This article proposes a non-inverting bidirectional buck-boost chopper accompanied by an auxiliary converter for battery storage that is installed in a light rail vehicle. The proposed chopper is composed of two half-bridge cells called the main converter, an auxiliary converter consisting of many full-bridge converters connected in cascade, and a small-sized inductor. It is controlled such that the capacitors of the auxiliary converter store and release most of the chopper energy instead of relying solely on a bulky and heavy inductor. As a result, it is lighter and smaller than the four-switch non-inverting buck-boost chopper. The operation, as well as the control of the proposed chopper, are experimentally tested using a down-scaled prototype. Further, a comparison with the four-switch non-inverting buck-boost chopper with respect to mass, volume, and efficiency is made to verify the efficacy of the proposed chopper.

Enhancing the performance of the voltage source inverters (VSIs) without changing the hardware structure has recently acquired an increased amount of interest. In this study, an optimization algorithm, enhancing the quality of the output power and the efficiency of three-phase grid connected VSIs is proposed. Towards that end, the proposed algorithm varies the switching frequency (f_sw) to maintain the best balance between switching losses of the insulated-gate-bipolar-transistor (IGBT) power module as well as the output power quality under all loading conditions, including the ambient temperature effect. Since there is a contradiction with these two measures in relation to the switching frequency, the theory of multi-objective optimization is employed. The proposed algorithm is executed on the platform of Altera^® DE2-115 field-programmable-gate-array (FPGA) in which the optimal value of the switching frequency is determined online without the need for heavy offline calculations and/or lookup tables. With adopting the proposed algorithm, there is an improvement in the VSI efficiency without degrading the output power quality. Therefore, the proposed algorithm enhances the lifetime of the IGBT power module because of reduced variations in the module’s junction temperature. An experimental prototype is built, and experimental tests are conducted for the verification of the viability of the proposed algorithm.

This paper presents an interleaved bidirectional chopper with auxiliary converters for onboard energy storage systems that is capable of achieving a significant reduction in the mass and volume of the current-smoothing inductors. The proposed chopper consists of multiple interleaved sub-converters, where each sub-converter consists of a bidirectional chopper cell, a current-smoothing inductor at the low-voltage side, and an auxiliary converter that is formed by the cascade connection of multiple full-bridge cells. Each auxiliary converter is connected in series with its respective inductor to suppress the ac voltage component that produces ripple current in the inductor of each sub-converter; hence, a smaller inductor can be used to meet the ripple current requirement when compared with the conventional bidirectional chopper. In addition, auxiliary converters can be used as dc-circuit breakers to protect the low-voltage side against faults in the power devices of the bidirectional chopper cells. This paper discusses the operating principles and control method of the proposed chopper, followed by experiments using a downscaled experimental model.

The performance optimization of the two-level, three-phase, grid-feeding, voltage-source inverter (VSI) is studied in this paper, which adopts an online adaptive switching frequency algorithm (OASF). A new degree of freedom has been added to the employed OASF algorithm for optimal selection of the weighting factor and overall system optimization design. Toward that end, a full mathematical formulation, including the impact of the coupling inductor and the controller response time, is presented. At first, the weighting factor is selected to favor the switching losses, and the controller gains are optimized by minimizing the integral time-weighted absolute error (ITAE) of the output active and reactive power. Different loading and ambient temperature conditions are considered to validate the optimized controller and its fast response through online field programmable gate array (FPGA)-in-the-loop. Then, the weighting factor is optimally selected to reduce the cost of the L-filter and the heat-sink. An optimization problem to minimize the cost design at the worst case of loading condition for grid-feeding VSI is formulated. The results from this optimization problem are the filter inductance, the thermal resistance of the heat-sink, and the optimal switching frequency with the optimal weighting factor. The VSI test-bed using the optimized parameters is used to verify the proposed work experimentally. Adopting the OASF algorithm that employs the optimal weighting factor for grid-feeding VSI, the percentages of the reductions in the slope of the steady state junction temperature profile compared to fixed frequencies of 10 kHz, 14.434 kHz, and 20 kHz are about 6%, 30%, and 18%, respectively.

Factors like pricing, transmission expansion, and capacity planning rely on accurate power demand forecasts. This paper intends to utilize time‐series models to forecast the peak electricity demand of Jordan's power grid amidst its energy transition, offering insights into necessary expansion and system adjustments over the next decade It explores the relationship between the country's peak load fluctuations over the last three decades and examining factors including the Gross Domestic Product (GDP) and population growth. Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Integrated Moving Average with Explanatory Variable (ARIMA‐X), are employed to forecast yearly peak loads, which are also compared with linear regression, providing an enhanced understanding of power generation and network expansion needs for the coming decade. The results show strong correlations between peak load, population growth, and GDP, with the models proving effective in forecasting future peak loads, albeit with caution regarding ARIMA‐X. Projections suggest a potential 41% increase in peak load by 2035, reaching around 5300 MW in 14 years. Assuming consistent growth rates in population and GDP, the projections of the peak load also indicate that the peak load might reach twice its current level in the next 2 to 2.5 decades.