In this paper, an adaptive event-triggered consensus problem considering the time delay of the communication network is studied for heterogeneous multi-agent systems. An event-triggered interval is here considered as a specific delay and unified round trip time (RTT) delay. Furthermore, an efficient optimal predictive-based coordination control strategy is introduced for balancing the non-ideal behaviors of communication channels. In order to evaluate the efficiency of the proposed method for controlling network-based multi-agent systems with coupled subsystems, two stages are studied. In the first stage, the very method is implemented on two coupled continuous stirred tank reactors while in the second one, it is used for controlling the voltage and current of a DC microgrid consisting of several distributed generation units. To prevent the unessential utilization of communication resources, the transfer of information will actually occur in this mechanism if a specific event is triggered. The simulation results show the fact that in spite of being non-ideal and time-delayed communication channels, the proposed technique is capable for improving the performance of power grids.

IIn this paper, an efficient method to detect and discriminate mechanical defects of transformer winding based on extracting the winding frequency responses using outlier data detection and ensemble algorithms ,which in total constitutes an efficient hybrid method has been proposed. First, the frequency response of the high voltage winding of a real model of transformer (1.6 MVA) was extracted in different condition and arranged as primary data. Then, due to the high standard deviation of the characteristics and the weight of the outlier samples above the threshold of 1.1, the Local Outlier Factor (LOF) method was used to clean the samples. Finally, data mining algorithms have been used to detect and distinguish mechanical defects. Based on the results, the decision tree bagging ensemble method reported the best accuracy compared to other techniques and improved the accuracy of the decision tree with total accuracy of 92.68% by LOF. These results also showed that all methods improved accuracy by LOF. Therefore, it can be claimed that the proposed method has the ability to discriminate the mechanical defects of the transformer winding with appropriate accuracy.

In this paper, a model predictive control approach based on a generic particle filter is proposed to synchronize two Josephson junction models with different parameters. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using a generic particle filter. Here, Josephson junction is described with Resistive Capacitive Inductive Shunted Josephson model, and the synchronization is obtained using the slave–master technique. Moreover, to verify the implementation capability of the proposed algorithm, a processor in loop experiment is performed. The results show that the open-loop system, without the controller, has a chaotic behavior. Numerical simulations are conducted to assess the performance of the proposed algorithm. The results show that the proposed approach can be implemented in a real-time application. Also, the performance of the suggested controller is compared with the proportional integral derivative controller and sliding mode controller.

In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the control law. First, a dynamic sliding mode control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the dynamic sliding mode control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the dynamic sliding mode controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive dynamic sliding mode control strategy.

The AC/DC converter is one of the popular power electronic converters in industrial applications such as in the railway, power supply systems and electric vehicle. In this paper, a three-phase controllable rectifier is considered and its linear model is extracted. Because of MPC controllers benefits, the continuous control set model predictive controller (CCs-MPC) is designed for controlling this rectifier output DC voltage. By considering rectifier dynamic response, the suitable criteria to choice the model predictive controller parameters such as sampling time, prediction horizon and control horizon is proposed. In experimental implantation the computing burden of microcontroller is limit therefore the reaching to optimal and minimum complexity in algorithms implantation is vital problem. In other words by using these proposed criteria for selection of sample time, prediction and control horizon the tradeoff between computational burden, system performance and dynamic stability is made. When using designed MPC controller, the rectifier and grid performance such as total harmonic distribution (THD), power factor (PF) and output voltage ripple have acceptable value. This controller can eliminated the effect of heavy load change on rectifier performance which is very common problem in indusial system. Also, this controller stability guaranteed is checked by using the dual-mode method. The simulation results are validated by using MATLAB software and showing the designed controller performance.

In this paper, a fault location approach is presented by using Whale Optimization Algorithm (WOA) strategy in two terminal transmission feeder. Also, Grey wolf Optimization (GWO) method is discussed. From both ends, affording the preparatory data for proposed strategies voltages and currents from both ends are measured. Several types of faults and simulations are considered in this paper and the objective function identifies the fault location with a high accuracy, correctness in a short time. Meanwhile, based on distributed model the line, the fault location is defined and since the optimization algorithm do not utilize compressed model of the line, the accuracy of the calculation is high. WOA based optimization method results in a notable reduction in the computational time. Accurate and timely location of the source of the fault greatly facilitates the job of the repair crew. This is the benefit of the proposed technique. Almost in all the cases, the accuracy of proposed procedure is very high and the error is kept below 1%.