CN110989363B - Electric energy quality control method and device based on deep learning - Google Patents
Electric energy quality control method and device based on deep learning Download PDFInfo
- Publication number
- CN110989363B CN110989363B CN201911379735.9A CN201911379735A CN110989363B CN 110989363 B CN110989363 B CN 110989363B CN 201911379735 A CN201911379735 A CN 201911379735A CN 110989363 B CN110989363 B CN 110989363B
- Authority
- CN
- China
- Prior art keywords
- electric energy
- energy quality
- data
- power quality
- quality control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application provides an electric energy quality control method and device based on deep learning, comprising the following steps: acquiring power quality data; inputting the power quality data into a trained power quality control model; and calculating through the electric energy quality control model to obtain an electric energy quality problem identification result output by the electric energy quality control model and an electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result, so as to control the electric energy quality of the power grid system according to the electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result. This application is through adding the degree of depth study, and based on the electric energy quality data input to the electric energy quality control model of training, through degree of depth study network discernment electric energy quality problem and obtain corresponding electric energy quality problem solution strategy to carry out real-time control adjustment to electric wire netting system according to the strategy that obtains, solved current electric energy quality control mode real-time, the poor technical problem of accuracy.
Description
Technical Field
The application relates to the technical field of power control, in particular to a method and a device for controlling electric energy quality based on deep learning.
Background
The power quality problem has been more and more concerned by power supply companies and power consumers, wherein the factors influencing the power quality mainly concentrate on three aspects of power factor reduction of a power distribution network, harmonic generation of a frequency converter during operation and three-phase load imbalance. Meanwhile, with the adoption of alternating current-direct current hybrid connection, new energy access and the application of a large number of power electronic devices, the modern power grid shows more complex random characteristics and multi-source big data characteristics, the uncertainty and complexity of the power grid operation environment are greatly increased, and the modern power grid dispatching system is deeply influenced.
The existing power quality control method adopts a fixed control method, and realizes control and treatment corresponding to limited fixed conditions of power quality by detecting specific indexes in a power grid and combining a set algorithm or a simulation module.
Disclosure of Invention
The application provides an electric energy quality control method and device based on deep learning, which are used for solving the technical problems of poor real-time performance and accuracy of the existing electric energy quality control.
In view of this, the first aspect of the present application provides a method for controlling power quality based on deep learning, including:
acquiring power quality data;
inputting the electric energy quality data into a trained electric energy quality control model, wherein the electric energy quality control model is a deep learning model obtained by training the electric energy quality data and the electric energy quality strategy data through a noise reduction self-coding learning network and a BP neural network, and the electric energy quality strategy data comprises: the power quality problem and the corresponding relation information of the power quality problem solving strategy;
through the operation is carried out to the electric energy quality control model, the electric energy quality problem identification result of obtaining the electric energy quality control model output and with the electric energy quality problem solution strategy that the electric energy quality problem identification result corresponds to according to with the electric energy quality problem solution strategy that the electric energy quality problem identification result corresponds carries out electric energy quality control to electric wire netting system.
Optionally, the method further comprises:
inputting the power quality data and the power quality strategy data into an initial noise reduction self-coding learning network model, and calculating through a noise reduction self-coding learning network to obtain the characteristic quantities of the power quality data and the power quality strategy data;
and fine tuning the noise reduction self-coding learning network through a BP neural network arranged at the top layer of the noise reduction self-coding learning network to obtain the electric energy quality control model.
Optionally, the acquiring the power quality data further includes:
and preprocessing the power quality data.
Optionally, the pre-processing comprises: normalization processing and noise reduction processing.
The second aspect of the present application provides an electric energy quality control device based on deep learning, including:
the data acquisition unit is used for acquiring power quality data;
the data input unit is used for inputting the electric energy quality data to a trained electric energy quality control model, the electric energy quality control model is a deep learning model obtained by training the electric energy quality data and the electric energy quality strategy data through a noise reduction self-coding learning network and a BP neural network, wherein the electric energy quality strategy data comprises: the power quality problem and the corresponding relation information of the power quality problem solving strategy;
and the solution strategy output unit is used for calculating through the electric energy quality control model to obtain an electric energy quality problem identification result output by the electric energy quality control model and an electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result, so that electric energy quality control is carried out on the power grid system according to the electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result.
Optionally, the method further comprises: a modeling unit;
the modeling unit is specifically configured to input the power quality data and the power quality policy data into an initial noise reduction self-coding learning network model, perform operation through a noise reduction self-coding learning network to obtain characteristic quantities of the power quality data and the power quality policy data, and perform fine tuning on the noise reduction self-coding learning network through a BP neural network arranged at a top layer of the noise reduction self-coding learning network to obtain the power quality control model.
Optionally, the method further comprises:
and the preprocessing unit is used for preprocessing the electric energy quality data.
Optionally, the pre-processing comprises: normalization processing and noise reduction processing.
A third aspect of the present application provides a terminal, comprising: a memory and a processor;
the memory is used for storing program codes corresponding to the deep learning-based power quality control method of the first aspect of the application;
the processor is configured to execute the program code.
A fourth aspect of the present application provides a storage medium having stored therein program code corresponding to the deep learning-based power quality control method according to the first aspect of the present application.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides an electric energy quality control method based on deep learning, which comprises the following steps: acquiring power quality data; with electric energy quality data input to the electric energy quality control model that trains, electric energy quality control model is through making an uproar self-coding study network and BP neural network of falling, to the deep study model that electric energy quality data and electric energy quality strategy data training obtained, wherein, electric energy quality strategy data include: the power quality problem and the corresponding relation information of the power quality problem solving strategy; and calculating through the electric energy quality control model to obtain an electric energy quality problem identification result output by the electric energy quality control model and an electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result, so as to control the electric energy quality of the power grid system according to the electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result.
According to the method, deep learning is added, actual power quality data are input into a trained power quality control model, power quality problems are identified through a deep learning network, corresponding power quality problem solving strategies are obtained, real-time control and adjustment are carried out on a power grid system according to the obtained strategies, and the technical problems that due to the fact that the power grid system is increasingly complex and uncertain, errors are prone to occurring, and real-time performance and accuracy are poor due to the fact that real-time adjustment and control cannot be achieved in the existing power quality control mode are solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a deep learning-based power quality control method according to a first embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a power quality control method based on deep learning according to a second embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of a modeling process of a deep learning-based power quality control method provided by the present application;
fig. 4 is a schematic structural diagram of a first embodiment of an electric energy quality control device based on deep learning according to the present application.
Detailed Description
The embodiment of the application provides an electric energy quality control method and device based on deep learning, and is used for solving the technical problem that the existing electric energy quality control is poor in real-time performance and accuracy.
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, 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 application.
Referring to fig. 1, a first embodiment of the present application provides a deep learning-based power quality control method, including:
It should be noted that, before the method of the present application is implemented to perform power quality control, power quality data needs to be acquired first, and preferably, data acquired in real time or data acquired in a short period, such as data acquired in the current day or data acquired in the previous day, may also be used.
Wherein, the power quality data of this embodiment includes: the monitoring method comprises the following steps of voltage and current unbalance parameters, harmonic parameters, surge, three-phase unbalance, frequency deviation, transient pulse and the like of each power quality monitoring point.
And 102, inputting the power quality data into the trained power quality control model.
It should be noted that, the electric energy quality control model of this embodiment is a deep learning model obtained by training electric energy quality data and electric energy quality strategy data through a noise reduction self-coding learning network and a BP neural network, wherein the electric energy quality strategy data includes: the power quality problem and the corresponding relation information of the power quality problem solving strategy.
And 103, operating through the power quality control model to obtain a power quality problem identification result output by the power quality control model and a power quality problem solution strategy corresponding to the power quality problem identification result, so as to control the power quality of the power grid system according to the power quality problem solution strategy corresponding to the power quality problem identification result.
It should be noted that, the electric energy quality data obtained in step 101 is input into the electric energy quality control model trained in advance, and then is calculated through the electric energy quality control model, so as to obtain the electric energy quality problem recognition result output by the electric energy quality control model and the electric energy quality problem solution strategy corresponding to the electric energy quality problem recognition result, wherein the electric energy quality problem recognition result output by the model of this embodiment includes: the type of the power quality problem and the output power quality problem solution decision are slightly the solution strategies corresponding to the type of the power quality problem output by the model.
