TPWRD2366436.pdf

Abstract -A method is proposed to prioritize the repair or replacement of out-of-service transformers that feed a heavily meshed secondary grid. Priority assigned to restoration of a specific transformer is based on the risk reduction that results from this replacement. Risk is defined as the reduction in the probable number of customers out of service should the transformer return to service. This measure of risk addresses both the possibility of network collapse following feeder failures (occasioned by load induced failure of transformers or feeders) and local customer impact on the secondary network. The prediction of risk makes extensive use of load predictions for feeder sections, network transformers, and secondary mains. A software tool has been developed implementing the equations proposed in this paper. This software gives system planners and operators the ability to quickly and economically select the next transformer to be repaired or replaced. Index Terms-Distribution system contingency analysis, line out distribution factor, network reliability, risk assessment. I. NOMENCLATURE Sum of impacted customers created by having a transformer remaining out of service. Probable number of customers interrupted as a result of transformer overloads. δ NC Number of customers served. NT Numbers of transformers that pick up new additional when a transformer is out of service. NF Numbers of feeders that pick up new load when a transformer is out of service. SM Number of secondary mains that are overloaded as a result of a transformer being out. operates the world's largest underground electric system Emergency Management Systems (EMS) makes use of realtime analysis of data obtained from the network to assist distribution system operators in making better decisions. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRD.2014.2366436, IEEE Transactions on Power Delivery 2 to efficiently identify where equipment overloads may occur because of out-of-service feeders and transformers. Network reliability software is used to predict the vulnerability of networks from cascading failures of primary feeders and widespread damage to the secondary grid. The most important computation tools used by the EMS utilize contingency analysis and security risk analysis algorithms A. Related Work The Line Outage Distribution Factor (LODF) method is used primarily in power systems to approximate the change in the flow on one line caused by the outage of another line ( Network transformers are monitored in real-time using the Remote Monitoring System (RMS) that employs Power Line Carrier (PLC) technology to collect the following real-time data: status of the transformers and associated network protectors, the load they carry, voltages and temperatures, a sample is shown in During a heat-wave, when temperature is up and equipment loading goes up, it is desirable that all transformers be in service to prevent overloads and maximize voltage quality. The current ad-hoc method employed for transformer restoration can be improved and streamlined to optimize cost and reduce dependence on the legacy resource intensive manual approach. The approach also requires running numerous power-flow studies to help identify heavily loaded areas. Forecasted increase in loading and temperature, moves network transformer ranking up or down in areas where demand changes significantly. Although contingency analysis tools and real-time system conditions have provided ample data on transformer loading, no rigorous tool is available as yet to prioritize the return of transformers to service. B. Contributions of the Paper The contributions of this paper are: (1) propose a rigorous and robust algorithm to rank the replacement of network transformers; and (2) validate and use the proposed algorithm to rank 300-600 network transformers replacement on a large meshed distribution network. The developed computer program runs an iterative process of power flow and network reliability evaluation, replacing one out of service network transformer at-a-time, which computes loading and reliability indices, then computes the load contribution while having individual transformers out of service. This computed load contributions is normalized by the number of customers per network to prioritize the return to service of network transformers. The algorithms analyze and quantify the contribution to risk of each out-of-service network transformer and rank the benefit that will ensue should a transformer be restored. III. FORMULATION OF RISK INDEX USING THREE TYPES OF DISTRIBUTION SYSTEM EQUIPMENT The proposed approach to prioritizing the restoration of outof-service transformers relies on experience, data and tools This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRD.2014.2366436, IEEE Transactions on Power Delivery 3 developed in recent years to predict loads in networks, and network reliability performance. The contribution to risk associated with an out-of-service transformer is defined in terms of the reduction in the anticipated number of network customers who will lose service in a specified time period (e.g., the duration of a heat wave) should the transformer not be returned to service, all other out-ofservice transformers remaining out of service. Risk, of course, can also be expressed in terms of a contribution to the likelihood customers would be exposed to low voltage; voltage reduction is a measure that is used to lower the risk of collapse on a highly stressed network. Risk might also be expressed in terms of the financial risk to customers and the utility that results from the loss of power or voltage reduction. An attractive feature of the proposed measures of risk is that it can be applied system-wide rather than be limited to providing guidance for transformer restoration within a single network. By examining all out of service transformers, the transformer that contributes most to risk is identified and its return to service is given priority. This process is then repeated to prioritize the subsequent return to service of other transformers. Both planning and emergency response functions within the utility benefit from this approach. Prioritizing the restoration of out-of-service transformers helps to prevent operational problems during the high-load summer months. This is particularly important to prevent failures caused by overloading during system peak-time when resources are in higher demand. This method avoids the costly and ineffective dispatch of field crews to return low priority transformers to service. The risk index proposal allows resources to be reallocated to where they are needed the most. A. Goal To identify the out-of-service transformers that contribute most to the risk of customer impact resulting from load shift on transformers, feeder sections, secondary mains, and other out-of-service transformers and feeders. B. Operations Risk can be accessed in response to the evolving network status in response to multiple feeder and transformer feeder during a heat wave. The restoration of a transformer to service results in lower loads on other transformers and therefore decreases the likelihood of their failure. The reduced risk a transformer out of service and the benefits obtained by returning it into service are characterized by the following equation: The risk index itself is expressed as a sum of the likely number of customer impact that will result from the failure, out of service, of overloaded transformers, feeders, and secondary mains as: where: δ 1 is the probable number of customers interrupted as a result of transformer overloads, δ 2 is the probable number of customers interrupted as a result of primary feeder overloads, and δ 3 is the probable number of customers interrupted as a result of secondary mains overloads. A factor α measures the relative load of every equipment and is computed as: The number of customers impacted as a result of other transformers that have increase loads is computed as: is the probability of transformer failure given its relative load j . is the conditional probability of network collapse after the failure of transformer j and the feeder that serves it. The probable number of customers interrupted as a result of other feeders picking up the load for a given set of transformers and feeders that are out of service is: NF is the number of feeders that are overload is the probability of a load-induced failure of feeders This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. is the probability of feeder k failing given its load is the conditional probability of network collapse after the failure of feeder k . Function is a monotonically increasing function of feeder load developed from the analysis of historical feeder failure data as a function of load, ambient temperature, and feeder composition. Finally, the predicted number of customer impact that will result from overloaded secondary mains is