A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

An Analytical Approach to Evaluate the Reliability of Offshore Wind Power Plants Considering Environmental Impact

The accurate quantitative reliability evaluation of off-shore wind power plants (OWPPs) is an important part in planning and helps to obtain economic optimization. However, loop structures in collector systems and large quantities of components with correlated failures caused by shared ambient influences are significant challenges in the reliability evaluation. This paper proposes an ana-lytical approach to evaluate the reliability of OWPPs considering environmental impact on failures and solve the challenges by protection zone models, equivalent power unit models and common cause failure (CCF) analysis. Based on investigation of the characteristics of OWPP and related failures mechanisms, the components are divided into three CCF subsets. With the aid of the protection zone model and equivalent power unit model merged with CCF, the faulty collector system state eval-uation is applied to reduce the computational burden. The case studies present the necessity and improved per-formance of merging CCF analysis into modeling via the comparison with other two simplified methods. A sensi-tivity analysis is also carried out to account for inaccu-racy of failure data. The results show that the assump-tion of independent failures in the conventional method might lead to over-optimistic or over-pessimistic evalua-tion depending on the CCF style

Reliability Evaluation of Common-Cause Failures and Other Interdependencies in Large Reconfigurable Networks

This work covers the impact of Interdependencies and CCFs in large repairable networks with possibility of "re-configuration" after a fault and the consequent disconnection of the faulted equipment. Typical networks with these characteristics are the Utilities, e.g. Power Transmission and Distribution Systems, Telecommunication Systems, Gas and Water Utilities, Wi Fi networks. The main issues of the research are: (a) Identification of the specific interdependencies and CCFs in large repairable networks, and (b)Evaluation of their impact on the reliability parameters (load nodes availability, etc.). The research has identified (1) the system and equipment failure modes that are relevant to interdependencies and CCF, and their subsequent effects, and (2) The hidden interdependencies and CCFs relevant to control, supervision and protection systems, and to the automatic change-over systems, that have no impact in normal operation, but that can cause relevant out-of-service when the above automatic systems are called to operate under and after fault conditions. Additionally methods were introduced to include interdependencies and CCFs in the reliability and availability models. The results of the research include a new generalized approach to model the repairable networks for reliability analysis, including Interdependencies/CCFs as a main contributor. The method covers Generalized models for Nodes, Branches and Load nodes; Interdependencies and CCFs on Networks / Components; System Interdependencies/CCFs; Functional Interdependencies/CCFs; Simultaneous and non-simultaneous Interdependencies/CCFs. As an example detailed Interdependency/CCFs analysis and generalized model of an important network structure (a "RING" with load nodes) has been analyzed in detail

Capacity reduction and Fire Load Factors for LRFD of Steel Members Exposed to Fire

A general reliability-based methodology is proposed for developing capacity reduction and fire load factors for design of steel members exposed to fire. The effect of active fire protection systems (e.g., sprinklers, smoke and heat detectors, fire brigade, etc.) in reducing the probability of occurrence of a severe fire is included. The design parameters that significantly affect the fire design of steel members are chosen as random variables. Raw experimental data published in the literature was analyzed to obtain the statistics of parameters for which no statistical information was available in the literature. Model errors associated with the thermal analysis models are also characterized based on experimental data. It is found that uncertainty associated with the fire design parameters is significantly higher than that of room temperature design parameters. To illustrate the proposed methodology, capacity reduction and fire load factors are developed for simply supported steel beams in U.S. office buildings, and it is shown that for consistent reliability these factors should vary depending on the presence of active fire protection systems in a building