It can be understood that after the corresponding solution strategy is obtained, the real-time power quality control of the power grid system can be realized only by regulating and controlling the power grid system according to the setting of the solution strategy, and the technical problems of poor real-time performance and accuracy caused by the fact that the existing power quality control mode is not easy to make mistakes and cannot realize real-time adjustment and control due to the fact that the power grid system is increasingly complex and the uncertainty is increased are solved.
The above is a detailed description of a first embodiment of a deep learning based power quality control method provided by the present application, and the following is a detailed description of a second embodiment of a deep learning based power quality control method provided by the present application.
Referring to fig. 2 and 3, on the basis of the first embodiment provided by the present application, a second embodiment provided by the present application includes:
before step 101 of the first embodiment of the present application, the method further includes:
It should be noted that the power quality data and the power quality strategy data are used as training set data, and the constructed self-encoder network is subjected to unsupervised pre-training by using the training set data. In the self-encoder network, an input layer unit and a hidden layer unit form an encoding part, the hidden layer unit and an output layer unit form a decoding part, and the neural units of the three layers are connected through a weight W but are not connected with the neural units of the same layer. Unsupervised pre-training, namely firstly, using the processed continuous sampling data values of a training set as the input of a built first self-encoder network, and carrying out unsupervised pre-training on the input; and then, the output of the first self-encoder network is utilized to continuously carry out unsupervised pre-training on the built next self-encoder network, and the like. A cost function is introduced in the unsupervised pre-training of the self-encoder network, and the minimum reconstruction error value between an input signal and an output signal is found out by adjusting the weight and the offset in the encoding and decoding processes after multiple iterations.
And 1002, fine tuning the noise reduction self-coding learning network through a BP neural network arranged at the top layer of the noise reduction self-coding learning network to obtain an electric energy quality control model.
It should be noted that the characteristic quantity of the power quality signal is obtained through multiple times of noise reduction self-coding learning, and the whole network model is further fine-tuned by adopting the BP neural network at the top layer, so that the power quality control model is obtained.
After the electric energy quality control model is obtained, the accuracy of the model can be verified by using the test set data, and the input electric energy quality data, the corresponding electric energy quality problem and the solution strategy can be automatically identified by using the model. The verification of the accuracy rate is to use the model generated by iterative training of the continuously sampled power quality data of the processed test set to judge the corresponding power quality problem and solution strategy by using the model, and finally match the judged power quality problem and solution strategy with the power quality problem and solution strategy corresponding to the input data, thereby testing the identification accuracy rate of the generated model.
The method further includes, after the step 101 of the first embodiment of the present application:
It should be noted that, in order to further improve the accuracy of the power quality control model, before inputting the power quality data into the model, the acquired power quality data may be preprocessed, and the training process and the implementation process may both preprocess the power quality data, wherein the preprocessing method includes: normalization processing and noise reduction processing. The data normalization processing aims to cancel energy level differences among all dimensional data, and avoids the trouble that network prediction errors are large due to large order difference between input and output. The normalization formula is as follows:
where x refers to the original raw signal value, xnorIs a normalized value, xmaxAnd xminRespectively representing the maximum and minimum values of each power quality signal.
The above is a detailed description of the second embodiment of the deep learning based power quality control method provided by the present application, and the following is a detailed description of the third embodiment of the deep learning based power quality control device provided by the present application.
The third embodiment of the present application provides an electric energy quality control device based on deep learning, including:
a data acquisition unit 301, configured to acquire power quality data;
data input unit 302 for with electric energy quality data input to the electric energy quality control model that trains, electric energy quality control model is through making an uproar self-encoding learning network and BP neural network of falling, to the deep learning model that electric energy quality data and electric energy quality tactics data training obtained, wherein, electric energy quality tactics data include: the power quality problem and the corresponding relation information of the power quality problem solving strategy;
and the solution strategy output unit 303 is configured to perform operation through the power quality control model to obtain a power quality problem identification result output by the power quality control model and a power quality problem solution strategy corresponding to the power quality problem identification result, so as to perform power quality control on the power grid system according to the power quality problem solution strategy corresponding to the power quality problem identification result.
More specifically, the method further comprises the following steps: a modeling unit 300;
the modeling unit is specifically used for inputting the electric energy quality data and the electric energy quality strategy data into an initial noise reduction self-coding learning network model, calculating through the noise reduction self-coding learning network to obtain the characteristic quantities of the electric energy quality data and the electric energy quality strategy data, and finely adjusting the noise reduction self-coding learning network through a BP neural network arranged at the top layer of the noise reduction self-coding learning network to obtain the electric energy quality control model.
More specifically, the method further comprises the following steps:
the preprocessing unit 3001 is configured to preprocess the power quality data.
More specifically, the pre-treatment comprises: normalization processing and noise reduction processing.
The above is a detailed description of a third embodiment of the deep learning based power quality control apparatus provided in the present application, and the following is an embodiment of a terminal and a storage medium provided in the present application.
A fourth embodiment of the present application provides a terminal, including: a memory and a processor;
the memory is used for storing program codes corresponding to the deep learning-based power quality control method according to the first embodiment or the second embodiment of the application;
the processor is configured to execute the program code.
A fifth embodiment of the present application provides a storage medium having stored therein program codes corresponding to the deep learning-based power quality control method according to the first or second embodiment of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (8)
1. A deep learning-based power quality control method is characterized by comprising the following steps:
acquiring power quality data;
inputting the electric energy quality data into a trained electric energy quality control model, wherein the electric energy quality control model is a deep learning model obtained by training the electric energy quality data and the electric energy quality strategy data through a noise reduction self-coding learning network and a BP neural network, and the electric energy quality strategy data comprises: the power quality problem and the corresponding relation information of the power quality problem solving strategy;
calculating through the electric energy quality control model to obtain an electric energy quality problem identification result output by the electric energy quality control model and an electric energy quality problem solving strategy corresponding to the electric energy quality problem identification result, so as to control the electric energy quality of a power grid system according to the electric energy quality problem solving strategy corresponding to the electric energy quality problem identification result;
the modeling process of the power quality control model specifically comprises the following steps:
inputting the power quality data and the power quality strategy data into an initial noise reduction self-coding learning network model, and calculating through a noise reduction self-coding learning network to obtain the characteristic quantities of the power quality data and the power quality strategy data;
and obtaining the characteristic quantity of the power quality data through multiple times of noise reduction self-coding learning, and further fine-tuning the whole network model by adopting a BP neural network at the top layer to obtain the power quality control model.
2. The deep learning-based power quality control method according to claim 1, wherein the obtaining of the power quality data further comprises:
and preprocessing the power quality data.
3. The deep learning-based power quality control method according to claim 2, wherein the preprocessing comprises: normalization processing and noise reduction processing.
4. An electric energy quality control device based on deep learning, characterized by comprising:
the data acquisition unit is used for acquiring power quality data;
the data input unit is used for inputting the electric energy quality data to a trained electric energy quality control model, the electric energy quality control model is a deep learning model obtained by training the electric energy quality data and the electric energy quality strategy data through a noise reduction self-coding learning network and a BP neural network, wherein the electric energy quality strategy data comprises: the power quality problem and the corresponding relation information of the power quality problem solving strategy;
the solution strategy output unit is used for carrying out operation through the electric energy quality control model to obtain an electric energy quality problem identification result output by the electric energy quality control model and an electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result, so that electric energy quality control is carried out on a power grid system according to the electric energy quality problem solution strategy corresponding to the electric energy quality problem identification result;
further comprising: a modeling unit;
the modeling unit is specifically configured to input the power quality data and the power quality policy data into an initial noise reduction self-coding learning network model, perform operation through a noise reduction self-coding learning network to obtain characteristic quantities of the power quality data and the power quality policy data, and perform fine tuning on the noise reduction self-coding learning network through a BP neural network arranged at a top layer of the noise reduction self-coding learning network to obtain the power quality control model.
5. The deep learning-based power quality control device according to claim 4, further comprising:
and the preprocessing unit is used for preprocessing the electric energy quality data.
6. The deep learning-based power quality control device according to claim 5, wherein the preprocessing comprises: normalization processing and noise reduction processing.
