This paper proposes a cyberspace asset mapping method for power monitoring systems. Firstly, based on the cyberspace modeling requirements and asset operation parameter information of various assets in the power monitoring system, a cyberspace asset model for the power monitoring system is constructed. Secondly, based on the cyberspace asset model and cyberspace mapping parameter information, a cyberspace asset mapping architecture for the power monitoring system is established. Finally, based on the cyberspace asset mapping architecture and cyberspace mapping requirement information, the actual network access behaviors between various assets are mapped to obtain the optimal network access relationship diagram between them. The asset discovery technology based on the cyberspace model proposed in this paper can more comprehensively map the actual network access behaviors between various assets, reducing data errors in cyberspace asset mapping within power monitoring systems and significantly improving the efficiency of cyberspace asset mapping in such systems.

To solve the problem that the existing security strategy of power monitoring system is insufficient to defend against new attacks,this paper proposes an automatic threat path construction method based on ATT&CK framework.Firstly,the network undirected graph based on the connectivity between devices is constructed.Then,the threat movement path is constructed according to the asset classification information.Finally,according to the asset classification information,ATT&CK framework and network killing chain,the threat movement path information and the threat path construction are completed.This method not only provides a theoretical basis and support for the formulation of power system security policy,but also provides the possibility to adaptively adjust network security policy when threats are detected.

Network anomaly detection techniques have been widely applied in the domain of smart distribution grid security, yet they still encounter numerous challenges in dealing with complex network attacks. Conducting research on efficient network anomaly detection methods holds significant importance for the security and stability of smart distribution grids. Due to issues such as the requirement for extensive feature extraction and poor handling of imbalanced data, leading to low detection rates and high false alarm rates, this paper proposes a method for network anomaly detection integrating autoencoders. Initially, a synthetic minority oversampling technique is employed to expand the samples of minority classes in network anomalies, addressing the problem of imbalanced data distribution and forming a more balanced dataset. Subsequently, autoencoders are utilized for data dimensionality reduction and denoising, preserving essential data information to enhance the precision of anomaly detection. Finally, an improved IDS-CNN network model is developed by adjusting the structure of convolutional and pooling layers and integrating it with multi-layer BiLSTM, thereby enhancing the detection capability and classification effects for network anomalies. In order to verify the performance of the proposed method, experiments are carried out on the data sets NSL-KDD and CICIDS2017. Compared with the mainstream methods, the detection rate of network abnormal behavior of the proposed method is improved by 1.32%, which has high detection accuracy.

Our previous study has demonstrated that the nuclear-origin supplementation of the PSII core subunit D1 protein stimulates growth and increases grain yields in transgenic rice plants by enhancing photosynthetic efficiency. In this study, the underlying mechanisms have been explored regarding how the enhanced photosynthetic capacity affects metabolic activities in the transgenic plants of rice harboring the integrated transgene RbcSPTP-OspsbA cDNA, cloned from rice, under control of the AtHsfA2 promoter and N-terminal fused with the plastid-transit peptide sequence (PTP) cloned from the AtRbcS. Here, a comparative metabolomic analysis was performed using LC-MS in flag leaves of the transgenic rice plants during the grain-filling stage. Critically, the dramatic reduction in the quantities of nucleotides and certain free amino acids was detected, suggesting that the increased photosynthetic assimilation and grain yield in the transgenic plants correlates with the reduced contents of free nucleotides and the amino acids such as glutamine and glutamic acid, which are cellular nitrogen sources. These results suggest that enhanced photosynthesis needs consuming more free nucleotides and nitrogen sources to support the increase in biomass and yields, as exhibited in transgenic rice plants. Unexpectedly, dramatic changes were measured in the contents of flavonoids in the flag leaves, suggesting that a tight and coordinated relationship exists between increasing photosynthetic assimilation and flavonoid biosynthesis. Consistent with the enhanced photosynthetic efficiency, the substantial increase was measured in the content of starch, which is the primary product of the Calvin–Benson cycle, in the transgenic rice plants under field growth conditions.

The dispatching automation master station system, monitors the operational status of power system equipment and plays a crucial role in the operation of the power grid. In the event of a system failure, a series of serious accidents may occur, endangering personnel safety and stable equipment operation. In response to these issues, this paper proposes a probability model for the functional failure of the dispatching automation master station system that takes into account human errors. The impact of human error on the automation master station system is comprehensively considered. Based on the risk events in the automation master station system, the risk events involved are regarded as nodes, and a probability model for the functional failure of the dispatching automation master station system based on Bayesian network is constructed. This model effectively combines qualitative expert knowledge with quantitative data information, inputs risk event nodes as evidence information into the Bayesian network model, obtains the posterior probability of event node variables through Bayesian inference, and thus realizes the probability calculation and analysis of functional failure of the automation master station system. This research has certain significance for the study of the dispatching automation master station system.

The Calvin–Benson cycle (CBC) consists of three critical processes, including fixation of CO2 by Rubisco, reduction of 3-phosphoglycerate (3PGA) to triose phosphate (triose-P) with NADPH and ATP generated by the light reactions, and regeneration of ribulose 1,5-bisphosphate (RuBP) from triose-P. The activities of photosynthesis-related proteins, mainly from the CBC, were found more significantly affected and regulated in plants challenged with high temperature stress, including Rubisco, Rubisco activase (RCA) and the enzymes involved in RuBP regeneration, such as sedoheptulose-1,7-bisphosphatase (SBPase). Over the past years, the regulatory mechanism of CBC, especially for redox-regulation, has attracted major interest, because balancing flux at the various enzymatic reactions and maintaining metabolite levels in a range are of critical importance for the optimal operation of CBC under high temperature stress, providing insights into the genetic manipulation of photosynthesis. Here, we summarize recent progress regarding the identification of various layers of regulation point to the key enzymes of CBC for acclimation to environmental temperature changes along with open questions are also discussed.

With the promotion of renewable energy generation, low-voltage distributed generation control system is widely applied nowadays, which also exposes more vulnerabilities to attacks due to its distributed nature and the fact that can be approached more easily, because it is close to the user side. Attackers launch their attacks through certain entry, for example, a communication device or a controlling device. Device been attacked will usually transfer malicious programs or commands to other devices belonging to the same system. In this paper, we proposed a method for cyber security monitoring in low-voltage distributed generation control system. This method can find out whether a terminal is transferring malicious programs or commands by analyzing its network traffic and side-channel information (power consumption). It is validated that proposed method is more comprehensive and accurate than traditional methods based only on network traffic analysis.

Characterization of the alterations in leaf lipidome in Begonia (Begonia grandis Dry subsp. sinensis) under heat stress will aid in understanding the mechanisms of stress adaptation to high-temperature stress often occurring during hot seasons at southern areas in China. The comparative lipidomic analysis was performed using leaves taken from Begonia plants exposed to ambient temperature or heat stress. The amounts of total lipids and major lipid classes, including monoacylglycerol (MG), diacylglycerol (DG), triacylglycerols (TG), and ethanolamine-, choline-, serine-, inositol glycerophospholipids (PE, PC, PS, PI) and the variations in the content of lipid molecular species, were analyzed and identified by tandem high-resolution mass spectrometry. Upon exposure to heat stress, a substantial increase in three different types of TG, including 18:0/16:0/16:0, 16:0/16:0/18:1, and 18:3/18:3/18:3, was detected, which marked the first stage of adaptation processes. Notably, the reduced accumulation of some phospholipids, including PI, PC, and phosphatidylglycerol (PG) was accompanied by an increased accumulation of PS, PE, and phosphatidic acid (PA) under heat stress. In contrast to the significant increase in the abundance of TG, all of the detected lysophospholipids and sphingolipids were dramatically reduced in the Begonia leaves exposed to heat stress, suggesting that a very dynamic and specified lipid remodeling process is highly coordinated and synchronized in adaptation to heat stress in Begonia plants.

With the rapid development of UHV AC/DC hybrid power grid, the safe and stable operation of interconnected large power grid is facing great pressure, which puts forward higher requirements for real-time analysis and dynamic early warning application of power grid control system. At present, the branch parameter identification of power grid regulation and control system is mainly carried out by the least square method, but this method has some shortcomings, such as easy over-fitting. One cross neural network model (CRSNet) is proposed in this paper, which adjusts the branch parameters to different scales in the calculation process and then updates the parameters through cross perception, so that the neural network can consider the characteristic information of other nodes in the power grid and it can fit accurately. Compared with the least square method commonly used in practice and other machine learning algorithms, the experiment shows that the proposed model has greatly improved the fitting accuracy and enhanced robustness, which has a broader application prospect.

Wireless based smart grid networks are widely applied due to its features of efficiency and agility to the power system. However, it is also vulnerable to malicious attacks via the communication channels, which has a great impact on stability of the whole power system. In this paper, a cooperative jamming attack strategy is proposed, in which a team of mobile attackers is tasked to jam the power price signals sent by the control center to power users, and halt jamming operation when the true price value monitored by attackers has been varied noticeably. Based on a dynamic coverage control scheme, attackers optimally coordinate their motion and continually disrupt wireless communication channels between power users and the control center during each jamming operation period. It ensures that the instantaneous imbalance of generated power and consumed power at the control center is gradually up to a maximum level when the true price is updated by local unit of each power user. The effect of such an attack is also discussed analytically. Simulation results are provided to validate the effectiveness of the proposed method.

随着信息技术的快速发展,通信、计算机和电网构成多功能复杂系统,通信设施的复杂化使智能电网网络安全问题日益严峻.为确保电力信息网络具有更高的安全性能,必须有效识别电力信息网络存在的入侵攻击.对此,提出了一种基于CNN(卷积神经网络)和LSTM(长短期记忆)网络的混合网络的异常检测方法,混合网络通过提取网络流量数据特征以获得较高的检测率,同时为减少模型训练样本中不同攻击类型样本数量不平衡对模型性能的影响,采用类别权重优化方法来提高模型鲁棒性.经实验证明,所提方法能够有效提高识别网络攻击的准确率.

In this paper, a new formula for AC unit commitment problem with safety constraints is proposed, which can express the uncertainty of wind power with bounded interval. The proposed SCUC model can flexibly adjust the width of the interval by selecting an appropriate confidence level, so as to achieve a good compromise between security and economy. In addition, the proposed SCUC model is equivalent to a two-stage stochastic programming problem in one expected case, while the second stage represents the corrective operation in two extreme cases. In order to deal with the large-scale and integer mixed nonlinear characteristics of the model, a new calculation strategy based on generalized Benders Decomposition is developed to solve this problem and obtain robust commitment plan to withstand the worst-case realization of uncertain wind energy. The proposed SCUC model is illustrated by IEEE 9 bus and 118 bus test system, and its advantages over the existing random programming technology are verified. The simulation results show that the proposed method is applicable to UC problem with uncertainty of wind power generation.

Based on the virtual power plant platform, this paper studies the aggregation adjustment and optimization strategy of multiple agents, and uses the multi-agent reinforcement learning strategy to realize the game behavior among multiple power generation companies, energy storage companies and load users, which meets the adjustment in the virtual platform. Nash equilibrium of the overall income of resource participants. Based on the modeling of the adjustable resource aggregation of the virtual power plant, the game strategy is divided into the overall cooperative game and the partial non-cooperative game according to the game characteristics, and different game strategies are adopted respectively. The comparison results of field operation examples and methods prove that the strategy has advantages in terms of model training time-consuming, execution time-consuming, convergence, etc., and it has theoretical guidance and field promotion value.

With the continuous development of Internet technology, the term "blockchain" has appeared in various industries. At present, blockchain is mainly used in power payment and settlement, power market transaction, new energy and distributed power generation, but it has not effectively solved the problems of mutual trust, data security and business evidence in the process of load regulation and control such as load-side resource perception, dispatching instruction and load response. This paper introduces the traditional power data storage model, and makes work for the problems of data tampering and loss in the traditional power data storage model and the existence of multi-party mutual trust in the centralized system as follows.1) In this paper, we have proposed a Hyperledger Fabric-based load regulation data on-chain technology. proposed.2) In this paper, we have developed a smart contract for uploading load regulation data and designed two uploading methods.In this paper, a prototype system of load regulation data uplink has been implemented to test the Fabric network based on load regulation data uplink.

Time series anomaly data detection has always been a research hotspot in different fields and is also one of the prerequisite tasks for effective data analysis. At present, a large number of research models are based on machine learning methods, which fail to well extract the correlation between data, fail to obtain the development trend of data, and often lead to wrong abnormal judgment. However, due to the large number of parameters, the new deep learning method takes too long to compute and is difficult to deploy. Aiming at the above problems, Light Gradient Boosting Machine (LightGBM) is used in this paper to significantly improve the detection accuracy of the model. Through experimental comparison, this method can obtain high accuracy in a very short time.

In order to reduce the subjectivity of information security risk assessment process and improve assessment efficiency, we propose a new method of information security risk assessment based on improved FAHP (Fuzzy Analytic Hierarchy Process) to analyse the information security-related standards for domestic and international risk assessment. We establish a Hierarchical Security Assessment Model and introduce refinement indicators and Intuitionistic Fuzzy Sets to reduce subjective judgment factors in the assessment of traditional risk. We then applied an e-commerce company in case analyse the security risk and the results are satisfactory and in line with the actual situation of the company. The indicator system of this method is more objective and comprehensive and the evaluation process is more efficient, which provide new ideas for risk assessment of existing information security companies.

Smart Grid Monitoring System(SGMS) is an important means to protect the security of smart grid. The high volumes of alerts generated by SGMS often confuse managers. Automatically handling alerts and extracting attack events is a critical issue for smart grid. Most of the existing security event analysis methods are designed for Internet, which will not be directly applicable to the power grid for high reliability and low attack tolerance requirements. In this paper, a multi-step attack detection model based on alerts of SGMS is proposed. In this model, an alert graph is constructed by IP correlation, and then transformed into candidate attack chains after being aggregated. Consequently, the candidate preliminary attack chains are pruned and denoised by negative causal correlation and non-cascading events. Finally, attack chains and visual attack graphs are formed. Our proposal model needs a little of priori knowledge while automatically extracting multi-step attack events and demonstrating the trajectories among IPs. The experimental results show the model performs well on China Grid data and DARPA 2000 data set.

with the complexity of the power system and the increasingly severe network security environment, the industry has urgently needed to improve the risk prediction ability of the power system security and the potential safety hazards brought about by the disposal. According to the experience and the features attributes of historical data, K-means unsupervised learning clustering is carried out. For supervised learning classification, this paper chooses SVM-KNN, and the risk assessment portrait after business disposal is constructed. After establishment and operation of the model, effective and rapid analysis and output of disposal recommendations and corresponding risk levels are carried out, and the original experience is intellectualized and rationalized to the relevant people. In order to make sure the stable, efficient and safe operation of the power monitoring system, model could give prompt safety advice as an expert.