CN116865442A - User side electric quantity loss analysis method - Google Patents
User side electric quantity loss analysis method Download PDFInfo
- Publication number
- CN116865442A CN116865442A CN202310828592.5A CN202310828592A CN116865442A CN 116865442 A CN116865442 A CN 116865442A CN 202310828592 A CN202310828592 A CN 202310828592A CN 116865442 A CN116865442 A CN 116865442A
- Authority
- CN
- China
- Prior art keywords
- data
- loss
- electric quantity
- user side
- power
- 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.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 76
- 238000012544 monitoring process Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 28
- 238000005516 engineering process Methods 0.000 claims abstract description 19
- 238000004891 communication Methods 0.000 claims abstract description 15
- 238000012423 maintenance Methods 0.000 claims abstract description 10
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000012806 monitoring device Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000004140 cleaning Methods 0.000 claims abstract description 5
- 238000011835 investigation Methods 0.000 claims abstract description 5
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 27
- 238000000034 method Methods 0.000 claims description 27
- 230000005611 electricity Effects 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 230000002068 genetic effect Effects 0.000 claims description 9
- 238000005065 mining Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 9
- 230000005856 abnormality Effects 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- 238000009434 installation Methods 0.000 claims description 4
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 3
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 claims description 3
- 238000007418 data mining Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000013136 deep learning model Methods 0.000 claims description 3
- 238000012804 iterative process Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000012067 mathematical method Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 7
- 230000009471 action Effects 0.000 description 4
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/0084—Arrangements for measuring currents or voltages or for indicating presence or sign thereof measuring voltage only
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/0092—Arrangements for measuring currents or voltages or for indicating presence or sign thereof measuring current only
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
- G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
-
- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Resources & Organizations (AREA)
- Biophysics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Marketing (AREA)
- Databases & Information Systems (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Primary Health Care (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Water Supply & Treatment (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Public Health (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Human Computer Interaction (AREA)
Abstract
The invention belongs to the technical field of electric quantity loss analysis, and discloses a user side electric quantity loss analysis method, which comprises the following steps: s1: the system collects the voltage, current and power parameters of the user side loop metering electric energy meter in real time through a sensor or a monitoring device, processes and compresses the collected data, and then records the processed data; s2: the collected electric quantity parameter data are transmitted to an object pipe monitoring platform or a data processing server through a communication network; s3: in the object pipe monitoring platform or the data processing server, the collected data is preprocessed by advanced data processing technology, which includes but is not limited to screening, cleaning and compressing the data stored in the database. The system can automatically remind or dispatch a work order to operation and maintenance personnel through a feedback early warning mechanism, and the located electric quantity loss points are subjected to investigation and treatment, so that the electric quantity loss is reduced, the energy utilization efficiency is improved, and the cost is saved.
Description
Technical Field
The invention belongs to the technical field of electric quantity loss analysis, and particularly relates to a user side electric quantity loss analysis method.
Background
The user side power loss refers to energy loss from a power supply point to a user terminal, and various losses are faced by electric energy in the power transmission and distribution process, including resistance loss of an electric wire and a cable, copper loss and iron loss of a transformer, line loss of electric equipment and the like, the losses can lead to electric energy which actually reaches the user terminal to be less than supplied electric energy, the purpose of user side power loss analysis is to evaluate and quantify the degree of electric energy loss, find out main reasons of loss, and take corresponding measures for improvement, which can comprise using more efficient electric equipment, improving selection and wiring of the electric wire and the cable and optimizing load management, and a traditional user side power loss analysis method mainly determines the power loss amount by analyzing and comparing electric meter data, but the method can lead to low accuracy of the analyzed and compared power loss amount, meanwhile, manual intervention is required, and hidden loss and non-technical loss cannot be detected.
Disclosure of Invention
The invention aims to provide a method for analyzing the power consumption of a user side, which aims to solve the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: the method for analyzing the electric quantity loss of the user side comprises the following steps of:
s1: the system collects the voltage, current and power parameters of the user side loop metering electric energy meter in real time through a sensor or a monitoring device, processes and compresses the collected data, and then records the processed data;
s2: the collected electric quantity parameter data are transmitted to an object pipe monitoring platform or a data processing server through a communication network;
s3: in the object pipe monitoring platform or the data processing server, preprocessing the acquired data by utilizing an advanced data processing technology, which comprises but is not limited to screening, cleaning and compressing the data stored in a database, screening out electric quantity data meeting the specified sampling frequency and power consumption range, clearing abnormal pulse data, filling the lost data, and then compressing the data;
s4: the method comprises the steps of carrying out real-time monitoring and evaluation on processed data by applying a comprehensive loss model algorithm and a model, wherein the monitoring and evaluation range comprises power consumption data and power consumption deviation value data, and then carrying out further mining analysis on the data;
s5: and pushing and displaying the analysis result, and automatically reminding or distributing a work order to operation and maintenance personnel through a feedback early warning mechanism to check and treat the positioned electric quantity loss point.
Preferably, the deployment manner of the sensor network in the electrical quantity loss analysis step S1 is as follows:
a1: in the user side power line, a plurality of sensor nodes are arranged, including a voltage sensor, a current sensor and a power sensor,
the sensor node installation position comprises any one of the following:
a, an input end of a distribution line at the inlet side;
b, an output end of the transformer equipment;
c, an input end of the power distribution room screen cabinet;
a2: the sensor node is connected with the object pipe monitoring system in a wired or wireless mode;
the real-time acquisition of the sensor or the monitoring device in the electric quantity loss analysis step S1 specifically comprises the following steps:
b1: collecting branch lines;
b2: collecting voltage parameters of a tail-end electric energy meter;
b3: collecting current parameters;
b4: the power parameters are collected.
Preferably, in the electrical quantity loss analysis step S2, a wireless communication technology is adopted for communication network transmission, and the processed data is transmitted to the object pipe monitoring system through the network;
wherein, the wireless communication technology includes: wi-Fi, zigbee, or LoRa.
Preferably, the method for preprocessing the data in the power loss analysis step S3 further includes:
c1: the object pipe monitoring system receives data transmitted by the sensor node, and decodes and restores the data;
c2: processing and analyzing the decoded data, and calculating the electricity consumption;
and C3: and carrying out deviation analysis based on the calculated actual power consumption and the theoretical line loss consumption value, and judging whether the loss analysis has fluctuation abnormality or not.
Preferably, the wave exception handling mode is:
d1: if the deviation between the analysis result and the preset loss fluctuation value exceeds a certain value, defining the abnormal event, and immediately sending out an alarm to inform related personnel or operators;
d2: the system can trigger to automatically remind relevant operation and maintenance personnel to take corresponding measures for processing while sending out alarm notification. For example, check whether the line ammeter wiring on the user side is reversed or check whether the ammeter multiplying power is accurately debugged.
Preferably, the comprehensive loss model algorithm and model in the electrical quantity loss analysis step S4 are as follows:
e1: based on the electric quantity parameters and characteristics of the user side acquired by the object management monitoring system, establishing a mathematical model to describe the behavior and performance of the system;
e2: analyzing and processing electric quantity data by using a higher mathematical method including linear algebra, calculus and discrete mathematics;
e3: adopting the idea of genetic algorithm optimization, and designing a multi-objective optimization model by combining a plurality of influencing factors and objective functions of electric quantity loss;
e4: and continuously optimizing parameters and weights in the loss model through an iterative process of the evolutionary algorithm, and searching an optimal electric quantity loss analysis result.
Preferably, the manner of performing further mining analysis on the data in the power loss analysis step S4 is as follows:
f1: deep analysis and mining are carried out on a large amount of collected electric energy data by means of a data mining technology;
f2: and predicting and identifying the running state and power consumption abnormality of the power system by using a machine learning and artificial intelligence method.
Preferably, in the electrical quantity loss analysis step S4, an integrated loss model is used to decompose the electrical quantity loss into different component parts, and calculate the total loss, so as to obtain a formula expression:
TotalLoss＝CopperLoss+IronLoss+StrayLoss+HarmonicLoss
component power loss model: for different components including wires and transformers, the power loss can be calculated using the following formula:
CopperLoss＝I^2*R
IronLoss＝K*f^α
StrayLoss＝K*V^2
HarmonicLoss＝K*THD*V^2
optimizing an objective function: using the objective function to optimize the power loss analysis results, the following form of objective function can be considered:
Minimize:Loss＝α*CopperLoss+β*IronLoss+γ*StrayLoss+δ*HarmonicLoss
fitness function of genetic algorithm: in the genetic algorithm, the fitness function is used to evaluate the fitness of an individual, and for the power loss analysis optimization problem, the following fitness function can be used:
Fitness＝1/(Loss+ε)
loss function of deep learning model: for a deep learning based power loss model, the model may be trained using the following loss function:
Loss＝MSE(Predicted,Actual)
preferably, in the step S5 of analyzing the power consumption, the analysis result is pushed and displayed, which is to output the analysis result to an application program, the application program can check the detailed power consumption condition of the user side, and can also classify and group the users to compare the power consumption conditions among different users, when the power consumption of a single user exceeds a preset threshold, the system can automatically send early warning information to the staff of the electric company, and remind the staff to perform investigation and processing.
