CN114493044A - Artificial intelligence prediction method for voltage quality of power distribution network - Google Patents
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Abstract
The invention relates to the technical field of power distribution networks, in particular to an artificial intelligence prediction method for the voltage quality of a power distribution network, which comprises the following steps: collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last years, and collecting and storing environmental condition factors corresponding to related data; the method provided by the invention establishes a voltage quality prediction model by acquiring the relevant data of the voltage quality, can effectively predict the voltage quality under various environmental conditions, enables enterprises to take relevant measures, reduces the influence of voltage quality fluctuation on enterprise production, and solves the problem that the enterprise production is influenced by the fluctuation of the voltage quality because the relevant production enterprises cannot obtain relevant predictions and take measures due to more environmental factors influencing the voltage quality and lack of prediction means of the voltage quality of the power distribution network at present.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to an artificial intelligence prediction method for the voltage quality of a power distribution network.
Background
The voltage is one of the important indexes of the power quality, and the voltage qualification rate is an important basis for evaluating the voltage quality of a power grid, managing production scheduling, making power grid planning and technical transformation plans and is also one of the important indexes for checking the operation management level of a system. Therefore, a perfect voltage monitoring system capable of reflecting the whole appearance is established, the monitoring data is enhanced and analyzed, and the method is very important for the dispatching operation management and planning and transformation work of power supply enterprises. The frequency deviation is the degree of the fundamental frequency of the power system deviating from the rated frequency, the power regulation of China stipulates that the frequency deviation of a large-capacity power system cannot exceed +/-0.2 Hz, the rotation speed of an asynchronous motor is changed due to the frequency change, the product quality of textile, paper-making and other machinery is affected, the excitation current of the asynchronous motor and a transformer is increased due to the reduction of the system frequency, the consumed reactive power is increased, and the voltage level of the power system is deteriorated. The voltage deviation is the percentage of the voltage at each part of the system deviating from the rated value, if the deviation is large, the voltage deviation has great harm to users, the safety and economic operation of the electric equipment are influenced, the yield and the quality of the produced products are influenced, and therefore the quality of the voltage is closely related to production enterprises.
The method comprises the steps of establishing a model for predicting the voltage quality by acquiring relevant data of the voltage quality, effectively predicting the voltage quality under various environmental conditions, enabling enterprises to take relevant measures, and reducing the influence of voltage quality fluctuation on enterprise production.
Disclosure of Invention
The invention aims to provide an artificial intelligence prediction method for the voltage quality of a power distribution network, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an artificial intelligence prediction method for the voltage quality of a power distribution network comprises the following steps:
(S1) collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last years, and collecting and storing environmental condition factors corresponding to related data, wherein the environmental condition factors comprise date, time, positions of nodes, weather, temperature and geographical environments of the nodes;
(S2), building an artificial intelligence prediction model in the power distribution network, and setting sensors and monitoring facilities for monitoring the climate, geography and human environment at each node of the power distribution network;
(S3) inputting the acquired five types of index data and the dates, the times and the positions of the nodes corresponding to the related data into an artificial intelligent prediction model for training, and simultaneously inputting the weather, the temperatures and the geographical environments of the nodes corresponding to the dates into the artificial intelligent prediction model for training;
(S4) manufacturing a corresponding factor condition training set according to the date, time, node position, weather, temperature and geographic environment corresponding to the five types of index data, randomly extracting factor condition data in the training set, inputting the factor condition data into the model for testing, if the error of the index data output by the artificial intelligence prediction model is small, indicating that the model is successfully trained, and if the error is large, continuing to train the model;
(S5), acquiring climate environment forecast of each node, inputting the variation of the climate environment and the date into the model together to obtain corresponding five types of index values, evaluating the quality of the voltage through the five types of index values to obtain a prediction result, and sending the related prediction result to a production enterprise.
Preferably, in the step (S1), the collected environmental condition factors further include artificial environmental factors, and the artificial environmental factors include development conditions near the distribution network nodes and artificial events affecting the distribution network nodes.
Preferably, in the step (S1), the data is collected with accuracy to each hour and the error of the data is within 3-2%.
Preferably, in the step (S1), in the process of collecting data, data with large fluctuation of the five types of indices is recorded, and the environmental condition factors corresponding to the relevant data are stored.
Preferably, in the step (S2), the artificial intelligence prediction model is built based on the global big data of the distribution network, and utilizes the related information obtained from different platforms and devices of various online monitoring systems, inspection systems, detection sensors, production management systems, power management systems, artificial overhaul monitoring systems and intelligent terminals.
Preferably, in the step (S2), the sensors include a temperature sensor, a humidity sensor and a vibration sensor, and the monitoring device includes a monitoring camera and a precipitation monitor.
Preferably, in the step (S3), the model training process is divided into four stages, namely, data-based prediction, reinforcement learning, neural network training and motion model-based prediction.
Preferably, in the step (S4), the artificial intelligence prediction model is trained by using the factor condition training set until the model meets the requirement of training precision or the training of the model reaches the maximum training number, and then the training of the model is stopped.
Preferably, in the step (S5), the climate environment forecast is obtained through a weather forecast, tv news, internet news and a related forecast APP.
Compared with the prior art, the invention has the following beneficial effects:
the method provided by the invention establishes a voltage quality prediction model by acquiring the relevant data of the voltage quality, can effectively predict the voltage quality under various environmental conditions, enables enterprises to take relevant measures, reduces the influence of voltage quality fluctuation on enterprise production, and solves the problem that the enterprise production is influenced by the fluctuation of the voltage quality because the relevant production enterprises cannot obtain relevant predictions and take measures due to more environmental factors influencing the voltage quality and lack of prediction means of the voltage quality of the power distribution network at present.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
an artificial intelligence prediction method for the voltage quality of a power distribution network comprises the following steps:
(S1), collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last few years, and simultaneously collecting and storing environmental condition factors corresponding to related data, wherein the environmental condition factors comprise date, time, positions of nodes, weather, temperature and geographical environments of the nodes, the collected environmental condition factors further comprise artificial environmental factors, and the artificial environmental factors comprise development conditions near the nodes of the power distribution network and artificial events influencing the nodes of the power distribution network so as to test the influence of the artificial factors on the voltage quality;
(S2), an artificial intelligence prediction model is built in the power distribution network, and sensors and monitoring facilities for monitoring the climate, geography and human environment are arranged at each node of the power distribution network, wherein the artificial intelligence prediction model is built on the basis of global big data of the power distribution network, and more comprehensive information is obtained by utilizing related information obtained from different platforms and equipment of various online monitoring systems, routing inspection systems, detection sensors, production management systems, electric energy management systems, artificial overhaul monitoring systems and intelligent terminals, so that the model can be built conveniently;
(S3) inputting the acquired five types of index data and the dates, the times and the positions of the nodes corresponding to the related data into an artificial intelligent prediction model for training, and simultaneously inputting the weather, the temperatures and the geographic environments of the nodes corresponding to the dates into the artificial intelligent prediction model for training, wherein the model training process comprises four stages of data-based prediction, reinforcement learning, neural network training and motion model-based prediction;
(S4) manufacturing a corresponding factor condition training set according to the date, time, node position, weather, temperature and geographic environment corresponding to the five types of index data, randomly extracting factor condition data in the training set, inputting the factor condition data into the model for testing, if the error of the index data output by the artificial intelligent prediction model is small, indicating that the model is successfully trained, if the error is large, continuing training the model, wherein the factor condition training set is used for training the artificial intelligent prediction model until the model meets the training precision requirement or the training of the model reaches the maximum training times, and stopping training the model;
(S5), acquiring climate environment forecast of each node, inputting the variation of the climate environment and the date into the model together to obtain corresponding five types of index values, evaluating the quality of the voltage through the five types of index values to obtain a prediction result, and sending the related prediction result to a production enterprise.
Example two:
an artificial intelligence prediction method for the voltage quality of a power distribution network comprises the following steps:
(S1) collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last few years, and simultaneously collecting and storing environment condition factors corresponding to related data, wherein the environment condition factors comprise date, time, positions of nodes, weather, temperature and geographic environments where the nodes are located, the collected environment condition factors also comprise artificial environment factors, the artificial environment factors comprise development conditions near the nodes of the power distribution network and artificial events influencing the nodes of the power distribution network so as to test the influence of the artificial factors on the voltage quality, and in the process of collecting the data, the data are accurate to each hour, meanwhile, the error of the data is within 3-2%, and the situation that the training of a prediction model is deviated due to large data error is prevented;
(S2), an artificial intelligence prediction model is built in the power distribution network, and sensors and monitoring facilities for monitoring the climate, geography and human environment are arranged at each node of the power distribution network, wherein the artificial intelligence prediction model is built on the basis of global big data of the power distribution network, and obtains more comprehensive information by utilizing related information obtained from various online monitoring systems, routing inspection systems, detection sensors, production management systems, electric energy management systems, artificial overhaul monitoring systems and different platforms and equipment of intelligent terminals so as to establish the model;
(S3) inputting the acquired five types of index data and the dates, the times and the positions of the nodes corresponding to the related data into an artificial intelligent prediction model for training, and simultaneously inputting the weather, the temperatures and the geographic environments of the nodes corresponding to the dates into the artificial intelligent prediction model for training, wherein the model training process comprises four stages of data-based prediction, reinforcement learning, neural network training and motion model-based prediction;
(S4) manufacturing a corresponding factor condition training set according to the date, time, node position, weather, temperature and geographic environment corresponding to the five types of index data, randomly extracting factor condition data in the training set, inputting the factor condition data into the model for testing, if the error of the index data output by the artificial intelligent prediction model is small, indicating that the model is successfully trained, if the error is large, continuing training the model, wherein the factor condition training set is used for training the artificial intelligent prediction model until the model meets the training precision requirement or the training of the model reaches the maximum training times, and stopping training the model;
(S5), acquiring climate environment forecast of each node, inputting the variation of the climate environment and the date into the model together to obtain corresponding five types of index values, evaluating the quality of the voltage through the five types of index values to obtain a prediction result, and sending the related prediction result to a production enterprise.
Example three:
an artificial intelligence prediction method for the voltage quality of a power distribution network comprises the following steps:
(S1), collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last years, and collecting and storing environment condition factors corresponding to related data, wherein the environment condition factors comprise date, time, positions of nodes, weather, temperature and geographical environments of the nodes, the collected environment condition factors further comprise artificial environment factors, the artificial environment factors comprise development conditions near the nodes of the power distribution network and artificial events influencing the nodes of the power distribution network so as to test the influence of the artificial factors on the voltage quality, in the process of collecting the data, the data are accurate to each hour, meanwhile, the error of the data is within 3-2%, the deviation of the training of a prediction model caused by large data error is prevented, in the process of collecting the data, recording data with large up-down fluctuation of five types of indexes, and storing environmental condition factors corresponding to related data;
(S2), an artificial intelligence prediction model is built in the power distribution network, and sensors and monitoring facilities for monitoring the climate, geography and human environment are arranged at each node of the power distribution network, wherein the artificial intelligence prediction model is built on the basis of global big data of the power distribution network, and obtains more comprehensive information by utilizing related information obtained from various online monitoring systems, routing inspection systems, detection sensors, production management systems, electric energy management systems, artificial overhaul monitoring systems and different platforms and equipment of intelligent terminals so as to establish the model;
(S3) inputting the acquired five types of index data and the dates, the times and the positions of the nodes corresponding to the related data into an artificial intelligent prediction model for training, and simultaneously inputting the weather, the temperatures and the geographic environments of the nodes corresponding to the dates into the artificial intelligent prediction model for training, wherein the model training process comprises four stages of data-based prediction, reinforcement learning, neural network training and motion model-based prediction;
(S4) manufacturing a corresponding factor condition training set according to the date, time, node position, weather, temperature and geographic environment corresponding to the five types of index data, randomly extracting factor condition data in the training set, inputting the factor condition data into the model for testing, if the error of the index data output by the artificial intelligent prediction model is small, indicating that the model is successfully trained, if the error is large, continuing training the model, wherein the factor condition training set is used for training the artificial intelligent prediction model until the model meets the training precision requirement or the training of the model reaches the maximum training times, and stopping training the model;
(S5) acquiring climate environment forecast of each node, inputting the variation of the climate environment and the date into the model together to obtain corresponding five types of index values, evaluating the quality of the voltage through the five types of index values to obtain a forecast result, and sending the relevant forecast result to a production enterprise, wherein the climate environment forecast is acquired through weather forecast, television news, network news and relevant forecast APP.
The method provided by the invention establishes a voltage quality prediction model by acquiring the relevant data of the voltage quality, can effectively predict the voltage quality under various environmental conditions, enables enterprises to take relevant measures, reduces the influence of voltage quality fluctuation on enterprise production, and solves the problem that the enterprise production is influenced by the fluctuation of the voltage quality because the relevant production enterprises cannot obtain relevant predictions and take measures due to more environmental factors influencing the voltage quality and lack of prediction means of the voltage quality of the power distribution network at present.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. An artificial intelligence prediction method for the voltage quality of a power distribution network is characterized by comprising the following steps: the method comprises the following steps:
(S1) collecting five types of index data of voltage deviation, frequency deviation, three-phase unbalance, harmonic voltage current and voltage fluctuation flicker of different node voltages in the power distribution network in the last years, and collecting and storing environmental condition factors corresponding to related data, wherein the environmental condition factors comprise date, time, positions of the nodes, weather, temperature and geographical environments of the nodes;
(S2) building an artificial intelligence prediction model in the power distribution network, and setting sensors and monitoring facilities for monitoring the climate, geography and human environment at each node of the power distribution network;
(S3) inputting the acquired five types of index data and the dates, the times and the positions of the nodes corresponding to the related data into an artificial intelligent prediction model for training, and simultaneously inputting the weather, the temperatures and the geographical environments of the nodes corresponding to the dates into the artificial intelligent prediction model for training;
(S4) manufacturing a corresponding factor condition training set according to the date, time, node position, weather, temperature and geographical environment corresponding to the five types of index data, randomly extracting factor condition data in the training set, inputting the factor condition data into the model for testing, if the error of the index data output by the artificial intelligent prediction model is small, indicating that the model is successfully trained, and if the error is large, continuing to train the model;
(S5), acquiring climate environment forecast of each node, inputting the variation of the climate environment and the date into the model together to obtain corresponding five types of index values, evaluating the quality of the voltage through the five types of index values to obtain a prediction result, and sending the related prediction result to a production enterprise.
2. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S1), the collected environmental condition factors further include artificial environmental factors, and the artificial environmental factors include development conditions near the distribution network nodes and artificial events affecting the distribution network nodes.
3. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S1), in the process of collecting data, the data is accurate to each hour, and the error of the data is within 3-2%.
4. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S1), in the process of collecting data, data with large fluctuation of the five types of indexes is recorded, and the environmental condition factors corresponding to the relevant data are stored.
5. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S2), the artificial intelligence prediction model is built based on the global big data of the distribution network, and utilizes the relevant information obtained from different platforms and devices of various online monitoring systems, inspection systems, detection sensors, production management systems, power management systems, artificial overhaul monitoring systems and intelligent terminals.
6. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S2), the sensors include a temperature sensor, a humidity sensor, and a vibration sensor, and the monitoring facility includes a monitoring camera and precipitation monitoring.
7. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S3), the process of model training is divided into four stages of data-based prediction, reinforcement learning, neural network training, and motion model-based prediction.
8. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S4), the artificial intelligence prediction model is trained using the factor condition training set until the model meets the training accuracy requirement or the training of the model reaches the maximum training times, and then the training of the model is stopped.
9. The artificial intelligence prediction method for the voltage quality of the power distribution network according to claim 1, characterized in that: in the step (S5), the weather environment forecast is obtained through weather forecast, tv news, internet news and related forecast APP.
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