Power Grid Data Research Articles

Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning

Power system load forecasting model plays an important role in all aspects of power system planning, operation and control. Therefore, accurate power load forecasting provides an important guarantee for the stable operation of the power grid system. This paper first analyzes the current status of power system load forecasting, and finds that there are still some deficiencies in the existing forecasting models. In order to make up for these shortcomings, this paper proposes construction of short-term and mid-term power system load forecasting models based on hybrid deep learning. In the data preprocessing part, this paper proposes to use the exponential weight moving average method to process missing values. The detection method of abnormal value is the GeneralizedESDTestAD(GESD). This paper analyzes the historical load data of a regional power grid and four industries, and proposes a short-term power system load forecasting model based on Bi-directional Long Short-Term Memory(BiLSTM); For mid-term load forecasting, this paper first uses random forest and Pearson correlation coefficient to select features. Then a hybrid deep learning model is constructed based on BiLSTM and random forest. After optimizing the parameters of the model, a mid-term power system load forecasting model based on hybrid deep learning is constructed. Finally, the benchmark models are selected for comparative experiments. The experimental results show that the MAPE of the proposed model is 2.36%, which is better than the benchmark models. This proves that the proposed hybrid model can effectively improve the accuracy of power system load forecasting.

Secondary Frequency Control Considering Optimized Power Support From Virtual Power Plant Containing Aluminum Smelter Loads Through VSC-HVDC Link

The growing number of renewable energy replacing conventional generators results in a loss of the reserve for frequency control in power systems, while many industrial power grids often have excess energy supply due to abundant wind and solar energy resources. This paper proposes a secondary frequency control (SFC) strategy that allows industrial power grids to provide emergency high-voltage direct current (HVDC) power support (EDCPS) for emergency to a system requiring power support through a voltage source converter (VSC) HVDC link. An architecture including multiple model predictive control (MPC) controllers with periodic communication is designed to simultaneously obtain optimized EDCPS capacity and minimize adverse effects on the providing power support (PPS) system. Moreover, a model of a virtual power plant (VPP) containing aluminum smelter loads (ASLs) and a high penetration of wind power is established for the PPS system. The flexibility and controllability of the VPP are improved by the demand response of the ASLs. The uncertainty associated with wind power is considered by chance constraints. The effectiveness of the proposed strategy is verified by simulation results using the data of an actual industrial power grid in Inner Mongolia, China. The DC voltage of the VSCs and the DC in the potlines of the ASLs are also investigated in the simulation.

New Energy Power Prediction and Warning Based on Multi-source Prediction and Scene Classification Recognition

This article proposes a new method for predicting and warning of new energy electricity based on a multi-source prediction data and scene classification recognition algorithm. The study uses ECMWF, GFS, and DERF2.0 multi-source prediction data to improve the accuracy of offshore wind power prediction and disaster warning. In order to overcome the limitations of traditional prediction methods, this study adopts a scene classification recognition method. Based on meteorological, power grid, and environmental data, the study uses a scene recognition algorithm to establish three types of factor feature extraction models and combines them with fault information to establish a wind power prediction and warning model. This model can achieve the prediction of wind power and warn of possible power grid faults under meteorological disasters. Case analysis shows that this method significantly improves the accuracy of new energy electricity prediction and meteorological disaster warning. This study emphasizes the importance of multi-source prediction data and scene classification recognition methods in improving offshore wind power prediction and other new energy fields. Additionally, this method has the potential to make valuable contributions to the renewable energy industry.

Research on Capability Maturity Evaluation Model of Power Grid Data Management

This paper compares the main data management maturity models at home and abroad, The main content and practical application of data management capability maturity model of power grid enterprises are expounded, and some suggestions on data management capability maturity evaluation of power grid enterprises are put forward, aiming at providing ideas and methods for data management and data management capability maturity evaluation of power grid enterprises.

Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision making

IN the trend of energy revolution, power data becomes one of the key elements of the power grid. And an advance power system with "electric power + computing power" as the core has become an inevitable choice. However, the traditional search approach based on directory query is commonly used for power grid operation data in domestic and international. The approach fails to effectively meet the user's need for fast, accurate and personalized retrieval of useful information from the vast amount of power grid data. It seriously affects the real-time availability of data and the efficiency of business-critical analytical decisions. For this reason, an intelligent retrieval approach for power grid operation data based on improved SimHash and multi-attribute decision making is proposed in this paper. This method elaborates the properties of SimHash and multi-attribute decision making algorithms. And an intelligent parallel retrieval algorithm MR-ST based on MapReduce model is designed. Finally, real time grid operation data from multiple sources are analyzed on the cloud platform for example. The experimental results show the effectiveness and precision of the method. Compared with traditional methods, the search accuracy rate, search completion rate and search time are significantly improved. Experiments show that the method can be applied to intelligent retrieval of power grid operation data.

