CN110277834A - A kind of grid responsive building interior load monitoring method, system and storage medium - Google Patents
A kind of grid responsive building interior load monitoring method, system and storage medium Download PDFInfo
- Publication number
- CN110277834A CN110277834A CN201910561463.8A CN201910561463A CN110277834A CN 110277834 A CN110277834 A CN 110277834A CN 201910561463 A CN201910561463 A CN 201910561463A CN 110277834 A CN110277834 A CN 110277834A
- Authority
- CN
- China
- Prior art keywords
- load
- data
- power
- building interior
- indicates
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 230000001052 transient effect Effects 0.000 claims abstract description 24
- 230000005540 biological transmission Effects 0.000 claims abstract description 10
- 238000005259 measurement Methods 0.000 claims abstract description 10
- 238000011068 loading method Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000000737 periodic effect Effects 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 230000002123 temporal effect Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 claims description 2
- 238000004590 computer program Methods 0.000 claims description 2
- 230000035772 mutation Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000005070 sampling Methods 0.000 abstract description 3
- 238000004378 air conditioning Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000010438 heat treatment Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Classifications
-
- H02J13/0006—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a kind of grid responsive building interior load monitoring method, system and storage medium, the program decomposes the operational plan individually loaded using signal processing technology by the power information of one group of electric loading of configuration load monitor measurement at breaker；And load monitor everywhere is connected to cloud, to the down-sampling power data of the cloud transmission small data file comprising transient mode and each monitored circuit, it realizes unsupervised load fault mode study and building system performance prediction, is classified using Lipchitz regularity to multi-scale edges various in transient state load section in learning process；Label appropriate is finally sent back into local platform and carries out load classification.Monitoring scheme proposed by the invention improves the potentiality of apparatus of electrical monitoring equipment, workload needed for reducing deployment and debugging monitoring solution.
Description
Technical field
The present invention relates to electric system elastic load monitoring technology fields, and in particular to a kind of grid responsive building interior is negative
Lotus monitoring method, system and storage medium.
Background technique:
It accepts extensively and accepts currently, the basic concept of demand side management has been obtained, but load side regulation and control object
Mainly for single air conditioner load or industrial load can be interrupted, adjustable burdened resource type is further enriched, studies electric power
Elastic load quick response regulating system, the condition monitoring capabilities for improving elastic load are becoming of gradually developing of demand response business
Gesture.
Scheme is managed for elastic load data resource, there are the general clothes for researching and proposing a kind of distributed energy management solutions system
Business system provides reference for the design of demand response system application layer services, but building internal loading type is more and every kind of load
Electricity consumption is smaller, lower for the independent Efficiency of each load and potentiality are smaller.
Summary of the invention
The purpose of the present invention is to provide a kind of grid responsive building interior load monitoring method, system and storage medium,
To solve the above-mentioned many defects caused by the prior art.
This programme is realized by following technical proposals:
First aspect: a kind of grid responsive building interior load monitoring method is provided, described method includes following steps:
The power information of configuration load monitor measurement load at breaker, and load monitor is connected to cloud；
Include the sampled power data under the small data file and monitored circuit of transient mode to cloud transmission；
Small data file and sampled power data are subjected to Multiple Time Scales marginal classification；
The data that cloud obtains Multiple Time Scales marginal classification are handled to obtain load indicative character；
Load indicative character is sent back into local platform and carries out load classification, obtains accurately loading electricity consumption condition monitoring knot
Fruit.
With reference to first aspect, further, the method for the load monitor measurement load power information includes following step
It is rapid:
Each load monitor measurement flows to the total current and line voltage distribution of one group of electric loading；
The time-varying estimated value that test line power frequency content is calculated according to total current and line voltage distribution, at m subharmonic,
Time-varying estimated value is as follows:
Sin (m ω t) and cos (m ω t) indicates the elementary item of Fourier space, and i (τ) is the table of former non-sinusoidal periodic current
Up to formula, τ is integration variable, and m indicates that frequency multiple, ω are angular frequency, and T indicates time window length, and t indicates moment, amAnd bmTable
Show spectral envelope coefficient.
With reference to first aspect, further, the transmission method of the small data file and sampled power data includes as follows
Step:
Measure the electric current on independent breaker；
Creation includes the small documents for the transient data collected around each event, an and mean power number of calculating per second
According to；
By transient data small documents and average power data periodic transmission to long-range cloud platform.
