CN109239456B - Harmonic tracing method based on dynamic programming time series similarity algorithm - Google Patents
Harmonic tracing method based on dynamic programming time series similarity algorithm Download PDFInfo
- Publication number
- CN109239456B CN109239456B CN201810879919.0A CN201810879919A CN109239456B CN 109239456 B CN109239456 B CN 109239456B CN 201810879919 A CN201810879919 A CN 201810879919A CN 109239456 B CN109239456 B CN 109239456B
- Authority
- CN
- China
- Prior art keywords
- data
- harmonic
- time
- power
- sequence
- 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
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 24
- 238000012544 monitoring process Methods 0.000 claims abstract description 31
- 230000005611 electricity Effects 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000011425 standardization method Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 238000002759 z-score normalization Methods 0.000 claims description 2
- 238000010219 correlation analysis Methods 0.000 abstract description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000001808 coupling effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a harmonic tracing method based on a dynamic programming time sequence similarity algorithm, which comprises the following steps: acquiring harmonic voltage monitoring data in a period of time, which is acquired by a power quality monitoring terminal installed on a certain bus, from a power quality monitoring system; acquiring the active power data of the electricity in the time period of all users powered by the bus from the electricity utilization information acquisition system; processing harmonic voltage monitoring data and power utilization active power data by using a data standardization method; and solving the correlation between the active power sequence data of each user power consumption and the harmonic voltage sequence data of the public connection point by using an algorithm for solving the similarity of the time sequence data based on the dynamic programming principle, and performing harmonic tracing. The method fully excavates the data value of a large number of deployed intelligent electric meters, deduces possible users causing harmonic problems through the data correlation analysis result of the power consumption condition and the harmonic condition of the users, and provides basis for accurate harmonic responsibility division, future quality-based pricing and accurate management.
Description
Technical Field
The invention relates to a harmonic tracing method based on a dynamic programming time sequence similarity algorithm.
Background
Power electronic loads are increasingly connected to a power grid, so that harmonic conditions in the power grid are more complex, normal work of the loads is influenced, and power equipment is damaged or power failure accidents are caused in severe cases. A bus in an actual power system is often connected with a plurality of users emitting harmonic waves, the harmonic waves among the users are mutually influenced, how to accurately position a harmonic wave source according to existing data is the basis of quantitatively dividing harmonic wave responsibility, and the method is also the premise of effectively managing the harmonic waves and ensuring safe and efficient operation of a power grid and the users.
At present, harmonic tracing methods can be divided into two types: a harmonic power direction method and a responsibility allocation method. The power direction method is used for distinguishing which side of the system side and the user side is the main harmonic source, and is difficult to use when a plurality of harmonic sources are connected on the same bus, so the harmonic responsibility apportionment method is more common. The harmonic responsibility apportionment method is used for actually and simultaneously carrying out harmonic traceability and harmonic responsibility apportionment, firstly, a suspected harmonic source is judged through engineering experience, then, the harmonic responsibility index is solved by using data obtained through special tests, and the result is larger and is considered as the main harmonic source.
Currently, the following technical defects exist in the problem:
1. the existing responsibility allocation methods such as a background harmonic voltage analysis method, a piecewise linearization method and the like need to accurately measure the harmonic voltage of a bus and the harmonic current of each feeder line, and are calculated by using sampling values containing amplitude values and phase angles, but the existing electric energy quality monitoring systems uniformly popularized by national power grid companies can only provide 3-minute statistical values of the harmonic voltage and the harmonic current, so that the existing methods cannot be applied.
2. Most of the existing responsibility allocation methods are based on transient data measured in a short time, but harmonic waves are steady-state quantities existing for a long time, harmonic wave source tracing and responsibility allocation through short-time transient data measurement are not accurate enough, and multi-party approval and acceptance are difficult to achieve.
3. At present, monitoring points of an electric energy quality monitoring system are limited, each special transformer user cannot be monitored, and the harmonic content of each user cannot be accurately measured only by monitoring data provided by an electric energy quality monitoring terminal installed on a bus of a transformer substation.
4. The existing harmonic responsibility calculation method is based on a Norton equivalent circuit, theoretically, the Norton equivalent current source harmonic current of a system side and each harmonic source needs to be known, however, as the current cannot be measured, branch current is generally used for approximate calculation, and due to coupling and interaction influence between harmonic sources, the approximate calculation causes a larger error in harmonic responsibility index calculation and even causes the problem of inaccurate harmonic traceability.
5. The existing harmonic responsibility apportionment method is greatly influenced by the voltage fluctuation of the background harmonic, and the influence of the voltage fluctuation of the background harmonic on the harmonic responsibility apportionment and solution can not be fundamentally solved by the existing data screening method or the harmonic source analysis method.
Therefore, in actual engineering, a new harmonic tracing method based on a time series similarity algorithm by using statistical data of an actual power quality monitoring system, fusing other power system data (such as user power consumption data of a power consumption information acquisition system) and is urgently needed.
Disclosure of Invention
The invention aims to provide a harmonic traceability method based on a dynamic programming time series similarity algorithm.
The invention adopts the following technical scheme: a harmonic tracing method based on a dynamic programming time series similarity algorithm comprises the following steps: step S1: harmonic voltage monitoring data within a period of time, which are acquired by a power quality monitoring terminal installed on a PCC of a common connection point of a certain bus, are acquired from a power quality monitoring system, and time series data are formed; step S2: acquiring the active power data of the electricity in the time period of all users powered by the bus from the electricity utilization information acquisition system, and forming time sequence data; step S3: processing the harmonic voltage monitoring data and the power utilization active power data by using a data standardization method to form the standardization sequence data of the harmonic voltage and the power utilization active power; step S4: and solving the correlation between the active power sequence data of each user power consumption and the harmonic voltage sequence data of the public connection point by using an algorithm for solving the similarity of the time sequence data based on the dynamic programming principle, and performing harmonic tracing.
Furthermore, the time interval of data obtained by the power quality monitoring system is 3 minutes, and each subharmonic voltage simultaneously comprises a maximum value, a minimum value, an average value and a 95% probability large value.
In an embodiment of the present invention, step S2 assumes that the PCCs are connected togetherN users exist, each user samples N points, and the N users are represented as P by using the electricity average active power time sequence data1＝<p11,…,p1n>、P2＝<p21,…,p2n>、…PN＝<pN1,…,pNn>(ii) a Selecting time series data of a 95% probability large value, an average value or a maximum value of PCC harmonic voltage at a common connection point for analysis, and obtaining m data in total, wherein the data is expressed as U ═<u1,…,um>(ii) a Step S3 is to perform z-score normalization on the acquired harmonic voltage time-series data and user average active power time-series data, and obtain a normalized time-series data sequence U' using a normalization method<u1’,…,um’>、P1’＝<p11’,…,p1n’>、P2’＝<p21’,…,p2n’>、…PN’＝<pN1’,…,pNn’>。
In an embodiment of the present invention, the algorithm for solving the similarity of the time-series data based on the dynamic programming principle in step S4 includes the following steps: step S41: taking harmonic voltage normalized time series data U ═<u1’,…,um’>Normalized time series data P with a certain average active powerk’＝<pk1’,…,pkn’>Calculating the Euclidean distance between two sequence points to obtain a two-sequence Euclidean distance matrix M, M [ i, j]Represents the sequence PkThe Euclidean distance between the ith point and the jth point of the sequence U';
step S42: definition McFor accumulating the distance matrix, M is given according to the following equation (1)cAssigning initial values to a first row and a first column of the matrix;
for each i in 2 ≦ i ≦ n, the cumulative distance M for j from 2 to M is calculatedc[i,j]，Mc[i,j]The calculation method of (2) is shown in the following formula;
step S43: to obtain Mc[n,m]The DTW distance under the optimal path matching can be calculated by the following equation (3):
wherein, DTW (P)k', U') is the DTW distance, which characterizes P under optimal matching conditionskSimilarity of 'to U' matches error.
Furthermore, N users are in total, and the power utilization average active power sequence data are respectively P1’、P2’、P3’…PNIf the sequence data of the harmonic voltage of the PCC point is U', calculating to obtain DTW (P)1’,U’)、DTW(P2’,U’)、DTW(P3’,U’)…DTW(PN', U'), so the user with the smallest DTW is selected as the dominant harmonic source.
