Effect of variations in load profile on power transformer cyclic ratings

Cyclic rating and demand side management (DSM) schemes are employed by electrical supply authorities to manage peak demand as an alternative to costly network upgrades. This project investigated the currently unknown impact of DSM altered load profiles on the cyclic ratings of power transformers. In response to electricity price increases, supply authorities are employing cyclic rating and DSM techniques to defer capital expenditure. A cyclic rating exploits transformer thermal inertia to permit loading above nameplate for parts of a 24 hour cycle, compensated for by loading below nameplate at other times such that insulation thermal limits are not exceeded. DSM aims to flatten load profiles commonly by transferring load from peak to off-peak times. Both techniques ensure that existing transformers can supply load peaks. Cyclic ratings achieve this by boosting capacity above demand while DSM reduces demand below capacity. In this way, the two techniques permit deferral of network upgrades. Since DSM alters the load profiles on which cyclic ratings are based, a relationship must exist between the two. The plant rating engineer must understand all such dependencies, hence the need for this project. Initially, thermal transformer models proposed by various researchers and AS 60076.7 for the purpose of predicting transformer oil and paper temperatures were studied. The AS 60076.7 models and one selected from the literature were then implemented in Matlab and compared to assess their suitability for use in the project and by plant rating engineers. The selected thermal model then became the basis of a cyclic rating calculator employed to automate the computation of cyclic ratings based on the AS 60076.7 specification. DSM techniques were researched to reveal the load shifting version as the method favoured by supply authorities and a simulator was constructed in Matlab to modify load profiles accordingly. 12 diverse Ergon Energy transformers were then selected along with a set of DSM modified load profiles as inputs to the cyclic rating calculator which computed 600 cyclic ratings for analysis. The final phase of the project then involved analysis of the ratings to determine and quantify the effect of DSM caused load profile changes on cyclic ratings. It was determined that the cyclic ratings of power transformers are negatively affected by load shifting DSM. That is, attening of load profiles causes reduction in cyclic ratings. The amount by which cyclic ratings change given a change in load profile varies according to several factors including: location, size and cooling mode of the transformer. Regression models for simple but approximate prediction of cyclic rating changes were developed. Generalised expressions for predicting the change in cyclic rating with change in peak load or load factor are: ΔCRFd = 0:45 x ΔPLd ΔCRFd = 0:37 x ΔLFi where: ΔCRFd is the percentage decrease in CRF; ΔPLd is the percentage decrease in peak load; & ΔLFi is the percentage increase in load factor. In addition to achievement of the main project objective - determination of the impact on cyclic ratings of load profile changes - a range of other outcomes from the project are useful. These include: a thorough investigation of the techniques and theory involved in transformer thermal modelling, insulation ageing, cyclic rating calculation and DSM; a program which automatically calculates cyclic ratings for power transformers; and a load shifting DSM simulation program, useful for generating altered load profiles for use with the cyclic rating calculator. This collection of knowledge and programs will be of particular use to Ergon Energy, the project sponsor. The impact of load shifting DSM on cyclic ratings, as identified in this dissertation, has the potential to negatively influence factors such as: peak capacity; transformer lifespan and maintenance; planning and budgeting for network augmentation; reduction of capital expenditure; and the valuation of DSM programs. The results and outcomes of this project have the potential to assist plant rating engineers in their understanding and application of cyclic ratings in the context of changing load profiles such that they may anticipate and therefore prevent many of the negative side-effects identified

Applying an abstract data structure description approach to parallelizing scientific pointer programs

Even though impressive progress has been made in the area of parallelizing scientific programs with arrays, the application of similar techniques to programs with pointer data structures has remained difficult. Unlike arrays which have a small number of well-defined properties that can be utilized by a parallelizing compiler, pointer data structures are used to implement a wide variety of structures that exhibit a much more diverse set of properties. The complexity and diversity of such properties means that, in general, scientific programs with pointer data structures cannot be effectively analyzed by an optimizing and parallelizing compiler.In order to provide a system in which the compiler can fully utilize the properties of different types of pointer data structures, we have developed a mechanism for the Abstract Description of Data Structures (ADDS). With our approach, the programmer can explicitly describe important properties such as dimensionality of the pointer data structure, independence of dimensions, and direction of traversal. These abstract descriptions of pointer data structures are then used by the compiler to guide analysis, optimization, and parallelization.In this paper we summarize the ADDS approach through the use of numerous examples of data structures used in scientific computations, we illustrate how such declarations are natural and non-tedious to specify, and we show how the ADDS declarations can be used to improve compile-time analysis. In order to demonstrate the viability of our approach, we show how such techniques can be used to parallelize an important class of scientific codes which naturally use recursive pointer data structures. In particular, we use our approach to develop the parallelization of an N-body simulation that is based on a relatively complicated pointer data structure, and we report the speedup results for a Sequent multiprocessor

Execution replay and debugging

As most parallel and distributed programs are internally non-deterministic -- consecutive runs with the same input might result in a different program flow -- vanilla cyclic debugging techniques as such are useless. In order to use cyclic debugging tools, we need a tool that records information about an execution so that it can be replayed for debugging. Because recording information interferes with the execution, we must limit the amount of information and keep the processing of the information fast. This paper contains a survey of existing execution replay techniques and tools.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AADebug 2000), August 2000, Munich. cs.SE/001003

The application of generalized, cyclic, and modified numerical integration algorithms to problems of satellite orbit computation

Generalized, cyclic, and modified multistep numerical integration methods are developed and evaluated for application to problems of satellite orbit computation. Generalized methods are compared with the presently utilized Cowell methods; new cyclic methods are developed for special second-order differential equations; and several modified methods are developed and applied to orbit computation problems. Special computer programs were written to generate coefficients for these methods, and subroutines were written which allow use of these methods with NASA's GEOSTAR computer program

A Simple and Scalable Static Analysis for Bound Analysis and Amortized Complexity Analysis

We present the first scalable bound analysis that achieves amortized complexity analysis. In contrast to earlier work, our bound analysis is not based on general purpose reasoners such as abstract interpreters, software model checkers or computer algebra tools. Rather, we derive bounds directly from abstract program models, which we obtain from programs by comparatively simple invariant generation and symbolic execution techniques. As a result, we obtain an analysis that is more predictable and more scalable than earlier approaches. Our experiments demonstrate that our analysis is fast and at the same time able to compute bounds for challenging loops in a large real-world benchmark. Technically, our approach is based on lossy vector addition systems (VASS). Our bound analysis first computes a lexicographic ranking function that proves the termination of a VASS, and then derives a bound from this ranking function. Our methodology achieves amortized analysis based on a new insight how lexicographic ranking functions can be used for bound analysis

BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction

A novel discrete mathematical approach is proposed as an additional tool for molecular systematics which does not require prior statistical assumptions concerning the evolutionary process. The method is based on algorithms generating mathematical representations directly from DNA/RNA or protein sequences, followed by the output of numerical (scalar or vector) and visual characteristics (graphs). The binary encoded sequence information is transformed into a compact analytical form, called the Iterative Canonical Form (or ICF) of Boolean functions, which can then be used as a generalized molecular descriptor. The method provides raw vector data for calculating different distance matrices, which in turn can be analyzed by neighbor-joining or UPGMA to derive a phylogenetic tree, or by principal coordinates analysis to get an ordination scattergram. The new method and the associated software for inferring phylogenetic trees are called the Boolean analysis or BOOL-AN

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods