This research study focused on the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms. Whereas test data has non-linearity, an artificial intelligence (AI) algorithm has been incorporated with different metaheuristic algorithms. About 317 datasets have been applied from the real test results to detect the promising factor of strength subjected to the seismic loads. Adaptive neuro-fuzzy inference system (ANFIS) was carried out as an AI beside the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Extreme Machine Learning (ELM) was also performed in order to approve the obtained results. According to the findings, it is demonstrated that ANFIS-PSO predicts the lateral load with promising evaluation indexes [R 2 (test) = 0.86, R 2 (train) = 0.90]. Mechanical performance prediction was also carried out in this study, and the results showed that ELM predicts the compressive strength with promising evaluation indexes [R 2 (test) = 0.66, R 2 (train) = 0.86]. Finally, both ANFIS-GA and ANFIS-PSO techniques illustrated a reliable performance for prediction, which encourage scholars to replace costly and time-consuming experimental tests with predicting utilities.
Variable speed wind turbines are commonly used as wind power generation systems because of their lower maintenance cost and flexible speed control. The optimum output power for a wind turbine can be extracted using maximum power point tracking (MPPT) strategies. However, unpredictable parameters, such as wind speed and air density could affect the accuracy of the MPPT methods, especially during the wind speed small oscillations. In this paper, in a permanent magnet synchronous generator (PMSG), the MPPT is implemented by determining the uncertainty of the unpredictable parameters using the extended Kalman filter (EKF). Also, the generator speed is controlled by employing a fuzzy logic control (FLC) system. This study aims at minimizing the effects of unpredictable parameters on the MPPT of the PMSG system. The simulation results represent an improvement in MPPT accuracy and output power efficiency.
One of the main challenges of a four-switch three-phase rectifier (FSTPR) is a DC imbalance in capacitor voltages. On the other hand, under unbalanced grid voltage conditions, unbalanced three-phase input current and the DC-link voltage affect the performance of the FSTPR. Although many papers focus on designing a controller to balance DC-link capacitor voltage, a few papers are available to cope with the imbalance of DC-link capacitor voltages and input current simultaneously under unbalanced grid voltage. In this paper, first, the operation of the FSTPR under unbalanced grid voltage conditions is investigated. It can be seen that under these conditions, the oscillatory parts of the active and reactive input power, i.e., sin and cos components, are the leading cause of the problems that can severely degrade the FSTPR performance of the controller. Therefore, this paper presents a promising control technique to eliminate the mentioned oscillation components. Aiming at this purpose, the current control loops in the dq axis are divided into two positive and negative sequences, i.e., idq+ and idq−. Simulation results in MATLAB/SimPowerSystem™ show that the proposed controller can reduce the output voltage ripple, the total harmonic distortion, and the unbalancing of input current compared to a conventional controller. Under these conditions, the DC-link capacitor voltages are more balanced, significantly reducing the voltage limiter of the FSTPR.
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