In the paper the squared voltage-current functionals are minimized, which represent the global power losses in the network. In that way it is possible to find the voltage-current distributions on the net without the use of immitance operators and basing only on the Kirchhoff laws. Farther the individual branch parameters are defined in the syntheses process. Many optimal power analysis examples are also shown to illustrate the thesis included in the paper.
The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.
The paper deals with the application of the feed-forward and cascade-forward neural networks to mechanical state variable estimation of the drive system with elastic coupling. The learning procedure of neural estimators is described and the influence of the input vector size and neural network structure to the accuracy of state variable estimation is investigated. The quality of state estimation by neural estimators of different types is tested and compared. The simple optimisation procedure is proposed. Optimised neural estimators of the torsional torque and the load machine speed are tested in the open-loop and closed-loop control structure of the drive system with elastic joint, with additional feedbacks from the shaft torque and the difference between the motor and the load speeds. It is shown that torsional vibrations of the two-mass system are damped effectively using the closed-loop control structure with additional feedbacks obtained from the developed neural estimators. The simulation results are confirmed by laboratory experiments.
Spectral properties of nonnegative and Metzler matrices are considered. The conditions for existence of Metzler spectrum in dynamical systems have been established. An electric RL and GC ladder-network is presented as an example of dynamical Metzler system. The suitable conditions for parameters of these electrical networks are formulated. Numerical calculations were done in MATLAB.