Research on Error Correction Method of Massive Data Based on Arbitrary Partition Area

With the UHV (Ultra High Voltage) power grid construction and the interconnection of regional power grids, the scale of power grids in China is increasing rapidly. At the same time, significant uncertainty and variability is being introduced into power grid operation with the integration of large-scale renewable energy in power systems. All of these pose an enormous challenge to the operation control of power systems in China. For a long time, online static security analysis, as an important part of EMS (Energy Management System), has been an effective tool for power grid operation. However, it is increasingly difficult for traditional static security analysis in serial computing mode to be online applied in bulk power grids in China. A new practical parallel approach for online static security analysis is put forward in this paper. A multithread parallelism is introduced into contingency screening, detailed contingency evaluation and decision support for reducing the execution time. By employing the multithread technology, the hardware resources of multi-processor/multi-core computer can be fully used and the program can be speeded up effectively. The performance of the parallel static security analysis is demonstrated by tests on two large-scale power systems. The test results show that the proposed method can be online applied in real bulk power grids.

Edge computing can process a large amount of data in the power grid locally, and then transmit the results and knowledge to the cloud platform, which reduces the use of network bandwidth and the processing response time. The process requires secure storage and privacy protection on the power terminal. This paper analyzes the demand of power edge computing, designs the power edge computing architecture, and proposes a distributed storage method using block chain technology. Through experiment combined with smart meter terminal data, the results show that this method is feasible and can be used for the safe storage and analysis of electric energy data in power grid.

Power grid operation and maintenance decision-making reform is an important part of power system reform. With the construction of massive historical quasi real-time data management platform, the reform of power system is also advancing. However, in the face of massive data explosion, the business level and business logic become disorganized and redundant. Based on the actual situation of Shenzhen Power Supply Bureau, the sg-erp data center is composed of structured data center, massive data center, unstructured data center and power grid GIS data center. With the unprecedented growth of business application data, the data center can improve business logic and promote power system reform. The experimental results show that big data technology has a broad application prospect in the reform of power industry.

In order to solve the problem of untargeted security classification and grading methods in the process of power grid data governance, this paper proposes a classification and grading method that combines the characteristics of power grid services by deeply analyzing the mainstream data security classification and grading standards at home and abroad. This method starts from combing the power grid business lines, goes through the process of service refinement, data classification, and data grading, and finally gives targeted protective measures. This method is in line with the actual operation of the power grid, and clarifies the responsible subject of data classification and grading, so it is easy to implement and facilitate the update of classification and classification. In the process of data grading, the degree of influence and scope of influence on multiple objects are fully considered, and the power grid data is divided into four levels to facilitate data management, which basically conforms to the status quo of power grid data classification and lays a foundation for power grid data governance.

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.

With the complexity and increasing demand of the power grid, more efficient scheduling methods are needed. Reinforcement learning, as an artificial intelligence technology, provides adaptive decision-making solutions that can optimize resource allocation and job scheduling based on the actual state and task requirements of the power grid. The experimental results indicate that the system has achieved significant performance improvement in different power grid operation scenarios. In terms of resource utilization, due to the higher utilization rate of the algorithm in this paper compared to the other two algorithms, the scheduling success rate is correspondingly higher than the other two algorithms. The success rate of the algorithm in this paper can reach 90.24%, which is 22.18% and 22.72% higher than GA (Genetic Algorithm) and DL (Deep Learning). Finally, in the cost-effectiveness ratio experiment, it is evident that the cost-effectiveness ratio of the algorithm proposed in this paper is much higher than that of the other two algorithms. This demonstrates the potential advantages of reinforcement learning algorithms in power grid spatial job scheduling. Future research can further explore more complex power grid operation and maintenance scenarios and algorithm optimization to continuously improve the efficiency and reliability of power grid operation and maintenance. The construction of an automatic scheduling and visualization system for power grid spatial operations provides strong support for the sustainable development of the power grid industry and the integration of renewable energy.

This contribution addresses the first assessment of the impact of geomagnetically induced currents (GIC) on the 400‐kV power grid of Mexico. As an initial approach, we modeled GIC using a uniform conductivity for the entire Mexican territory and spatially uniform geomagnetic disturbance. Power grid data were provided by the electric operator of Mexico; the geophysical data were inferred from the main features of Mexican geology. We calculate the power grid response during four geomagnetic storms from Solar Cycles 23 and 24 (i.e., 15 July 2000, 20 October 2003, 17 March 2015, and 7 September 2017), as well as during an extreme scenario (a Carrington‐like event). The results show that the Mexican power grid can be affected by three‐phase GIC ranging from 20 to 75 A during geomagnetic disturbances. According to the model, sites located in coastal areas or close to the edges of the network can experience large GIC during time intervals between 3 and 10 hr, depending on the intensity of the geomagnetic disturbance. It is an interesting result that these sites are of the major economic and strategic significance for the country. In the case of a Carrington‐like event, the power grid could be affected by GIC ranging between 25 and 150 A under a uniform 1 V/km EW geoelectric field. Such an event might produce significant distortions in the grid hardware (i.e., transformers and static VAR compensators), potentially leading to widespread damage.

