Aiming at the radiated electromagnetic interference (EMI) noise of electronic equipment, a novel method of radiated EMI noise analysis based on non-linear principal component analysis (NLPCA) algorithm is proposed in this paper. In order to obtain multiple independent common-mode / differential-mode radiated sources, and to find the sources that cause the radiated noises that exceed the limit of standard, NLPCA algorithm is used to process the near-field radiated signals superimposed by multiple radiated sources. The simulation results show that NLPCA can successfully screen out the radiated EMI noises which exceed the limit of standard. Moreover, the experiments are carried out with three models: double-common-mode hybrid sources, double-differential-mode hybrid sources and common-differential-mode hybrid sources. Compared with the traditional independent component algorithm (ICA), the method proposed in this paper can separate the radiated EMI noise sources more accurately and quickly. It can be concluded that the accuracy of NLPCA algorithm is 10% higher than ICA algorithm. This work will contribute to trace the radiated EMI noise sources, and to provide the theoretical basis for the future suppression.

In the current distribution system, the participation of solar power generation system in operation has been the main topic in the field of distribution system research. However, because of climate and other factors, it will increase the difficulty of calculation to study the 8760 hours of sunshine of solar power distribution system at least one year, so it is necessary to simplify the data. Firstly, this paper designs matlab program based on SOM clustering analysis method, which integrates solar power generation and load, and simplifies at least several cases. Then, a matlab program is designed based on the Markov key to integrate the generating capacity and load change in multi scenario, and the weight value of each scenario is calculated by Markov, so as to achieve the goal of multi scenario integration.

In this paper, a new method for conducting EMI noise source identification is proposed. Firstly, the basic principle of the classical KPCA method is analyzed. Then, the core principal component (KPCA) data is selected, and the input space is transformed into the feature space through nonlinear transformation. This method analyzes the relationship between the time and frequency of the electromagnetic interference signal from the frequency domain, so as to extract the time-domain characteristics of the noise signal, and finally diagnose the characteristics that cause the conduction of excessive signal, and according to the characteristics of the noise signal targeted suppression experiment.