Power Quality Improvement for Grid-Connected Photovoltaic Panels Using Direct Power Control

This chapter displays a control strategy for a photovoltaic system (PV) linked to the network with two phases of a PWM converter, where the first phase is a DC-DC converter linked among the photovoltaic source and the DC-AC converter. The second phase is a DC-AC converter linked to the grid. The maximum power point (MPP) is tracked by DC-DC converter, which increases the DC bus voltage. The P&O (perturbation and observation) technique is utilized as a direct current (DC-DC) converter controller to make the PV arrays work at greatest value of power under changing weather conditions. The DC-AC converter transfers the maximum power extracted from the PV cell into the grid. To improve the energy quality produced by the photovoltaic field other than the performance of the pulse width modulation (PWM) inverter, direct power control (DPC) is used to achieve these improvements. The simulation results showed a good performance of the suggested controller. Decoupled power control is achieved successfully, and a good power quality with low harmonic distortion rate (THD) is obtained.

Artificial Neural Network for PWM Rectifier Direct Power Control and DC Voltage Control

In this chapter, a new technique has been proposed for reducing the harmonic content of a three-phase PWM rectifier connected to the networks with a unit power factor and also providing decoupled control of the active and reactive instantaneous power. This technique called direct power control (DPC) is based on artificial neural network (ANN) controller, without line voltage sensors. The control technique is based on well-known direct torque control (DTC) ideas for the induction motor, which is applied to eliminate the harmonic of the line current and compensate for the reactive power. The main idea of this control is based on active and reactive power control loops. The DC voltage capacitor is regulated by the ANN controller to keep it constant and also provides a stable active power exchange. The simulation results are very satisfactory in the terms of stability and total harmonic distortion (THD) of the line current and the unit power factor.

Fuzzy Logic-Based Direct Power Control Method for PV Inverter of Grid-Tied AC Microgrid without Phase-Locked Loop

A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.

New application of artificial neural network-based direct power control for permanent magnet synchronous generator

Purpose. This article proposes a new strategy for Direct Power Control (DPC) based on the use of Artificial Neural Networks (ANN-DPC). The proposed ANN-DPC scheme is based on the replacement of PI and hysteresis regulators by neural regulators. Simulation results for a 1 kW system are provided to demonstrate the efficiency and robustness of the proposed control strategy during variations in active and reactive power and in DC bus voltage. Methodology. Our strategy is based on direct control of instant active and reactive powers. The voltage regulator and hysteresis are replaced by more efficient and robust artificial neuron networks. The proposed control technique strategy is validated using MATLAB / Simulink software to analysis the working performances. Results. The results obtained clearly show that neuronal regulators have good dynamic performances compared to conventional regulators (minimum response time, without overshoots). Originality. Regulation of continuous bus voltage and sinusoidal currents on the network side by using artificial neuron networks. Practical value. The work concerns the comparative study and the application of DPC based on ANN techniques to achieve a good performance control system of the permanent magnet synchronous generator. This article presents a comparative study between the conventional DPC control and the ANN-DPC control. The first strategy based on the use of a PI controller for the control of the continuous bus voltage and hysteresis regulators for the instantaneous powers control. In the second technique, the PI and hysteresis regulators are replaced by more efficient neuronal controllers more robust for the system parameters variation. The study is validated by the simulation results based on MATLAB / Simulink software.

A power quality enhanced for the wind turbine with sensorless direct power control under different input voltage conditions

Introduction. The quality of electrical energy is essential during disturbances, at the level of power electronic devices will suffer serious operating problems causing dangerous damage. Aim. A new approach to direct power control without grid voltage sensor improves the quality and control of instantaneous active and reactive power converters. Methodology. First, the technique without network voltage sensor with a direct power control based on a switching table, which is a classic approach, is discussed and its performance is analyzed under increasing and decreasing load. In addition, the performance of the proposed technique is also analyzed under the same circumstances and their performance is compared. Originality. The new method consists of a nonlinear grid voltage modulated controller and a conventional controller which guarantees very good results in a polluted network. The proposed method is verified using MATLAB/Simulink. Results. The simulation results under different input voltage conditions show that the proposed method not only has good tracking performance in active and reactive power, but also reduces the current total harmonic distortion to 1.9 %, which is good lower than the requirement for network operation.

A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine

<p class="Abstract">In this work, a 24-sector direct power control (24-sector DPC) of a doubly-fed induction generator (DFIG) based dual-rotor wind turbine (DRWT) is studied. The major disadvantage of the 24-DPC control is the steady-state ripples in reactive and active powers. The use of 24 sectors of rotor flux, a feedforward neural network (FNN) algorithm is proposed to improve traditional 24-sector DPC performance and minimize significantly harmonic distortion (THD) of stator current and reactive/active power ripple. The proposed method is modeled and simulated by using MATLAB/Simulink software under different tests and compared with conventional 24-sector DPC.</p>