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Data analysis has become most widespread field of research and it has extended in almost every field of study. Considering the recent trends and developments in the field of communication and information technology, there is a scope of combining the monitoring of substation equipment with big data analysis technology. That will result in an improve...
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Citations
... They highlight the transition from traditional hardware-based systems to flexible, softwaredefined control systems for dynamic adaptation to changing power resources and flows. In their study, Ruiling Yu et al. [19] enhance substation monitoring using big data analysis, applying distributed data techniques like Hive, Impala, and H Base for improved efficiency and storage. Their experimental validation showcases the model's advantages over traditional methods, coupled with a comprehensive review of data platform architecture, data cleaning, and storage relevant to substation monitoring. ...
... This data can be viewed with false colour composites (FCC) for the land feature identification. The FCC formed by bands 5, 4 and 3, which can be called composite band image, was classified by the ISO unsupervised classification method in Arc GIS map 10.5.1 (Yu et al. 2021). The five land features can be identified to assess LULC change. ...
In this paper, the assessment of seasonal water quality and land use land cover change in the Subarnarekha watershed in Ranchi stretch was analysed. Agricultural runoff along with climate change adds to the pollution risk to the Subarnarekha River of Ranchi stretch in Jharkhand. Water quality indicators, like acidity alkalinity (ALK), total dissolved solids (TDS), hardness (H), dissolved oxygen (DO), biochemical oxygen demand (BOD), chlorides (CL−), electrical conductivity (EC), salinity (SAL), resistivity (RES) and pH, were assessed as per the standard method. During monsoon season, acidity, alkalinity, hardness, chlorides, salinity, pH and DO decreased, whereas EC, TDS, BOD and resistivity increased in comparison to pre-monsoon season. In post-monsoon, chloride problem was observed very high. Hardness was least in monsoon and maximum in post-monsoon season. EC and BOD increased in monsoon season in comparison to other seasons. Statistical analysis like HCA (hierarchical cluster analysis) and PCA (principal component analysis) also confirmed the problem of TDS, EC, chloride and hardness in the area. WQI (water quality index) analysis showed that the water quality was poor to unsuitable on all the sampling points throughout the study area in all seasons. LULC (land use land cover) and NDWI (normalized difference water index) analysis had also concluded that due to high rate of urbanization, the area has undergone a massive change in terms of forest cover and water bodies. The need for afforestation, forest protection and wetland protection can be clearly seen from the result of this study.
... In general, keeping this value around 2-3 % indicates effective asset management. However, to achieve and maintain these levels, it is necessary to establish a technology-driven asset management system [10]. In this sense, online monitoring systems used in today's technology are an excellent solution for asset management of critical components with high financial value. ...
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... The system uses logic modeling technology to achieve panoramic modeling of the logical relationships of various models. The logic modeling technology establishes logical relationships between the AC system of the substation, the DC system, and the primary and secondary equipment supplied by it [9][10][11] . ...
For the problems of complicated work content and error-prone workload of secondary circuits, a three-dimensional visualization method is proposed for the visualization and management of the physical circuits of the control and protection system of the converter station based on digital twin technology. The system can be used to improve the work efficiency of operation and maintenance personnel by constructing a secondary circuit information data model, generating corresponding data through artificial intelligence image recognition and manual review of secondary circuit drawings and schematics, and performing accurate corresponding circuit data analysis and 3D visualization management of the secondary physical circuits of control and security equipment, which can visualize the circuit information of control and security equipment in the station from outside to inside at multiple levels and multiple dimensions.
... Using computers in monitoring and control the power system elements leads to a huge data collection of the network and its parameters. These data need specific algorithms to be manipulated and treated optimally to reduce the computation based on it, and how it can be used efficiently to improve the performance of the DS [9], [10]. Many researchers in the last decade are focusing on smart grids and how they can be managed, i.e. solving the location of different distributed generators, to reduce the grid losses and improve the reliability [11]- [14]. ...
Electric power quality is one of the most challenging topics for electrical companies throughout the world. The planning and the structure of distribution stations (DS) play an important role in the power quality and the performance of these stations. This paper studies the structure and the components of the distribution substation and presents some technical ideas in order to enhance its performance. The effect of (33/0.4) kV transformer, ONAF transformers, and power factor correction, will be discussed for higher power distribution reliability in terms of peak-load management and power delivery interruptions. The medium voltage outdoor distribution station of (33/11) kV in AL-Tur in Palestine is adopted as a case study to illustrate the proposed technical actions.
... An ELM is a simple and efficient structure, has no need to set a learning rate, and requires few calculations [9]. It can minimize the experience risk as well as the generalization error, showing good extensibility. ...
Background
To improve the modeling efficiency of nonlinear load electric energy metering evaluation system, a method based on artificial intelligence algorithm was proposed.
Methods
First introduces the artificial glowworm swarm optimization extreme learning machine, a powerful ability of global optimization using artificial firefly algorithm. Then, find the hours of training error, extreme learning machine model, input weighting matrix, and hidden layer offset matrix. Moreover, there is a certain period in a given area in our country, power load simulation through the experiment, verifying the validity and superiority of the model.
Results
The experimental results show that the traditional BP neural network has the largest prediction relative error, and the stability of BP neural network is poor, and the relative error time is large, which is related to the defect of the neural network itself. The prediction effect of SVM method is better than that of BP neural network, because SVM has a strict theoretical and mathematical basis, so its generalization ability is better than that of BP neural network, and the algorithm has global optimality.
Conclusion
As can be seen from the chart analysis, GSO-ELM algorithm performs better than both in terms of stability and test error. It is proved that the modeling of nonlinear load electrical energy measurement and evaluation system based on artificial intelligence algorithm is superior and effective. The proposed algorithm outperforms very well over the existing literature.
... Ruiling Yu, Mohammad Asif Ikbal, and Abdul Rahman contributed an article entitled "Improvement of substation Monitoring aimed to improve its efficiency with the help of Big Data Analysis" [21]. In this article, the authors have introduced the big data analysis and its corresponding application in the monitoring of substations. ...
Digital recognition of meters aims to identify numbers in complex environments. Existing methods of digital recognition of meters are dependent on deep networks supported by high-quality large-scale data and features extraction, whereas low-quality small-scale digital datasets are not effective in recognition. Moreover, occlusion of digital images obstructs the extraction of fixed features. Multi-classifier under Feature Engineering (MC-FE) is proposed to solve the above problems. To be specific, MC-FE builds a feature library containing a variety of mainstream features. It directly selects the optimal combination of distinguishing features applicable to the current dataset, rather than using the fixed features. In addition, 10 regression machines integrated by support vector machines are adopted. The respective regression machine determines the probability of every number from 0 to 9. The positioning of the meter is the premise of accurate identification. A multi-layer kernel regression positioning (ML-KRP) is designed to increase the accuracy of meter identification. The results of the experiments on several digital recognition datasets reveal that MC- FE and ML-KRP outperform the state-of-the-arts in digital recognition under this small-scale setting.
