An extended configurable UML activity diagram and a transformation algorithm for business process reference modeling

Enterprise Resource Planning (ERP) solutions provide generic off-the-shelf reference models usually known as best practices . The configuration !individualization of the reference model to meet specific requirements of business end users however, is a difficult task. The available modeling languages do not provide a complete configurable language that could be used to model configurable reference models. More specifically, there is no algorithm that monitors the transformation of configurable UML Activity Diagram (AD) models while preserving the syntactic correctness of the model. To fill these gaps we propose an extended UML AD modeling language which we named Configurable UML Activity Diagram (C-UML AD). The C-UML AD is used to represent a reference model while showing all the variation points and corresponding dependencies within the model. The C-UML AD covers the requirements and attributes of a configurable modeling language as prescribed by earlier researchers who developed Configurable EPC (C-EPC). We also propose a complete algorithm that transforms the C-UML AD business model to an individual consistent UML AD business model, where the end user\u27s configuration values are consistent with the constraints of the model. Meanwhile, the syntactic correctness of the transformed model is preserved. We validated the Transformation Algorithm by showing how all the transformation steps of the algorithm preserve the syntactic correctness of any given configurable business model, as prescribed by earlier researchers, and by running it on different sets of test scenarios to demonstrate its correctness. We developed a tool to apply the Transformation Algorithm and to demonstrate its validity on a set of test cases as well as a real case study that was used by earlier researchers who developed the C-EPC

Robust Fuzzy Control for Uncertain Nonlinear Power Systems

This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel distributed compensation (PDC) is applied for the stabilization of the fuzzy system, where the design of control laws is based on the Lyapunov function method and the stability conditions are solved using a linear matrix inequalities (LMI)-based framework. Due to the high number of system nonlinearities, two steps are followed to reduce the number of fuzzy rules. Firstly, the power network is subdivided into sub-systems using Thevenin’s theorem. Actually, each sub-system corresponds to a generator which is in series with the Thevenin equivalent as seen from this generator. This means that the number of sub-systems is equal to the number of system generators. Secondly, the significances of the nonlinearities of the sub-systems are ranked based on their limits and range of variation. Then, nonlinearities with non-significant variations are assumed to be uncertainties. The proposed strategy is tested on the Western systems coordinating council (WSCC) integrated with a wind turbine. The disturbances are assumed to be sudden variations in wind power output. The effectiveness of the suggested fuzzy controller is compared with conventional regulators, such as an automatic voltage regulator (AVR) and power system stabilizers (PSS)

A Novel Hybrid Chaotic Jaya and Sequential Quadratic Programming Method for Robust Design of Power System Stabilizers and Static VAR Compensator

This paper proposes a novel hybrid algorithm combining chaotic Jaya (CJaya) and sequential quadratic programming (SQP), namely CJaya-SQP, for solving the coordinated design problem of static var compensator (SVC) and power system stabilizers (PSSs). The CJaya serves as a global optimizer and the SQP as a local optimizer for fine-tuning the solution. In the proposed algorithm, chaotic maps are used to generate the initial solutions and control the search process. In order to prove the performance of the CJaya-SQP, a set of benchmark optimization problems is used where the results are compared with those of the basic Jaya and other recognized algorithms. The proposed optimization method is then applied for the optimal tuning of PSSs and SVC controllers in such a way that damping ratios and damping factors of the electromechanical modes are optimally improved. To illustrate the robustness of the CJaya-SQP-based coordinated PSSs and SVC controllers, the four-machine, two-area system is used. Eigenvalue analysis and nonlinear time-domain simulation vividly show that the CJaya-SQP-based coordinated controllers improve greatly the system’s dynamic stability with a robust damping of local and inter-area power oscillations