CN104950192B - A kind of electricity consumption energy efficiency monitoring method of compressed sensing - Google Patents
A kind of electricity consumption energy efficiency monitoring method of compressed sensing Download PDFInfo
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Abstract
The present invention discloses the wireless sensor network electricity consumption energy efficiency monitoring method of one of electricity consumption energy efficiency monitoring technical field compressed sensing.It include: that electricity consumption efficiency Source Data Acquisition is carried out by concentrator；The electricity consumption energy efficiency monitoring data aggregate model of compressed sensing is established, concentrator and energy efficiency monitoring main website carry out compressed sensing rarefaction operation and the reconstructed operation of data respectively；Energy efficiency monitoring main website carries out the analysis of electricity consumption energy efficiency monitoring according to the reconstruct data of compressed sensing.The electricity consumption energy efficiency monitoring method of compressed sensing proposed by the present invention can effectively reduce network energy efficiency monitoring uplink transmission data amount, guarantee the reliable and stable operation of system.
Description
Technical Field
The invention relates to the technical field of power utilization energy efficiency monitoring, in particular to a compressed sensing power utilization energy efficiency monitoring method focusing on a common platform with power utilization information acquisition.
Background
The power utilization efficiency monitoring means that the stable operation of a power grid is ensured by analyzing the power utilization trend and power grid fluctuation of a user side, the power grid efficiency is improved, and the power utilization efficiency monitoring method is a powerful guarantee for the power supply reliability of users, good power quality and the economic effectiveness of the power grid operation. The construction requirement of the intelligent power grid is that power grid intelligent service is realized through automatic demand response, diversified demands of power consumers are met, and a novel power supply and utilization relation of real-time interaction of power flow, information flow and service flow between the power grid and the customers is constructed on the basis. The electricity utilization efficiency information acquisition system based on the intelligent electric meter provides a real-time data source and technical support for the informatization, automation and interaction of the intelligent power grid.
The problem that the communication traffic of the information communication support platform is increased in multiples due to the real-time interactive service requirement of intelligent power utilization is solved, and if the power utilization energy efficiency information acquisition system is taken as an example, the communication data volume is increased suddenly due to the fact that the communication data volume is increased for 96 times every day (namely, the power utilization energy efficiency information acquisition system is acquired periodically every 15 minutes). An increase in the load on the communication network may cause congestion, which may affect the Quality of Service (QoS) of the communication when the network congestion cannot be controlled in a timely manner.
Compressed sensing is an effective data compression method, and can break through the bottleneck of processing, transmitting and storing a large amount of data of a system. The compressive sensing theory is a brand new mathematical theory and is widely applied in many fields, and the compressive sensing theory can be used for restoring data by observing a small amount of data and operating a reconstruction algorithm at a terminal, so that the data volume circulating in a network is greatly reduced.
The invention provides a power utilization energy efficiency monitoring method based on compressed sensing, aiming at the problem of huge data volume in power utilization energy efficiency information energy efficiency monitoring. Firstly, a concentrator acquires electricity utilization energy efficiency data; secondly, establishing a compressed sensing power utilization energy efficiency data aggregation model, and respectively performing compressed sensing sparsification operation and compressed sensing reconstruction operation on data by the concentrator and the energy efficiency monitoring master station; and finally, the energy efficiency monitoring master station carries out energy efficiency monitoring analysis according to the compressed sensing reconstruction data. According to the method, the type of the power utilization information data is divided, and the power utilization efficiency monitoring data part is subjected to data aggregation by adopting a compressed sensing structure model, so that the uplink transmission data volume of network energy efficiency monitoring is effectively reduced, and the stable and reliable operation of the system is ensured.
Disclosure of Invention
The invention aims to provide a power utilization energy efficiency monitoring method based on compressed sensing, which comprises the following specific steps.
Step 1: energy efficiency power source data acquisition for concentrator
And collecting user electricity utilization efficiency data of the electricity meter terminal of the distribution area covered by the power concentrator through the power concentrator to serve as an electricity utilization efficiency monitoring data source.
Step 2: compressive sensing electricity utilization energy efficiency data aggregation structure modeling
The method comprises the steps that a power utilization energy efficiency data aggregation structure model of compressed sensing is established between a concentrator and an energy efficiency monitoring master station, the concentrator achieves data sparseness of the compressed sensing, the energy efficiency monitoring master station achieves data reconstruction of the compressed sensing, and compressed sensing processing parameters are synchronized between the concentrator and the energy efficiency monitoring master station through information exchange.
And step 3: compressed sensing data sparsification operation of concentrator
The method comprises the following steps of establishing a compressed sensing data aggregation implementation framework between a concentrator and an energy efficiency monitoring master station, and compressing power utilization energy efficiency information data by the concentrator configured in a public distribution transformer area according to a compressed sensing theory data compression method, wherein the compressed sensing theory data compression method specifically comprises the following steps: the concentrator sends out signalsExpressed as the sum of the common part and the unique part:. Taking users under the same concentrator and adopting the same FFT sparse basisTo common componentAnd specific ingredientsAre respectively thinned and are expressed asVector group of，The following formula is given.
The realization from high (a) to high (b) is realized by a random Gaussian matrix measurement systemN) Dimension to low: (M) The projection of the dimensional space is as follows.
。
Wherein,is composed ofM×NThe measurement matrix of (a) is,is composed ofNThe dimension matrix is a matrix of dimensions,Xis composed ofMThe dimension matrix is a matrix of dimensions,N>>M. Therefore, the data volume required to be acquired is reduced, and the purpose of data compression is achieved through data sparsification.
And 4, step 4: compressed sensing data reconstruction operation of energy efficiency monitoring main station
Establishing a compressed sensing data aggregation implementation framework between the concentrator and the energy efficiency monitoring master station, and completing compressed sensing data reconstruction operation at the energy efficiency monitoring master station, wherein the compressed sensing data aggregation implementation framework specifically comprises the following steps: the energy efficiency monitoring master station performs data reconstruction through an iterative algorithm according to data uploaded by the concentrator: firstly, setting an initial value to be reconstructed, an iteration step length and a maximum iteration number; estimating first iteration data, calculating an objective function value of the reconstructed data, and updating iteration times; updating the value of the next iteration by using the reconstructed values of the previous two times, and calculating a reconstructed objective function value; and comparing the objective function values of two times until the objective function value of the time is less than or equal to that of the previous time and the current iteration number is equal to the maximum iteration number, stopping iteration, returning the reconstructed data, and otherwise, continuing the updating of the previous step.
And 5: energy efficiency monitoring and analyzing of electricity utilization efficiency of energy efficiency monitoring master station according to compressed sensing reconstruction data
The energy efficiency monitoring master station completes analysis of the power utilization energy efficiency characteristics of the power grid through real-time monitoring of the power utilization energy efficiency information data of the reconstructed data, and then effectively schedules and manages the power grid according to the current power utilization energy efficiency monitoring condition.
And (3) the concentrator in the step (1) acquires the energy utilization efficiency power supply data.
And 2, establishing a compressive sensing electricity utilization energy efficiency data aggregation structure model.
And the concentrator in the step 3 performs compressed sensing sparsification operation on the data.
And in the step 4, the energy-efficient monitoring master station carries out compressed sensing reconstruction operation on the data.
And 5, the energy efficiency monitoring master station reconstructs data according to the compressed sensing and carries out power utilization energy efficiency monitoring analysis.
The power utilization energy efficiency monitoring method based on compressed sensing can effectively reduce the network energy efficiency monitoring uplink transmission data volume and ensure the stable and reliable operation of the system.
Drawings
FIG. 1 is an overall flow chart of the present invention.
FIG. 2 is a compressive sensing aggregation architecture framework for the method of the present invention.
FIG. 3 is a comparison curve of compressed sensing reconstruction data and original data according to the method of the present invention.
Detailed Description
The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
The example is implemented in the context of a power usage energy efficiency data acquisition system. The parameter sample data is commercial power consumption and typical power consumption data of a user in one day. The compression sensing compression ratio is set to 70%.
