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Aiming at the accuracy of modeling in existing integrated energy system planning methods, a comprehensive energy system p lanning method that takes into account the actual operating efficiency of the system is proposed. First, the output model of various equipment units based on the basic structure of the integrated energy system is established. Then, based on the traditional energy hub model, a correction model for the work efficiency of energy equipment to construct an improved dynamic efficiency model of energy equipment is established. With the goal of minimizing the comprehensive cost of the integrated energy system, an integrated energy system planning operation model that takes into account the operating characteristics of the equipment is proposed. Finally, the planning method proposed in this paper is verified through simulation. The results show that: compared with the traditional planning method based on energy hubs, the method proposed in this paper improves the actual part-load performance of the system by improving the accuracy of energy coupling equipment, making the proposed planning model more reasonable and economical.

As the carrier of electric energy transmission, transmission lines undertake the important task of electric energy distribution and transfer. However, with the increasing frequency of construction using large machinery such as tower cranes and excavators under the transmission channels, transmission line accidents occur frequently. Therefore, this paper proposes a refined identification method for the hidden dangers of external damage in transmission lines based on the generation of effective regions driven by vanishing points. The comprehensive and accurate perception of external damage targets through the perception model of scene elements based on slicing-aided hyperinference was realized. Secondly, the accuracy and robustness of the calculation of the transmission line's vanishing point were improved based on Canny edge detection and Hough linear detection. The effective region on the visual images was generated by combining the vanishing point and the bottom of transmission tower coordinates. Finally, the relative position relationship between areas with hidden dangers of external damage and the effective warning regions were compared, and the refined identification of hidden dangers was realized. The experimental data show that the proposed method realized a perception accuracy of 82.9% in identifying hidden dangers of external damage caused by ground- and aerial-moving targets, which shows better detection performance and practical value compared with the existing method. [ABSTRACT FROM AUTHOR]

Oil-immersed transformers play a pivotal role owing to their environmentally friendly characteristics, compact footprint, and cost-effectiveness. Ensuring the online monitoring of oil-immersed transformers is a fundamental measure to ensure the secure and stable operation of modern power systems. In this paper, metal particle cluster-doped SnS is firstly used in the adsorption and sensing of decomposition components (CO, C2H2) under fault conditions in oil-immersed transformers. The study comprehensively analyzed band structure, differential charge density, density of states, and molecular orbital theory to unveil the adsorption and sensing mechanisms of target gases. The findings suggest that the modification of metal particle clusters can enhance the surface electronic properties of single-layer SnS. In the regions of metal particle clusters and the gas–surface reaction area, electronic activity is significantly heightened, primarily attributed to the contribution of d-orbital electrons of the metal cluster structures. The modified SnS exhibits adsorption capacity in the following order: Ru3-SnS > Mo3-SnS > Au3-SnS. Additionally, the modified material demonstrates increased competitiveness for C2H2, with adsorption types falling under physical chemistry adsorption. Different metal elements exert diverse effects on the electronic distribution of the entire system, providing a theoretical foundation for the preparation of corresponding sensors. The findings in this work offer numerical insights for the further preparation and development of SnS nanosensors, concurrently shedding light on the online monitoring of faults in oil-immersed transformers. [ABSTRACT FROM AUTHOR]

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy. [ABSTRACT FROM AUTHOR]

In keeping with China's dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power grid and the correlation properties of carbon emission flow, an optimal power flow calculation model targeting the total carbon emission rate of the power system's power generation cost, active network loss, and load and network loss was constructed. Next, the NSGA-III algorithm was used to solve the model, and the decision was to coordinate and optimize the output schemes of various types of power plants, such as wind, water, and thermal. The modified IEEE39 node simulation system was built with Matlab software (MATLAB R2020b). The results of the calculation showed that, compared to the traditional method of determining the optimal power flow, the proposed method reduced the system carbon emissions by 20% while the power generation cost increased by less than 2%, which proves the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

As an important index of risk protection, the safety distance is crucial to ensure the safe and stable operation of the power system and the safety of personnel's life. Traditional monitoring methods are difficult to balance recognition accuracy and convenience. Therefore, this paper presents a power safety distance sensing method based on monocular visual images to achieve the recognition of the safety distance of external damage in complex scenes of transmission corridors, and proposed a power density depth distance model. In this model, a codec network with skip-connection to extract features and aggregate shallow and deep features for input power system images. Then, the regularization method, migration learning strategy, cosine annealing learning strategy, and data enhancement strategy are used to further optimize the model, so as to obtain a model with good accuracy and generalization in complex conditions. The effectiveness and superiority of the proposed method are verified in comparison to other external damage monitoring methods. The experimental results showed that the proposed method has high accuracy for the distance of external damage in the actual scenario. Moreover, the method has good generalizability, which can be easily deployed in video monitoring systems on different transmission corridors. [ABSTRACT FROM AUTHOR]

With the deep coupling between the cyber side and the physical side of power systems, the failure of any link of both sides may lead to power outages, so it is necessary to analyze their vulnerability and vulnerable links for targeted improvement of systems. By dynamically attacking the coupled network nodes, this paper proposes a multilevel model and node-to-edge cyber-physical power system and the corresponding indexes system to analyze the vulnerability of the coupled power grid and its key components. The results showed that in the order of the indexes proposed in this paper, attacking surviving power nodes and cyber nodes results in a network crash rate of 25.0% and 66.7% faster than that in the order of "betweenness" and that attacking surviving cyber nodes results in a network crash rate of 89.4% faster than that in the order of "degree." In terms of attacking power nodes, the index proposed in this paper has the same rate as "degree." Therefore, the proposed model can better describe the vulnerability of the power grid to withstand attacks. [ABSTRACT FROM AUTHOR]

The contradiction between the demand of massive intelligent scene caused by the interconnection of things in power system and the bottleneck of the chip itself is becoming more and more serious. How to realize the self-controllable and domestic substitution of power chips has been widely concerned in the power field. Therefore, this paper analyzes the demand for power chips in power system and discusses the scene specificity of power chips. Then, it combs the design architecture of power chips and discusses the application scenarios and research focuses of power chips. Finally, the key scientific problems and major technical bottlenecks of power chips are prospected. [ABSTRACT FROM AUTHOR]

Microwave ablation (MWA) is a key alternative therapy to conventional surgery for the treatment of lung cancer. In addition to eliminating local tumors, MWA may promote antitumor immunological responses, such as abscopal effects in distant lesions. However, the intensity of MWA is limited and the underlying mechanisms are not well-defined. The present study assessed the impact of MWA on immune cell subsets and cytokines in patients with lung cancer. A total of 45 patients with lung cancer who underwent percutaneous lung tumor MWA were enrolled. Peripheral blood samples were collected before and 24 h after MWA and changes in immune cell subsets [lymphocytes, CD3+, CD4+ and CD8+ T cells, B cells and natural killer (NK) cells] and serum cytokine levels (IL-1β, IL-2, IL-4-6, IL-8, IL-10, IL-12p70, IL-17A and F, IL-22, TNF-α, TNF-β and IFN-γ) were assessed by flow cytometry and ELISA. The number of total lymphocytes, CD4+ T and NK cells in the peripheral blood significantly decreased 24 h after MWA, while number of CD8+ T cells remained stable, leading to a higher proportion of CD8+ T cells. In addition, the serum levels of IL-2, IL-1β, IL-6, IL-12p70, IL-22, TNF-α and IFN-γ were significantly increased 24 h after MWA, indicating a T helper 1 type immune response. The immune response in patients with advanced stage disease was comparable with patients in the early stage group; however, the number of total lymphocytes and CD3+ T cells significantly decreased and the ratio of CD4/CD8 and IL-2 levels significantly increased. The early immune response after MWA may contribute to systemic antitumor immunity in patients with both early and advanced disease. Thus, MWA may exhibit potential as a local therapy and trigger abscopal effects in distant lesions in patients with lung cancer. [ABSTRACT FROM AUTHOR]

The icing monitoring of transmission lines is of great significance to prevent freezing disasters of transmission lines and ensure the security and stability of power system. Measurement methods based on image recognition is gradually applied for icing monitoring but fails to distinguish the icing thickness under the extreme meteorological conditions. Therefore, this article built an ice monitoring system based on edge intelligence to achieve the ice thickness identification at the icing monitoring terminal and proposed a lightweight vision identification method based on discriminative-driven channel pruning for icing monitoring terminals with limited computing resources. In this method, the lightweight convolutional neural network (CNN) MobileNetV3 is utilized for feature extraction, and the multiscale target detection network SSD is used to extract the high-dimensional feature information for ice monitoring. Then, the channel pruning driven by discrimination is used to further compress model, so as to obtain a lightweight identification method suitable for ice monitoring terminals with limited resources. The effectiveness and superiority of the proposed method are verified in comparison with other CNN-based target detection methods and traditional ice monitoring methods. The experimental results showed that the proposed method has 74.5% recognition accuracy for the ice images collected under extreme meteorological conditions. Moreover, the model size is only 11 MB, which can be deployed in ice thickness monitoring terminals with limited computing resources. [ABSTRACT FROM AUTHOR]

The quality of the camera image directly determines the accuracy of the defect identification of the transmission line equipment. However, complex external factors such as haze can seriously affect the image quality of the aircraft. The traditional image dehazing methods are difficult to meet the needs of enhanced image inspection in complex environments. In this paper, the image enhancement technology in haze environment is studied, and an image dehazing method of transmission line based on densely connection pyramid network is proposed. The method uses an improved pyramid network for transmittance map calculation and uses an improved U-net network for atmospheric light value calculation. Then, the transmittance map, atmospheric light value, and dehazed image are jointly optimized to obtain image dehazing model. The method proposed in this paper can improve image brightness and contrast, increase image detail information, and can generate more realistic deblur images than traditional methods. [ABSTRACT FROM AUTHOR]

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods. [ABSTRACT FROM AUTHOR]

To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery. [ABSTRACT FROM AUTHOR]