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Aiming at the optimization of power source capacity in multi-energy industrial parks, an economic optimization model with the lowest comprehensive cost of the system as the objective function was established, and an improved particle swarm optimization algorithm with natural selection strategy and chaos theory was proposed to optimize the model. This algorithm initialized particle fitness by chaotic mapping, added natural selection strategy to the iterative optimization process, and used chaotic ergodicity to search solution space. The test function simulation showed that the algorithm had the characteristics of fast convergence, high precision and being not easy to fall into local optimum. A case study of a certain area in Hebei Province, China, was selected to analyze the example, and the power source capacity optimization design scheme was obtained. The analysis results verified the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

To solve the problem of harmonic distortion generated by the output AC of inverters without considering the relationship between carrier switching frequency and carrier frequency, by analyzing the influence of carrier switching frequency on random carrier signal, it was concluded that the phase mismatch between the front and back carriers was the fundamental reason for the distortion of the output voltage of inverters when the preset carrier was switched randomly. And the switching frequency was randomized by switching the preset carrier, i.e., an improved kind of random carrier frequency modulation (RCFM). The simulation and experimental results showed that the proposed random PWM method could not only suppress the high-order harmonics of the output voltage more effectively, but also have better sinusoidal waveform than the conventional random PWM method. [ABSTRACT FROM AUTHOR]