The information produced today is enormous and is increasing more quickly than computer speeds can keep up. As a result, it is no longer advantageous and often takes a long time to maintain or process information using traditional methods, or as we may say, a single machine. Under the terms of the Apache License, In the cloud hardware of the distributed file system, this research also covers optimizing the number of computing nodes and the message passing pathways. According to HIVE.apache.org, the Apache HIVE data storage software makes it easier to read, write, and manage huge datasets that are stored in distributed storage using SQL. The installation and launch of Hadoop and HIVE, setting up the rules in a Hadoop cluster, and a few use cases for performance testing are all covered at the end of the chapter.

Large-scale Information Management using Hadoop Platform

Line loss is one of the key links in the management of power supply enterprises. Line loss rate directly affects the economic benefits of power supply companies. Line loss management explains the management level of a power supply company to a certain extent. This paper proposes a massive data analysis technology based on the integration of business allocation and deployment. In this paper, the key technologies of the power big data platform are used to build a power grid line loss and electricity stealing early warning analysis system to realize the integrated calculation, analysis and display of line loss. By using Hadoop offline distributed computing, Spark in-memory computing and other big data technologies, the author conducts in-depth mining of lines or stations with abnormal line loss rates. This can identify possible electricity theft. The line loss multi-level and multi-dimensional early warning system designed in this paper provides electricity stealing early warning and data support services for the anti-stealing inspection of power supply enterprises.

Research on Multi-Level and Multi-Dimensional Early Warning System of Line Loss under the Condition of Improving Real-Time Monitoring of Equipment Operation Status

For different signals, whether it is synchronous acquisition, or phase synchronization and traditional detection technology, or the acquisition of partial discharge data, there are very big differences. This is because, for the three kinds of signals, the acoustic-electrical joint detection device can implement multi-channel synchronous acquisition and processing. One is high-frequency current; the other is ultrasonic; the third is ultra-high frequency. In addition to the PD pulse self-triggering technology, this paper studies the FIFO ultra-high-speed segmented acquisition and storage technology. In this way, for the multi-channel signals of the three types of sensors, its high-speed synchronous acquisition can be realized.

Research on Data Processing in Comprehensive Live Detection of Partial Discharge

Transmission lines are the main arteries of power systems, which inevitably pass-through geological areas such as coal mining areas, desert areas, soft soil areas, etc. Transmission towers are at risk of tilting and settling. Traditional tower monitoring methods such as GPS/Beidou method and static level have high price, inconvenient installation and risk of external breakage. In this paper, we propose a multi-sensor fusion method for tower settlement monitoring, using ultrasonic distance sensor and tilt sensor fusion to achieve the monitoring of tower settlement, and design a monitoring system and carry out a simulation of settlement experiments, the experimental results show that the average measurement error of the system is 5%, the system has the advantages of good economy, easy installation and good measurement accuracy.

Multi-Sensor Fusion-based Settlement Monitoring Method for Power Transmission Towers

Aiming at the low measurement accuracy and poor time lag of traditional power system harmonic and interharmonic detection methods, a new power harmonic detection method is proposed, namely, the neural network power system harmonic detection analysis based on big data. It combines wavelet transform with artificial neural network, uses wavelet transform to extract the characteristic components in the signal. And then separates and extracts the different frequency band signals, and then uses the corresponding artificial neural network to analyze and identify the extracted frequency band signals to obtain each Harmonic type, amplitude and phase parameters in the hierarchy. The final simulation experiment shows that this research proposes to combine wavelet transform with artificial neural network to give full play to the time and frequency locality advantages of wavelet transform, as well as the advantages of artificial neural network self-adaptability and model maximum value advantages, which are effective for periodic changes and interference in electric power. It has strong tracking performance and recognition ability, which further shows that the power harmonic detection method proposed in this study is feasible and effective.

Harmonic and Interharmonic Detection and Analysis of Neural Network Power System Based on Big Data

The reduction of power grid losses is of great significance to the economic operation of the power system. This paper proposes a series of limit line loss analysis indicators. The contents include limit line loss, section limit line loss rate and section limit loss reduction rate, reactive power compensation limit line loss rate and reactive power compensation limit loss reduction rate, optimized transformer limit line loss rate and optimized transformer limit loss reduction rate, three-phase Balance limit line loss rate and three-phase balance limit loss reduction rate, optimized operation limit line loss rate and optimized operation limit loss reduction rate, and comprehensive limit line loss rate and comprehensive limit loss reduction rate indicators. It is found that the decision-making method proposed in this paper can optimize each planning scheme, and has good operability and economy.

Research on Multi-Dimensional Early Warning System under the Integrated Display of Precise Loss Reduction Based on Intelligent Management and Control of Loss Reduction

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