Signal Processing and Renewable Energy
Volume:4 Issue: 4, Autumn 2020

Many researchers have proposed small signal analysis methods and numerous power system stabilizers designed by system linearization. In these approaches, it is assumed that disturbances are so small and the linear approximation error of a nonlinear power system remains in an acceptable range. However, the approximation can restrict the validity of linearized models to a neighborhood of equilibrium points. In this paper, in order to reduce the concerns related to the linearization of the power system and damping of electromechanical oscillations, the synergistic nonlinear control method has been used. In addition, a decentralized control strategy was proposed for the use of power system stabilizers in multi-machine power systems. The relation between generators was modelled as a function of variables and its effects on a single machine connected to an infinite bus power system were examined under various disturbances. Also, three test systems were used to verify the efficiency of the proposed decentralized synergetic method. The simulation results showed that the proposed power system stabilizer was more effective than other stabilizers such as a multi-band power system stabilizer in local and inter-area mode oscillations and under different disturbances.

Regarding the significance of energy distribution smart grids and the operation of smart grids, this paper presents a novel method to reduce power loss in these grids. The proposed method determines the injected reactive power and tap of an on-load tap changer (OLTC) optimally using an optimization algorithm. Loss reduction, voltage profile improvement, costs caused by reactive power injection by the capacitor in the grid, conservation voltage reduction (CVR), and transformer loss are considered as objective functions. The new Whale Optimization Algorithm (WOA) is employed as the Volt/VAR Optimization (VVO) algorithm in this paper. The algorithm inspired by the hunting behavior of humpback whales has a high convergence speed, fewer parameters to adjust, and a balance between exploitation and exploration phases. In addition to the above advantages, the WOA has accurate convergence and an effective variety of solutions. The suggested method is applied to the standard IEEE 33-bus system. According to the optimization results, the operating conditions of the distribution smart grid has been improved and the loss has been significantly reduced. Furthermore, the WOA provides better performance in solving the given optimization problem.

Photovoltaic (PV) systems are widely used due to low maintenance costs and being non-pollutant. Selecting proper parameters for the inverter is essential for its stable performance. The inverter connected to the grid should be able to transfer maximum PV energy to the power grid. To have the complete transmission, the output current of the inverter should be synchronous with the voltage of the power network in terms of phase and frequency. The inverter affects the quality of the power generated by the PV and chaotic behavior can affect the performance of the PV system, negatively. Due to chaotic behavior, by determining the correlation coefficient, PV voltage and circuit parameters, phase and frequency can be synchronized. Therefore, in this paper, determining parameters of the inverter connected to the single-phase full-bridge PV system for phase and frequency synchronization is studied. To increase the accuracy of estimating system parameters and reduce synchronization error, the adaptive chaotic grey wolf Algorithm is used. Simulations are compared with PSO and GWO indicating the superiority of the proposed method in terms of phase and frequency synchronization.

As grid-connected Photovoltaic (PV) based inverters are being used more, these systems play a more important role in the electricity generation by distributed power generators. Power injection to the grid needs to meet predefined standards.In order to meet the harmonics requirement of standards, they need an output filter. The connection through an LCL filter offers certain advantages, but it also brings the disadvantage of having a resonance frequency.LCL filter can easily help the system to satisfy these requirements but also introduce a resonance peak which makes the system control a challenging task. In this paper, a three-level Neutral Point Clamped (NPC) inverter is connected to the grid through an LCL filter. The injected current of the inverter is controlled using Proportional-Resonant (PR) controllers. The resonant peak of the filter is also damped using capacitor current feedback. A systematic mathematical design procedure for controller and filter capacitor current feedback coefficients is investigated in details. Simulations are carried out in MATLAB/Simulink environment and results depict suitable performance of the system with designed parameters.

The eye is one of the sensitive organs of the body that is affected by various factors. One of these diseases is glaucoma. Glaucoma is one of the most common ophthalmic diseases that affects the optic disc area and changes this area in terms of size, color and texture. For this reason, the detection of the optic disc area in retinal fundus images is one of the most basic steps in the process of automatic diagnosis of ocular diseases, including glaucoma. Due to the importance of eye diseases and their high incidence, the introduction of new methods in the process of automatic detection of optic disc area by analysis of retinal color images can reduce the volume and computational load, and it helps us to improve the process of early diagnosis of eye diseases. For the reasons mentioned, in this paper, a new method based on the graph-based visual saliency model, along with the watershed algorithm and region growing algorithm to detect optic disc area in retinal fundus images have been suggested to help diagnose eye diseases including glaucoma. According to the proposed method, in this paper, we were able to detect the optic disc area with a 99.1% standard success rate in DRIONS database.

Detecting attacks and anomalies is one of the new challenges in commercializing and advancing IOT technology. One of the most effective methods for detecting attacks is the machine learning algorithms. Until now, many ML models have been suggested to detect attacks and anomalies, all of them use experimental data to model the detection process. One of the most popular and efficient ML algorithms is the artificial neural network. Neural networks also have different classical learning methods. But all of these classic learning methods are problematic for systems that have a lot of local optimized points or have a very complex target function so that they get stuck in local optimal points and are unable to find the global optimal point. The use of evolutionary optimization algorithms for neural network training can be an effective and interesting method. These algorithms have the capability to solve very complex problems with multi-purposed functions and high constraints. Among the evolutionary algorithms, the particle swarm optimization algorithm is fast and popular. Hence, in this article, we use this algorithm to train the neural network to detect attacks and anomalies of the Internet of Things system. Although the PSO algorithm has so many merits, in some cases it may reduce population diversity, resulting in premature convergence. So, in order to solve this problem, we make use of the TLBO algorithm and also, we show that in some cases, up to 90% accuracy of attack detection can be obtained.