Transformer-less inverters are necessary parts for grid-connected renewable energy resources. Owing to its cost effectiveness, downsize and less weight, great attention has been paid to these inverters development. With these aforementioned advantages, these inverters have limitations like the flow of leakage current through photovoltaic arrays, high total harmonic distortion (THD) at inverter's output and DC current injection to the grid. This study presents coupled inductor-based single-phase transformer-less semi-Z-source inverter topology to lessen those limitations. Since the DC input and AC output voltage share a common ground, the presented inverter system is categorised under doubly grounded topologies. For the purpose of handling, the non-linearity of the voltage gain of semi-Z-source inverter, a non-linear sinusoidal pulse-width modulation technique has been employed. The prototype of the suggested inverter has been constructed. The performance and compatibility of modulation technique are verified under different loading conditions. The feasibility of the configuration is ensured based on the mitigated common-mode leakage current, the substantially lower THD as well as DC current injected to the grid. Moreover, the presence of coupled inductor significantly contributes in reducing input current ripple, installation area of the inverter and enhancing the efficiency. Finally, this topology exhibits appreciable performance to operate synchronously and transfer power to the grid.

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Abstract:In this paper, a single phase doubly grounded semi-Z-source inverter with maximum power point tracking (MPPT) is proposed for photovoltaic (PV) systems. This proposed system utilizes a single-ended primary inductor (SEPIC) converter as DC-DC converter to implement the MPPT algorithm for tracking the maximum power from a PV array and a single phase semi-Z-source inverter for integrating the PV with AC power utilities. The MPPT controller utilizes a fast-converging algorithm to track the maximum power point (MPP) and the semi-Z-source inverter utilizes a nonlinear SPWM to produce sinusoidal voltage at the output. The proposed system is able to track the MPP of PV arrays and produce an AC voltage at its output by utilizing only three switches. Experimental results show that the fast-converging MPPT algorithm has fast tracking response with appreciable MPP efficiency. In addition, the inverter shows the minimization of common mode leakage current with its ground sharing feature and reduction of the total harmonic distortion (THD) as well as DC current components at the output during DC-AC conversion.

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Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.