With reference to first aspect, further, the method for the Multiple Time Scales marginal classification includes the following steps:
Using these edges of the conceptual description of Lipchitz regularity, the power absorbed on given circuit is considered as letter by us
Number f (t), if f has a singular point at t=v, it is meant that f non-differentiability at t=v, Lipchitz index characteristic α are characterized
This singular behavior；
This concept is the viewpoint based on approximate Taylor polynomial of the f on section [v-h, v+h]:
The absolute value of approximate error is as follows:
F (t) indicates that the power absorbed on t moment circuit, Pv indicate that the approximate Taylor of f (t) on section [v-h, v+h] is more
Item formula, t indicate the moment, and m indicates that the derivative order that f (t) has, k indicate that kth order derivative, v indicate the v moment, and h is one minimum
Time quantum, for indicating the neighborhood at v moment, fmIndicate that the m order derivative of f, u indicate the time variable in [v-h, v+h] range；
Above formula shows that when t is intended to v, f constitutes the upper limit of e (t) error in the m rank differential of v neighborhood.If f is in v neighbour
Domain isSecondary continuously differentiable, then pv(t) it is Taylor expansion at v；One bounded but at v discontinuous letter
Number has 0≤α < 1, and the value of α can be used for characterizing the variation of different singular points, such as pulse, stepping, smooth stepping.
With reference to first aspect, further, it is described load indicative character include load transient border sequences time mode,
Average steady state power when cycle-index, the gross energy of absorption, the beginning of circulation and end time and operation.
With reference to first aspect, further, the method for the load classification includes the following steps:
Edge detection algorithm positions true or reactive power signals mutation first；
The data (transient state and stable state) of the subsequent each perimeter of detection of classifier identify specific ON/OFF event；
Steady state data includes the letter for other spectral envelopes differences that active power, reactive power and event front and back record
Breath, single load often have repeatable steady state characteristic；
Temporal profile information also contributes to remained capacity, field observation to most of loads all there is repeatable transient state
Section；
When an event occurs, the transient state of generation is fitted to each example using least square method and (determined before i.e. by classifier
The temporal profile of justice), and example appropriate is determined using best fit measurement (i.e. the 2- norm of residual error).
Second aspect provides a kind of grid responsive building interior elastic load monitoring system, comprising:
Acquisition module: for acquiring the power information of load in real time；
Data transmission module: for being transmitted data mutually between local platform beyond the clouds；
Data processing module: for carrying out calculating and classification processing to data.
The third aspect provides a kind of grid responsive building interior elastic load monitoring system, comprising: memory and processing
Device；
The memory is for storing instruction；
The processor is used to carry out operation one according to described instruction to execute according to the described in any item methods of first aspect
The step of.
Fourth aspect provides a kind of computer readable storage medium, is stored thereon with computer program, which is located
Manage the step of any one of first aspect the method is realized when device executes.
The present invention has the advantages that this kind of grid responsive building interior load monitoring method, the configuration load at breaker
Monitor measures the power information of one group of electric loading, and the operational plan individually loaded is decomposed using signal processing technology；And it will be each
The load monitor at place is connected to cloud, and the small data file for including transient mode and each monitored circuit are transmitted to cloud
Down-sampling power data realizes unsupervised load fault mode study and building system performance prediction, utilizes benefit in learning process
Pu Xici regularity classifies to multi-scale edges various in transient state load section；Label appropriate is finally sent back into local platform
And carry out load classification.
For the present invention compared with the generic service system of existing distributed energy management solutions system, the monitoring scheme proposed can
Collect the loaded power information of institute in building, and then elastic load characteristic is divided under more sufficient data information
Analysis, monitors it and uses electricity condition.
Detailed description of the invention
Fig. 1 is grid responsive building interior load monitoring method flow diagram of the present invention；
Fig. 2 is the unsupervised trained schematic diagram realized that load monitor is connected with cloud in the present invention；
Fig. 3 is that load condition monitors schematic diagram in the present invention.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
As shown in Figure 1 to Figure 3, a kind of grid responsive building interior load monitoring method, as shown in Figure 1, implementation steps are such as
Under:
Step 1 configures a load monitor at each breaker to detect the load running on single circuit, and
Load monitor everywhere is connected to cloud, a distributed terminator monitor network is created, uses their collective data
It is trained, obtains useful load model, as shown in Figure 2；
Step 2, each non-intrusion type load monitor measurement flow to the total current and line voltage distribution of one group of electric loading, base
The time-varying estimated value of test line power frequency content is calculated in the preprocessor of software, at m subharmonic, time-varying estimated value is such as
Under:
Sin (m ω t) and cos (m ω t) indicates the elementary item of Fourier space, and i (τ) is the table of former non-sinusoidal periodic current
Up to formula, τ is integration variable, and m indicates that frequency multiple, ω are angular frequency, and T indicates time window length, and t indicates moment, amAnd bmTable
Show spectral envelope coefficient.
This is the fourier series analysis equation calculated on the moving window that length is t, coefficient am(t) and bm(t) include
When m- local message about i (t) frequency content.Assuming that elementary item sin (m ω t) and cos (m ω t) are synchronous with line voltage, then
Spectrum envelope coefficient has active and reactive and harmonic power useful physical interpretation.Preprocessor calculates the spectrum packet of 120HZ sampling
Winding thread, and the switch events on each circuit are detected using these envelopes；
In view of the main loads such as air-conditioning equipment and electric appliance have the special circuit of oneself, event detection threshold value is easy to adjust
It is whole, to detect all associated loadings.Event is positioned using a simple FIR filter, because system is not known initially
It is mounted with that is loaded on each breaker, therefore can not classify to any event；
Step 3, electric current of the low profile edge computer on the several independent breakers of local measurement, processing locality
Creation of standing includes the small documents for the transient data collected around each event, an and average power data of calculating per second is several
According to small-sized transient affair file periodic transmission to platform long-range, based on cloud.Cloud solution APMB package simultaneously executes feature extraction, leads to
Known mode is crossed to identify the load on each circuit.
When checking load profile, not only there is significant and useful mode on day scale, but also for occurring secondary
Second, also useful transient mode, cloud platform can dispose computing resource appropriate to the identical load on second and minute scale,
It is trained in these different time scales using common-mode；
Step 4, a key element of training process are divided Multiple Time Scales edges various in transient state load section
Class.To describe these edges, we use the concept of Lipchitz regularity.We regard the power absorbed on given circuit
For signal f (t), if f has a singular point at t=v, it is meant that f non-differentiability at t=v, Lipchitz index characteristic α table
This singular behavior is levied.This concept is the viewpoint for the Taylor polynomial being similar on section [v-h, v+h] based on f:
The absolute value of approximate error is as follows:
F (t) indicates that the power absorbed on t moment circuit, Pv indicate that the approximate Taylor of f (t) on section [v-h, v+h] is more
Item formula, t indicate the moment, and m indicates that the derivative order that f (t) has, k indicate that kth order derivative, v indicate the v moment, and h is one minimum
Time quantum, for indicating the neighborhood at v moment, fmIndicate that the m order derivative of f, u indicate the time variable in [v-h, v+h] range.
Above formula shows that when t is intended to v, f constitutes the upper limit of e (t) error in the m rank differential of v neighborhood.If f is in v neighbour
Domain isSecondary continuously differentiable, then pv(t) it is Taylor expansion at v.One bounded but at v discontinuous letter
Number has 0≤α < 1, and the value of α can be used for characterizing the variation of different singular points, such as pulse, stepping, smooth stepping.
Above formula shows that when t is intended to v, f constitutes the upper limit of e (t) error in the m rank differential of v neighborhood.If f is in v neighbour
Domain isSecondary continuously differentiable, then pv(t) it is Taylor expansion at v.One bounded but at v discontinuous letter
Number has 0≤α < 1, and the value of α can be used for characterizing the variation of different singular points, such as pulse, stepping, smooth stepping.
Since across the scale decaying of Wavelet transformation amplitude is related with the Lipchitz rule of signal, and when power signal is discrete
Between sequence, therefore can be used discrete wavelet transformer bring description transient power signal various edges.
Step 5 handles to obtain related load indicative character by cloud, the time mode including transient edge sequence, one
It load cycle number, the start and end time of load cycle, average steady state power when operation, one day total energy absorbed
Amount etc..These features combine in a multidimensional characteristic vectors device, and are sent back to local platform and classify and tracking performance.One
The denier process starts, and local unit continuation is communicated with cloud system, to help to monitor abnormal behaviour.Fig. 3 shows that given place exists
The relationship between Heating,Ventilating and Air Conditioning energy and external air temperature in two different time sections.Under normal conditions, Heating,Ventilating and Air Conditioning energy
Amount is in piecewise linear relationship with temperature, is negative slope when low temperature, and when high temperature is positive slowly.And in Fig. 3 the left side data and temperature
There is no very strong correlation, illustrates that the compressor in Heating,Ventilating and Air Conditioning heat pump system breaks down, and the figure on the right then shows and repairs
Behavior after multiple.
As known by the technical knowledge, the present invention can pass through the embodiment party of other essence without departing from its spirit or essential feature
Case is realized.Therefore, embodiment disclosed above, in all respects are merely illustrative, not the only.Institute
Have within the scope of the present invention or is included in the invention in the change being equal in the scope of the present invention.
Claims (9)
1. a kind of grid responsive building interior load monitoring method, which is characterized in that described method includes following steps:
The power information of configuration load monitor measurement load at breaker, and load monitor is connected to cloud；
Include the sampled power data under the small data file and monitored circuit of transient mode to cloud transmission；
Small data file and sampled power data are subjected to Multiple Time Scales marginal classification；
The data that cloud obtains Multiple Time Scales marginal classification are handled to obtain load indicative character；
Load indicative character is sent back into local platform and carries out load classification, obtains accurately loading electricity consumption condition monitoring result.
2. a kind of grid responsive building interior load monitoring method according to claim 1, it is characterised in that: the load
The method of monitor measurement load power information includes the following steps:
Each load monitor measurement flows to the total current and line voltage distribution of one group of electric loading；
The time-varying estimated value that test line power frequency content is calculated according to total current and line voltage distribution, at m subharmonic, time-varying
Estimated value is as follows:
Sin (m ω t) and cos (m ω t) indicates the elementary item of Fourier space, and i (τ) is the expression of former non-sinusoidal periodic current
Formula, τ are integration variable, and m indicates that frequency multiple, ω are angular frequency, and T indicates time window length, and t indicates moment, amAnd bmIt indicates
Spectral envelope coefficient.
3. a kind of grid responsive building interior load monitoring method according to claim 1, it is characterised in that: the decimal
Transmission method according to file and sampled power data includes the following steps:
Measure the electric current on independent breaker；
Creation includes the small documents for the transient data collected around each event, an and average power data of calculating per second；
By transient data small documents and average power data periodic transmission to long-range cloud platform.
4. a kind of grid responsive building interior load monitoring method according to claim 1, it is characterised in that: when described more
Between the method for scale marginal classification include the following steps:
Check the mode of time-varying estimated value；
The edge of small data file and sampled power data is described；
The power absorbed on given circuit is considered as signal f (t), if f has a singular point at t=v, it is meant that f is in t=v
Locate non-differentiability；
The viewpoint of approximate Taylor polynomial based on f on section [v-h, v+h]:
The absolute value of approximate error is as follows:
F (t) indicates the power absorbed on t moment circuit, PvIndicate the approximate Taylor polynomial of f (t) on section [v-h, v+h],
T indicates the moment, and m indicates that the derivative order that f (t) has, k indicate that kth order derivative, v indicate the v moment, and h is a very small time
Amount, for indicating the neighborhood at v moment, fmIndicate that the m order derivative of f, u indicate the time variable in [v-h, v+h] range；
Above formula shows that when t is intended to v, f constitutes the upper limit of e (t) error in the m rank differential of v neighborhood, if f is in v neighborhoodSecondary continuously differentiable, then pv(t) it is Taylor expansion at v；Still discontinuous function has one bounded at v
There is 0≤α < 1, and the value of α can be used for characterizing the variation of different singular points, such as pulse, stepping, smooth stepping.
5. a kind of grid responsive building interior load monitoring method according to claim 1, it is characterised in that: the load
Indicative character includes the beginning and end of the time mode, cycle-index, the gross energy of absorption, circulation of load transient border sequences
Average steady state power when time and operation.
6. a kind of grid responsive building interior load monitoring method according to claim 1, it is characterised in that: the load
The method of classification includes the following steps:
True or reactive power signals mutation are positioned first；
Detect the data (transient state and stable state) of each perimeter then to identify specific ON/OFF event；
Steady state data includes the information for other spectral envelopes differences that active power, reactive power and event front and back record, single
A load often has repeatable steady state characteristic；
Temporal profile information also contributes to remained capacity, field observation to most of loads all there is repeatable transient state to cut open
Face；
When an event occurs, the transient state of generation is fitted to each example using least square method, and is measured using best fit
Determine example appropriate.
7. a kind of grid responsive building interior elastic load monitoring system characterized by comprising
Acquisition module: for acquiring the power information of load in real time；
Data transmission module: for being transmitted data mutually between local platform beyond the clouds；
Data processing module: for carrying out calculating and classification processing to data.
