Methods of de-noising the low frequency electromagnetic data

The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.

An Improved Power Flow Method to Cope with Non-smooth Constraints of Integrated Energy Systems

Integrated energy system applications can significantly improve energy efficiency.In this paper,we establish an integrated energy system containing heat,electricity and gas.The existing power flow(PF)calculation method applied to integrated energy systems(IESs)does not consider non-smooth constraints,such as the piecewise pipeline friction coefficient and generator buses reactive power limits,etc.Mixed integer nonlinear programming(MINLP)is conventionally used to deal with piecewise pipeline friction coefficients in gas network parts,but it is both complex and inefficient.Hence,we develop a piecewise linear function-based fitting method that can reduce the number of integer variables and enhanced the computational efficiency.In the electric network part,if the reactive power of the PV bus violates limits,it will be converted into a PQ bus,which is a non-differentiable and non-smooth constraint.Mixed complementarity problems are conventionally introduced to represent the PV-PQ buses type switching relationship and are addressed by the Newton-Raphson(NR)method.However,the above method is sensitive to the initial point.Here,we introduce a robust projected Levenberg-Marquardt(PLM)algorithm to cope with this issue.We demonstrate the advantages of our method and validate it both in a small-scale system and largescale network test cases.