EDN more efficiently considering the planned outages.

Evaluation of Operational Reliability of a Microgrid Using a Short-Term Outage Model

Island-capable microgrids can potentially improve customer reliability, but protection-related issues can adversely affect this reliability benefit. At the same time, average annualized indices often do not effectively convey a complete picture of load-point reliabilities in microgrids integrated with multiple weather-dependent microsources. The primary goal of this work is to investigate the impacts of protection system and operating conditions on the reliability indices of a microgrid. The proposed method utilizes a short-term outage model that quantifies the relationship between state variables and the outage rate of component. A hybrid approach is proposed, which combines scenario selection and enumerative analysis. Simulations on a test system illustrate the dependence of operational reliability on factors such as local meteorological conditions, correlation between variable generation and loads, as well as the effects of protection system operations. Finally, some recommendations on the parameter settings of protection system are provided to enhance the system reliability.

Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Methodology for Evaluation of Risk of Subsynchronous Resonance in Meshed Compensated Networks

This paper proposes a methodology for evaluation of risk of subsynchronous resonance (SSR) in meshed compensated AC networks. The developed methodology takes into account the severity of SSR problem in different network configurations caused by fixed series compensation and probabilistic behavior of the power systems due to random outages of the lines. SSR risk evaluation considers severity of SSR problem, probabilities of different network configurations for different contingencies and different operating conditions of turbine generator. Probability of each contingency is determined through line outage model while the probabilities of different operating conditions are calculated from multilevel load duration curve. Using developed risk matrix, it is demonstrated that the degree of risk of SSR that the generators in the network are exposed to can be evaluated for different network configurations, different contingencies and different line compensation levels.

Power transmission risk assessment considering component condition

This paper proposes a new method for power transmission risk assessment considering historical failure statistics of transmission systems and operation failure risks of system components. Component failure risks are integrated into the new method based on operational condition assessment of components using the support vector data description (SVDD) approach. The traditional outage probability model of transmission lines has been modified to build a new framework for power transmission system risk assessment. The proposed SVDD approach can provide a suitable mechanism to map component assessment grades to failure risks based on probabilistic behaviors of power system failures. Under the new method, both up-to-date component failure risks and traditional system risk indices can be processed with the proposed outage model. As a result, component failure probabilities are not only related to historical statistic data but also operational data of components, and derived risk indices can reflect current operational conditions of components. In simulation studies, the SVDD approach is employed to evaluate component conditions and link such conditions to failure rates using up-to-date component operational data, including both on-line and off-line data of components. The IEEE 24-bus RTS-1979 system is used to demonstrate that component operational conditions can greatly affect the overall transmission system failure risks.

Incorporating Hurricane Forecast Uncertainty into a Decision-Support Application for Power Outage Modeling

A variety of decision-support systems, such as those employed by energy and utility companies, use the National Hurricane Center (NHC) forecasts of track and intensity to inform operational decision making as a hurricane approaches. Track and intensity forecast errors, especially just prior to landfall, can substantially impact the accuracy of these decision-support systems. This study quantifies how forecast errors can influence the results of a power outage model, highlighting the importance of considering uncertainty when using hurricane forecasts in decision-support applications. An ensemble of 1,000 forecast realizations is generated using the Monte Carlo wind speed probability model for Hurricanes Dennis, Ivan, and Katrina. The power outage model was run for each forecast realization to predict the spatial distribution of power outages. Based on observed power outage data from a Gulf Coast utility company, the authors found that in all three cases the ensemble average was a better predictor of power...

Analysis of the Effect of Environmental Factors on Outage Rates of Overhead Distribution Feeders: A Case Study of Zaria Distribution Network

The paper is aimed at analysing the environmental factors on the outage rates of the overhead distribution feeders. This was facilitated using daily outage data on the six 33kV feeders of the Zaria distribution system collected from the Zaria Transmission substation of the Power Holding Company of Nigeria (PHCN) for twelve months. Outage models were developed from where power losses due to the feeder outages were obtained. Frequent and delayed forced outages occur during the rainy season, the peak being in August and during harmattan, especially in October. There were relatively high outage rates during the hot season, the maximum occurring in May. Feeders 2, 3 and 6 were most frequently on forced outages. The causes of the high outage rates are attributable to damages done to the overhead lines due to heavy winds, thunder strikes and other disturbances associated with rain. Other reasons include animal-caused outages, increase in the simultaneous use of weather sensitive devices, trees and vehicular accidents. As a result, energy loss was found to be 2947.62GWh, which translates to high revenue loss if the entire nation is considered. The forced outage rates are mapped to external environmental factors while the scheduled outages are due to the intrinsic factors. The paper proposes recommendations for minimizing the outage rates and hence improving the performance of the feeders.