Compared with the prior art, the method combines the characteristics of the data of the electricity utilization information acquisition system and the data of the electric energy quality monitoring system, uses the dynamic programming principle to solve the algorithm of time series data similarity, calculates the correlation between the electricity consumption of each user and the harmonic voltage of the PCC points, compares the calculated correlation with the obtained user with the strongest correlation, and considers the user as a main harmonic source. The tracing method fully excavates the data value of a large number of deployed intelligent electric meters under the condition that the power quality monitoring points are limited, deduces possible users causing harmonic problems through the data correlation analysis result of the power utilization condition and the harmonic condition of the users, has simple principle, convenient use and objective and credible result, can be used for long-term responsibility division, does not need to increase a special monitoring device for installation, does not need the wiring form and the line parameters of the bus to the users, has more engineering practicability and stronger popularization and application values, and provides a basis for accurate harmonic responsibility division, future quality-based pricing and accurate management.
Drawings
FIG. 1 is a schematic diagram of a harmonic responsibility engineering problem.
Fig. 2 is a flow chart of a harmonic tracing algorithm.
Detailed Description
The invention is further explained below with reference to the figures and the specific embodiments.
The invention provides a harmonic tracing method based on a dynamic programming time sequence similarity algorithm, which mainly comprises the following steps:
(1) harmonic voltage monitoring data in a period of time, which are acquired by a power quality monitoring terminal installed on a certain bus (a common connection point, namely a PCC point), are acquired from a power quality monitoring system, and time series data are formed;
(2) acquiring the active power data of the electricity in the time period of all users powered by the bus from the electricity utilization information acquisition system, and forming time sequence data;
(3) processing the harmonic voltage monitoring data and the power utilization active power data by using a data standardization method to form the standardization sequence data of the harmonic voltage and the power utilization active power;
(4) and solving the correlation between the active power sequence data of each user power consumption and the harmonic voltage sequence data of the public connection point by using an algorithm for solving the similarity of the time sequence data based on the dynamic programming principle, and performing harmonic tracing.
As shown in fig. 1, harmonic voltage monitoring data of the power quality monitoring terminal at the 10kV bus and average active power metering data of each user connected to the bus in the power consumption information acquisition system (each user metering point in fig. 1) are obtained, the correlation between the average active power sequence data of each user and the harmonic voltage sequence data is analyzed, and the user with the strongest correlation is determined to be a harmonic source. As shown in fig. 1, in the power supply system of the power grid company, a power quality monitoring Point is located on a 10kV bus, that is, a Point of Common Coupling (PCC), a data time interval obtained by the power quality monitoring system is 3 minutes, and each sub-harmonic voltage simultaneously includes a maximum value, a minimum value, an average value, and a large 95% probability value. This patent selects harmonic electricityAnalyzing the time series data of the 95% probability maximum value, the average value or the maximum value, and obtaining m data in total, wherein the data is expressed as U ═<u1,…,um>. As shown in fig. 1, each user is equipped with an electric energy meter (i.e., a metering point in the power consumption information acquisition system of each user in fig. 1), and power consumption average active power data of the user for a period of time can be acquired through the power consumption information acquisition system, and the data time interval in the general power consumption information acquisition system is 15 minutes. If the PCC is connected with N users in total, and each user samples N points, the time series data of the power utilization average active power of the N users can be represented as P1＝<p11,…,p1n>、P2＝<p21,…,p2n>、……PN＝<pN1,…,pNn>。
In order to avoid the influence of each group of sequence data dimension on subsequent calculation, z-score standardization processing is carried out on the acquired harmonic voltage time sequence data and user average active power time sequence data, and a time data sequence U' after standardization processing is obtained by using a standardization method<u1’,…,um’>、P1’＝<p11’,…,p1n’>、P2’＝<p21’,…,p2n’>、……PN’＝<pN1’,…,pNn’>。
The average active power of the electricity consumption of the users is consistent with the trend that the harmonic waves generated by the average active power of the electricity consumption of the users change along with the time, and the harmonic waves are traced by comparing the correlation between the harmonic voltage of the PCC points and the electricity consumption of the users connected with the PCC points. Because the data of the power quality monitoring system and the data of the user power consumption information acquisition system adopt different statistical methods and different time scales, the traditional Euler distance cannot effectively represent the similarity of the variation trends of the two data, the patent provides a harmonic traceability method based on a Dynamic programming time series similarity (DTW) algorithm, and the specific algorithm is as follows:
taking harmonic voltage normalized time series data U ═<u1’,…,um’>And a certain average active power normalized time seriesData Pk’＝<pk1’,…,pkn’>. Firstly, the Euclidean distance between two sequence points is calculated to obtain a two-sequence Euclidean distance matrix M, M [ i, j]Represents the sequence PkThe euclidean distance between the ith point and the jth point of the sequence U'.
Definition McFor accumulating the distance matrix, M is given according to the following equation (1)cThe first row and the first column of the matrix are assigned initial values.
For each i in 2 ≦ i ≦ n, the cumulative distance M for j from 2 to M is calculatedc[i,j]，Mc[i,j]The calculation method (2) is shown below.
Finally obtaining Mc[n,m]The DTW distance under the optimal path matching can be calculated by the following equation (3):
wherein, DTW (P)k', U') is the DTW distance, which characterizes P under optimal matching conditionskSimilarity of 'to U' matches error.
The harmonic traceability algorithm of the present invention is illustrated in the flow chart of fig. 2.
In the invention, N users are assumed to be in total, and the power utilization average active power sequence data are respectively P1’、P2’、P3’……PN', the sequence data of the harmonic voltage of the PCC point is U', then DTW (P) can be calculated1’,U’)、DTW(P2’,U’)、DTW(P3’,U’)……DTW(PN', U'), the user with the smallest DTW is selected as the dominant harmonic source, since the smaller the value of DTW, the higher the similarity of the two sequences.
The above embodiments are provided only for illustrating the present invention, and those skilled in the art can make various changes or modifications without departing from the spirit and scope of the present invention, and therefore, all equivalent technical solutions should also fall within the scope of the present invention.
Claims (5)
1. A harmonic tracing method based on a dynamic programming time series similarity algorithm is characterized in that: the method comprises the following steps:
step S1: acquiring harmonic voltage monitoring data within a period of time, which is acquired by a power quality monitoring terminal installed on a PCC (point of common coupling) of a certain bus, from a power quality monitoring system;
step S2: acquiring the active power data of the electricity in the time period of all users powered by the bus from the electricity utilization information acquisition system;
step S3: processing the harmonic voltage monitoring data and the power utilization active power data by using a data standardization method to form the standardization sequence data of the harmonic voltage and the power utilization active power;
step S4: and solving the correlation between the active power sequence data of each user power consumption and the harmonic voltage sequence data of the public connection point by using an algorithm for solving the similarity of the time sequence data based on the dynamic programming principle, and performing harmonic tracing.
2. The harmonic traceability method based on the dynamic programming time series similarity algorithm of claim 1, wherein: the time interval of data obtained by the power quality monitoring system is 3 minutes, and each subharmonic voltage simultaneously comprises a maximum value, a minimum value, an average value and a 95% probability maximum value.
3. The harmonic traceability method based on the dynamic programming time series similarity algorithm of claim 1, wherein: in step S2, assuming that the PCC has N users connected together, and each user samples N points, the electricity average active power time-series data of the N users is represented as P1＝＜p11，...，p1n＞、P2＝＜p21，...，p2n＞、...PN＝＜pN1，...，pNn>; selecting time sequence data of a 95% probability large value, an average value or a maximum value of PCC harmonic voltage at a common connection point for analysis, and obtaining m data in total, wherein the data is expressed as U ═ U1，...，um＞；
Step S3 is to perform z-score normalization on the acquired harmonic voltage time-series data and user average active power time-series data, and obtain a normalized time-series data sequence U' < U using a normalization method1’，…，um’＞、P1’＝＜p11’，…，p1n’＞、P2’＝＜p21’，...，p2n’＞、...PN’＝＜pN1’，...，pNn’＞。
4. The harmonic traceability method based on the dynamic programming time series similarity algorithm of claim 3, wherein: the algorithm for solving the similarity of the time-series data based on the dynamic programming principle in the step S4 includes the following steps:
step S41: taking harmonic voltage normalized time sequence data U' < U1’，...，um' > and a certain mean active power normalized time series data Pk’＝＜pk1’，…，pkn' >, calculating the Euclidean distance between two sequence points to obtain a two-sequence Euclidean distance matrix M, M [ i, j]Represents the sequence PkThe Euclidean distance between the ith point and the jth point of the sequence U';
step S42: definition McFor accumulating the distance matrix, M is given according to the following equation (1)cAssigning initial values to a first row and a first column of the matrix;
for each i in 2 ≦ i ≦ n, the cumulative distance M for j from 2 to M is calculatedc[i，j]，Mc[i，j]The calculation method of (2) is shown in the following formula;
step S43: to obtain Mc[n，m]The DTW distance under the optimal path matching can be calculated by the following equation (3):
wherein, DTW (P)k', U') is the DTW distance, which characterizes P under optimal matching conditionskSimilarity of 'to U' matches error.
5. The harmonic traceability method based on the dynamic programming time series similarity algorithm according to claim 4, wherein: the total number of N users is P respectively by the electricity average active power sequence data1’、P2’、P3’...PNIf the sequence data of the harmonic voltage of the PCC point is U', calculating to obtain DTW (P)1’，U’)、DTW(P2’，U’)、DTW(P3’，U’)...DTW(PN', U'), the user with the smallest DTW is selected as the dominant harmonic source.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810879919.0A CN109239456B (en) | 2018-08-03 | 2018-08-03 | Harmonic tracing method based on dynamic programming time series similarity algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810879919.0A CN109239456B (en) | 2018-08-03 | 2018-08-03 | Harmonic tracing method based on dynamic programming time series similarity algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109239456A CN109239456A (en) | 2019-01-18 |
| CN109239456B true CN109239456B (en) | 2020-12-25 |
Family
ID=65070210
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810879919.0A Active CN109239456B (en) | 2018-08-03 | 2018-08-03 | Harmonic tracing method based on dynamic programming time series similarity algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109239456B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109829652B (en) * | 2019-02-12 | 2023-04-07 | 福州大学 | Long-time scale dynamic harmonic responsibility division method |
| CN112257013A (en) * | 2020-10-19 | 2021-01-22 | 长沙理工大学 | Electricity stealing user identification and positioning method based on dynamic time warping algorithm for high-loss distribution area |
| CN113435490B (en) * | 2021-06-18 | 2022-06-28 | 国网宁夏电力有限公司电力科学研究院 | Harmonic tracing method based on dynamic programming time series similarity algorithm |
| CN115051477B (en) * | 2022-08-16 | 2022-11-18 | 山东大学 | Harmonic traceability system and method based on cloud edge-end cooperation |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102939572A (en) * | 2010-06-11 | 2013-02-20 | 伊顿公司 | Automatic matching of sources to loads |
| CN104793082A (en) * | 2015-04-23 | 2015-07-22 | 江苏中凌高科技股份有限公司 | Harmonic correlation analysis based electricity system harmonic source recognition device |
| CN106019026A (en) * | 2016-07-20 | 2016-10-12 | 东南大学 | Distribution method based on waveform matching for harmonic voltage responsibility |
| CN106370180A (en) * | 2016-08-29 | 2017-02-01 | 常州市钱璟康复股份有限公司 | Inertial sensor initial position recognition method based on dynamic time wrapping algorithm |
| CN106383257A (en) * | 2016-08-17 | 2017-02-08 | 云南电网有限责任公司电力科学研究院 | Harmonic source identification method based on correlation analysis |
