نشریه سامانه های غیرخطی در مهندسی برق
سال نهم شماره 2 (پیاپی 16، پاییز و زمستان 1401)

Currently, renewable energy is rapidly developing across the world in response to technical, economic, and environmental developments, as well as political and social initiatives. Moreover, the excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its Hosting Capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems. Distributed Generation (DG) sources are one of the important componeents of novel distribution systems, among which renewable and clean energy sources have received more attention due to the important role of these sources in reducing greenhouse pollutants. The use of renewable sources such as wind and photovoltaic sources is expanding day by day with the increasing demand for electric energy supply. However, the limitations in the amount of the penetration of DG resources are one of the main challenges in the development of the use of these resources. This paper is looking for a method to improve the HC of the distribution network from DG sources by using reactive power compensation and the reconfiguration of distribution systems. The results of the simulation show the advantages of using the proposed method in increasing the HC and as a result the development of the use of renewable resources.

In this paper, three different finite-time nonlinear controllers are proposed to steer a robotic surgical needle in prostate tissue subject to parametric and modeling uncertainties. The torque generated by each type of these controllers is injected to the surgical needle’s closed-loop structure and, in consequence, the system’s state variable precisely converges to the desired path in prostate tissue within an adjustable finite time. The mentioned controllers are constructed based on the developed terminal sliding mode control method (as the main approach of robust-nonlinear control) incorporated with the adaptive control technique (for designing adaptation laws and estimation of unknown physical constants). It is worth noting that the basic difference between these controllers is in the definition of their nonlinear sliding manifolds. By utilizing the Lyapunov stability theory and several applicable lemmas, it is mathematically proven that all types of the introduced control approaches are able to accomplish the finite-time steering objective and guarantee the global finite-time stability for the needle-tissue dynamical system. Adaptation laws (existing in the proposed nonlinear controllers) continuously estimate the unknown physical constants and it is demonstrated that time responses of these estimations exactly reach the constants values over the finite time. Finally, by using MATLAB software, three types of the proposed controllers are separately simulated onto a second-order needle-tissue system to illustrate their proper performance.

The control of unmanned aerial vehicles is a challenging problem due to their lightweight and intense coupling between longitudinal and lateral motion. Considering this issue, in this article, an automatic landing system for a fixed-wing unmanned aircraft exposed to wind disturbances and parametric uncertainties is designed using the backstepping algorithm and the disturbance observer-based sliding mode control. Two controllers are designed based on the backstepping algorithm and sliding mode control to stabilize the attitude angles. The longitudinal speed controller uses the sliding mode technique to maintain the total speed relative to the ground at a constant desired value in all landing phases. A nonlinear disturbance-observer is considered in the sliding mode controller structure to estimate wind disturbance and parametric uncertainty. The new robust automatic landing system is software implemented, and its performance is investigated by several numerical simulations; Lateral deviation relative to the runway is eliminated while the unmanned aerial vehicle maintains its desired trajectory slope angle in all phases of the landing at the desired value. Therefore, the results of numerical simulations prove that the new control structure is stable and robust against different initial conditions, different types of wind disturbances (wind shear and discrete gust), and parametric uncertainty.

In an islanded microgrid, power electronic converters are used to exchange power, and these converters have very low inertia, thus compromising the frequency stability of the microgrid. Virtual inertia control is used to improve the frequency stability of an islanded microgrid. The derivative control technique is usually used to implement virtual inertia control in the microgrid. Factors such as disturbance and uncertainty of parameters of the islanded microgrid compromise the performance of virtual inertia control and may cause system frequency instability. Therefore, the virtual inertia control structure, a complementary controller is needed that can weaken the effect of disturbance on the microgrid as much as possible and be resistant to the uncertainty of parameters of the microgrid. In this paper, a robust control method is used in a virtual inertia control structure that uses system output feedback. The proposed method is expressed based on linear matrix inequality and is proved based on the Lyapunov criterion. Among the advantages of the proposed method is the attenuation of disturbance, resistance to the uncertainty of parameters of the microgrid, and increasing the degree of freedom to control the system in this method. The results of the proposed method to improve the performance of virtual inertia control in several different scenarios by considering the uncertainty of parameters of the two-zone microgrid and disturbances on the microgrid are compared with several methods and the effectiveness of the proposed method in terms of improving frequency stability is shown.

Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.

In order to reduce the cost of electric power generation and also reduce greenhouse gas emissions in ships with electric propulsion system, renewable energy resources and energy storage systems are used along with thermal units. Therefore, in this paper, a stochastic mixed integer linear model has been suggested for optimal management of electrical energy of a ship with electric propulsion system, energy storage, heat generators and renewable solar resources with the aim of minimizing the cost of electricity production and determining the optimal ship speed. In this paper, the Monte Carlo simulation method is used to model the uncertainty in predicting the solar power and the ship's electric load. The proposed model is implemented and analyzed in GAMS optimization software. The simulation results show the efficiency of the proposed model and the introduced energy management strategy.