Building a Unified Spatio-Temporal Data Model for Grid Resources Based on Microservice Architecture

Under the background of accelerating the process of power grid construction, the unified spatial-temporal data model of power grid resources has become a necessary means to describe the relationship between spatial objects and power grid data. Affected by the defect of the information island, some unified spatiotemporal data models of power grid resources have poor updating performance. Therefore, a unified spatiotemporal data model of power grid resources based on microservice architecture is designed. The architecture can obtain the spatial structure elements of the power grid area, identify the spatial correlation characteristics of the modeling object through the distribution of power energy supply lines, eliminate the dimension of meteorological data variables, design a unified resource scheduling scheme based on the microservice architecture, calculate the space-time weight matrix, and build a space-time data model. Test results are that under the two update task scenarios, the average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 11363 times/second. The average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 9958 times/second). And the average update performance of the unified spatiotemporal data model of power grid resources based on the genetic algorithm is 9771 times/second. It shows that the designed unified spatiotemporal data model of power grid resources is perfect after combining the microservice architecture.

Research and Implementation of Power Grid Resource Information data-sharing Technology

To avoid the waste of power grid data resources, under the condition of ensuring data security, the design of power grid resource information data-sharing is carried out. Firstly, the security problems that may be encountered in the process of resource sharing are analyzed, and the data resources are encrypted. Then, when that data carries out information interaction between the terminal equipment and the resource sharing cent, the transfer engine is utilized to process the transferred data and transfer the transfer data. Finally, the sharing of data resources is realized by building a calculation model. The experimental results show that the designed information and data-sharing technology consumes less energy on the terminal equipment, and reduces the workload of the terminal equipment for data transmission. It is hoped that the research of this paper can further promote the research of power grid information resource data-sharing.

Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction

Abstract Problems exist in power grid data management that have unclear relationships, weak security and low accuracy. By analysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, the grid data management platform is reshaped architecturally, and the knowledge graph construction technology is embedded in the grid data management framework. For the aforementioned problems, the knowledge graph construction and Internet of Things optimisation framework of power grid data knowledge extraction are proposed in this article. Firstly, the semantic search (KGSS) algorithm based on the knowledge graph is used. The KGSS algorithm extracts knowledge from structured, semi-structured and unstructured grid data through the massively parallel processing acquisition model and Hadoop database, and constructs knowledge entities, attributes and inter-entity relationships. Then, it optimises and predicts through the knowledge graph construction and Internet of Things optimisation framework extracted from power grid data knowledge. Finally, the experimental results show that the accuracy rate of the KGSS algorithm is 92%. The experimental results also show that it provides new ideas and research directions for power grid data under big data in the future.

Multi-target prediction model of urban distribution system rainfall-caused outage based on spatiotemporal fusion

Improving the prediction accuracy of weather-related outages in the distribution system is crucial for disaster prevention and mitigation. Due to the vulnerability of the distribution system in rainfall, we propose a multi-target prediction model of the rainfall-caused outages of the distribution system based on the fusion of spatiotemporal attributes. In this article, the ground subdivision model based on element reconstruction is firstly proposed to establish a spatial grid. Then the rainfall waterlogging model considering seepage, drainage and confluence is established to provide a targeted reference for distribution system outage prediction. Then the multi-target random forest based on superposition features is proposed to deal with the meteorological data, geographic environment data, power grid data and waterlogging data. The model outputs the rainfall outage probability and visualization results of ground and overhead equipment branded with time and space labels. Finally, the model is tested on the instance data and compared with other models.

Digital power grid based on digital twin: Definition, structure and key technologies

Under the background of the 4th industrial revolution, digital twin technology has been constantly integrated with the power grid business. Power grid will build a digital twin grid covering the whole physical grid and production process. This paper first proposes the digital power grid based on digital twin. A closed-loop power grid data empowerment system is introduced to define the digital power grid based on digital twin, the three features and properties are described to enrich the digital power grid connotation. It constructs the digital power grid architecture including physical power grid, perception layer, transport layer, data layer, platform layer and application layer. The key technologies are also discussed to embody digital power grid.

Application of power grid wind monitoring data in transmission line accident warning and handling affected by typhoon

With the increasing number of transmission line monitoring points, it is possible to obtain meteorological monitoring data of power grid, and the meteorological monitoring data of power grid can be further used to guide typhoon disaster early prevention, fault repair and disposal of transmission faults. This paper innovatively proposes a typhoon microscale (100 m) wind field simulation method suitable for the complex terrain conditions in different coastal areas of China, and uses the power grid meteorological monitoring data to improve the early warning accuracy and credibility in the local terrain space of typhoon disasters. The prediction robustness and accuracy of the scale reduction prediction model are improved through continuous observation and statistical correction, several engineering examples show that the method is correct and effective. The dynamic typhoon prevention strategy of the power grid and the timely adjustment of the power grid operation mode can be guided for the preliminary analysis of the cause of the transmission failure after the typhoon transit, so as to provide scientific guidance for the subsequent emergency repair. This method has a general practical and guiding value for the coastal power grid of the typhoon influence area.

A Data-Driven Self-Learning Evaluation Method of Voltage Sag Severity

Lightning is one of the main causes of voltage sags, and it is of great significance for subsequent analysis and governance to accurately evaluate the severity of the sag events caused by lightning. There are many uncertain factors between lightning fault events and voltage sag events. To evaluate the severity of voltage sag events caused by lightning, a data-driven self-learning evaluation method for voltage sag severity is proposed. According to a large number of online monitoring data of lightning positioning system and power quality monitoring system, the association rule mining algorithm based on incremental learning is used. And the rules are kept updating through the accumulation of historical data, which may give it the abilities of self-learning. The empirical analysis is carried out based on the monitoring data of a regional power grid. The results show that the method in this paper can accurately mine more valuable rules in reality and solve the problem of low efficiency of mining algorithm when the database changes dynamically.

Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably.

Research on future parallel power grid architecture

Abstract This paper introduces the six distribution levels of physical object layer, object perception layer, storage layer, operation layer, analysis layer and parallel control layer of the future parallel power grid architecture. On this basis, a more targeted analysis model is proposed. Starting from the resource operation level of parallel power grid, the abstract model method is used to analyse the changes of the architecture of resource nodes in different virtual agent sets. The detailed changes of the resource system are described through multi-layer nodes, and the random matrix is used to systematically analyse the operation data of the parallel power grid in the future so as to obtain the architecture mode with the highest adaptability when the actual space and virtual space are associated. Considering that the virtual community in the future parallel power grid is the core part of the architecture, the guidance information received by the virtual community in the power grid is regarded as a three-dimensional state transition probability matrix, which captures the real-time changes of the virtual community. On this basis, taking the future parallel power grid macro-economy as the development prospect, the supply and demand balance conditions of the future power grid are obtained to help the power grid achieve strong development. The experimental results show that the parallel power grid architecture optimised by this method is more in line with the actual development needs; the resource utilisation rate and operation income have a positive growth trend and finally achieve an impressive result of approximately 100%, which is of great help to the development of parallel power grid in the future.

Intelligent Identification and Order-Sensitive Correction Method of Outliers from Multi-Data Source Based on Historical Data Mining

In recent years, outliers caused by manual operation errors and equipment acquisition failures often occur, bringing challenges to big data analysis. In view of the difficulties in identifying and correcting outliers of multi-source data, an intelligent identification and order-sensitive correction method of outliers from multi-data sources based on historical data mining was proposed. First, an intelligent identification method of outliers of single-source data is proposed based on neural tangent kernel K-means (NTKKM) clustering. The original data is mapped to high-dimensional feature space using Neural Tangent Kernel, where the features of outliers are acquired by K-means clustering to realize the accurate identification of outliers. Second, an order-sensitive missing value imputation framework for multi-source data (OMSMVI) was proposed. The similarity graph of sources with missing data was constructed based on multidimensional similarity analysis, and the filling order decision was transformed into an optimization problem to realize the optimal filling order decision of missing values in multi-source data. Finally, a neighborhood-based imputation (NI) algorithm is proposed. Based on the traditional KNN filling algorithm, neighboring nodes of sources with missing data are flexibly selected to the achieve accurate correction of outliers. The case experiment was operated on actual power grid data, and the results show that the proposed clustering method can identify outliers more accurately, and the determined optimal imputation sequence has higher accuracy, which provide a feasible new idea for the identification and correction of outliers in the process of data preprocessing.

