Symmetrical Pole Placement Method-Based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller With Harmonic Compensator for Single Phase Grid-Connected PV Inverters Academic Projects in Electrical


In this paper, a symmetrical pole placement Method-based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller is presented. The proposed PR-P controller resolved the issues that are tracking repeating control input signal with zero steady-state and mitigating of 3rd order harmonic component injected into the grid associated with the use of PI controller for single-phase PV systems. Additionally, the PR-P controller has overcome the drawbacks of frequency detuning in the grid and increase in the magnitude of odd number harmonics in the system that constitute the common concerns in the implementation of conventional PR controller developed as an alternative to PI controller. Moreover, the application of an unprecedented design process based on changing notch filter dynamics with symmetrical pole placement around resonant frequency overcomes the limitations that are essentially complexity and dependency on the precisely modelled system associated with the use of various controllers such as Adaptive, Predictive and Hysteresis in grid connected PV power generation systems. The proposed PR-P controller was validated employing Photovoltaic emulator (PVE) consisting of a DC-DC Buck power converter, a maximum power point tracking (MPPT) algorithm and a full-bridge grid connected inverter designed using MATLAB/Simulink system platform. Details of the proposed controller, Photovoltaic emulator (PVE) simulations, analysis and test results were presented in the paper.


  1. Proportional resonant current controller
  2. Harmonic compensator
  3. Buck converter based PV emulator
  4. MPPT



Figure 1. PVE Based Single Phase Grid-Connected Inverter System.


Figure 2. The PVE Current For Varying Irradiance.

Figure 3. The PVE Voltage For Varying Irradiance.

Figure 4. System Outputs With The Use Of Proposed PR-P Controller.

Figure 5. Generated Power With Delivered And Reactive Powers.

Figure 6. Closed-Loop Error In Terms Of 3rd Order Harmonics.

Figure 7. PR-P And PI Controlled Grid Currents With Scaled Grid Voltage.


This paper has presented an alternative unprecedented design process for a Proportional-Resonant (PR) controller with a selective harmonic components (3rd and 5th order) compensator for Photovoltaic Emulator (PVE) supported single phase Grid Connected Inverter (GCI) systems. The design procedure of the proposed controller unity proportional resonant (PR) path is conducted based on notch filter dynamics regulated by symmetrical pole placement methods. Addition of scheduled proportional gain designed by loop shaping method to the resonant path increased the performance of the controller in terms of robustness, achieving better results in the presence of non-linear load and weak grid. The performance of the proposed controller and harmonic compensator is validated employing a PVE consisting of a DC-DC Buck converter, a Maximum Power Point Tracking (MPPT) algorithm and a full-bridge GCI designed using MATLAB/Simulink platforms. Frequency and time domain analysis of the system elements showed satisfactory behaviors. A comparative analysis with different PR controller design techniques used in various papers is performed and resulted in confirming that the proposed technique is robust and simple to implement. The performance of the Proposed PR-P controller with the harmonic compensator is compared with a PI in stationary reference frame and conventional PR current controllers in terms of steady-state error and harmonics mitigation. The simulation results demonstrated that the proposed PR-P controller with harmonic compensator is superior at tracking sinusoidal reference current with zero steady-state error and lower total harmonic distortion with eliminated 3rd and 5th order harmonics. The overall system is under development and experimental results will be presented in the near future.


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[4] G. Price, Renewable Power and Energy, 1st ed. New York, NY, USA: Momentum Press, 2018.

[5] B. Carrera and K. Kim, “Comparison analysis of machine learning techniques for photovoltaic prediction using weather sensor data,” Sensors, vol. 20, no. 11, p. 3129, Jun. 2020.

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