Capacitive energy storage from single pore to porous electrode identified by frequency response analysis

Rate capability,peak power,and energy density are of vital importance for the capacitive energy storage(CES)of electrochemical energy devices.The frequency response analysis(FRA)is regarded as an efficient tool in studying the CES.In the present work,a bi-scale impedance transmission line model(TLM)is firstly developed for a single pore to a porous electrode.Not only the TLM of the single pore is reparameterized but also the particle packing compactness is defined in the bi-scale.Subsequently,the CES properties are identified by FRA,focused on rate capability vs.characteristic frequency,peak power vs.equivalent series resistance,and energy density vs.low frequency limiting capacitance for a single pore to a porous electrode.Based on these relationships,the CES properties are numerically simulated and theoretically predicted for a single pore to a porous electrode in terms of intra-particle pore length,intra-particle pore diameter,inter-particle pore diameter,electrolyte conductivity,interfacial capacitance&exponent factor,electrode thickness,electrode apparent surface area,and particle packing compactness.Finally,the experimental diagnosis of four supercapacitors(SCs)with different electrode thicknesses is conducted for validating the bi-scale TLM and gaining an insight into the CES properties for a porous electrode to a single pore.The calculating results suggest,to some extent,the inter-particle pore plays a more critical role than the intra-particle pore in the CES properties such as the rate capability and the peak power density for a single pore to a porous electrode.Hence,in order to design a better porous electrode,more attention should be given to the inter-particle pore.

Nonlinear Dynamic Analysis of the Whole Vehicle on Bumpy Road

Through the research into the characteristics of 7-DoF high dimensional nonlinear dynamics of a vehicle on bumpy road, the periodic movement and chaotic behavior of the vehicle were found.The methods of nonlinear frequency response analysis, global bifurcation, frequency chart and Poincaré maps were used simultaneously to derive strange super chaotic attractor.According to Lyapunov exponents calculated by Gram-Schmidt method, the unstable region was compartmentalized and the super chaotic characteristic of ...

Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm

Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.

Advancement in Materials for Supercapacitors

As future improvement to the energy density and power density of supercapacitors relies on the availability of new materials, worldwide research has been undertaken to address this need. The recent advancement in new materials used for fabricating supercapacitors is reviewed in this paper. Among the newly emerged materials covered in this review are the activated graphene, conductive polymers, CNT （carbon nantotubes）, AC （activated carbons）, carbon additives and metal oxides for EDLC （electric double layer capacitors） and pseudocapacitors applications.

Well-being analysis of GSU transformer insulation incorporating the impact on power generation using fuzzy logic

With the prevailing power scenario, every watt-second of electrical energy has its own merit in satisfying the consumer demand. At the state of such a stringent energy demanding era, failure of a power generation equipment compounds the energy constraints which will not only result in a huge loss of generation but also have an impact on capital revenue. The unexpected failure of generator step-up （GSU） transformer is espe- cially a major disturbance in the power system operation and leads to unscheduled outages with power delivery problems. The time lag in bringing back the equipment in service after rectification or replacement may increase the criticality as the process involves mobilization of spares and maintenance professionals. Hot atmosphere existing in the vicinity of thermal power stations running round-the- clock with more than 100% plant load factor （PLF） increases the thermal stress of the electrical insulation which leads to premature failure of windings, bushings, core laminations, etc. The healthy state of the GSU transformer has to be ensured to minimize the loss of power generation. As the predication related to failure of a GSU transformer is associated with some uncertainties, a fuzzy approach is employed in this paper along with actual field data and case studies for the well-being analysis of GSU transformer.