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Overvoltage transient is the major danger facing large offshore wind farms, as it may lead to a complete failure of the system components. A 150 MW offshore wind farm was modeled using the ATP-EMTP package to analyze the effect of the temporary overvoltage resulting from de-energization conditions, and the ferro resonance phenomenon resulting from the asymmetric opening of circuit breaker pole(s). The results showed that the best scenario to shut down such a system from the grid with minimum values of temporary overvoltage is to disconnect it, feeder, by a feeder. The stuck of one or more poles during the opening operation of the circuit breaker is a dangerous phenomenon, as it induces a high overvoltage value, which lasts for a long time causing more electric stress on the sensitive components of the system. The use of the Pre-Insertion Resistor PIR was recommended as a mitigation technique for the temporary overvoltage in large offshore wind farms, as it proves a significant reduction in the overvoltage values reaches to 79.44 % compared to surge arrester and shunt reactor which reduce the over voltage by 68.33%, 66.29% respectively. The genetic algorithm was used to find the optimum value of the PIR and shunt reactor that leads to the minimum TOV. The optimum value of PIR which was recorded is equal to 57.352 Ohm, which leads to more reduction in the overvoltage value by 9.67%. Also, the optimum values of the resistor and inductor of the shunt reactor were found to be 11.238 Ohms and 37.246 mH which leads to more reduction in the overvoltage value by 21.97% compared to the reduction without optimization.

Improving spontaneity, shifting control, and efficiency are the main goals of actuators for hydraulic automated transmissions. Friction losses of piston-seals play an essential role in achieving these goals. Therefore, modeling the complex friction behavior of piston seals leads to a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission actuators. This paper proposes a piston-seal friction model based on the Generalized Maxwell-Slip model. The proposed model introduces an additional hydraulic-pressure dependency that emulates the influence of cylinder-pressure on the displacement variable while accounting for various piston-seal structures. A Genetic Algorithm is also applied to identify and optimize the parameters of the proposed friction model. Simulations with O-Ring, D-Ring, and Bonded Piston seals were developed to show the validity of the proposed model in practical scenarios. The results were also compared with the original Generalized Maxwell Slip friction model to show the superiority of the proposed model in representing the experimental data.

This paper presents an efficient artificial neural network (ANN) electrothermal modeling approach applied to GaN devices. The proposed method is based on decomposing the device nonlinearity into intrinsic trapping-induced and thermal-induced nonlinearities that can be simulated by low-order ANN models. The ANN models are then interconnected in the physics-relevant equivalent circuit to accurately simulate the transistor. Genetic algorithm (GA)-based training procedure has been implemented to find optimal values for the weights of the ANN models. The modeling approach is used to develop a large-signal model for a 1-mm gate-width GaN high-electron mobility transistor (HMET). The model has been implemented in the advanced design system (ADS) and it has been validated by pulsed and continues small- and large-signal measurements. The model simulations showed a very good agreement with the measurements and verify the validity of the developed technique for dynamic electrothermal modeling of active devices.