The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing scheme. Owing to the pulse width of tens of nanoseconds, the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level, which seriously restricts the development of the optical time-domain reflection system. In this paper, a chaos laser is proposed in the context of the physical principle of the Raman scattering effect, and a novel theory of chaos Raman distributed optical fiber sensing scheme is presented. The scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing fiber. Further, the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is demonstrated. Then, the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight time. Based on this novel optical sensing mechanism, an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted, and the spatial resolution was found to be independent of the sensing distance. Within the limit of the existing spatial resolution theory, the spatial resolution of the proposed scheme is 50 times higher than that of the traditional scheme. The scheme also provides a new research direction for optical chaos and optical fiber sensing.

Raman distributed optical fiber temperature sensors possess the unique capability of measuring spatial environmental temperatures, which can be of great interest in several fields of application. The key physics barrier to spatial resolution for most optical time-domain reflection (OTDR) systems is the positioning principle of pulse-time-flight. It obtains the spatial resolution of the existing Raman distributed optical fiber temperature sensor, with the kilometer-level sensing distance being limited to the meter-level. Here, we propose a chaotic laser Raman distributed optical fiber temperature sensing scheme that replaces the traditional OTDR positioning principle used for more than 40 years with the chaotic correlation positioning principle. The proposed scheme possesses the characteristics of the chaos Raman scattering light excited by the chaos signal along the sensing fiber. A novel measurement mechanism based on chaotic time-domain differential reconstruction and chaotic correlation demodulation is developed, and a relationship between the temperature variation information and the chaotic correlation peak is experimentally demonstrated. Importantly, the proposed optics mechanism scheme overcomes the physics limitation of the effect of a wide pulse width on sensing spatial resolution; its spatial resolution is optimized from 50 to 0.3 m under the modulation of a 500 ns pulse width. This scheme provides a new concept for chaos optics and fiber sensing research.

As a key state parameter of high-voltage cables, conductor temperature is an essential determinant of the current carrying capacity of cables, but in practice, this is difficult to measure directly during the operation of high-voltage cables. In this paper, the electromagnetic-thermal coupling analysis model of a 110 kV high-voltage cable is established using the finite element analysis software COMSOL. By analyzing the temperature distribution law of high-voltage cables under different load currents and ambient temperatures, the relationship between the change in the high-voltage cable surface temperature and the conductor temperature is deduced, which allows the monitoring of the high-voltage cable conductor temperature. Taking the 110 kV cable of the Yanzhong line in Shanxi Province as an example and using the electromagnetic-thermal coupling temperature field analysis method, the conductor temperature of the high-voltage cable can be measured using the data obtained from the cable surface temperature, which is measured by the self-developed Raman Distributed Temperature Sensor (RDTS) system with a maximum measurement error of about 2 °C. The method is easy to use and can achieve the accurate measurement of the conductor temperature without damaging the cable body.

Raman distributed optical fiber sensing has the unique ability to measure the spatially distributed profile of temperature that are of great interest to numerous field applications. However, the sensing performance is severely limited by the signal-to-noise ratio (SNR). The existing SNR enhancement schemes have drawbacks such as increased system complexity, degradation of sensor performance metrics such as spatial resolution, poor denoising performance, etc. Here, we report the Raman residual composite dual-convolutional neural network (RRCDNet), a novel convolutional neural network-based denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNet-enhanced Raman distributed fiber sensor system dramatically improves the temperature precision by more than a factor of 100, from 7.57 °C to 0.06 °C, without hardware modification or degradation of other performance metrics. At the same time, RRCDNet can also enhance other optical fiber sensor systems with one-dimensional signals, such as Rayleigh and Brillouin sensing systems.

The practical implementation of Raman distributed optical fibre sensing has been fundamentally constrained by the inherent low signal-to-noise ratio (SNR), particularly for sensing distance exceeding 30 km. We propose a paradigm that combines enhanced anti-distortion coding processing, advanced Raman scattering waveform reconstruction preprocessing, and Haar wavelet denoising to transcend this physical limitation. The proposed pre-processing framework simultaneously optimises complementary sequences correlation, effectively mitigates disturbances of transient effects and improves sensing performance. The experimental demonstration achieves performance metrics: 70.0 km sensing distance with 1.58 m spatial resolution, while maintaining 0.91 °C measurement accuracy and 5.39 °C temperature resolution. The tripartite synergistic mechanism, consisting of the waveform reconstruction pre-processing framework compensating for and suppressing transient effects, coding gain improving the baseline SNR, and the Haar wavelet transform removing residual noise, breaks through the theoretical trade-off constraint between SNR and sensing distance in traditional schemes. The proposed approach demonstrates a potential for application in the fields of long-range infrastructure monitoring and environmental sensing.