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A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results. [ABSTRACT FROM PUBLISHER]

This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window). [ABSTRACT FROM AUTHOR]

North Carolina (NC) is a large producer of utility-scale solar power and most of the photovoltaics are located on former agricultural lands (solar farms). In 2017 4% of NC power was generated by solar, but plans are in place for higher percentages, which could have consequences for food production and water quality. Geospatial data sets were used to characterize NC watersheds by solar farm development potential. Twenty-seven watersheds were identified as good candidates due to abundance of agricultural lands, presence of high-power transmission lines, and existence of agriculturally impaired waterbodies. These watersheds were somewhat distributed throughout the state, but especially focused in Union County, and 33% contain at least one solar farm. An energy generation model found that for the 27 targeted watersheds, only 2.6% of agricultural land would need to be converted to increase solar's energy portfolio to 5%; and 21.7% would need to be converted to increase it to 12.5%. [ABSTRACT FROM AUTHOR]