Improving the area of harmonic pollution and vulnerability to voltage sag for compatible and reliable operation of sensitive harmonic loads

Nowadays, sensitive harmonic loads (SHLs) are being increasingly used in the industrial sector. SHLs generate harmonics and may become disconnected from the network when voltage sags occur at the point of common coupling (PCC). Here, a new approach is proposed to reduce harmonics distortion and establish a more reliable annual operation of SHLs by reducing the area of pollution (AOP) with harmonic and the area of vulnerability (AOV) to voltage sag, respectively. The proposed approach includes an innovative optimization problem for planning passive harmonic filters which is solved by the Non-dominant Sorting Genetic Algorithm (NSGA-II) on the basis of historical data of the industrial power grid. By doing so, a solution is obtained in which the costs of passive harmonic filters and financial costs due to grid losses and voltage sags are minimized. In order to assess the new proposed approach, an Iranian large copper mine electrical network equipped with SHLs is used. Results of using the new approach show that total harmonic distortion and grid losses decrease by nearly 80% and 15%, respectively and also, the voltage value at the PCC is improved which can assure the secure operation of SHLs. Moreover, decreased financial losses is obtained since voltage sags are reduced by 65%.

Hadoop-Based Power Grid Data Quality Verification and Monitoring Method

Hadoop-Based Power Grid Data Quality Verification and Monitoring Method

Data Governance Architecture of Digital Grid Based on Blockchain Technology and Nanomaterial Technology

As the traditional grid data architecture develops with enterprises, its security risks will become greater and greater, and it will not be able to satisfy customers' differentiated services. Predecessors' research on the application of blockchains and nanomaterials is still insufficient. To solve the various shortcomings of the traditional power grid data governance architecture, a new type of sensor designed using blockchain technology and nanomaterials is designed through new technical means. It is applicable to the current digital grid data management architecture. The study found that the scale of the system gradually increased from 4 nodes to 16 nodes, and the TPS entered the smoothing stage after about 10, 50, 70, and 100 s, and stabilized at a small fluctuation point. We found that for every 4 nodes increase in the cluster size, the throughput drops by about 8%. Clusters of different sizes have entered a state of stable throughput. Although TPS has shown an overall downward trend of 8%, the peak situation of TPS has shown a certain different trend. This shows that the digital grid data governance network framework designed through blockchain technology and nanomaterial technology can meet the various needs of modern digital grid data centers for data center networks, such as: business scalability, reliability, security, etc.

A Residual Voltage Data-Driven Prediction Method for Voltage Sag Based on Data Fusion

Voltage sag is the most serious power quality problem in the three-phase symmetrical power system. The influence of multiple factors on the voltage sag level and low computational efficiency also pose challenges to the prediction of residual voltage amplitude of voltage sag. This paper proposes a voltage sag amplitude prediction method based on data fusion. First, the multi-dimensional factors that influence voltage sag residual voltage are analyzed. Second, these factors are used as input, and a model for predicting voltage sag residual voltage based on data fusion is constructed. Last, the model is trained and debugged to enable it to predict the voltage sag residual voltage. The accuracy and feasibility of the method are verified by using the actual power grid data from East China.

The design of power grid data management system based on blockchain technology and construction of system security evaluation model

The construction of smart grid has triggered comprehensive changes in power equipment. At the same time, massive data are waiting to be screened, analyzed and processed. However, there are many data sources and complex data structure in smart grid, and smart grid requires higher security, reliability, traceability, non-repudiation and real-time performance of data processing, which poses unprecedented challenges to the traditional data management. Blockchain technology has the advantages of decentralization, autonomy, leakage prevention and openness. It can match the demands of power data management in terms of operation mode, security protection, complement power data, and better meet the security and credibility requirements of energy Internet. Therefore, this paper designs the smart grid data management system based on blockchain, and constructs the system security evaluation model by using AHP and expert consultation method. Firstly, the characteristics of smart grid big data and blockchain technology are analyzed; Then the technical architecture of data management system is designed by using alliance chain, and the technical architecture is analyzed in detail; Finally, the index factors affecting system security are combed, and the system security evaluation model is constructed by AHP. Distributed storage, asymmetric encryption, consensus mechanism, and other technologies of blockchain technology can well solve the problems of power grid data storage and application, and better ensure the security of smart grid data.