Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.Since the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application difficult.In order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this paper.Meanwhile,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage stability.To begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of LMPS.Meanwhile,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.

Loaded-frequency selective surface

A new frequency-selective surface(FSS)with loadings is introduced in this paper and analyzed by way of period moment methods(PMMs).The simulated results show that FSS may operate in different bands and especially generate a large reduction in the resonant frequency for a fixed unit cell size through different loadings.This provides a new orientation in the development for FSS.Practical circuits are fabricated,and the measured results agree well with the simulated results.

Propane catalytic cracking on pretreated La-ZSM-5 zeolite during calcination for light olefins production

The purpose of this study was to increase light olefins yield and selectivity in catalytic cracking of propane. Pretreatment of ZSM-5 zeolite was performed during calcination. First, lanthanum was loaded on the catalyst by wet impregnation method and then the effect of parameters, such as temperature（400–800 oC）, time（120–480 min） and type of stream, on La-ZSM-5 zeolite was examined during calcination using central composite design（CCD） method. Based on the proposed models, the optimized condition for maximizing selectivity of light olefins for air stream was 680 oC and 310 min and for the nitrogen stream was 735 oC and 173 min that under these conditions, selectivity amount increased 24% for nitrogen stream and 19% for air stream as compared with H-ZSM-5 catalyst. The synthesized catalysts were characterized by X-ray diffraction（XRD） and temperature-programmed desorption（TPD） techniques. According to the results of NH3-TPD, as the temperature increased, the number of strong acid sites（specially Bronsted acid sites） reduced and resulted in increased production of light olefins as well as catalyst stability. Accordingly, stability of the samples calcined with nitrogen stream was higher than the samples calcined with the air stream.