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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.

 

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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.
 

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This paper presents a new method of the adaptive non-singular Second-order Terminal Sliding Mode (SOTSM) control for the fast and finite time stabilization of Cyber-Physical Systems (CPSs) in the simultaneous presence of parametric uncertainties, unwanted disturbances and actuator cyber-attacks. By utilizing the presented non-linear manifold and sliding surface, the reaching mode is deleted and the entire system’s robust performance is improved. The proposed online adaptive laws deal with parametric uncertainties, unwanted disturbances and cyber-attacks, so that there is no need to identify their upper bounds. The designed adaptive non-singular SOTSM control method guarantees the robust performance of the system in the mentioned conditions along with fast and smooth response, high accuracy and flexibility, without transient fluctuations and chattering, as well as proper convergence in finite time. The numerical simulation results show the effectiveness and success of the adaptive non-singular second-order terminal sliding mode control method in comparison with the results of adaptive integral sliding mode control, traditional sliding mode control and state feedback control.
 

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Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles.