A series of temperature-and pH-responsive hydrogels were prepared from acrylic acid (AAc), acrylamide (AAm), oligo(ethylene glycol)monoacrylate (OEGMA), and oligo(ethylene glycol)diacrylate by varying the AAc:AAm molar ratio and the OEGMA content. Phase-transition temperatures and swelling ratios of the obtained poly(AAc-co-AAm)-graft-OEG gels were measured as a function of temperature and pH. At pH Ͻ 5, the obvious transition temperatures ranging from 5 to 35°C were obtained as the AAc : AAm molar ratio was varied. The highest transition temperature was obtained at the AAc : AAm ratios of 5 : 5 and 6 : 4, and the sharp transition curves were observed at the AAc : AAm ratios from 5 : 5 to 8 : 2. The transition temperature further increased with increasing OEGMA content. It was suggested that OEG graft chains with a large mobility played an important role for the formation of hydrogen bonding in the hydrogels. The gels prepared here showed obvious reproducibility of the phase transition in response to temperature changes, which suggests the feasibility of their practical applications.
The growing pervasiveness of the Wind Energy Conversion System (WECS) in power systems has a great influence on the electrical system reliability in relation to other conventional sources for power generation. This current study offers a binary particle swarm optimisation (BPSO) application with Weibull model to reliably evaluate the generation systems with a WECS. The proposed methodology is based on hourly time series wind speed and uses Weibull model and simulation of the operation of generation system, taking into consideration the random failures of conventional units of the system and the fluctuating wind energy of a WECS. The BPSO algorithm adopts intelligent research to explore the meaningful system states and accelerate their integrated convergence, so that makes it feasible to locate all possible failure states in the system states space in order to calculate the reliability indices with WECS. The numerical simulation of the suggested solution is compared with the established Monte Carlo simulation (MCS). The reliability test system (IEEE-RTS-79) is employed to show the effectiveness of the proposed algorithm.
The optimal phasor measurement unit (PMU) placement problem in power systems has been considered and investigated by many researchers for accurate and fast state estimation by PMUs. However, the current channel cost of the PMU affects the total placement cost. This paper proposes a novel formulation in the multi‐objective optimal PMU placement, which minimizes the PMU placement cost with the current channel selection and the state estimation error. The current channel selection is represented as a decision variable in the optimization. For trade‐off objective functions, the Pareto approach by nondominated sorting genetic algorithm II (NSGA‐II) is applied in the optimization. The result of the numerical experiment in this paper demonstrates the advantage of considering the appropriate PMU current channel allocation, compared with the conventional method that ignores it, in the modified IEEE New England 39‐bus test system. As a result, the proposed method obtained a better Pareto solution compared with the conventional one because of the consideration for the current channel selection. An advantage of the proposed PMU placement is that it is able to reduce the total PMU placement cost while maintaining the state estimation accuracy.
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