Development of a multi-megahertz frequency converter for wireless power applications

A novel wave-shifting, frequency-reducing converter topology is developed, with the target application of capacitive wireless power transmission. The new topology combines a Class-E synchronous rectifier and Class-D inverter. Zero-voltage switching is achieved, and the converter is load-independent. The developed converter is an AC-AC converter, which can divide the input frequency by any even whole number. The power throughput of the rectifier is less than that of the total converter, especially when the ratio of input to output frequency is small. Therefore, the wave-shifting converter has a potential advantage in efficiency over AC-DC-AC frequency reducers. The feasibility of the converter is demonstrated by simulation and experimental results. The intended application of the developed converter is to drive an isolation transformer in a capacitive wireless power rectifier. High-frequency transformers have been investigated, and it is shown that a suitable transformer is compatible with the developed converter

Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour. In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications