AI-based intelligent diagnosis method, system and device

A diagnostic method and energy technology, applied in the field of smart grids, can solve problems such as misjudgment, large time span, and ineffective data, and achieve fast network service response, low-latency business response, and improved energy indicators.

Active Publication Date: 2019-11-29
马欣 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] 1. "Energy index management is not predictable" - the current energy index management system is mainly based on monitoring and data storage, a large amount of data does not play a role, and it is impossible to predict energy indexes in real time, so that it is impossible to accurately judge the abnormal situation of energy indexes;
[0009] 2. "Difficulty in diagnosing the cause of abnormal energy indicators" - most of the energy indicator management systems currently in use are based on data calculation, with weak analysis capabilities, and cannot accurately locate the real-time abnormalities of each station, equipment, line, station a

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  • AI-based intelligent diagnosis method, system and device

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Embodiment 1

[0062] like figure 1 As shown, the present invention provides a kind of intelligent diagnosis method based on AI, comprising:

[0063] Collect electricity consumption data;

[0064] Input the collected electricity consumption data into the trained artificial intelligence AI model to obtain relevant data.

[0065] The artificial intelligence AI model includes a power stealing AI model to detect power stealing data, wherein the power stealing AI model is trained according to the following procedures: collecting labeled power consumption data; preprocessing the collected power consumption data; The preprocessed electricity consumption data is used to train the electricity stealing AI model.

[0066] Specifically, for the training of the model, the real power consumption data of the station area is collected through the IOT edge device for the training of the model. The data feature set includes time, single-phase / three-phase voltage, single-phase / three-phase current, neutral l...

Embodiment 2

[0092] A kind of intelligent diagnosis method based on AI of the present invention comprises:

[0093]We have collected a large amount of real power consumption data in the station area through IOT edge devices for model training. The total amount of training data collected by each IOT device is about 20w, including time, single-phase / three-phase voltage, single-phase / three-phase current, neutral line voltage / current, phase angle, whether to open the cover and other index data as features set, and also contains the actual user classification data (normal users, power-stealing users) as the actual results of user classification as labels to train the model.

[0094] The 20w data collected through IOT edge devices contains some missing and erroneous data, so we need to preprocess the data first so that the data can reach the standard of training data. Through the EDA (Exploratory Data Analysis) method, we found that the distribution of high-quality data is more obvious, showing...

Embodiment 3

[0102] An AI-based intelligent diagnosis method of the present invention also includes:

[0103] Collect electricity consumption data;

[0104] Input the collected electricity consumption data into the trained artificial intelligence AI model to obtain relevant data.

[0105] Preferably, the artificial intelligence AI model also includes the station area list AI model, the technical loss AI model, the household change relationship AI model, the collection AI model, the measurement AI model, and the archives AI model.

[0106] Among them, the relevant models are as follows:

[0107] Table area AI model

[0108] The station area meter is used to measure the difference between power supply and electricity sales (called line loss electricity). The station area meter counts the power supply and electricity sales of the 10kV / 400V low-voltage sub-area, and provides the basic data of the line loss in the station area. The AI ​​module of the station area table mainly completes the f...

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Abstract

The invention relates to the field of intelligent power grids, in particular to an AI-based intelligent diagnosis method, a system and a device. The core of the method is AI + IoT. The method is composed of standardized and industrialized IoT edge computing hardware equipment and a customized AI module, and comprises the following steps: an acquisition unit of IoT equipment acquires power consumption data; the cloud platform trains an AI model according to historical data, the AI model is continuously and dynamically perfected according to new data, and an edge computing unit of the IoT equipment inputs acquired power consumption data into the trained artificial intelligence AI model to obtain related data. Real-time prediction of governance is achieved, the real-time monitoring level of abnormal standard exceeding of energy indexes is practically improved, and a previous working mode of semi-manual system detection and manual verification is changed into a full-automatic intelligent AI analysis mode. Finally, a brand-new software and hardware form of a cloud platform, edge computing equipment, an intelligent field terminal and an intelligent IoT chipset module is realized. A user-side energy system and a power distribution management system are redefined, and the age of software-defined energy is started.

Description

technical field [0001] The invention relates to the field of smart grids, in particular to an AI-based intelligent diagnosis method, system and device. Background technique [0002] At present, the construction of the ubiquitous electric power Internet of Things is progressing steadily, and tasks in six major fields, including basic support, data sharing, and internal and external business, have been carried out successively. As one of the core economic and technical indicators of power grid enterprises, line loss reflects the operating costs and economic benefits of the enterprise. Strengthening line loss management is a long-term strategic task and systematic project for power grid enterprises. The smart grid is a multi-objective function constrained by balance. Its factors are numerous, the conditions are random, the line loss is changeable, and the causes are complex. Factors change dynamically, making physical modeling difficult. Manual experience and existing methods ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06K9/62
CPCG06Q10/04G06Q10/06393G06Q50/06G06F18/24323G06F18/214Y02P90/82
Inventor 马欣孙钊
Owner 马欣
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