Improved voltage tracking of autonomous microgrid technology using a combined resonant controller with lead-lag compensator adopting negative imaginary theorem

Growing application of distributed generation units at remote places has led to the evolution of microgrid(MG)technology.When an MG system functions independently,i.e.,in autonomous mode,unpredictable loads and uncertainties emerge throughout the system.To obtain stable and flexible operation of an autonomous MG,a rigid control mechanism is needed.In this paper,a robust high-performance controller is introduced to improve the performance of voltage tracking of an MG system and to eliminate stability problems.A combination of a resonant controller and a lead-lag compensator in a positive position feedback path is designed,one which obeys the negative imaginary(NI)theorem,for both single-phase and three-phase autonomous MG systems.The controller has excellent tracking performance.This is investigated through considering various uncertainties with different load dynamics.The feasibility and effectiveness of the controller are also determined with a comparative analysis with some well-known controllers,such as linear quadratic regulator,model predictive and NI approached resonant controllers.This confirms the superi-ority of the designed controller.

Proton exchange membrane fuel cell voltage-tracking using artificial neural networks

Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells.The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature,pressure,and flow rate.To overcome this problem,fast and delay-free systems are necessary for predicting control signals.In this paper,we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance.This is done through an input air pressure control signal.The proposed artificial neural network was constructed based on a back propagation network.A fuel cell nonlinear model,with and without feed forward control,was investigated and compared under random current variations.Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage.Also,less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.