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ANN-Controlled Z-Source Inverter Integrated Wind Energy System with DSTATCOM for Grid Support and Power Quality Improvement

International Journal of Electrical and Electronics Engineering
© 2026 by SSRG - IJEEE Journal
Volume 13 Issue 3
Year of Publication : 2026
Authors : Rajesh K, Suresh Babu Daram
10.14445/23488379/IJEEE-V13I3P114
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How to Cite?

Rajesh K, Suresh Babu Daram, "ANN-Controlled Z-Source Inverter Integrated Wind Energy System with DSTATCOM for Grid Support and Power Quality Improvement," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 3, pp. 178-199, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I3P114

Abstract:

This paper presents a grid-integrated wind energy system designed to enhance the reliability of electrical power supply to consumers while reducing dependence on the conventional distribution grid. In this system, an AC output from a Permanent Magnet Synchronous Generator (PMSG) is rectified and fed to a Z-source inverter for connection to the grid. The Z-source inverter performs the function of boosting the DC link voltage while converting it to AC power in one step, and therefore, it has been shown to reduce the overall size, cost, and voltage ripples of the system. A control strategy is developed to regulate the DC link voltage across the inverter bridge terminals, to maximize the power extracted from the wind turbine based on the Perturb and Observe (P&O) MPPT tracking method, and to ensure a unity power factor. The proposed control strategy utilizes two independent control variables, i.e., the shoot-through duty cycle and the modulation index, to separate DC side and AC side control. For addressing issues associated with weak grids, such as voltage fluctuations, harmonic distortion, and reactive power imbalance, a Distribution Static Compensator (DSTATCOM) is used. An adaptive Artificial Neural Network (ANN)-based control strategy is utilized to improve the performance of both the Z-source inverter and the DSTATCOM used in the hybrid renewable energy system proposed in this work. The ANN-based control strategy is able to mitigate transient disturbances, improve dynamic responses, and provide smart reactive power compensation under nonlinear and time-varying loading conditions. Compared to the traditional Proportional–Integral (PI) controller, the ANN-based controller has a fast response time, high adaptability, and high accuracy for improving the quality of power. Simulation results obtained from using MATLAB/Simulink show that the ANN-based controlled system has a lower Total Harmonic Distortion (THD) than the PI-controller system, nearly unity power factor, and complies with the IEEE-519 standard. Therefore, the proposed control strategy allows for maximum power extraction from renewable energy resources, regulates the DC-link voltage stably, and enhances the grid-side performance, proving its suitability for modern renewable-energy-integrated electric power systems.

Keywords:

Artificial Neural Network (ANN), Z-Source Inverter, Wind Energy Conversion System, Permanent Magnet Synchronous Generator (PMSG), DSTATCOM, Power Quality Improvement, Total Harmonic Distortion (THD), Maximum Power Point Tracking (MPPT).

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