Grid Component Outage Probability Model Considering Weather and Aging Factors

Component outage model plays an important role in risk assessment of the power grid safety. In this paper, three aspects resulting in failures were taken into consideration, which are: repairable failure, aging factor, and the weather dependent factor. Repairable failure model can be obtained based on statistical date in the past, while aging failure model were simulated according to normal distributions. Moreover, the weather dependent characteristics of both models were combined, and ultimately, it was possible to derive the components outage probability model. Besides, the feasibility of this model was proved by an actual line in the IEEE-RTS96.

Analysis of a Transformer Time-Varying Outage Model for Operational Risk Assessment

Firstly, the operating mechanism in developing process of transformer internal latent fault and external accessory fault is analyzed. Then, dissolved gas analysis (DGA) outcome and average degree of polymerization (DP) are used as an indicator to classify transformers operating condition, the multi-state Markov process model based transformers operating condition is proposed to estimate the internal failure rate. According to the method of weather condition classification, the external accessory failure rate model is proposed by considering the influence of weather change. A time-varying transformer outage model for operational risk assessment is proposed by using the multi-state Markov process to estimate the time-varying failure probability. Finally, a numerical is presented.

Probabilistic analysis of system outage in distributed antenna systems with composite channels

Distributed antenna systems (DAS) play a key role in future wireless communications. Despite the increasing previous research work, the channel models of DAS lack generality. This paper presents a holistic and generic channel model, which incorporates path loss, lognormal shadowing, and generalized-gamma fast fading into consideration. This overarching composite channel model encompasses well-known composite channel models as special cases. A probabilistic system outage model is attained by selective transmission scheme in terms of the proposed channel model and the distribution of mobile stations in the cell. Experimental results substantiate that the theoretical model provides sufficient precision for evaluating the outage performance of DAS.

A Weather Dependent Failure Rate Model for Overhead Lines

In power system planning and operation, accurate assessment of overhead lines outage rate is essential for making timely decisions. The overhead lines are often influenced by many random events, such as accidents and weather conditions, resulting in power outages. Especially, the reliability of the overhead lines is subjected to weather conditions to a great extent[9]. At present, research about weather conditions dependent reliability is abundant, but many of them are based on synthetical weather, little of them base on specific weather conditions such as typhoon and thunder .This paper proposed outage model of overhead lines dependent upon typhoon an thunder. In power system planning and operation, accurate assessment of failure rate is essential for making informed decisions, The outage model proposed in this paper has significant meaning for the research of risk assessment of power systems.

Aircraft Rerouting due to Abrupt Facility Outages

Abrupt airport outages resulting from terrorism, natural disasters, and incidents can cause reroutes and fuel-critical situations for flights. Although aircraft carry reserve fuel for holding and diversions, poor situational awareness and incomplete information about conditions may cause disordered reroutes. In this paper a deterministic routing model for airport outages that assigns flights an arrival time at a diversion airport is developed. The model includes a parameter for decision time, allowing one to investigate the value of faster response with advanced technologies. The model minimizes flight time after the disaster to ensure that flights avoid fuel emergencies. Model formulations include an unconstrained allocation of flights to diversion airports and a constrained allocation of flights according to routing rules; this formulation is suboptimal but provides a powerful tool because of its potential ease of implementation. The models are used to determine how coordinated traffic flow management actions could have diverted flights and reduced fuel-critical situations immediately following the destructive 2011 Tωhoku earthquake. It is found that under reasonable assumptions about diversion airports and capacities, all flights can be accommodated without reaching a fuel-critical state for a decision occurring 20 min after the disaster. A longer diversion decision-making process results in some flights reaching emergency conditions, requiring the use of other, less preferred diversion airports and expedited handling procedures. The more constrained model requires faster response for full accommodation of flights.

On the Earth-Space Site Diversity Modeling: A Novel Physical-Mathematical Outage Prediction Model

A novel model based on the bivariate inverse Gaussian (IG) distribution is presented for the prediction of the outage performance of a dual site diversity Earth-space system. In the present paper, the exceeded rain attenuation induced on each Earth-space path is assumed that follows the IG distribution. The joint rain attenuation exceedance probability is calculated using the long-term statistical parameters of rain attenuation on each individual slant path and adopting a spatial correlation coefficient of rain attenuation variables as a function of the separation distance of the two Earth-stations. The Earth-Space path Database of Study Group 3 (DBSG3) of International Telecommunication Union (ITU) is employed in order to compare the performance of the new method with the ITU-R P.618-10 outage model. The numerical results from the comparison tests are very encouraging.

