Sectional type direct-current electric arc noise model and parameter optimization and identification method
A DC arc and noise model technology, applied in noise factor or signal-to-noise ratio measurement, chaotic model, calculation model, etc., can solve the problems of falling into local optimum, slow convergence speed, genetic algorithm and particle swarm optimization algorithm prone to premature maturity, etc. , to achieve the effect of speeding up the convergence speed and improving the accuracy
Active Publication Date: 2021-03-19
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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However, the genetic algorithm and particle swarm optimization algorithm have the problems of being premature, falling into local optimum and slow convergence.
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[0041] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0042] Step 1: collect the DC arc current signal, and perform Fourier decomposition on the collected current signal to obtain the frequency domain distribution characteristics of the current;
[0043] Step 2: Establish a segmented DC arc noise model according to the spectral distribution characteristics of the DC arc current, and its expression is:
[0044]
[0045] In the formula, f is the frequency value, f 0 is the inflection point frequency, L is the control parameter of the spectrum energy amplitude, c is the control parameter of the spectrum energy decline rate, S(f) is the spectrum distribution of the model output, S w (f) is the spectrum distribution of the model input signal WS. WS conforms to a normal distribution, and its expression is shown in formula (2):
[0046] WS~N(0,1) (2)
[0047] Step 3: Set the expression of the o...
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The invention discloses a sectional type direct-current electric arc noise model and a parameter optimization and identification method. The sectional type direct-current electric arc noise model parameter identification method comprises the following steps: 1) obtaining direct-current electric arc current data; 2) establishing a sectional type electric arc noise model according to the frequency domain distribution characteristics of the direct current electric arc current; 3) determining a target function and parameters to be identified; and 4) initializing a chaotic quantum cuckoo optimization algorithm by using chaotic mapping to optimize model parameters. According to the invention, the sectional type direct current electric arc noise model is adopted, so that the model output can flexibly fit the characteristics of electric arc current spectrum distribution under different working conditions. The traditional quantum cuckoo optimization algorithm is improved on the basis of three mechanisms of chaos initialization, chaos random parameter generation and chaos local search, and the convergence rate of the algorithm and the ability of the algorithm to jump out of a local minimum point are enhanced, so that sectional type direct-current electric arc noise model parameters can be identified more accurately.
Description
technical field [0001] The invention relates to a segmented DC arc noise model and its parameter optimization and identification methods, belonging to the field of arc fault detection. [0002] technical background [0003] DC power distribution systems are used in more and more fields, such as more electric aircraft, residential buildings, and electric vehicles. However, the increase of system complexity and voltage level has brought great challenges to the safe and stable operation of DC power distribution system. Among them, the DC arc fault is an important fault form in the DC power distribution system. Compared with the AC arc fault, the DC arc fault has no zero crossing point, it is difficult to extinguish itself, and its harm is more serious. DC arc faults are usually caused by loose connectors, frayed, broken wires, and aging. In the process of breakdown air discharge, DC arc fault will produce strong light, noise, electromagnetic radiation and release a lot of hea...
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Login to View More IPC IPC(8): G01R29/26G06F30/20G06N3/00
CPCG06N3/006G06F30/27G06N7/08G06N10/00
Inventor 王莉尹振东杨善水张瑶佳高杨
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS