重庆电力高等专科学校

This study investigated the synergistic characteristics of co-hydrothermal carbonization (co-HTC) of sewage sludge (SS) and wheat straw (WS). Co-HTC of SS and WS synergistically promoted the conversion of organic matter into combustible hydrochar components. The synergistic coefficients for hydrochar yield (YHC), energy recovery efficiency (R-EN), carbon recovery efficiency (R-C) and hydrogen recovery efficiency (R-H) first increased and then decreased as the WS blending ratio or hydrothermal carbonization (HTC) temperature increased. Additionally, blending WS or increasing the HTC temperature shifted the main combustion process of hydrochar to the low-temperature region. The average activation energy of hydrochar combustion first decreased and then increased as the WS blending ratio increased. However, the average combustion activation energies of hydrochars prepared via HTC at 200, 230 and 260 degrees C were essentially identical (160.61, 161.88, and 161.04 kJ mol(-1), respectively), with a relative difference of < 1%. Maintaining an HTC temperature of 200 degrees C avoids the higher energy input required for elevated temperatures (230 or 260 degrees C), which is crucial for reducing the industrial application costs of HTC while preserving hydrochar quality. Consequently, from the perspective of energy consumption, 200 degrees C is more suitable for the HTC. The minimum average activation energy for hydrochar combustion (160.61 kJ mol(-1)) was achieved at a SS:WS mass ratio of 2:1 and an HTC temperature of 200 degrees C. These research findings provide a theoretical basis for the energy-oriented utilization of SS and waste biomass.

Due to their ease of access and low cost,unmanned aerial vehicle(UAV)visible remote sensing images have been widely used for the statistical analysis of agricultural resources.To obtain more representative features of UAV visible remote sensing images and achieve accurate land-cover classification,a land-cover classification algorithm based on the joint distribution of color-spatial features is proposed.First,the index of the golden rectangular patch is defined to select patches for sampling from the labeled data.Based on the golden rectangles of the selected patches,a logarithmic spiral was constructed to choose the training samples.Color feature reference points and neighborhood pixels were then applied to calculate the difference information and extract the color-space joint feature for each sample.Subsequently,the objective function of the joint feature is constructed using Jensen's inequality and fuzzy classification maximum likelihood.Next,the multidimensional mixed Weibull distribution of each sample is solved using several iterations.Finally,a similarity measure corresponding to the multidimensional mixed Weibull distribution was defined to classify each sample under analysis.Experimental results show that the overall accuracy of the proposed algorithm reaches 98.6%,which is better than that of local binary pattern,gray level cooccurrence matrix,random forest,ResNet,and VGG,proving the effectiveness of the proposed algorithm.

Accurate short-term load forecasting (STLF) is vital for modern power systems to be safe and efficient. STLF presents difficulties because of the cyclic, non-stationary, seasonal, and nonlinear nature of electricity consumption. With advanced metering infrastructure (AMI) collecting massive power con-sumption data, machine learning (ML) methods have emerged as promising tools to improve STLF accuracy. However, one critical challenge faced by existing ML methods is to deal with the domain shift problem when applying ML models into the industrial scenario. To address the limitation, we propose a novel partial adversarial adaptation enhanced deep forecasting (PADF) framework. Specifically, we identify the outlier samples in source domain using a domain classifier without gradient reversal layer (GRL) and reduce the domain shift between normal target and source domain samples at the same time. We demonstrate the effectiveness of PAFF on real-world large-scale datasets from the state grid corporation of China (SGCC).

Non-technical losses (NTL) in power grid not only cause significant financial loss but also reduce the quality of supply for the distribution network operators (DNOs). In recent years, the frequency of power theft activities related to NTL have been increased rapidly. The data-based detection approach for NTLs has been studied by researchers from all over the world thoroughly, but the region domain shift issues and the lack of labelled data limited its applicability. In this paper, an adversarial learning-based approach is proposed to alleviate the domain shift problem, named Cross-Regional Adaptive Network (CAN). First of all, a multilayer Convolutional Neural Networks (CNN) feature extractor maps the training and application scenario data into the same feature space. After that the feature extractor and the domain classifier are trained against each other by inversing extractor's gradients. Finally, the training and application scenario data are indistinguishable for the domain classifier, and the feature extractor can extract domain invariant features. Experiments are conducted on two sets of electricity consumption data captured by utility company to verify the effectiveness of the proposed method.

Based on the survey data of the employment questionnaire for computer network technology majors graduating in 2022 in Chongqing Electric Power College, by means of the principal component analysis, 21 influencing factors of employment were transformed into several comprehensive factors to determine the main influencing factors of employment.The grading of some experts together with the survey verified the validity and unity of the results of the model calculation.This paper points out that employment education and ideological and political education, in addition to specialized education, are significant in enhancing the graduates’ quality of employment.Besides, it puts forward relevant suggestions in the aspects of perfecting the system of employment guidance, improving comprehensive ability of employment, expanding channels of employment and deepening the school-enterprise cooperation, in order to perfect the work of employment guidance for computer network technology majors and provide reference for the work of employment guidance for related majors in similar colleges.