calculated as follows: The equation of is determined from overloaded secondary mains that are then assumed to be burned out. It is assumed that the unrelieved overload of secondary mains will result in their failure, thereby affect customers. After their failure, loads are dropped, and this is then converted to number of customers that are dropped as a result of the burned out secondary mains. C. Planning The application of the approach described above in planning the non-emergency restoration of out-of-service transformers can be made by either evaluating the risk associated with a number of diverse heat wave scenarios or by simulating the reliability performance of the network and secondary grid and then ascertaining the contribution to customer impact made by each out-of-service transformer. D. Network Reliability functions and Conditional Probabilities When the software developed is executed, variables and are calculated in real-time, using current network condition with possible primary feeders out, using the network reliability simulation module. The Network Reliability Evaluator (NRE) is an operating tool that predicts the likelihood of cascading network failures and possible network collapse in a heat wave, given existing feeder contingencies, predicted network loads, and ambient temperatures. In this tool a what-if scenario computes the conditional probability of cascading failures should another feeder fail. For feeder k, the network collapse after its failure is . Similarly, for transformer j fed by feeder k , the conditional probability of network collapse after the failure of the transformer j is . E. Implementation Once the reductions in risk have been calculated for the restoration of out-of-service transformers, one at a time, transformers are assigned a rank. The control center can use the resulting list to restore transformers in order of priority. If there are nine transformers on the banks-off list, all are taken out of the network model and power flow is run. All overloads (transformer, primary feeder sections, and secondary mains) are identified. With one of the banks replaced and all other eight banks out. This process is repeated for each outof-service bank and results in a total of ten power flow simulations. IV. FAILURE RATES OF EQUIPMENT The failure rates used for feeder sections, joints, transformers, and other equipment are those used in network reliability models. They reflect temperature, load, the age and type of equipment presence of multiple feeders within a manhole, and other factors found to impact reliability. As illustrated in V. IMPACT OF TEMPERATURE AND LOAD A. Temperature Impact on Transformer Reliability Transformer ratings reflect a number of factors: tank design, mass of oil and metal, design criteria such as top-oil and hot-spot temperature, vault conditions, daily load factors, and operating ambient temperature. The maximum allowable load on a transformer is defined as the maximum peak load that can be safely applied to a given transformer such that neither the calculated hot spot nor the top oil temperatures exceed their respective maximum allowable temperatures limits. In the IEEE algorithm This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRD.2014.2366436, IEEE Transactions on Power Delivery 5 program, thereby giving each transformer on the network a calculated rating based on its individual characteristics. Should transformers be allowed to operate at loads in excess of this rating, their failure rates will increased rapidly; see Fig. 5. As the percent loading goes up on the transformer, its failure rate goes up. This was computed using historical data of loading and temperatures collected from transformers over the past 10 years. B. Temperature and Load Impact on Primary and Secondary Cables The continued exposure of secondary cable to loads in excess of their normal ratings will result in cable failure. A secondary network served by network transformers is depicted in The effect of temperature on primary underground cable has been well characterized. As the loading shift to other cable sections on the secondary grid, the temperatures in the ducts also rise. Joints and cable sections of specific types and age exhibit widely different failure rates; see VI. RISK INDEX ALGORITHM The customers' impact calculation is performed as shown in The program is also used under real-time system conditions. System known conditions with all banks-off (transformers that are out of service), feeder(s) out, and open-mains (secondary cables that are burned out) data are input into the program. Operations use the program to make last minute decisions on system hardening before the next-day heat wave. The following operations are performed: 1) Power Flow: compute α of feeder, transformers, and secondary mains before and after the restoration of an out-of-service transformert 2) Identify failure rates of individual components. 3) Network Reliability: compute probability of contingencies needed for individual transformers and feeder calculation ( . where: α Equipment loading divided by equipment rating Conditional probability of equipment failure This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. VII. NUMERICAL EXAMPLE A numerical example is presented in this section to illustrate the process of using the proposed risk index equations. All calculations are performed for a network with 16 primary network feeders and 347 network transformers serving 38,275 customers. Network performance during a heat wave is considered with two primary feeders, 15 transformers, and 121 secondary mains out of service. A transformer that is taken out of service poses risk to other transformers by having them pick-up its load and therefore become overloaded. As a result of the overload, the secondary mains and primary feeders in the vicinity of the overloaded transformer become overloaded in turn. These changes in feeder load also result in an increased probability of failure. 0885-8977 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRD.2014.2366436, IEEE Transactions on Power Delivery 7 These effects are described in (3) with an example given in Finally, secondary network mains are also impacted by having a transformer taken out of service and therefore, secondary mains sections overload contributes to the risk index. If there are 35 secondary mains that get overloaded, we assume that they get burned out and therefore remove all 35 from the model and then identify the total load that gets drop. The reduction in risk resulting from the restoration of a single transformer to service is calculated for each out-of-service transformer. Table VI presents the results from ranking one distribution network with five transformers out of service. There are five network transformers that are out of service in the example. The wide variation in risk reduction makes prioritization easy in this case. If one would replace one transformer on this network, it should be transformer VS7769. A second transformer to be replaced is V508. The two transformers that have zero impact will be considered the next day when the software analyzes the new system conditions. The network condition changes daily as a result of work being done, failure of equipment, or newly added customers. It was determined that 44% of network transformers that were out of service have no system impact on network loading under contingency N-2. These transformers will be reconsidered on a daily bases when the software is run. With a change in customer demand and new customers added to the neighborhood, these transformers may deem needed again; else they can become candidates for relocation. VIII. CONCLUSIONS A method to prioritize the repair or replacement of network transformers has been proposed. The method has been implemented in software that is used to rank all existing networks on a large distribution system with 300-600 transformers out of service at a given time. Because of the large number of transformers to be replaced, manual efforts needed to run numerous power flow studies and visual identification of maps have proven to be costly and error prone. The method proposed in this paper intends to target spending and concurrently maximize system reliability. Transformers with high impact on the networks will have high prioritization for replacement and are replaced immediately. Transformers with no impact at all will be left unchanged for the next year and then be considered again (because of load growth in the area). The method developed in this paper is of general applicability. It has been used for the prioritization of transformer replacement. However, it can also be applied for the prioritization of secondary mains replacement. ACKNOWLEDGMENT The authors would like to thank the following people for their time and support: David Allen, Peter Van Olinda, and Mohamed Kamaludeen. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication

