Reliable Test Cases Research Articles

Demand bidding vs. demand response for industrial electrical loads

We investigate a novel optimal demand bidding model for industrial loads based on an extended optimal power flow problem for the day-ahead market. The objective function accounts for generation costs and revenue resulting from the sale of products made by the bidder using electricity. As a bidding entity, the industrial load participates in electricity price formation, and is described using a model whose parameters can be determined mostly from public information. Constraints specific to industrial loads, such as satisfying product demand, are also included. The model is validated using simulations of a modified IEEE 24-bus reliability test case with a chlor-alkali plant as the load entity. We find that the proposed participation model, which is simple enough to include in grid dispatch software, performs well relative to other demand-side management strategies such as price-based demand response and cooperative scheduling of industrial load by the grid operator.

Investigation on the Application of Computer System Reliability and Data Security in GPN Networks

With the progress of science and the development of human society, the demand for complex software and hardware systems has sharply increased. Its development speed far exceeds the ability to design, implement, test, and maintain software and hardware. This article mainly studied the computer system reliability and data security of GPN networks. The automatic testing module of the program called the program to be tested into the simulator, started these test cases, and automatically simulated the situations in the test cases for testing. The test results were automatically saved to the reliability test case result document, and the database was encrypted using a GPN network. In password processing, a random key was generated and encrypted with a public key associated with a specific user identification code. Finally, the key was attached to the specified database. During the testing process, the average clock period of multiple experiments was used. The CPU (Central Processing Unit) time estimated the number of clock cycles required by the processor to execute tasks without considering factors such as pipeline. After introducing the GPN network in this article, the system throughput was 175 mpbs. Therefore, GPN networks had a certain positive impact on the reliability of computer systems and data security.

Merchant and regulated storage investment in energy and reserve markets: A Stackelberg game

AbstractWith large‐scale integration of renewable generation, energy storage is expected to play an important role in providing flexibility to energy systems. In this paper, the authors construct a trilevel Stackelberg game model to study the co‐investment of merchant and regulated storage in energy and reserve markets. The upper‐level problem is a profit‐maximizing storage investment problem with a desired rate‐of‐return solved by a merchant investor. In the middle‐level problem, the system operator (SO) makes regulated storage investment decisions to minimize system cost. In the lower‐level problem, the SO clears energy and reserve markets. The proposed model captures interactions of regulated and merchant storage investment. Also, it clarifies how different ownership structures of storage influence merchant storage profitability and system cost structures in different capital cost of storage investment and wind penetration level scenarios. The numerical results conducted on a 6‐bus illustrative example and the IEEE 24‐bus Reliability test case validate the proposed model. The results show that both regulated and merchant storage can increase social welfare, and social welfare remains almost the same under different ownership structures of storage.

Dynamic Reserve and Transmission Capacity Allocation in Wind-Dominated Power Systems

The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility of flexible generating units. To this end, we propose a novel reserve procurement approach, formulated as a two-stage stochastic bilevel model, in which the upper level identifies a number of contiguous reserve zones using dynamic grid partitioning and sets zonal requirements based on the total expected operating costs. Using two standard IEEE reliability test cases, we show how the efficient partitioning of reserve zones can reduce expected system cost and promote the integration of stochastic renewables.

Setting Reserve Requirements to Approximate the Efficiency of the Stochastic Dispatch

This paper deals with the problem of clearing sequential electricity markets under uncertainty. We consider the European approach, where reserves are traded separately from energy to meet exogenous reserve requirements. Recently, proposed stochastic dispatch models that co-optimize these services provide the most efficient solution in terms of expected operating costs by computing reserve needs endogenously. However, these models are incompatible with existing market designs. This paper proposes a new method to compute reserve requirements that bring the outcome of sequential markets closer to the stochastic energy and reserves co-optimization in terms of cost efficiency. Our method is based on a stochastic bilevel program that implicitly improves the inter-temporal coordination of energy and reserve markets, but remains compatible with the European market design. We use two standard IEEE reliability test cases to illustrate the benefit of intelligently setting operating reserves in single and multiple reserve control zones.

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

In software testing, testing of all program statements is a very crucial issue as it consumes a lot of time, effort and cost. The time, effort and cost can be reduced by using an efficient technique to reduce the test case and a good optimization algorithm to generate efficient, reliable and unique test cases. In this paper, the concept of dominance tree is used which covers all edges/statement by using minimum test case. Nature inspired algorithm - PSO (Particle Swarm Optimization) by applying different inertia weights is used to generate unique, reliable and efficient test cases to cover the leaf nodes of dominance tree. Inertia weights like fixed inertia weight (FIW), global-local best (GLbestIW), Time-Dependent weight (TDW), and proposed GLbestRandIW weights are used with PSO to investigate the effect of inertia weights on the execution of PSO with respect to number of generation required, percentage coverage , total test cases generated to test the software under consideration.

Towards the Validation of a CFD Solver for Non-ideal Compressible Flows

Mini ORC power systems with the capability to deliver 3-50 kWe are receiving increased recognition for applications such as heat recovery from automotive engines, or distributed power generation from geothermal reservoirs and solar irradiation. Efficient and reliable expanders are the enabling components of such power systems, and all the related developments are currently at the research stage [1]. In the open literature experimental gas dynamic data is limited concerning the fluids and the flow conditions of interest for ORC expanders [2]. Therefore, CFD tools used for the fluid dynamic design of these components cannot be validated against reliable test cases. To bridge this gap, new experimental facilities are currently being built, such as the ORCHID setup [3]. The availability of proper experimental datasets is not, however, the sole requirement for validating a CFD code. Another precondition, equivalently important, is to define an appropriate validation methodology. This paper introduces the first steps towards the validation of a CFD solver for non-ideal compressible flows. Notably, a numerical procedure based on uncertainty quantification analysis has been conceived to assess the accuracy of the thermophysical sub-model of the code, i.e. the equation of state (EoS). Due to the lack of suitable experimental data, a synthetic dataset is generated and used to investigate the validity of the procedure. The associated validation exercise confirms the applicability of the proposed procedure, but also points out that the adopted validation metrics should be complemented with additional statistical indicators.

Research on building software usage model based on UML model

Software usage model can be used as a guide when generate reliability test cases and it helps to evaluate the reliability of the software. Recent researches of deriving Markov Chain usage model from UML Model are mostly based on the UML sequence diagram and UML component diagram. But there are some problems with the existing methods: for some large software systems, there are many kinds of users who use the software in different ways, while a general usage model is not targeted and not suitable for all users. For this reason,this paper puts forward a new method to construct user-based software usage model. The method constructs usage model according to the UML activity diagram and UML use-case diagram, the model constructed by this method is user-based: the model describes, for every kind of users, the specific interacting process between the user and the system. What’s more, the model can be used to guide test cases generation.

Test Case Reduction Using Data Mining Technique

Software testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors' goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain selection techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. The state of practice among test case selection heuristics is cyclomatic complexity and code coverage. The authors used clustering algorithm which is a data mining approach to reduce the number of test cases. Their approach was able to obtain 93 unique effective test cases out a total of 504.

Uncertainty quantification for functional dependent random variables

This paper proposes a new methodology to model uncertainties associated with functional random variables. This methodology allows to deal simultaneously with several dependent functional variables and to address the specific case where these variables are linked to a vectorial variable, called covariate. In this case, the proposed uncertainty modelling methodology has two objectives: to retain both the most important features of the functional variables and their features which are the most correlated to the covariate. This methodology is composed of two steps. First, the functional variables are decomposed on a functional basis. To deal simultaneously with several dependent functional variables, a Simultaneous Partial Least Squares algorithm is proposed to estimate this basis. Second, the joint probability density function of the coefficients selected in the decomposition is modelled by a Gaussian mixture model. A new sparse method based on a Lasso penalization algorithm is proposed to estimate the Gaussian mixture model parameters and reduce their number. Several criteria are introduced to assess the methodology performance: its ability to approximate the functional variables probability distribution, their dependence structure and their features which explain the covariate. Finally, the whole methodology is applied on a simulated example and on a nuclear reliability test case.

Natural Computing for Automatic Test Data Generation Approach Using Spanning Tree Concepts

The weight is assigned to all edges of graph according to a uniform policy and focus is on the leaves nodes of the Spanning Tree of Control Flow Graph of Program. If all leaves nodes of Spanning tree are covered by the test cases, then number of test cases required or testing the program can be reduced to great extent. Further the spanning tree is based on the concept of maximum spanning tree of CFG which helps in finding the critical paths of CFG. The chances of finding errors in critical are more as compare to the normal path. Covering of all the leaves nodes of Spanning Tree give guarantee for the coverage of Critical path also. Further the paper also shows that number of test cases required in case of Spanning tree is less as compare to the Dominance tree concept. For the generation of fast, unique and reliable test cases a optimization technique which is based on natural computing concept is used which is called as on Swine Influenza Models Based Optimization (SIMBO). The relation between nodes has been used as fitness function in this paper. Finally, the results in the paper show the effectiveness of SIMBO techniques as compare to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Composition dependence of the band offsets in wurtzite nitride-based heterojunctions

We theoretically calculate the composition dependence of the valence- and conduction- band discontinuities at the interfaces between selected III-nitride ternary materials with wurtzite structure, e.g. AlxGa1−xN/AlxGa1−xN, InxGa1−xN/InxGa1−xN and InxA1−xN/InxA1−xN. Calculations are performed using a theoretical model, initially proposed by Chuang et al. 1997 [1]. Depending on a particular set of input parameters, simulation results show that band offsets change more or less with strain. The valence band offsets, together with the resulting conduction band offsets, indicate that a type-I, type-II band line-up forms at InxGa1−xN/GaN, AlxGa1−xN/GaN heterojunctions with varying In, Al contents respectively. Also, based upon the same model, we propose a type I Indium-dependent band alignment in InxAl1−xN/AlN interfaces. The failure of the transitivity rule, which is often used to determine the band offsets in heterojunctions, was demonstrated and its cause was explained. The obtained results are well compared with experiment and theory in various reliable test cases and therefore provide a basis for optimization and design of novel interface structures.

A Novel Method for Allocating Software Test Cases

Abstract Operational Profile is found to be an important technique for critical fault removal .Software Operational Profile (SOP) plays a vital role in reliability estimation, quality valuation, performance analysis and test case allocation of software. However, some problems are detected by software experts with the development of SOP as data resources are very limited and large effort is required to convert gathered data into point estimate. Also uncertainty is involved with SOP as it often changes during operational phase. Software operational profile plays a crucial role in improving the software quality. Therefore, in this paper, software test case allocation using fuzzy logic based on operational profile based is proposed and it takes usage data in terms of linguistic variable. The model is validated with the existing literature. Validation result is satisfactory.

Determining the Minimal Software Reliability Test Effort by Stratified Sampling

Software reliability testing provided the necessary guarantee for improving software reliability level and estimation. The traditional method for generating software reliability test cases could be seen as a simple random sampling, which was randomly choosen according to the operational profile defined by Musa. The stratified sampling was well known as a complex but more accurate sampling approach which could make the sampling results more accurate and less variance by dividing the population into several subsets and randomly sampling from each subset respectively. First, this paper introduced the traditional approach to determine the number of test cases in the discrete software reliability demonstration testing. Second, the background of the stratified sampling and necessary condition for the minimal test effort based on the stratified sampling were put forward. Third, the new method’s principle and details were analyzed and a sample was given to show the method’s effectiveness.Finally, the future work was discussed. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3041

Minimum Test Case for Discrete-Type Software Reliability Testing by Stratified Sampling

The traditional approach for generating software reliability test cases according to operational profile (OP) can be regarded as a simple random sampling. The stratified sampling is a complex but more accurate sampling approach which can make the sampling results more accurate and less variance by dividing the population into several subsets and randomly sampling from each subset respectively. Firstly, this paper introduced the traditional method which can be used to determine the number of test cases in the discrete-type software reliability demonstration testing. Secondly, the necessary condition for the minimum test case based on the stratified sampling was studied. Then a new approach was proposed and its principle was analyzed for determining the minimum test case of the discrete-type software by using the stratified sampling. Finally, the future work was discussed.

Density functional theory study of the vertical ionization energies of the valence and core electrons of cyclopentadiene, pyrrole, furan, and thiophene

The procedure abbreviated as ΔPBE0(SAOP)/et-pVQZ, based on density functional theory, was developed recently for the calculation of vertical ionization energies of the valence electrons of organic and other small molecules and succeeded in giving results with an average absolute deviation of 0.21 eV from experiment for a collection of 115 reliable test cases of nonperhalo molecules. The objective of this work is to add a number of test cases to the benchmark database. We chose the set of molecules cyclo-C4H4X, with X = CH2, NH, O, and S, previously studied by many workers both experimentally and theoretically. The results show that the ΔPBE0(SAOP)/et-pVQZ procedure is not only as good as ab initio methods such as SAC-CI, OVGF, and ADC(3) in performance, but also handles inner valence ionized cations more efficiently. Although the core-electron binding energies of the titled molecules have not been as well investigated theoretically, we apply the methods we developed in recent years to calculate the binding energies of C1s, N1s, O1s, S1s, and S2p, which compare well with available experimental data.

Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques

The main goal of this paper is to document a comparative study of different computational-fluid-dynamics-based optimization techniques applied to the solution of a three-dimensional wing drag minimization problem. To achieve this objective, three optimization tools were used: SYN107 (Intelligent Aerodynamics International), MDOPT (The Boeing Company), and OPTIMAS (Israel Aerospace Industries). The first tool employs gradient-based search techniques using the continuous adjoint equation, the second one is a response-surface method, and the last one uses a floating-point genetic algorithm as its search engine. As the starting geometry, the public domain DPW-W1 wing (a test case for the Third AIAA Drag Prediction Workshop) was used. The comparisons included herein are provided in three stages: cross analysis of the initial geometry by the computational fluid dynamics tools employed in the optimizations, optimization of the initial geometry to minimum drag, and cross analysis of optimal shapes achieved by the optimization tools using all computational fluid dynamics tools employed. The cross analysis also includes results from an independent computational fluid dynamics method that was not used in any of the optimization efforts. These results help quantify the level of variation that is inherent in, and can be expected from, application of the current state-of-the-art aerodynamic optimization methods. The present work may be regarded as a move toward the construction of reliable test cases for an aerodynamic shape optimization problem. Another goal of this collaborative investigation is to collect lessons learned from this pilot project to help develop a model for an aerodynamic optimization workshop.

Experimental Study of 3D Unsteady Flow Around Oscillating Blade With Part-Span Separation

This paper documents an investigation conducted on the aerodynamic response of a turbine blade oscillating in a three-dimensional bending mode under massive tip separation. Flow separation near the tip of the blade was realized by use of a step placed upstream of the blade’s leading edge. Extensive blade surface steady and unsteady pressure measurements were obtained from a test facility with clearly defined boundary conditions for a range of reduced frequency. The experiment is designed to produce detailed and reliable 3D test cases for aeroelastic applications. A complete set of steady and unsteady blade surface pressure measurements is provided for five spanwise sections at 10, 30, 50, 70, and 90 percent of span. In addition, the issue of linearity is addressed. Experimental results demonstrate a predominant linear behavior of the unsteady pressure response.