Journal of Majlesi Journal of Mechatronic Systems
Volume:10 Issue: 4, Dec 2021

In electric power distribution networks, most of the times are single-phase and therefore their connection is between one of the phase wires and the neutral wire. In most cases, the number of branches on each of the phases does not feed the same subscribers, and in case of equality, their consumption is different due to different uses of single-phase consumers. Therefore, unlike balanced three-phase loads, in single-phase distribution systems, current flows through the neutral wire, and for this reason the distribution network is generally an unbalanced network. The imbalance of this system has various consequences such as reduced efficiency, performance of the protection system, increased losses, heating of three-phase equipment, voltage drop due to unbalanced current, neutral wire current and reduced power quality. Especially from the point of view of power quality criteria, this problem manifests itself as the ratio of zero sequence size to positive sequence outside the standard. In this paper, a method based on the use of FACTS devices and fuzzy control is described, which reduces the adverse effects of imbalance. In this method, the voltage imbalance index is within the allowable standard of distribution networks. The simulation results show that the stated control method reduces the effects of imbalance and the control method has established the objectives with appropriate speed and accuracy.

In this paper, a new method is introduced to locate emitters using the angle of arrival (AOA), when the navigation systems are unable to report the location of the receiver. In our proposed framework, a moving receiver equipped with a direction-finding system locates the targets using the directions from which these targets are sensed. For this purpose, the receiver would need to know its own exact location using navigation systems. However, in certain circumstances such as long time flights, the reports from these navigation systems might be unavailable or unreliable. Therefore, in such cases, known emitters’ direction of arrival and location can be used to locate the receiver and target using regular AOA position finding (PF) method in two stages. In the first stage, receiver’s location is estimated using the signals received from known emitters and in the second stage the unknown emitter’s location is obtained utilizing the estimated receiver’s location and the direction of unknown emitters. In this work, we introduce a new scheme for such circumstances to locate the receiver and unknown emitter in a single step with a closed- form formulation and improved precision. Additionally, three different approaches are proposed to solve the new formulation. The simulations demonstrate that the proposed method offers more accurate results in comparison to the conventional schemes.

With the growing relevance of reliability concerns in power grids and the rising penetration of renewable energy sources in power systems, we've moved on to assessing a model grid in terms of dependability. We also looked at a sample network with and without renewable energy sources, in light of the rising penetration of renewable energy sources. The findings were compared for various circumstances, and the conclusion was that, when used appropriately, these tools can help improve dependability indicators. Finally, a basic guideline is provided for decreasing operational challenges in managing dependability and generation adequacy indicators.

In the present study, the identification of unknown parameters of structural systems with hysteresis behavior has been investigated by the particle swarm optimization algorithm and the Bouc-Wen model has been used to describe the nonlinear behavior as well as the system formulation for simulation. The particle swarm optimization algorithm can detect the Bouc-Wen parameters of a structural system with hysteresis behavior faster and closer than other detection methods due to the lack of need for initial values of model parameters and the lack of early convergence to local optimal regions. In previous studies, the performance of identifying and estimating the unknown parameters of linear systems has been investigated to some extent, but the function of particle swarm optimization algorithm in systems with hysteresis, which is a nonlinear model, has not been investigated. In this study, first, the good performance of particle swarm optimization algorithm in terms of speed and accuracy in identifying unknown parameters of a nonlinear system with hysteresis behavior is investigated and then by comparing the final value of the identified parameters from the simulation, the superiority of this method is shown by estimating the least squares as well as the genetic algorithm with three methods of random selection, roulette wheel and competitive. In the following, the ability of methods to immediately follow the parameters of a structural system, in case the system stiffness changes due to failure, has been evaluated in this study.

Renewable sources for power generation are being popular day by day and solar PV is mostly first choice. In addition, the energy output of solar PV is highly affected by weather conditions like temperature, irradiance, sky conditions etc. Therefore, an intelligent model based on weather conditions is essential for estimation of solar energy output to meet the needs of energy required. The prediction of PV power output is critical to security, operation, scheduling and energy management. Stability of power grid can also be increased if accuracy of power production in PV plants is further enhanced. This paper has worked on LSTM and used recurrent neural networks (RNN) for forecasting of power production and it is seen that the results of RNNs are nearly compatible with the realistic power production which is evident from less mean absolute error (MAE), mean absolute percentage error (MAPE), Root Mean Square Percentage Error (RMPSE) of magnitude. The comparison with different layers of LSTM model for each season of weather is analyzed.

The main purpose of this study is edge detection in MRI brain images. This study presents an efficient method in which a band pass filter is designed to apply to two sub-images of frequency sub bands resulted from the discrete wavelet transform of the original MRI image simultaneously and enhance the edge of the image. The simulation results show that band pass filter with noise elimination and amplification of properties with the desired brightness of the texture, created convenient accuracy in identifying the edges of the image compared to existing filters, it is also time-efficient with less computational process compared to complex methods. This method has a useful solution for identifying diseases in which the diagnostic method of MRI imaging has been used. It can also provide good insight to overcome the difficulties in the field of scale and noise in edge recognition.