This paper presents a new boost inverter topology with nine level output voltage waveform using a single dc source and two switched capacitors. The capacitor voltages are self-balancing and thus is devoid of any sensors and auxiliary circuitry. The output voltage is twice higher than the input voltage, which eliminates the need for an input dc boost converter especially when the inverter is powered from a renewable source. The merits of the proposed topology in terms of the number of devices and cost are highlighted by comparing the recent and conventional inverter topologies. In addition to this, the total voltage stress of the proposed topology is lower and have a maximum efficiency of 98.25%. The operation and dynamic performance of the proposed topology have been simulated using PLECS software and are validated using an experimental setup considering a different dynamic operation. INDEX TERMS Multilevel inverter, nine-level inverter, step-up inverter, switched capacitor, reduce switch count.
The development and deployment of an effective wind speed forecasting technology can improve the stability and safety of power systems with significant wind penetration. However, due to the wind's unpredictable and unstable qualities, accurate forecasting of wind speed and power is extremely challenging. Several algorithms were proposed for this purpose to improve the level of forecasting reliability. A common method for making predictions based on time series data is the long short-term memory (LSTM) network. This paper proposed a machine learning algorithm, called adaptive dynamic particle swarm algorithm (AD-PSO) combined with guided whale optimization algorithm (Guided WOA), for wind speed ensemble forecasting. The proposed AD-PSO-Guided WOA algorithm selects the optimal hyperparameters value of the LSTM deep learning model for forecasting purposes of wind speed. In experiments, a wind power forecasting dataset is employed to predict hourly power generation up to forty-eight hours ahead at seven wind farms. This case study is taken from the Kaggle Global Energy Forecasting Competition 2012 in wind forecasting. The results demonstrated that the AD-PSO-Guided WOA algorithm provides high accuracy and outperforms a number of comparative optimization and deep learning algorithms. Different tests' statistical analysis, including Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), confirms the accuracy of proposed algorithm.
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