8. a kind of grid responsive building interior elastic load monitoring system characterized by comprising memory and processor；
The memory is for storing instruction；
The processor is used to carry out operation one according to described instruction to execute described in any item methods according to claim 1~6
The step of.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
The step of any one of claim 1~6 the method is realized when row.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910561463.8A CN110277834B (en) | 2019-06-26 | 2019-06-26 | Power grid response building internal load monitoring method and system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910561463.8A CN110277834B (en) | 2019-06-26 | 2019-06-26 | Power grid response building internal load monitoring method and system and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110277834A true CN110277834A (en) | 2019-09-24 |
| CN110277834B CN110277834B (en) | 2021-02-05 |
Family
ID=67963267
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910561463.8A Active CN110277834B (en) | 2019-06-26 | 2019-06-26 | Power grid response building internal load monitoring method and system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110277834B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021056281A1 (en) * | 2019-09-25 | 2021-04-01 | 西门子股份公司 | Load monitoring apparatus, load monitoring method, program product, and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104954285A (en) * | 2015-05-13 | 2015-09-30 | 北京蓝山科技股份有限公司 | Dynamic power control method and device for OTN (optical transport network) system |
| CN105612546A (en) * | 2014-07-11 | 2016-05-25 | 英科德技术股份有限公司 | Apparatus, server, system and method for energy measuring |
| CN106329522A (en) * | 2016-11-10 | 2017-01-11 | 国家电网公司 | Multi-energy flexible control system and method for urban and rural residents based on demand side response |
| CN106786539A (en) * | 2016-12-30 | 2017-05-31 | 江苏金风科技有限公司 | Load data harvester, prediction control device and load monitoring system |
-
2019
- 2019-06-26 CN CN201910561463.8A patent/CN110277834B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105612546A (en) * | 2014-07-11 | 2016-05-25 | 英科德技术股份有限公司 | Apparatus, server, system and method for energy measuring |
| CN104954285A (en) * | 2015-05-13 | 2015-09-30 | 北京蓝山科技股份有限公司 | Dynamic power control method and device for OTN (optical transport network) system |
| CN106329522A (en) * | 2016-11-10 | 2017-01-11 | 国家电网公司 | Multi-energy flexible control system and method for urban and rural residents based on demand side response |
| CN106786539A (en) * | 2016-12-30 | 2017-05-31 | 江苏金风科技有限公司 | Load data harvester, prediction control device and load monitoring system |
Non-Patent Citations (1)
| Title |
|---|
| 栾开宁等: "需求侧多负荷协调能效优化评价指标体系的建立及应用", 《水电与新能源》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021056281A1 (en) * | 2019-09-25 | 2021-04-01 | 西门子股份公司 | Load monitoring apparatus, load monitoring method, program product, and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110277834B (en) | 2021-02-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106777984A (en) | A kind of method that photovoltaic array Working state analysis and fault diagnosis are realized based on density clustering algorithm | |
| CN112149873B (en) | Low-voltage station line loss reasonable interval prediction method based on deep learning | |
| CN105954632A (en) | Zinc oxide lightning arrester on-line monitoring and diagnostic method | |
| CN106845562B (en) | The fault monitoring system and data processing method of photovoltaic module | |
| CN109190950A (en) | A kind of use energy abnormality recognition method of ammeter and the perforation of water meter archives | |
| CA3167552A1 (en) | Method and apparatus for recognizing operating state of photovoltaic string and storage medium | |
| CN111209976A (en) | Non-invasive load monitoring method and system | |
| CN110703009A (en) | Abnormal analysis and processing method for line loss rate of transformer area | |
| CN109272713A (en) | A kind of the breakdown judge method for early warning and system of photovoltaic plant group set of strings | |
| CN113806420A (en) | Power grid data monitoring method and device | |
| CN106341467A (en) | State analysis method of power utilization information collector based on big data parallel computing | |
| CN113595246B (en) | Micro-grid state online monitoring method and device, computer equipment and storage medium | |
| CN109543993B (en) | Method for analyzing photovoltaic power station, computer storage medium and computer device | |
| CN110277834A (en) | A kind of grid responsive building interior load monitoring method, system and storage medium | |
| CN109613372A (en) | A kind of electric network failure diagnosis method based on polynary electric network database | |
| Dong et al. | Fault diagnosis and classification in photovoltaic systems using scada data | |
| CN117394535A (en) | Digital twin system of AC/DC hybrid power distribution network | |
| CN113034305A (en) | Non-invasive load monitoring event classification method and storage medium | |
| CN112348207A (en) | Power grid disaster prevention early warning method and device | |
| CN110703019A (en) | Power consumption abnormity accurate positioning method based on collected information of power transmission line | |
| CN115934268A (en) | Power edge calculation method, system, computer equipment and storage medium | |
| CN115908082A (en) | Enterprise pollution discharge monitoring method and device based on electricity utilization characteristic indexes | |
| CN114839462A (en) | Intelligent anti-electricity-stealing monitoring method and system | |
| Liang et al. | Health assessment of wind turbine based on laplacian eigenmaps | |
| CN116937758B (en) | Household energy storage power supply system and operation method thereof |
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 |