In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines Convolutional Denoising Autoencoder (CDAE) and one-dimensional Convolutional Neural Network (1-DCNN), where CDAE is used for denoising FBG reflection spectra and 1-DCNN is employed for temperature state recognition and temperature demodulation of FBG sensors. The proposed method applies to FBG reflection spectra with different input SNR levels ranging from 0dB to 20dB. Experimental results demonstrate that this CDAE is effective in high-fidelity denoising of the original spectral signals and it outperforms other machine learning techniques. The 1-DCNN model achieves a recognition accuracy of 98.2% for FBG temperature states, with a goodness-of-fit value of 0.9994 for the relationship curve between predicted and actual temperatures, and a root mean square error (RMSE) of only 0.3049°C. This research provides an efficient solution for FBG-based sensor networks.

In this paper, we present a microwave photonic filter bandpass filter with narrow bandwidth and high quality (Q) factor is presented which with a bandwidth of 7.8MHz, a gain response of 24dB, and Q factor is 2412. This performance is achieved through tailoring one Brillouin gain spectrum by two Brillouin loss spectrums. By precise control of the individual pump power and frequency, a narrowed bandpass filter is generated. We achieve this performance through the mathematical formulation of the Stimulated Brillouin scattering (SBS) spectrum relative frequency shift and SBS pumps power ratio, resulting in precise paramet ers resolution of the narrowest filter bandwidth leading to experimentally demonstration. We further show the variation trend of the filter bandwidth and gain response, leading to exploit the general discipline of this technology.

This research provides a detection approach based on Markov transition field (MTF) and convolutional neural network (CNN) for substation perimeter intrusion event recognition. Because of the complexity and variety of external signals, which makes sensor detection more challenging, determining and analyzing the intrusion behavior of vibration signals induced by intrusion has become critical to improving the identification rate of intrusion-like occurrences. The obtained one-dimensional signals are mapped into two-dimensional pictures using MTF to reflect better the properties of intrusion-like signals, which can yield deeper signal details than the usual feature extraction approach. The CNN-vgg19 model’s excellent image feature identification capabilities are utilized to detect and categorize the acquired 2D feature pictures. The experimental results show that the average recognition rate of the six intrusion events is 96.7%, and the average recognition rate of the noise events is 99%, which can effectively identify the noise events and reduce the false-positive rate. The study’s findings are valuable for substation and peripheral security intrusion products.

With the development and utilization of distributed energy and microgrid, distributed energy storage has become a new development trend. However, small pumped storage units have the advantages of flexible engineering location, low investment, quick effect, low requirements on transmission lines, and a better solution to the peak load demand of the system. Therefore, it is more and more used in the microgrid, and it conducts joint dispatching with wind power, photovoltaic, and other clean energies. To solve the capacity problem of small pumped storage units within the microgrid, a new control strategy is proposed in this paper. Two pumped storage units are used for joint operations. Taking the smoothed combined output power of wind power, photovoltaic power, and pumped storage power as the target, and considering the limitations of transmission lines, the constraints of wind power and photovoltaic power fields as well as the restrictions of pumped storage power units and corresponding reservoirs are taken into account. In this paper, social particle swarm optimization (SPSO) with improved weight is used to calculate and solve the model. The effectiveness of the new control strategy is verified.