A renewal-process-based component outage model considering the effects of aging and maintenance

The development of accurate component outage models for reliability assessment in power systems is a fundamental problem. Traditional outage models with constant failure rate cannot reflect the impact of changes in operating conditions and repairs after a failure. In this paper, a staircase function is used to approximate the aging failure rate curve, and a renewal-process-based model is introduced to calculate time-varying failure probabilities. The proposed time-varying outage model is able to reflect the effects of component aging and repair activities on the failure rate. Application to an actual transformer shows that given the aging failure rate curve, the new model can evaluate the life probability distribution and steady-state availability of a component as accurate as the simulation method. Compared to the traditional constant-rate model, the proposed model is more accurate and practical.

A Model for Assessing the Power Variation of a Wind Farm Considering the Outages of Wind Turbines

Most of the published models cannot properly incorporate the outages of wind turbines in estimating the stochastic variation of the wind power output. In reality, wind turbines are exposed to an open and uncontrolled environment, sometimes harsh, to harvest energy from wind. The environmental condition of wind turbines is thus worse than that of the conventional generators. This results in a relatively higher outage probability of the wind turbines, which significantly affects the power output from a wind farm. This paper proposes a model for including the outage probability of wind turbines in simulating the power output of wind farms. The contribution of this paper is in introducing a model to represent the relationship between the outage probabilities of wind turbines and wind speed and then integrating this model with a frequency-domain wind power output model. A numerical simulation approach based on the Monte Carlo method is then proposed to simulate the wind power variation including the outage probability of wind turbines. A validation study based on the field-measured data from a real wind farm shows that the simulated wind power variation matches well with the actual field measurements when the probability-based outage model is included in the calculations.

Evidence-based approach to power transmission risk assessment with component failure risk analysis

This study presents a new framework for power transmission risk assessment, which considers not only historical failure statistics of transmission systems but also operation failure risks of system components. The component failure risks are integrated into the new framework based on operation condition assessment of components using the Evidential Reasoning (ER) theory. In this study, the traditional outage probability model of transmission line has been modified to build a new framework for power transmission system risk assessment. The proposed ER-based approach can provide a suitable mechanism to map component assessment grades to failure risks based on the probabilistic behaviours of power system failures. Under the new framework, both up-to-date component failure risks and traditional system risk indices can be processed with the proposed outage model. As a result, failure probabilities are related not only to historical statistic data but also to operation data of components and derived risk indices can reflect current operation conditions of components. In simulation studies, the ER approach is used to evaluate component conditions and link such conditions to failure rates using up-to-date component operation data, including both online and offline data of components. The IEEE 24-bus RTS-1979 system is used to demonstrate that component operation conditions greatly affect the overall transmission system failure risk.

A time-varying transformer outage model for on-line operational risk assessment

The failure probabilities of system components may vary with changes in the operating conditions. Performing a probabilistic risk assessment in real-time is challenging, since component failure probabilities are difficult to predict. Accordingly, this paper introduces a delayed semi-Markov process that incorporates real-time data from advanced sensors, as a means of efficiently calculating time-varying or condition-based failure probabilities. To demonstrate the feasibility of the procedure, a time-varying transformer outage model with numerical examples is presented. In the proposed technique, an analytic random model is developed to accommodate the impact of real-time dissolved gas analysis data, as well as other conditions pertaining to the failure probabilities of system components.

Importance of soil and elevation characteristics for modeling hurricane-induced power outages

Hurricanes can severely damage the electric power system, and therefore, predicting the potential impact of an approaching hurricane is of importance for facilitating planning and storm-response activities. A data mining approach, classification and regression trees (CART), was employed to evaluate whether the inclusion of soil and topographic variables improved the predictive accuracy of the power outage models. A total of 37 soil variables and 20 topographic variables were evaluated in addition to hurricane, power system, and environmental variables. Hurricane variables, specifically the maximum wind gust and duration of strong winds, were the most important variables for predicting power outages in all models. Although the inclusion of soil and topographic variables did not significantly improve the overall accuracy of outage predictions, soil type and soil texture are useful predictors of hurricane-related power outages. Both of these variables provide information about the soil stability which, in turn, influences the likelihood of poles remaining upright and trees being uprooted. CART was also used to evaluate whether environmental variables can be used instead of power system variables. Our results demonstrated that certain land cover variables (e.g., LC21, LC22, and LC23) are reasonable proxies for the power system and can be used in a CART model, with only a minor decrease in predictive accuracy, when detailed information about the power system is not available. Therefore, CART-based power outage models can be developed in regions where detailed information on the power system is not available.