Journal of Majlesi Journal of Mechatronic Systems
Volume:11 Issue: 2, Jun 2022

In this paper, the issue of locating and determining the capacity of dispersed production resources is considered by the owner of the source and the distribution company of the modelling and solved. The benefit / cost ratio of the project is considered as the owner's objective function and the difference in losses, the improvement of the voltage profile, the reduction of environmental pollutants, the delay of investment, reliability and the difference in the cost of supplying power from the network as the interests of the distribution company. Also, the multi-objective optimization problem, which is modeled by the particle pool method, has been solved with comprehensive training based on the epsilon approach. Finally, the parrot responses obtained by the fuzzy set are classified and the optimal final response is extracted.

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

Management information system (MIS), decision support system (DSS), and executive support system (EES) are the inevitable constituents of the intelligent systems which are being integrated with the infrastructural and technological development of the organizations to address non-routine decisions. The intelligent systems are incorporated with methodologies that support providing solutions to unpredicted decisions by employing mathematical and statistical tools and incorporating software programs embedded with cutting-edge algorithms. We investigate the applicability of several algorithms in the healthcare domain and propose mechanisms of development of machine learning techniques in the area of artificial intelligence. Artificial intelligence (AI) encompasses integer linear programming (ILP) and machine learning (ML) that further motivates us to dig up the algorithms and learning techniques to find the best solution in the field of predictive analytics for the supervised learning environments in correlating blood glucose concentration and hematocrit volume.

In this paper, an approach for synchronization chaotic gyros proposed using fuzzy sliding mode control. The proposed method will be investigated in the presence of trigonometric function uncertainty and trigonometric function external disturbance in the dynamic equations of the gyroscope. Proportional-Integral (PI) sliding mode technique can reduce the controller steady state error. In the fuzzy design, a low number of membership functions are selected, which will help speed up the performance of the processors. By combining proportional-integral sliding mode and fuzzy logic, the final controller is designed. It is proved that the derived robust controller based on Lyapunov stability theory can guarantee that all states of the closed-loop system are globally uniformly ultimately bounded, and lead the system tracking error to zero. In numerical simulation, the controller signal is depicted. It shows that the proposed method can be built and implemented in the real world, because it has low amplitude and oscillations. Finally, simulation results are provided to show the effectiveness of the proposed approach.

This paper presents a discrete-time control for a Linear Induction Motor (LIM). First, an identifier is proposed with a nonlinear block controllable form (NBC) structure. This identifier is based on a discrete-time high order neural network trained on-line with an extended Kalman filter (EKF)-based algorithm. The backstepping control and Artificial Neural Networks (ANN ) are combined in order to design a robust controller that is capable of preserving the drive system robustness subject to all parameter variations and uncertainties. The overall system stability is proved by Lyaponuv theory.. The neural control performance is illustrated via simulations.

This paper presents a comparative study between two nonlinear methods represented in synergetic control and sliding mode control, where these two strategies are used to improve the performance and effectiveness of direct power control of the rotor side converter of the doubly-fed induction generator (DFIG) connected to a multi-rotor wind power system (MRWP). A comparative study is carried out in terms of the degree of complexity, simplicity, ease of implementation, durability, ripple reduction ratio, current quality, ...etc. In order to achieve the desired goal of this paper, three different tests are proposed using Matlab software. Moreover, the proposed methods are applied to a large-capacity generator (1.5MW). This study makes a comparison, research, and detailed analysis and presents the best solution for the control of the electric power generation systems based on DFIG. Therefore, this study is of great importance in the field of renewable energies intending to obtain a quality electric current of high value. The numerical results showed the characteristic/effectiveness of the synergetic control in improving the quality of reactive power, torque, current, and reactive power compared to the sliding mode control of the DFIG-based variable-speed MRWP systems.