| CN107345983A (en) * | 2017-06-29 | 2017-11-14 | 苏州市技术性贸易措施咨询服务中心 | Multi-harmonic Sources system harmonicses transmitting appraisal procedure based on subharmonic source correlation |
| CN107367647A (en) * | 2017-06-22 | 2017-11-21 | 上海理工大学 | The detection of mains by harmonics source and localization method based on EEMD SOM |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7567900B2 (en) * | 2003-06-11 | 2009-07-28 | Panasonic Corporation | Harmonic structure based acoustic speech interval detection method and device |
-
2018
- 2018-08-03 CN CN201810879919.0A patent/CN109239456B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102939572A (en) * | 2010-06-11 | 2013-02-20 | 伊顿公司 | Automatic matching of sources to loads |
| CN104793082A (en) * | 2015-04-23 | 2015-07-22 | 江苏中凌高科技股份有限公司 | Harmonic correlation analysis based electricity system harmonic source recognition device |
| CN106019026A (en) * | 2016-07-20 | 2016-10-12 | 东南大学 | Distribution method based on waveform matching for harmonic voltage responsibility |
| CN106383257A (en) * | 2016-08-17 | 2017-02-08 | 云南电网有限责任公司电力科学研究院 | Harmonic source identification method based on correlation analysis |
| CN106370180A (en) * | 2016-08-29 | 2017-02-01 | 常州市钱璟康复股份有限公司 | Inertial sensor initial position recognition method based on dynamic time wrapping algorithm |
| CN107367647A (en) * | 2017-06-22 | 2017-11-21 | 上海理工大学 | The detection of mains by harmonics source and localization method based on EEMD SOM |
| CN107345983A (en) * | 2017-06-29 | 2017-11-14 | 苏州市技术性贸易措施咨询服务中心 | Multi-harmonic Sources system harmonicses transmitting appraisal procedure based on subharmonic source correlation |
Non-Patent Citations (2)
| Title |
|---|
| 供电系统谐波对电能计量影响的研究;张海霞;《电气技术》;20141231(第12期);35-39 * |
| 基于数学形态学与动态时间扭曲的电压扰动分类;赵静等;《西安交通大学学报》;20090430;第44卷(第2期);208-214 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109239456A (en) | 2019-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109239456B (en) | Harmonic tracing method based on dynamic programming time series similarity algorithm | |
| US9292794B2 (en) | Voltage-based clustering to infer connectivity information in smart grids | |
| Lee | Electric load information system based on non-intrusive power monitoring | |
| CN109146265B (en) | Three-phase unbalanced source tracing method based on dynamic programming time sequence similarity | |
| CN111027408A (en) | Load identification method based on support vector machine and V-I curve characteristics | |
| CN113011481B (en) | Electric energy meter function abnormality assessment method and system based on decision tree algorithm | |
| CN103617568A (en) | Setting method for abnormal data determination threshold in steady-state power quality early-warning mechanism | |
| Iksan et al. | Appliances identification method of non-intrusive load monitoring based on load signature of VI trajectory | |
| CN103424620B (en) | A kind of 10kV distribution harmonic source identification method based on the time series degree of correlation | |
| CN109767054A (en) | Efficiency cloud appraisal procedure and edge efficiency gateway based on deep neural network algorithm | |
| Therrien et al. | Assessment of measurement-based phase identification methods | |
| EP3642636B1 (en) | Method for detecting impedances in an electrical power distribution grid | |
| Chang et al. | A novel nonintrusive fault identification for power transmission networks using power-spectrum-based hyperbolic S-transform—Part I: Fault classification | |
| CN105548809A (en) | Harmonic wave responsibility determining method of power system | |
| CN109829652B (en) | Long-time scale dynamic harmonic responsibility division method | |
| CN108614192B (en) | Distribution decision method for lightning monitoring device of power distribution network | |
| CN110047013B (en) | Anti-private-transformer user intermittent electricity stealing method | |
| CN112558000A (en) | Correlation screening-based electric energy meter metering error analysis method | |
| Buzachis et al. | Development of a smart metering microservice based on fast Fourier transform (FFT) for edge/internet of things environments | |
| CN109613372B (en) | Power grid fault diagnosis method based on multi-element power grid database | |
| Jaramillo et al. | Non-intrusive load monitoring algorithm for PV identification in the residential sector | |
| Hartmann et al. | Suspicious electric consumption detection based on multi-profiling using live machine learning | |
| CN103529337B (en) | The recognition methods of nonlinear correlation relation between equipment failure and electric quantity information | |
| CN110084719A (en) | A kind of distribution network load type device for identifying | |
| CN114266457A (en) | Method for detecting different loss inducement of distribution line |
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 |