Based on the concept of switched-capacitor based multilevel inverter topology, a new structure for a boost multilevel inverter topology has been recommended in this paper. The proposed topology uses 11 unidirectional switches with a single switched capacitor unit to synthesize nine-level output voltage waveform. Apart from the twice voltage gain, self-voltage balancing of capacitor voltage without any auxiliary method along with reduced voltage stress has been the main advantages of this topology. The merits of proposed topology have been analyzed through various comparison parameters including component counts, voltage stresses, cost and efficiency with a maximum value of 98.3%, together with the integration of switched capacitors into the topology following recent development. Phase disposition pulse width modulation (PD-PWM) technique and nearest level control PWM (NLC-PWM) have been used for the control of switches. Different simulation and hardware results with different operating conditions are included in the paper to demonstrate the performance of the proposed topology. INDEX TERMS Multilevel inverter, boost inverter topology, switched-capacitor, single dc source, reduce switch count, PWM.

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Considering the aim of having low switching losses, especially in medium-voltage and high-power converters, the pre-programmed pulse width modulation technique is very useful because the generated harmonic content can be known in advance and optimized. Among the different low switching frequency techniques, the Selective Harmonics Elimination (SHE) modulation method is most suitable because of its direct control over the harmonic spectrum. This paper proposes a method for obtaining multiple solutions for selectively eliminating specific harmonics in a wide range of modulation indices by using modified Newton-Raphson (NR) and pattern generation techniques. The different pattern generation and synthesis approach provide more degrees of freedom and a way to operate the converter in a wide range of modulation. The modified Newton-Raphson technique is not complex and ensures fast convergence on a solution. Moreover, multiple solutions are obtained by keeping a very small increase in the modulation index. In the previous methods, solutions were not obtainable at all modulation indices. In this paper, only exact solutions to the low-order harmonics elimination for Cascaded H-bridge inverter are reported for all modulation indices. Analytical and simulation results prove the robustness and correctness of the technique proposed in this paper.

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

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