Fig. 1 is an overall flowchart of a power consumption energy efficiency monitoring method according to the present invention. According to the illustration in fig. 1, the method according to the invention comprises the following implementation steps:
step 1: energy efficiency power source data acquisition for concentrator
And collecting user electricity utilization efficiency data of the electricity meter terminal of the distribution area covered by the power concentrator through the power concentrator to serve as an electricity utilization efficiency monitoring data source.
Step 2: compressive sensing electricity utilization energy efficiency data aggregation structure modeling
Fig. 2 is a power consumption energy efficiency data aggregation structure model of compressed sensing, a concentrator realizes data sparseness of compressed sensing, an energy efficiency monitoring master station realizes data reconstruction of compressed sensing, and compressed sensing processing parameters are synchronized between the concentrator and the energy efficiency monitoring master station through information exchange.
And step 3: compressed sensing data sparsification operation of concentrator
The method comprises the following steps of establishing a compressed sensing data aggregation implementation framework between a concentrator and an energy efficiency monitoring master station, and compressing power utilization energy efficiency information data by the concentrator configured in a public distribution transformer area according to a compressed sensing theory data compression method, wherein the compressed sensing theory data compression method specifically comprises the following steps: the concentrator sends out signalsExpressed as the sum of the common part and the unique part:. Taking users under the same concentrator and adopting the same FFT sparse basisTo common componentAnd specific ingredientsAre respectively thinned and are expressed asVector group of，The following formula is given.
The realization from high (a) to high (b) is realized by a random Gaussian matrix measurement systemN) Dimension to low: (M) The projection of the dimensional space is as follows.
。
Wherein,is composed ofM×NThe measurement matrix of (a) is,is composed ofNThe dimension matrix is a matrix of dimensions,Xis composed ofMThe dimension matrix is a matrix of dimensions,N>>M. Therefore, the data volume required to be acquired is reduced, and the purpose of data compression is achieved through data sparsification.
And 4, step 4: compressed sensing data reconstruction operation of energy efficiency monitoring main station
Establishing a compressed sensing data aggregation implementation framework between the concentrator and the energy efficiency monitoring master station, and completing compressed sensing data reconstruction operation at the energy efficiency monitoring master station, wherein the compressed sensing data aggregation implementation framework specifically comprises the following steps: the energy efficiency monitoring master station performs data reconstruction through an iterative algorithm according to data uploaded by the concentrator: firstly, setting an initial value to be reconstructed, an iteration step length and a maximum iteration number; estimating first iteration data, calculating an objective function value of the reconstructed data, and updating iteration times; updating the value of the next iteration by using the reconstructed values of the previous two times, and calculating a reconstructed objective function value; and comparing the objective function values of two times until the objective function value of the time is less than or equal to that of the previous time and the current iteration number is equal to the maximum iteration number, stopping iteration, returning the reconstructed data, and otherwise, continuing the updating of the previous step.
And 5: energy efficiency monitoring and analyzing of electricity utilization efficiency of energy efficiency monitoring master station according to compressed sensing reconstruction data
The energy efficiency monitoring master station completes analysis of the power utilization energy efficiency characteristics of the power grid through real-time monitoring of the power utilization energy efficiency information data of the reconstructed data, and then effectively schedules and manages the power grid according to the current power utilization energy efficiency monitoring condition.
Step 2: compressive sensing electricity utilization energy efficiency data aggregation structure modeling
The drawings of the present invention are explained below. FIG. 3 is a comparison curve of compressed sensing reconstruction data and original data according to the method of the present invention. The average error value of the compression sensing reconstruction data compared with the original data under the condition of 70% compression ratio is 4.05%, and the trend observation requirement of power utilization energy efficiency monitoring can be met.
In conclusion, the method provided by the invention can effectively reduce the uplink transmission data volume of network energy efficiency monitoring under the condition of meeting the requirement of accuracy of the power utilization energy efficiency monitoring data; meanwhile, collected data are efficiently transmitted back to the energy efficiency monitoring master station in real time, so that power consumption energy efficiency monitoring is realized, and stable and reliable operation of the system is guaranteed.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A compressed sensing power utilization energy efficiency monitoring method is characterized by comprising the following steps:
step 1: energy efficiency power source data acquisition for concentrator
Collecting user electricity utilization efficiency data of an electricity meter terminal of a distribution area covered by the power concentrator as an electricity utilization efficiency monitoring data source;
step 2: compressive sensing electricity utilization energy efficiency data aggregation structure modeling
Step 2, establishing a compressed sensing power utilization energy efficiency aggregation model, and performing compressed sensing data sparsification operation on power utilization energy efficiency information data by a concentrator; the energy efficiency monitoring master station performs data reconstruction operation of compressed sensing;
and step 3: compressed sensing data sparsification operation of concentrator
The concentrator represents the signal f as the sum of the common part and the unique part: z ═ fc+zjA common component zcAnd a characteristic component zjRespectively expressed as a set of vectors x under the sparse basis psic,xjOf the formula
zc＝ψxc,||xc||0＝Kc；
zj＝ψxj,||xj||0＝Kj
The projection from the high (N) -dimensional space to the low (M) -dimensional space is realized by a random Gaussian matrix measurement system as follows
Y＝φf＝φ(zc+zj)＝φψ(xc+xj)＝φψX
Where ψ is a measurement matrix of M × N, xjIs an N-dimensional matrix, X is an M-dimensional matrix, N>>M；
And 4, step 4: compressed sensing data reconstruction operation of energy efficiency monitoring main station
The energy efficiency monitoring master station performs data reconstruction through an iterative algorithm according to data uploaded by the concentrator: firstly, setting an initial value to be reconstructed, an iteration step length and a maximum iteration number; estimating first iteration data, calculating an objective function value of the reconstructed data, and updating iteration times; updating the value of the next iteration by using the reconstructed values of the previous two times, and calculating a reconstructed objective function value; comparing the two objective function values until the objective function value is less than or equal to the previous objective function value and the current iteration number is equal to the maximum iteration number, stopping iteration, returning reconstructed data, and otherwise continuing the updating of the previous step;
and 5: energy efficiency monitoring and analyzing of electricity utilization efficiency of energy efficiency monitoring master station according to compressed sensing reconstruction data
The energy efficiency monitoring master station completes analysis of the power utilization energy efficiency characteristics of the power grid through real-time monitoring of the power utilization energy efficiency information data of the reconstructed data, and then effectively schedules and manages the power grid according to the current power utilization energy efficiency monitoring condition.
2. The power consumption energy efficiency monitoring method based on compressed sensing of claim 1, wherein the step 4 is performed with data reconstruction operation of compressed sensing of the energy efficiency monitoring master station, and the energy efficiency monitoring master station performs data reconstruction through an iterative algorithm according to data uploaded by the concentrator to recover the data.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN201378753Y (en) * | 2008-12-12 | 2010-01-06 | 长沙大家网络科技有限责任公司 | Electric power wireless network regional management concentrator |
| CN103280084A (en) * | 2013-04-24 | 2013-09-04 | 中国农业大学 | Data acquisition method for multi-parameter real-time monitoring |
| CN103368578A (en) * | 2013-07-01 | 2013-10-23 | 中国农业大学 | Compressed-sensing-based signal sampling method for distributed wireless sensor network nodes |
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| US8775490B2 (en) * | 2011-02-04 | 2014-07-08 | Alcatel Lucent | Method and apparatus for compressive sensing with reduced compression complexity |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN201378753Y (en) * | 2008-12-12 | 2010-01-06 | 长沙大家网络科技有限责任公司 | Electric power wireless network regional management concentrator |
| CN103280084A (en) * | 2013-04-24 | 2013-09-04 | 中国农业大学 | Data acquisition method for multi-parameter real-time monitoring |
| CN103368578A (en) * | 2013-07-01 | 2013-10-23 | 中国农业大学 | Compressed-sensing-based signal sampling method for distributed wireless sensor network nodes |
Non-Patent Citations (3)
| Title |
|---|
| Compressed Sensing;David L. Donoho;《IEEE TRANSACTIONS ON INFORMATION THEORY》;20060430;第52卷(第4期);第1289-1306页 * |
| 压缩感知及应用;李卓凡 等;《微计算机应用》;20100331;第31卷(第3期);第12-16页 * |
| 基于能效监测平台的能源管理与优化;董奥;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20130715(第07期);第5-34页 * |
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