Bubble formation phenomena in a two-phase gas/liquid system occur in many industries that involve boiling; such as desalination stations, nuclear reactors, chemical plants, and fluid piping transportation and processes. Bubble formation phenomena cause problems, such as a decrease in equipment efficiency, vibration, noise, and solid surface erosion. Applications of the acoustic emission (AE) technique for monitoring bubble formation and burst stages in boiling processes are marginal in terms of extension in comparison to other applications of the AE technique. The use of the AE technique in this experimental investigation covers the frequency range between 100 and 1000 kHz, showing that the AE sensor can detect acoustic emissions from an occurrence of bubble formation. Statistically, it was found that the best AE parameter indicator for bubble formation was AE-RMS (root mean square).

SUMMARY Distributed generation (DG) systems are small‐scale power generation technologies that offer better reliability of supply, reduced transmission, and distribution losses and cost. The benefits of DG greatly depend on their placement and size. Most of the existing analytical methods developed so far are applicable for single DG installation only. Further, in meta‐heuristic‐based methods, location and sizing problem of DG were dealt separately in which optimal locations are identified with sensitivity‐based methods, and optimal sizing is obtained using meta‐heuristic algorithms which may lead to incorrect location and suboptimal sizing. In this paper, a novel method that employs harmony search algorithm with differential operator is proposed to install multiple DG units optimally in distribution system with an objective of minimizing active power loss and improving voltage profile. The proposed method is tested on 33‐bus and 69‐bus radial distribution systems to demonstrate its performance and efficacy. Results show that the proposed method is superior to existing methods in quality of solution. Copyright © 2012 John Wiley & Sons, Ltd.

The variable nature of the renewable energy resources (RES) complicates their modelling, operation, and integration to the grid. Therefore, it is difficult to choose optimal RES with a proper energy storage system (ESS) for the economic and reliable operation of the grid-integrated hybrid renewable energy system (HRES). There is a need to solve this optimal HRES problem using efficient algorithms due to the high cost and model complexity involved. In this study, optimal photovoltaic, wind, biomass, and battery-based gridintegrated HRES is proposed using a multi-objective artificial cooperative search algorithm (MOACS) to minimise annual life cycle costing and loss of power supply probability. ESS is chosen to provide a backup power supply for at least 30 min during peak load condition. A probabilistic approach is used to consider the time-varying nature of the RES and load while solving optimal HRES design problem by employing MOACS. A comparative analysis is provided at the end, which shows that MOACS can provide a better optimal design of HRES. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Utilizing electric vehicles (EVs) that don't pollute the atmosphere is one way to solve environmental pollution. This chapter as per the author proposes Harris Hawk optimized algorithm (HHOA) based on fuzzy logic controller with battery management system in electric vehicles. The suggested system is made to alter and regulate the current values of battery characteristics like battery voltage, current, power usage, and state of charge (SOC). To estimate the model temperature dependency, at various temperatures, the heat parameters can be found. Then, these temperature-dependent parameters are extracted for model decoupling. With offline data, the provided model is verified to attain good reliability over a wide range of temperatures and charge states. Extended Kalman filter (EKF) is used to monitor and control BMS operation.