Synchronization is a crucial problem in the grid-connected inverter’s control and operation. A phase-locked loop (PLL) is a typical grid synchronization strategy, which ought to have a high resistance to power system uncertainties since its sensitivity influences the generated reference signal. The traditional PLL catches the phase and frequency of the input signal via the feedback loop filter (LF). In general, to enhance the steady-state capability during distorted grid conditions generally, a filter tuned for nominal frequency is used. This PLL corrects large frequency deviations around the nominal frequency, which increases the PLL’s locking time. Therefore, this paper presents an adaptive feed-forward PLL, where the input signal frequency and phase under large frequency deviations are tracked precisely, which overcomes the above-mentioned limitations. The proposed adaptive PLL consists of a feedback loop that reduces the phase error. The feed-forward loop predicts the frequency and phase error, and the frequency adaptive FIR filter reduces the ripples in output, which is due to input distortions. The adaptive mechanism adjusts the gain of the filter in accordance with the supply frequency. This reduces the phase and frequency error and also decreases the locking time under wide frequency deviations. To verify the effectiveness of the proposed adaptive feed-forward PLL, the system was tested under different grid abnormal conditions. Further, the stability analysis has been carried out via a developed prototype test platform in the laboratory. To bring the proposed simulations into real-time implementations and for control strategies, an Altera Cyclone II field-programmable gate array (FPGA) board has been used. The obtained results of the proposed PLL via simulations and hardware are compared with conventional techniques, and it indicates the superiority of the proposed method. The proposed PLL effectively able to tackle the different grid uncertainties, which can be observed from the results presented in the result section.
The complex industrial processes exhibiting nonstationary and multivariable with time‐varying dynamics result in low accuracy. Also, stability compensation is difficult to be obtained by a conventional PID controller. Hence, a deep learning‐based data‐driven PID controller is designed for unmodeled dynamics compensation for complex industrial processes. In this research work, a nonlinear PID controller is designed with a deep neural network (DNN) model from unmodeled dynamics of the complex industrial processes. To validate the performance, results from stability compensation and convergence of the model parameters for closed‐loop systems were obtained. When tested on a real‐time twin tank system, it achieved an accurate output flowrate with 97.65% accuracy and 1.89% peak overshoot compared with conventional PID controller. Both simulated and experimental results validate that proposed controller has improved stability and uniform convergence of system variables. The proposed deep learning‐based PID controller was employed on a twin tank control system. This confirms the feasibility and practical application of a real‐time complex process.
This paper presents a solar dynamic voltage restorer for hybrid series‐connected solar‐wind farms to mitigate the power quality problems. The operating region of the proposed hybrid system is derived and studied through graphical analysis. Upon examination of the system's operation under various grid conditions, the feasibility of solar PV power injected into the grid is verified. It is further proved that the series injection of voltage is also capable of mitigating the effects of voltage sag/swell, and unbalance, which have adverse effects on wind‐connected induction generators. Further, the effectiveness of Solar DVR to mitigate the fault ride‐through capability of the wind farm is analyzed. The non‐requirement for an energy storage device such as a battery is also validated. A control system for the series PV inverter is proposed and the computer simulations are performed to confirm the efficacy and ride‐through capability of the system.
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