Calculation of electricity sales based on multi-factor correlation analysis

Electricity sales is one of the important assessment indexes of a power grid company’s operation. Since electricity sales is closely related to many factors, how to consider the influence of multiple factors and improve the accuracy of the calculation of electricity sales is a difficult problem that needs to be solved urgently. In this paper, we first analyze the six dimensions affecting electricity sales and select the key influencing factors that can be quantified statistically. Secondly, the key influencing factors are screened according to Pearson’s correlation coefficient and then the calculation model of electricity sales is established based on the random forest algorithm. Finally, we validate the feasibility and validity of the proposed calculation method for electricity sales through a case study

Antimicrobial peptide temporin derivatives inhibit biofilm formation and virulence factor expression of Streptococcus mutans

IntroductionTemporin-GHa obtained from the frog Hylarana guentheri showed bactericidal efficacy against Streptococcus mutans. To enhance its antibacterial activity, the derived peptides GHaR and GHa11R were designed, and their antibacterial performance, antibiofilm efficacy and potential in the inhibition of dental caries were evaluated.MethodsBacterial survival assay, fluorescent staining assay and transmission electron microscopy observation were applied to explore how the peptides inhibited and killed S. mutans. The antibiofilm efficacy was assayed by examining exopolysaccharide (EPS) and lactic acid production, bacterial adhesion and cell surface hydrophobicity. The gene expression level of virulence factors of S. mutans was detected by qRT-PCR. Finally, the impact of the peptides on the caries induced ability of S. mutans was measured using a rat caries model.ResultsIt has been shown that the peptides inhibited biofilm rapid accumulation by weakening the initial adhesion of S. mutans and reducing the production of EPS. Meanwhile, they also decreased bacterial acidogenicity and aciduricity, and ultimately prevented caries development in vivo.ConclusionGHaR and GHa11R might be promising candidates for controlling S. mutans infections

Semi-supervised learning-based satellite remote sensing object detection method for power transmission towers

It is well known that as the power grid becomes more and more complex, the traditional manual survey of transmission towers is inefficient and cannot meet the requirements of safe and stable operation. Although the development of the satellite remote sensing technology provides a new prospect for the efficient and stable survey of transmission towers, there are still many problems that need to be solved. Due to the harsh climate and limitations of the imaging equipment, some of the transmission tower objects in the remote sensing images are blurred, which makes it extremely difficult to generate datasets and to achieve high precision transmission tower object detection. To further develop the detection precision of transmission towers, the image enhancement algorithm based on the dark channel priori is firstly applied for remote sensing images, which can boost the image interpretability. Then, considering that there are still some transmission towers in the enhanced images that cannot be manually labeled, a pseudo-label-based semi-supervised learning method is employed to maximize the use of the existing data. Based on this high-quality dataset, a satellite remote sensing object detection model for transmission towers is constructed using mobile inverted bottleneck convolution and deformable convolution. Finally, the ablation and comparative experiments are conducted according to the satellite remote sensing image dataset of a certain area in China. The experimental results indicate that both the image enhancement and the semi-supervised learning methods can improve the detection precision, and compared with the existing mainstream model, the proposed method performs better

Adaptability Analysis of Fault Component Distance Protection on Transmission Lines Connected to Photovoltaic Power Stations

Photovoltaic (PV) power stations tend to have a relatively weak infeed characteristic, unlike conventional synchronous generators. The limited overcurrent capability of power electronic devices and the controllability of grid-connected inverters mean that PV power stations will cause changes in the characteristics of faults on transmission lines. To analyze the adaptability of fault component distance protection on transmission lines connected to PV power stations, a unified phasor expression for the fault current of a PV power station side under various control strategies was deduced in this paper. This expression is then used to derive the equivalent impedance on the PV power station side and the additional impedance. The equivalent impedance and additional impedance are affected greatly by the active and reactive power commands, control targets, and fault conditions. These aspects of a PV power station may cause malfunctions, which can thereby reduce the reliability of fault component distance protection on transmission lines connected to PV power stations. A simulation model of a PV power station was established in PSCAD/EMTDC and the correctness of theoretical analysis was verified by the simulation results

Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method

The development trend of influenza in China from 2010 to 2019

In this study, we quantify and evaluate the transmission capacity of different types of influenza, and evaluate the flu vaccination effect. Taking the influenza cases reported by the National Influenza Center of China from 2010 to 2019 as the research object (http://www.chinaivdc.cn/cnic), we established the SEIABR model to calculate the influenza infection rate and R0 for each year from 2010 to 2019, and calculate the influenza A and B influenza infection rates. We further added vaccination measures to the SEIABR model, and analysis the impact of different vaccination rates on the spread of influenza. We find that the range of β(infection rate) is $$6.03 \times {10^{ - 10}}$$ to $$9.66 \times {10^{ - 10}}$$, and the average is $$\left({7.95 \pm 1.27} \right) \times {10^{ - 10}}$$, the range of R0 is .98 to 1.47, and the average is 1.21. Simulation result suggest that vaccine coverage needed to reach 60%-80% to control the spread of influenza virus in China when the vaccine effectiveness was 20%-40%. When the vaccine effectiveness is 40%-60%, vaccine coverage needs to reach 40%-60% to control the spread of influenza virus in China. In China, the infection rate of influenza A is higher than influenza B, to better control the spread of the flu virus, we suggest that we also need to increase the number of people vaccinated or improve the efficiency of vaccines(the current vaccination coverage is probably less than 20%)