Energy management system and supervision in a micro-grid using artificial neural network technique

Nowadays, the combination of conventional and renewable energy sources such as solar energy is one of the most widespread solutions to surmount the challenge of the climate and energy crisis. In the presence of random behavior of photovoltaic systems and variable power demand by consumers, energy management is a real challenge. In this paper, we propose a new energy management technique based on artificial neural networks in a smart grid. This will ensure the continuous supply of electricity to the consumer in the presence of random operation in energy consumption and generation. The global system is modeled and simulated under the MATLAB/Simulink tool.

An Automated Aerosol Collection and Extraction System to Characterize Electronic Cigarette Aerosols

Electronic cigarette (e-cigarette) market increased by 122% during 2014–2020 and is expected to continue growing rapidly. Despite their popularity, e-cigarettes are known to emit dangerous levels of toxic compounds (e.g., carbonyls), but a lack of accurate and efficient testing methods is hindering the characterization of e-cigarette aerosols emitted by a wide variety of e-cigarette devices, e-liquids, and use patterns. The aim of this study is to fill this gap by developing an automated E-cigarette Aerosol Collection and Extraction System (E-ACES) consisting of a vaping machine and a collection/extraction system. The puffing system was designed to mimic e-cigarette use patterns (i.e., power output and puff topography) by means of a variable power-supply and a flow control system. The sampling system collects e-cigarette aerosols using a combination of glass wool and a continuously wetted denuder. After the collection stage, the system is automatically washed with absorbing and extracting liquids (e.g., methanol, an acetaldehyde-DNPH solution). The entire system is controlled by a computer. E-ACES performance was evaluated against conventional methods during measurements of nicotine and carbonyl emissions from a tank type e-cigarette. Nicotine levels measured using glass fiber filters and E-ACES were not significantly different: 201.2 ± 6.2 and 212.5 ± 17 μg/puff (p = 0.377), respectively. Differences in formaldehyde and acetaldehyde levels between filter-DNPH cartridges and the E-ACES were 14% (p = 0.057) and 13% (p = 0.380), respectively. The E-ACES showed reproducible nicotine and carbonyl testing results for the selected e-cigarette vaping conditions.

GMDH Neural Networks - Based Modeling of Variable Power Inductor

In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.

Predictive and comparative analysis of NPP modernization with an autonomous hydrogen power complex and gas turbine unit

Optimizing existing nuclear power plants adding developing power technology can help find effective ways of improving variable power loads in an electric power system. One of the most promising options is combining a nuclear power plant with a newly developed autonomous hydrogen complex reported in our research. The ability of storing unused energy and releasing it when needed will raise contribution of nuclear power plants in compensating improving variable power loads, shorten emissions as well as contribution of conventional thermal power plants into electric power generation. Also, as we demonstrated in our previous research results, a low-power steam turbine plant used in the said autonomous hydrogen complex can support an auxiliary power system of a nuclear power plant reusing residual reactor heat in case of an outage.

Adaptive Mode Selection Strategy for Series-Parallel Hybrid Electric Vehicles Based on Variable Power Reserve

When the series-parallel hybrid electric vehicle exits the pure electric mode, the battery provides power for the drive motor and integrated starter generator (ISG) to drive the vehicle and start the engine. If the battery discharge power is insufficient, the driving power will drop, which will inhibit the vehicle from accelerating and impair drivability. Considering that the mode selection strategy determines the timing of mode switching, this paper proposes an adaptive mode selection strategy based on variable power reserve to allow the vehicle to switch mode considering the battery power limitation. The effectiveness of this strategy is verified by simulation, and its influence on fuel consumption and battery utilization is analyzed. Compared with the mode selection strategy based on logic thresholds at the same initial battery state of charge (SOC), under the high-speed and aggressive US06 cycle, the total driving power drop is reduced by 74.2%, and the over-discharge power of the battery is fully restrained while keep almost the same fuel consumption; under the city FTP cycle, the total driving power drop is reduced 65%, and fuel consumption is reduced while maintaining SOC at a reasonable level.