The Impact of Protection System Failures on Power System Reliability Evaluation

The reliability of protection systems has emerged as an important topic because protection failures have critical influence on the reliability of power systems. The goal of this research is to develop novel approaches for modeling and analysis of the impact of protection system failures on power system reliability. It is shown that repairable and non-repairable assumptions make a remarkable difference in reliability modeling. A typical all-digital protection system architecture is modeled and numerically analyzed. If an all-digital protection system is indeed repairable but is modeled in a non-repairable manner for analysis, the calculated values of reliability indices could be grossly pessimistic. The smart grid is emerging with the penetration of information-age technologies and the development of the Special Protection System (SPS) will be greatly influenced. A conceptual all-digital SPS architecture is proposed for the future smart grid. Calculation of important reliability indices by the network reduction method and the Markov modeling method is illustrated in detail. Two different Markov models are proposed for reliability evaluation of the 2-out-of-3 voting gates structure in a generation rejection scheme. If the model with consideration of both detectable and undetectable logic gate failures is used as a benchmark, the simple model which only considers detectable failures will significantly overestimate the reliability of the 2-out-of-3 voting gates structure. The two types of protection failures, undesired-tripping mode and fail-to-operate mode are discussed. A complete Markov model for current-carrying components is established and its simplified form is then derived. The simplified model can appropriately describe the overall reliability situation of individual components under the circumstances of complex interactions between components due to protection failures. New concepts of the self-down state and the induced-down state are introduced and utilized to build up the composite unit model. Finally, a two-layer Markov model for power systems with protection failures is proposed. It can quantify the impact of protection failures on power system reliability. Using the developed methodology, we can see that the assumption of perfectly reliable protection can introduce errors in reliability evaluation of power systems

A Probabilistic Assessment of Failure for Air Force Building Systems

Deteriorating and failing federal facilities represent a cost to leaders and organizations as they attempt to manage and maintain these assets. Currently the Air Force employs the BUILDERTM Sustainment Management System to predict the reliability of building components. At different system levels, however, the probabilities of failure are not predicted. The purpose of this research is to provide probabilistic models which predict the probability of failure at the system level of a building s infrastructure hierarchy. This research investigated the plumbing, HVAC, fire protection, and electrical systems. Probabilistic models were created for these systems by using fault trees with fuzzy logic on the basis of risk by weighting the probabilities of failure by the consequences of failure. This thesis then validated each of the models using real-world Air Force work order data. Through contingency analysis, it was found that the current BUILDERTM condition index model possessed no predictive ability due to the resulting p-value of 1.00; the probabilistic models possessed much more predictive ability with a resulting p-value of 0.12. The probabilistic models are statistically shown to be a significant improvement over the current condition index model; these models lead to improved decision making for infrastructure assets

Multi-Mission Earth Vehicle Subsonic Dynamic Stability Testing and Analyses

Multi-Mission Earth Entry Vehicles (MMEEVs) are blunt-body vehicles designed with the purpose of transporting payloads from outer space to the surface of the Earth. To achieve high-reliability and minimum weight, MMEEVs avoid use of limited-reliability systems, such as parachutes, retro-rockets, and reaction control systems and rely on the natural aerodynamic stability of the vehicle throughout the Entry, Descent, and Landing (EDL) phase of flight. The Multi-Mission Systems Analysis for Planetary Entry (M-SAPE) parametric design tool is used to facilitate the design of MMEEVs for an array of missions and develop and visualize the trade space. Testing in NASA Langley?s Vertical Spin Tunnel (VST) was conducted to significantly improve M-SAPE?s subsonic aerodynamic models. Vehicle size and shape can be driven by entry flight path angle and speed, thermal protection system performance, terminal velocity limitations, payload mass and density, among other design parameters. The objectives of the VST testing were to define usable subsonic center of gravity limits, and aerodynamic parameters for 6-degree-of-freedom (6-DOF) simulations, for a range of MMEEV designs. The range of MMEEVs tested was from 1.8m down to 1.2m diameter. A backshell extender provided the ability to test a design with a much larger payload for the 1.2m MMEEV

Integrated assessment of quality of supply in future electricity networks

Although power system reliability analysis is a mature research area, there is a renewed interest in updating available network models and formulating improved reliability assessment procedures. The main driver of this interest is the current transition to a new flexible and actively controlled power supply system with a high penetration of distributed generation (DG) and energy storage (ES) technologies, wider implementation of demand-side management (DSM) and application of automated control, monitoring, protection and communication infrastructures. One of the aims of this new electricity supply network (’the smart grid’) is an improved reliability and power quality performance, realised through the delivery of an uninterrupted and high-quality supply of electrical energy. However, there is currently no integrated methodology to measure the effects of these changes on the overall system reliability performance. This PhD research aims to update the standard power system simulation engine with improved numerical software models offering new capabilities for the correct assessment of quality of supply in future electricity networks. The standard reliability analysis is extended to integrate some relevant power quality aspects, enabling the classification of short and long supply interruptions by the correct modelling of network protection and reconfiguration schemes. In addition, the work investigates the formulation and analysis of updated reliability indicators for a more accurate validation and benchmarking of both system and end-user performance. A detailed database with typical configurations and parameters of UK/European power systems is established, providing a set of generic models that can correctly represent actual distribution networks supplying a mix of residential, commercial and industrial demand for different load sectors. A general methodology for reducing system complexity by calculating both electrical and reliability equivalent models of LV and MV distribution networks is also presented. These equivalent models, based on the aggregation of individual component models, help to reduce calculation times while preserving the accuracy assessment of network’s reliability performance at bulk supply points. In addition, the aggregated counterparts (same and mixed-type) of different ’smart’ component models (DG, ES and DSM) are also included in the analysis, showing how their co-ordinated implementation and control could improve quality of supply. Conventional reliability assessment procedures are also extended in this thesis to include accurate reliability equivalent models, network contingency statistics, actual load profiles and empirical fault probability distributions, which are employed to assess the frequency and duration of interruptions in the supply system for different scenarios. Both analytical and probabilistic simulation techniques (Monte Carlo method) are developed to include up-to-date security of supply legislation, introducing a new methodology for calculating the standard set of indices reported annually to energy regulators

Data-driven method for enhanced corrosion assessment of reinforced concrete structures

Corrosion is a major problem affecting the durability of reinforced concrete structures. Corrosion related maintenance and repair of reinforced concrete structures cost multibillion USD per annum globally. It is often triggered by the ingression of carbon dioxide and/or chloride into the pores of concrete. Estimation of these corrosion causing factors using the conventional models results in suboptimal assessment since they are incapable of capturing the complex interaction of parameters. Hygrothermal interaction also plays a role in aggravating the corrosion of reinforcement bar and this is usually counteracted by applying surface protection systems. These systems have different degree of protection and they may even cause deterioration to the structure unintentionally. The overall objective of this dissertation is to provide a framework that enhances the assessment reliability of the corrosion controlling factors. The framework is realized through the development of data-driven carbonation depth, chloride profile and hygrothermal performance prediction models. The carbonation depth prediction model integrates neural network, decision tree, boosted and bagged ensemble decision trees. The ensemble tree based chloride profile prediction models evaluate the significance of chloride ingress controlling variables from various perspectives. The hygrothermal interaction prediction models are developed using neural networks to evaluate the status of corrosion and other unexpected deteriorations in surface-treated concrete elements. Long-term data for all models were obtained from three different field experiments. The performance comparison of the developed carbonation depth prediction model with the conventional one confirmed the prediction superiority of the data-driven model. The variable importance measure revealed that plasticizers and air contents are among the top six carbonation governing parameters out of 25. The discovered topmost chloride penetration controlling parameters representing the composition of the concrete are aggregate size distribution, amount and type of plasticizers and supplementary cementitious materials. The performance analysis of the developed hygrothermal model revealed its prediction capability with low error. The integrated exploratory data analysis technique with the hygrothermal model had identified the surfaceprotection systems that are able to protect from corrosion, chemical and frost attacks. All the developed corrosion assessment models are valid, reliable, robust and easily reproducible, which assist to define proactive maintenance plan. In addition, the determined influential parameters could help companies to produce optimized concrete mix that is able to resist carbonation and chloride penetration. Hence, the outcomes of this dissertation enable reduction of lifecycle costs