The data in the power Internet of Things has the characteristics of multiple types, large amounts of data, and obvious heterogeneity. In order to solve the storage and scheduling problem of massive heterogeneous data in the power grid, this paper designs a hybrid heterogeneous data storage scheduling architecture based on container technology. In order to solve the problem of sensor data analysis with unknown types and dynamic changes, this paper uses the K-Means clustering method to propose a data analysis process based on the idea of unsupervised learning to realize the analysis and adaptation of massive sensor data. In addition, in order to improve scheduling efficiency in big data scenarios, a two-layer scheduling architecture based on YARN and Kubernetes is proposed to achieve unified scheduling at the task level and node level, thereby improving cluster resource utilization. The heterogeneous data storage scheduling architecture proposed in this paper combines the container technology to meet the needs of massive data storage and processing for low latency and improved interrupt response rate and can be applied to power Internet of Things scenarios.KeywordsBig dataTask schedulingContainerKubernetesYARN

In recent years, with the deepening of smart grid construction, the data generated by power grid operation have increased rapidly, and the power grid big data environment has gradually formed. The power grid data management based on graph data has gained more and more attention, and its efficient storage and processing have become a research hotspot. In this paper, a subgraph partitioning algorithm for power grid graph data is developed. The power grid graph data are partitioned into multiple subgraphs, and then a parallel topology analysis algorithm for multiple subgraphs is proposed. Related experiments show that the parallel analysis scheme of power grid graph data is effective and meets the actual needs of power grid graph data.

Affected by the north of the Jianghuai cyclone on November 17,2020, a rare rain and snow freezing weather occurred in Jilin Province, which is the largest and widest range of rainstorm, snowstorm, freezing rain and gale disaster weather since its own meteorological records. This paper provides a reference and basis for future power grid planning, design and operation, and maintenance of disaster prevention and mitigation by analyzing the causes of this weathering process and its impact on power grid operation.

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

Power grid anomaly detection is a basic task of the power system, and the evaluation results are of great significance for the power grid to find out the potential dangers of operation as soon as possible and take timely measures to prevent them from happening. In recent years, with the rapid development of green energy and high technology, the power grid is developing towards the smart grid. A large number of measurement devices are deployed in the power grid, and the power grid operation data is collected, transmitted and analyzed in real time. The data-driven power big data analysis approach emerges as the times require. In this paper, a data-driven approach is proposed. It constructs a high-dimensional random matrix based on power system operating data, uses the sliding window method and the random matrix single-ring theorem to continuously analyze the high-dimensional matrix, and introduces the mean spectral radius (MSR) as a specific evaluation index to realize the power grid operation situation assessment. The simulation data test results of IEEE 39 bus system verifies the feasibility and correctness of the approach for evaluating the operation situation of the power grid. This developed approach can identify abnormal conditions at the early stage of system failure, and has great guiding significance for fault inspection of power grids.

Land compensation for power grid construction is a very important link in the process of power grid construction. In the process of power grid construction, we often encounter land compensation and other related problems that need to be solved. Based on the law currently being implemented in our country, this paper designs a reasonable compensation method for land expropriation for power grid construction to solve the related problems faced by land compensation for power grid high-voltage transmission lines. This method can not only promote the normal construction of the national power grid but also ensure the rights and interests of the broad masses of the people, in particular. It is the interests of the peasants who have been requisitioned for land in construction. It can reduce the cost of electric power enterprises to the greatest extent, at the same time, the profit of electric power enterprises can be improved, so that the construction of electric power infrastructure can be a sustainable development. At the same time, by analyzing a case, this paper illustrates that the land compensation for high-voltage transmission lines based on easement has certain rationality and development prospects, which can alleviate the problem of the sharp increase of compensation costs to a certain extent and solve the contradictions existing in the external environment of power grid construction.

When the power grid is disturbed to a certain degree, it may be extremely deteriorated. At this time, corresponding control measures need to be taken to eliminate or reduce the risks. For an unsafe normal state, the system can be restored to a safe state through preventive control. This paper proposes a risk-based power grid operational control strategy by using the optimal power flow method to solve the problem of power grid operational control based on risk parameters with the goal of minimizing the power grid operating risks. Under the condition of satisfying steady-state operational constraints, by adjusting the control measures that can be used in the system, such as active power generation, the safe operational state of the system with the least operational risk is realized. To avoid the poor effectiveness of alternating current power flow of large power grid, linearized power flow model is employed. Simulation results using an IEEE 118-node system and East China Power Grid system. The proposed operational control strategy can achieve the effect of eliminating or reducing operating risks, which provide effective decision-making for the dispatch operators to deal with risks.