This is a preprint of an article accepted for publication in the Journal of the American Society for Information Science & Technology, Special Bioinformatics Issue, Fall 2003

With the rapid expansion of scientific research, the ability to effectively find or integrate new domain knowledge in the sciences is proving increasingly difficult. While development of information systems to promote scientific discovery is being explored on a number of fronts, much of this work is based on traditional search and retrieval approaches and the bibliographic citation format remains unchanged. In the Telemakus system, aggregated citation information and content-based research findings are displayed in a conceptual schema. Dynamic mapping provides graphical displays of research interrelationships from documents across a domain. We describe a working implementation of a system designed to enhance the knowledge discovery process through graphical presentation and interaction tools to mine and map author-reported research findings and their associated methods

Initial studies on proton computed tomography using a silicon strip detector telescope”, Nucl Instr Meth A 514

Abstract We report initial results of a feasibility study of Proton Computed Tomography (PCT) and Proton Transmission Radiography (PTR) for applications in proton therapy treatment planning and patient positioning. The aim of the study is to explore experimentally if PCT, which is based on the measurement of the specific energy loss of protons traversing tissues of different density, may be preferred to X-ray Computed Tomography (CT) and X-ray radiography, which are presently used for radiation treatment planning and patient positioning in proton treatment centers. We present first data from proton transmission studies through a hollow aluminum cylinder taken with a telescope of silicon detectors with very high spatial and good energy resolution. In addition, we report the results of GEANT4 simulations of proton transport through the same object, which show good agreement with experimental results and explain the observed features of the proton transmission image