FESTO Compact Workstation is a well known didactic tool in process control. This paper aims at providing an improved transfer function model of this system's level and flow control loops. This higher order model is compared to existing first order system approximations of the level control loop in various input-output scenarios to verify its applicability and superiority. Results are obtained using MATLAB System Identification Toolbox after data acquisition in LabVIEW. MATLAB Simulink is used for cascade PI and single loop PI experiments to show the improvement cascade control on the new model brings. Together with the practical value the results have, the procedure conducted here can serve as a primer and a tutorial for system identification class using this or similar apparatus.

This paper presents a method to solve electrical network reconfiguration problem in the presence of distributed generation (DG) with an objective of minimizing real power loss and energy not supplied function in distribution system. A method based on NSGA II multi-objective algorithm is used to simultaneously minimize two objective functions and to identify the optimal distribution network topology. The constraints of voltage and branch current carrying capacity are included in the evaluation of the objective function. The method has been tested on radial electrical distribution network with 213 nodes, 248 lines and 72 switches. Numerical results are presented to demonstrate the performance and effectiveness of the proposed methodology.

Abstract This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm. Keywords radial distribution network, multiobjective optimization, reconfiguration, genetic algorithms, NSGA II