7. A terminal, comprising: a memory and a processor;
the memory is used for storing program codes corresponding to the deep learning-based power quality control method of any one of claims 1 to 3;
the processor is configured to execute the program code.
8. A storage medium having stored therein a program code corresponding to the deep learning based power quality control method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911379735.9A CN110989363B (en) | 2019-12-27 | 2019-12-27 | Electric energy quality control method and device based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911379735.9A CN110989363B (en) | 2019-12-27 | 2019-12-27 | Electric energy quality control method and device based on deep learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110989363A CN110989363A (en) | 2020-04-10 |
| CN110989363B true CN110989363B (en) | 2022-01-25 |
Family
ID=70078146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911379735.9A Active CN110989363B (en) | 2019-12-27 | 2019-12-27 | Electric energy quality control method and device based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110989363B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111478327B (en) * | 2020-05-18 | 2021-05-18 | 山东山大世纪科技有限公司 | New energy power quality control system and method based on dynamic strategy analysis |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726678A (en) * | 2008-10-30 | 2010-06-09 | 华北电力科学研究院有限责任公司 | Electricity quality evaluation system and method |
| CN103647276A (en) * | 2013-12-10 | 2014-03-19 | 国家电网公司 | Electric energy quality early warning system and method thereof |
| CN103729804A (en) * | 2014-01-02 | 2014-04-16 | 东南大学 | On-line decision support method for responding to power quality early warning |
| EP2810223A1 (en) * | 2012-02-03 | 2014-12-10 | Thales | Method for processing uncertain data from a plurality of heterogeneous sources and associated automatic decision support method |
| CN104459373A (en) * | 2014-11-11 | 2015-03-25 | 广东电网有限责任公司东莞供电局 | Method for calculating node voltage temporary drop magnitudes based on BP neural network |
| CN106778923A (en) * | 2017-03-23 | 2017-05-31 | 广东电网有限责任公司珠海供电局 | A kind of Power Quality Disturbance sorting technique and device |
| CN107016241A (en) * | 2017-04-05 | 2017-08-04 | 重庆交通大学 | Based on the rotating machinery lifetime stage recognition methods for adding sample enhancing depth own coding learning network of making an uproar |
| CN107123987A (en) * | 2017-05-11 | 2017-09-01 | 华东交通大学 | Electrical energy power quality disturbance recognition methods based on on-line training weighed SVM |
| CN108664950A (en) * | 2018-05-22 | 2018-10-16 | 天津大学 | A kind of electrical energy power quality disturbance identification and sorting technique based on deep learning |
| CN109596942A (en) * | 2018-12-06 | 2019-04-09 | 华北电力大学 | A kind of voltage sag reason recognition methods based on depth confidence network |
| CN109635928A (en) * | 2018-12-06 | 2019-04-16 | 华北电力大学 | A kind of voltage sag reason recognition methods based on deep learning Model Fusion |
| CN109659933A (en) * | 2018-12-20 | 2019-04-19 | 浙江工业大学 | A kind of prediction technique of power quality containing distributed power distribution network based on deep learning model |
| CN110009529A (en) * | 2019-04-15 | 2019-07-12 | 湖南大学 | A kind of transient frequency acquisition methods based on storehouse noise reduction autocoder |
| CN110399796A (en) * | 2019-09-02 | 2019-11-01 | 国网上海市电力公司 | A kind of electrical energy power quality disturbance recognition methods based on improvement deep learning algorithm |
-
2019
- 2019-12-27 CN CN201911379735.9A patent/CN110989363B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726678A (en) * | 2008-10-30 | 2010-06-09 | 华北电力科学研究院有限责任公司 | Electricity quality evaluation system and method |
| EP2810223A1 (en) * | 2012-02-03 | 2014-12-10 | Thales | Method for processing uncertain data from a plurality of heterogeneous sources and associated automatic decision support method |
| CN103647276A (en) * | 2013-12-10 | 2014-03-19 | 国家电网公司 | Electric energy quality early warning system and method thereof |
| CN103729804A (en) * | 2014-01-02 | 2014-04-16 | 东南大学 | On-line decision support method for responding to power quality early warning |
| CN104459373A (en) * | 2014-11-11 | 2015-03-25 | 广东电网有限责任公司东莞供电局 | Method for calculating node voltage temporary drop magnitudes based on BP neural network |
| CN106778923A (en) * | 2017-03-23 | 2017-05-31 | 广东电网有限责任公司珠海供电局 | A kind of Power Quality Disturbance sorting technique and device |
| CN107016241A (en) * | 2017-04-05 | 2017-08-04 | 重庆交通大学 | Based on the rotating machinery lifetime stage recognition methods for adding sample enhancing depth own coding learning network of making an uproar |
| CN107123987A (en) * | 2017-05-11 | 2017-09-01 | 华东交通大学 | Electrical energy power quality disturbance recognition methods based on on-line training weighed SVM |
| CN108664950A (en) * | 2018-05-22 | 2018-10-16 | 天津大学 | A kind of electrical energy power quality disturbance identification and sorting technique based on deep learning |
| CN109596942A (en) * | 2018-12-06 | 2019-04-09 | 华北电力大学 | A kind of voltage sag reason recognition methods based on depth confidence network |
| CN109635928A (en) * | 2018-12-06 | 2019-04-16 | 华北电力大学 | A kind of voltage sag reason recognition methods based on deep learning Model Fusion |
| CN109659933A (en) * | 2018-12-20 | 2019-04-19 | 浙江工业大学 | A kind of prediction technique of power quality containing distributed power distribution network based on deep learning model |
| CN110009529A (en) * | 2019-04-15 | 2019-07-12 | 湖南大学 | A kind of transient frequency acquisition methods based on storehouse noise reduction autocoder |
| CN110399796A (en) * | 2019-09-02 | 2019-11-01 | 国网上海市电力公司 | A kind of electrical energy power quality disturbance recognition methods based on improvement deep learning algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 基于降噪自编码的电能质量扰动识别;肖露欣 等;《南昌大学学报（理科版）》;20171231;第41卷(第6期);第591-595页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110989363A (en) | 2020-04-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107909118B (en) | Power distribution network working condition wave recording classification method based on deep neural network | |
| CN112041693A (en) | Power distribution network fault positioning system based on mixed wave recording | |
| CN110726898B (en) | Power distribution network fault type identification method | |
| CN110826789A (en) | Power load prediction method and device based on power system and terminal equipment | |
| CN113159341A (en) | Power distribution network aid decision-making method and system integrating deep reinforcement learning and expert experience | |
| CN113657622B (en) | Multi-dimensional state data fusion method, device, terminal and storage medium for power equipment | |
| CN108139432A (en) | The method of saturation effect and its intelligent electronic device in correcting current mutual inductor | |
| CN111426905A (en) | Power distribution network common bus transformation relation abnormity diagnosis method, device and system | |
| CN110808580A (en) | Quick identification method for voltage sag source based on wavelet transformation and extreme learning machine | |
| CN116707331B (en) | Inverter output voltage high-precision adjusting method and system based on model prediction | |
| CN110610226A (en) | Generator fault prediction method and device | |
| CN108092996B (en) | Authentication channel selection method and terminal equipment | |
| CN110989363B (en) | Electric energy quality control method and device based on deep learning | |
| CN112147474A (en) | XLPE power cable typical defect partial discharge type identification system and method | |
| CN112486137A (en) | Method and system for constructing fault feature library of active power distribution network and fault diagnosis method | |
| CN111371088A (en) | Method and system for correcting SVG control strategy based on BP neural network | |
| CN115685046A (en) | Mutual inductor metering abnormity identification method, device, equipment and storage medium | |
| CN115577851A (en) | Energy consumption prediction method, device, equipment and storage medium | |
| CN114626487A (en) | Line-variable relation checking method based on random forest classification algorithm | |
| CN114184870A (en) | Non-invasive load identification method and equipment | |
| CN111967919A (en) | System and method for analyzing electricity consumption behavior of residents based on autoregressive and adaptive boosting algorithm | |
| CN116488149A (en) | Method and device for determining micro-grid power generation strategy and micro-grid | |
| CN112232381B (en) | Model parameter post-processing method and device for dominant load parameter noise identification | |
| CN115409132A (en) | Method and system for processing power distribution network data | |
| CN112308463A (en) | Evaluation method and device based on power grid constraint conditions, storage medium and processor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |