Research On Detection Technology Of Weak Signal Based On Chaos Theory

With the increase of equipment of electric power,machinery and other fields,weak signals are often collected to judge the operating condition of the equipment in the process of daily equipment maintenance.Therefore,weak signal detection has become an important research.The traditional detection methods of weak signal have low efficiency and high SNR threshold.At the same time,the combination of chaos theory research and weak signal detection has attracted much attention.This paper used the sensitivity of chaotic system to weak signal and outstanding noise immunity to study the detection of weak signal and to applied to detection of partial discharge signal in the electric power equipment.Firstly,through the analysis of dynamic behaviors,it was verified that the novel chaotic system has rich dynamic characteristics.The working principle of this system for detecting weak sinusoidal signal was explained and several possibilities of missing detection were discussed.Compared with the Duffing system and the coupling Duffing system,the advantage of the novel chaotic system in terms of noise immunity was shown.The experiments were performed under Gaussian white noise and colored noise and this system was found to have a very low SNR threshold.By increasing the critical threshold accuracy,the identified phase difference range of the weak signal can be expanded.The number of the chaotic system used in the detection model is further reduced.The combination algorithm of scale transform and chaotic system array was used to identify the unknown signal and recognize its frequency.Then,the amplitude and phase of the unknown signal were estimated by phase shifting.Aiming at the limitations of commonly used phase state identification methods,an improved phase state identification method with universality and real-time performance was proposed.The principle of this method is simple.It is not affected by the transition process and it can identify intermittent chaotic state.By the way,the simulation circuits of a novel chaotic system and an improved phase state identification method were designed to realize the function of automatically judging the system states and to achieve accurate detection of nanovolt-level sinusoidal signals.Secondly,a double coupling Van der pol-Duffing system was proposed and its dynamic characteristics were analyzed.The working principle of detecting weak pulse signal by using transient out-of-synchronization phenomenon was discussed.Detection effect were studied in the absence of the coupling terms of the restoring force and the damping force respectively.By adjusting the system to detect pulse signal in different states,it was found that the double coupling system can detect weak pulse signal in any system state.The three major indexes which are pulse synchronization difference,output noise variance and corrected time ratio were proposed to compare the detection capability of the double coupled system under different phase states.Then,the detection probabilities of several types of coupled systems under different SNR conditions were calculated.The simulation experiments verified that this system has outstanding noise immunity and significant detection effect in quasi-periodic and periodic states.It can accurately detect positive and negative alternating pulse signals in real time and overcome the shortcomings that the ring coupled Duffing system generates twoway oscillation and relies on specific system state when it is detecting pulse signals.The double coupling system can achieve lower SNR detection than several detection models based on Duffing system.Finally,the field of partial discharge signal detection was studied and chaos theory was introduced.The novel chaotic system was used to detect the partial discharge signals of the attenuated oscillating form and the double coupled Van der pol-Duffing system was used to detect the partial discharge signal of the attenuated form.This method provides a new idea for the weak signal detection and fault identification of the power system.