Design of an Immune-Genetic Algorithm-Based Optimal State Feedback Controller as UPFC

Abstract-an optimal design of state feedback controller as an UPFC using immune genetic algorithm (IGA) is presented. The potential of the UPFC supplementary state feedback controllers to enhance the dynamic stability is evaluated. The selection of the state feedback gains for the UPFC controllers is converted to an optimization problem with the time domain-based objective function which is solved by an IGA. The effectiveness of the new controller is demonstrated through time-domain simulation studies. The results of these studies show that the designed controller has an excellent capability in damping power system oscillations

Assessment security protection system the distribution network in presence of distributed generation using Petri net

Creation and installation of DG in distribution networks, a source of new challenges in maintaining and protecting the network that has attracted much attention in research and practice. In this regard, we need to all cases and incidents in the system are investigated and simulation. The purpose of the technical review and how to implement security settings, need to develop new tools and methods for the study of certain or probable sequence of conservation practices in the system. Sequence analysis of the distribution network protection ‌, ‌ will be promoting in the presence of distributed provided. This paper presents a new approach to model the performance of a distributed network security system using generalized stochastic Petri nets, to evaluate the protective systems of distribution network, the models are dealt with. The proposed method is a 32 bus IEEE distribution network with high penetration of DG to study the impact of DG on distribution network protection systems have been applied

Cooperative Energy Management of Hybrid DC Renewable Grid Using Decentralized Control Strategies

This paper attempted to control a hybrid DC microgrid in islanded operation mode using decentralized power management strategies. Proposed adaptive I/V characteristic for hybrid photovoltaic (PV) and battery energy storage system (BESS) and wind turbine generator (WTG) adapts the distributed energy resources (DER) behavior independently in accordance with the load demand. Hence, the PV module can spend its maximum power on load demand and spend the extra power for charging the BESS, which will regulate DC bus voltage and maintain the power balance within the microgrid. When load demand is beyond the maximum generation power of PV unit, WTG will supply the energy shortage. The proposed control system was applied on the DC microgrid in order to achieve control objectives through a decentralized procedure, without telecommunication links. In order to validate the proposed strategies, the control system was implemented on a DC microgrid within MATLAB/SIMULINK, where the simulation results were analyzed and validated

Sensor-less Predictive Control of Induction Motor with Super-Twisting Sliding Observer and Fuzzy Adaptive Estimator

Induction motor is one of the most widely used electric motors in the industry and even in everyday life. Various controllers are provided for this motor, one of which is the predictive control method. The main problem with this new method is the inaccuracy of the motor parameters and the lack of access to accurate system information. This inaccuracy can lead to errors in the controller operation. In this paper, the predictive control of induction motors is considered. To improve the performance of this controller, a super twisting sliding observer is used to reduce the effect of uncertainties in the structure of the induction motor. A model reference adaptive estimator has also been used to improve speed estimation in sensor-less control and online estimation of stator resistance. Finally, by adding a fuzzy controller, improvements in the performance of this estimator have been achieved. The simulation results indicate the optimal performance of this method in eliminating the uncertain effect and optimal control of the induction motor