To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm...
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To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.
The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low *** of the reliable,safe and easy-to-operate technology provided by deep ...
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The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low *** of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation ***,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance *** results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 *** segmentation model proposed can provide a focusing technical means for the intelligent management of powersystems.
Accurate multi-energy load forecasting is pivotal for achieving supply–demand balance and enabling economic dispatch in Integrated energysystems (IES). However, prediction accuracy is significantly compromised by me...
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Accurate multi-energy load forecasting is pivotal for achieving supply–demand balance and enabling economic dispatch in Integrated energysystems (IES). However, prediction accuracy is significantly compromised by meteorological fluctuations, seasonal coupling variations, and multi-energy interactions affecting multi-energy loads. This study proposes a joint multi-energy load forecasting framework integrating meteorological variation rate features with multi task learning (MTL) and single task learning (STL). First, historical load variation rates and meteorological variation rates are calculated, with rapid maximal information coefficient (RapidMIC) employed to identify dominant meteorological variation rates influencing multi-load fluctuations. Subsequently, a weighted average of correlation analysis method quantifies both linear and nonlinear impacts of meteorological variation rates on load fluctuations, enabling precise screening of critical meteorological input features. Second, addressing potential accuracy degradation in MTL models caused by coupling strength disparities among multi-energy loads, a joint forecasting method based on MTL-STL is proposed to enhance prediction precision. Furthermore, a loss function optimization strategy combining homoscedastic uncertainty (HU) and dynamic weight averaging (DWA) achieves real-time weight allocation in MTL. Finally, validation through case studies on IES at Arizona State University (ASU) and an industrial park in Gansu Province, China demonstrates the model’s effectiveness and generalizability: Across all seasons, MAPE values for electricity, cool, and heating loads remain stable within 1.068%-3.022%, 1.877%-5.331%, and 1.697%-3.999% respectively.
Accompanied by energy structure transformation and the depletion of fossil fuels, large-scale distributed power sources and electric vehicles are accessed to distribution network that result in the load peak-valley ga...
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Antenna is widely used in wireless communication equipment, which is developing towards miniaturization and high frequency. The application of fractal theory in antenna design can make the antenna more miniaturized an...
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Load peak and valley periods as the key scheduling scenarios, the effectiveness of wind power prediction becomes extremely important, in order to minimize the impact of wind power on the powersystem due to prediction...
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作者：
Wei, XiuyanZou, GuibinShandong University
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Ji'nan250061 China Northeast Electric Power University
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China
The existing directional pilot protection (DPP) is susceptible to the line distributed capacitance and the fault resistance, and mostly rely on the current limiting reactance (CLR) at the line ends. To solve the above...
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To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is *** regi...
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To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is *** region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature *** the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image *** study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional *** methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other *** algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)*** experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.
This paper proposes an economic analysis method for distributed energy storage applications in distribution networks, and constructs a visual simulation platform. Firstly, the influence of photovoltaic / wind power fl...
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In recent years, arcing faults inside large oil-immersed power transformers have been frequent. Suspended bubbles, as the main product of high temperature, high humidity, material aging and cracking gas production ins...
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