A Simple Decentralized Charging Control Scheme of Plug-in Electric Vehicles for Alleviating Wind Farm Intermittency

AbstractVariable power output from large-scale wind farms present new challenge of balancing power system load with generation. To alleviate this problem, this paper proposes a decentralized charging control scheme for plug-in electric vehicles (PEVs) to neutralize wind power fluctuations. In the proposed scheme, each PEV autonomously adjusts its power in response to a real-time directing signal and based on its own urgency level of charging. No intelligent central control entity is needed. Simulation results demonstrate the effectiveness of the proposed charging control in directing PEV power to counteract wind power fluctuations. Also, proportionally fair distribution of counteracting duties among PEVs can be achieved so as to meet heterogeneous charging requirements of PEV users, and the total utility of the PEV fleet is proven to be maximized

A Framework for Self-healing Smart Grid with Incorporation of Multi-Agents

AbstractA hierarchical framework incorporated with multi-agents is proposed for enabling the self-healing of smart grid. While the central control agent in the upper layer adopts the multiple-step Taylor series function (MTSF) method to efficiently predict the system stability using wide area measurement system (WAMS) data, agents with shared information in the lower layer protect the devices in plug-in micro grids more effectively and adaptively compared with traditional protection. The proposed framework shows the self-healing capability for ensuring the security of smart grid by reliably preventing faults and flexibly coordinating generations. Simulation results of modified WSCC 3-generator system with plug-in micro grids have confirmed the validity of the proposed framework

Probability weighted four-point arc imaging algorithm for time-reversed lamb wave damage detection

Damage imaging based on scattering signals of ultrasonic Lamb waves in plate structure is considered as one of the most effective ways for structural health monitoring area. To improve location accuracy and reduce the impact of artifacts, a probability weighted four-point arc imaging algorithm for time reversal Lamb wave damage detection is proposed in this paper. By taking the defect as a secondary wave source, the four-point arc positioning method is used to calculate the propagation time of the signal from transducer to defect. And the amplitude of damage signal corresponding to the time of flight is used for imaging. In order to eliminate the artifacts, a damage probability weighting is combined with four-point circular arc imaging algorithm. The effectiveness of the proposed method is experimentally verified in aluminum plate. Experimental results indicate that damage location accuracy and imaging quality has been improved in both single-flaw and double-flaw samples compared with conventional delay-and-sum method

A modified damage index probability imaging algorithm based on delay-and-sum imaging for synthesizing time-reversed Lamb waves

Imaging for damage in plate structure by Lamb waves is one of the most effective methods in the field of structural health monitoring. In order to improve the accuracy of damage localization, a novel method is proposed to modify damage exponent probability imaging algorithm based on delay-and-sum imaging by using time reversal Lamb waves. A new probability distribution function is introduced to improve the damage index probability method and is combined with delay-and-sum method for damage localization. Experimental results on aluminum plate show that the hybrid algorithm achieves better accuracy of damage location and imaging quality than the conventional delay-and-sum method

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real-time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programmes, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared with three different strategies, which interpret the effectiveness of the proposed strategy.In part by the National Natural Science Foundation of China [61304090] and the Department of Education of Liaoning Province, China [L2013132].http://www.tandfonline.com/loi/tprs202016-07-30hb201

Damage detection of composite plate based on an improved DAS algorithm by time difference due to anisotropy

This paper proposes a damage detection method based on an improved DAS imaging algorithm by introducing time difference due to anisotropy of composite material. First, the finite element characteristic frequency method is used to obtain the dispersion curve of the composite plate, and the validity of the dispersion curve is verified. Next, the average phase velocity of the Lamb wave at mixed modes in the composite plate is obtained by two-dimensional Fourier transform (2-D FFT). The mixed modal group velocity is calculated according to the corresponding phase velocity, the mean change rate of the phase velocity and the dispersion curve obtained by simulation. The time difference due to anisotropy of composite material is investigated, and the damage location is estimated by the delay-and-sum (DAS) imaging algorithm. Experiments on carbon fiber multilayer composite plates verify the effectiveness of the proposed method

Co-delivery of doxorubicin and oleanolic acid by triple-sensitive nanocomposite based on chitosan for effective promoting tumor apoptosis

Nanocomposites as “stevedores” for co-delivery of multidrugs hold great promise in addressing the drawbacks of traditional cancer chemotherapy. In this work, our strategy presents a new avenue for the stepwise release of two co-delivered agents into the tumor cells. The hybrid nanocomposite consists of a pH-responsive chitosan (CS), a thermosensitive poly(N-vinylcaprolactam) (PNVCL) and a functionalized cell-penetrating peptide (H6R6). Doxorubicin (DOX) and oleanolic acid (OA) are loaded into the nanocomposite (H6R6-CS-g-PNVCL). The system displayed a suitable size (∼190 nm), a high DOX loading (13.2%) and OA loading efficiency (7.3%). The tumor microenvironment triggered the nanocomposite to be selectively retained in tumor cells, then releasing the drugs. Both in vitro and in vivo studies showed a significant enhancement in antitumor activity of the co-delivered system in comparison to mono-delivery. This approach which relies on redox, pH and temperature effects utilizing co-delivery nanosystems may be beneficial for future applications in cancer chemotherapy