Multi-source electricity information fusion methods: A survey

The current wireless network information fusion matching method is based on the calculation of the uncertainty of a large number of data samples, and gets the information fusion results according to the corresponding decision. However, due to the uncertainty of data samples, it is difficult to complete statistics, which leads to low processing accuracy and poor information fusion effect of information fusion matching method. In order to solve the above problems, a multi-source information matching and fusion method based on fuzzy set is studied. After matching with multi-source information, Markov distance is used to allocate the information. According to the probability of supporting information conflict, information fusion rules are formulated, and multi-source fusion is realized by using fuzzy sets. Simulation results show that the proposed information fusion method can reduce the energy consumption of network nodes by 81.4%, and has high fusion accuracy and reliability.

A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.

Identifying the hierarchical emotional areas in the human brain through information fusion

Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

PurposeThe study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.Design/methodology/approachA multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.FindingsThe results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.Research limitations/implicationsThe study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.Practical implicationsFrom the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.Originality/valueThe research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In the complex and harsh environment in which bearings operate, the fault diagnosis approach of multi-source information fusion can extract fault features more stably and extensively than the traditional single-source fault diagnosis method. However, most existing multi-source fusion methods are in infancy, and there are a number of pressing issues to address, such as subjective elements having a significant impact, excessive data redundancy, fuzzy multi-source signal fusion strategy, and insufficient accuracy. As a result, a new multi-source fusion fault diagnosis method is proposed in this paper. First, the residual pyramid algorithm is utilized to fuse the acoustic and vibration signals of multiple spatial positions respectively and then form two fused acoustic and vibration signals. Second, two improved 2D-CNN are used to extract the fault features contained in the above two signals separately to form a multi-source fault feature set. Third, an AdaBoost algorithm with a dynamic deletion mechanism is designed to fuse multi-source fault feature sets and produce the fault diagnosis findings. Finally, six different experimental data sets are used to test the performance of the model. The results reveal that the model has better generalization, higher and more stable fault diagnostic accuracy, and stronger anti-interference capacity.

In the Big Data computing, improving performance with memory computing is one of hot spots. In the memory computing, the data deployment directly affects load balance and task efficiency. In the scene of memory computing of electric power data, two unsolved problems are: (1) only memory space, without the CPU frequency and nuclear number, could be considered for load balance and improving performance, (2) there are so many manual operations that it is difficult to complete data deployment automatically. This paper provides an electric power data deployment solution for distributed memory computing to solve the above challenges. In the solution, according to business logic and hardware configuration of cluster nodes, the data deployment strategy can be established. Then, the deployment scheme can be implemented with interface operation. Lastly, cluster nodes load data according to the deployment scheme. The solution has been applied to the Objectification Parallel Computing (OPC). The application result shows that OPC can achieve the best performance which can meet the demand of system efficiency and the operation of data deployment is simple.

Abstract. The process of building a digital city is the construction of a three-dimensional city model. The construction of 3 d data model is the process of multi-source spatial information collection and fusion. The current means of spatial information collection are mainly divided into three-dimensional laser technology and UAV photogrammetry technology. The three-dimensional laser technology is mainly based on ground laser, which has high acquisition accuracy for the bottom part of the building, but it is insufficient for the information collection at the top of the building. UAV photogrammetry has a large area and high efficiency spatial information collection method. At the same time, a large three-dimensional city model of the building can also be constructed. However, due to practical problems such as flight height and ground occlusion, the model constructed by UAV photogrammetry has a perfect top and poor bottom quality. The two methods are combined to build a more perfect point cloud model. Aiming at the above problems, a three-dimensional modeling method of buildings under multi-source data fusion is proposed. Firstly, the point cloud data and image data obtained by 3D laser scanner and UAV are preprocessed, and the two data forms are transformed into high-precision point cloud data. Then two sets of point cloud data are fused to generate the whole point cloud data. Finally, based on the patch generated by the whole point cloud, the point cloud model is reverse modeled. Get the corresponding building entity model, in order to get a more perfect 3D city model.

In order to enhance the suppleness of a lower limb rehabilitation medical robot during the re-habilitation process, this study proposes a multi-source information fusion lower limb motion intention recognition method based on surface electromyographic signals (sEMG) and lower limb joint angles. To solve the problem of data traffic surge during the collection process, a multi-source current limiting sliding time window algorithm (MLS) is proposed. The MLS algorithm controls the data flow through a flow limiting and sliding time window mechanism to ensure the efficiency and stability of the system in handling large data volumes. On this basis, the study combines the Back Propagation Generalized Algorithm Neural-network (BPGN) to construct a prediction model for lower limb joint angles. The experimental results show that under the same conditions of the algorithm, the fusion of multi-source information reduces the average error of knee joint angle prediction by 10.8° and the average error of ankle joint angle prediction by 7.2° compared with the method using a single lower limb joint angle signal. Under the same condition of input signal, the multivariate flow-limiting sliding time-window BPGN reduced the average knee joint error by 13.6° and the average ankle joint angle error by 8.5° compared to the BPGN intent recognition. The multivariate flow-limited sliding time window BPGN reduced the mean knee error by 11.2° and the mean ankle angle error by 7.4° compared to Radial Basis Function (RBF) Neural-network intent recognition. By integrating the sEMG signal and lower limb joint angle information, the system can more accurately capture the patient’s movement intention and realize more precise lower limb rehabilitation training.

With the promotion of artificial intelligence technology in various industries in China, deep learning technology driven by electric power data has begun to be applied on a large scale in the electric power industry, but in actual application we have also found some problems, for example, the current process and tools, seemingly clear and independent, but are actually interdependent and not flexible enough; for common requirements such as retrieval, functions are not flexible enough and often re-developed. Accumulated algorithm exploration will cause the same sample to appear repeatedly in multiple training sets, The waste of resources is serious. This article elaborated on the causes and solutions to solve these problems, redesigned the process and architecture, and developed software tools. First of all, this article believes that deep learning models have a life cycle, and proposes to reorganize the deep learning tool chain with the model warehouse as the center. Secondly, combined with the actual application of electric power AI, we decoupled the original data, labeled data, and sample definitions. Finally, we designed an expression of public functions such as retrieval, taking into account flexibility and versatility.

There are significant identification errors in the vibration displacement identification of deep-water pile foundation bridge piers on soft soil foundations. Therefore, this paper proposes a new method for identifying the vibration displacement of deep-water pile foundation bridge piers on soft soil foundations based on multi-source information fusion. The characteristics of soft soil structure are analyzed and the stress relationship between soft soil and solid soil skeleton is calculated. Deep water pile foundation bridge piers on soft soil foundation determine the structural characteristics of deep water pile foundation bridge piers on soft soil foundation, construct displacement curves based on the elastic deformation curvature of deep water pile foundation bridge piers on soft soil foundation, and determine the curvature of each point when the bridge pier displacement occurs. Analyze the factors that cause the vibration of deep-water pile foundation bridge piers on soft soil foundations, and determine the characteristics of the vibration of deep-water pile foundation bridge piers on soft soil foundations by calculating the amplitude under different factors. Analyze the different failure modes and shear strength, bending strength, displacement ductility coefficient under the vibration conditions of bridge piers, and analyze the load on the bridge piers to complete the extraction of multi-source information on the vibration displacement of deep-water pile foundation bridge piers on soft soil foundation. Analyze the multi-source information fusion structure, introduce the least squares support vector machine method to complete the fusion of information on the vibration displacement impact of deep-water pile foundation bridge piers on soft soil foundation. Based on this, select strain measurement points for deep-water pile foundation bridge piers on soft soil foundation, and arrange displacement identification along the entire bridge pier at equal intervals to achieve accurate displacement identification. The experimental results show that the proposed method reduces the error of displacement recognition.

It is very important for the development of electric power big data technology to use the electric power knowledge. A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data. Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data. Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method. Based on this, this paper proposes the structure, elements, basic calculations and multidimensional reasoning method of the new knowledge model. A modeling example of the regulations defined in electric power system operation standard is demonstrated. Different forms of the model and related technologies are also introduced, including electric power system standard modeling, multi-type data management, unstructured data searching, knowledge display and data analysis based on semantic expansion and reduction. Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data. With the development of electric power big data technology, it is expected that the knowledge model will be improved and will be used in more applications.

In the coming decades, electricity's share of total global energy is expected to continue to grow, I and more intelligent processes will be introduced into the electric power delivery (transmission and distribution) networks. It is envisioned that the electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades. A key challenge is how to redesign, retrofit, and upgrade the existing electromechanically controlled system into a smart self-healing grid that is driven by a well-designed market approach. Revolutionary developments in both information technology and materials science and engineering promise significant improvements in the security, reliability, efficiency, and cost effectiveness of electric power delivery systems. Focus areas in materials and devices include sensors, smart materials and structures, microfabrication, nanotechnology, advanced materials, and smart devices.

With the wide application of Gas Insulated Switchgear (GIS) in the field of ultra-high voltage, the identification and evaluation of insulation state in GIS equipment has been a hot topic in this field, Therefore, how to effectively integrate the multi-source information representing GIS insulation state and establish a scientific state evaluation model is a major problem to be solved. In this paper, the insulation degradation process of typical insulation defect inside GIS were simulated, and ultra-high frequency (UHF) and ultrasonic information of defect in different degradation stages were obtained. At the same time, the genetic algorithm is used to further characterize the PD severity assessment feature, and the PD assessment index set based on UHF and ultrasonic information was constructed. Based on this, the fuzzy hierarchy analysis was used as the evaluation method framework, the factor analysis weighting method and fuzzy evaluation function based on mutual information was used to obtain the preliminary evaluation result. Finally, the preliminary evaluation result was used to form a complete comprehensive evaluation model. The evaluation scheme constructed in this paper can effectively complement the multi-source information, thereby improving the reliability of the GIS insulation state evaluation result.

This paper develops models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations using Monte-Carlo simulation, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data are used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study is developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the NREL Futures Study 2050 Texas scenarios for 50% and 80% renewable energy.