The beneficial effects of the invention are as follows:
1. the invention collects key parameters such as voltage, current, frequency, power and the like of the user-side electric energy meter in real time through the sensor or the monitoring device, and records the key parameters; then, the collected electric energy data is transmitted to an object pipe monitoring platform or a data processing server through a communication network so as to be further analyzed and processed; preprocessing the acquired data by utilizing an advanced data processing technology, including but not limited to screening, cleaning and compressing the data stored in a database, screening out electric quantity data meeting the specified sampling frequency and power consumption range, removing abnormal pulse data, filling lost data, and then compressing the data; then, a comprehensive loss model algorithm and a model are applied to monitor and evaluate the processed data in real time, wherein the data comprise loss amount, loss deviation value and the like; and pushing and displaying the analysis result, the system can automatically remind or dispatch a work order to operation and maintenance personnel through a feedback early warning mechanism, and the positioned electric quantity loss point is subjected to investigation and treatment, so that the electric quantity loss is reduced, the energy utilization efficiency is improved, and the cost is saved.
Drawings
Fig. 1 is a diagram showing steps of the power loss analysis according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides a method for analyzing the power loss of a user side, which comprises the following steps:
s1: the system collects the voltage, current and power parameters of the user side loop metering electric energy meter in real time through a sensor or a monitoring device, processes and compresses the collected data, and then records the processed data;
s2: the collected electric quantity parameter data are transmitted to an object pipe monitoring platform or a data processing server through a communication network;
s3: in the object pipe monitoring platform or the data processing server, preprocessing the acquired data by utilizing an advanced data processing technology, which comprises but is not limited to screening, cleaning and compressing the data stored in a database, screening out electric quantity data meeting the specified sampling frequency and power consumption range, clearing abnormal pulse data, filling the lost data, and then compressing the data;
s4: the method comprises the steps of carrying out real-time monitoring and evaluation on processed data by applying a comprehensive loss model algorithm and a model, wherein the monitoring and evaluation range comprises power consumption data and power consumption deviation value data, and then carrying out further mining analysis on the data;
s5: and pushing and displaying the analysis result, and automatically reminding or distributing a work order to operation and maintenance personnel through a feedback early warning mechanism to check and treat the positioned electric quantity loss point.
The working principle and beneficial effects of the technical scheme are as follows: through real-time monitoring and analysis, the technology can help identify potential fault risks and problems, so that preventive measures can be timely taken, and system faults are avoided. In addition, according to the analysis result, the system can be optimized and adjusted, and the electric quantity loss at the user side is reduced.
The monitoring and analysis of the user-side electric energy meter are more automatic and intelligent, and the requirement of manual intervention is reduced. The operation and maintenance efficiency can be improved, the manpower resource cost can be saved, and the fault removal and maintenance time can be shortened.
Through real-time monitoring and analysis, the technology can help the user to know the loss condition of the whole energy consumption of the user side, provide timely alarm and prompt information, and enhance the trust and satisfaction degree of the user to the system.
Through loss model analysis, the technology can effectively position the electric quantity loss point, and realize accurate treatment, thereby improving the energy utilization rate and saving the cost.
In conclusion, the method can effectively locate and analyze the electricity consumption condition of the user side, and improve the stability, reliability and performance of the system, so that better operation effect and user experience are brought, accurate, timely and comprehensive electricity consumption analysis service is provided, the energy utilization efficiency is improved, the energy consumption is reduced, and the core competitiveness is enhanced.
As shown in the figure, in one embodiment, the deployment manner of the sensor network in the power consumption analysis step S1 is as follows:
a1: in the user side power line, a plurality of sensor nodes are arranged, including a voltage sensor, a current sensor and a power sensor,
the sensor node installation position comprises any one of the following:
a, an input end of a distribution line at the inlet side;
b, an output end of the transformer equipment;
c, an input end of the power distribution room screen cabinet;
a2: the sensor node is connected with the object pipe monitoring system in a wired or wireless mode;
the real-time acquisition of the sensor or the monitoring device in the electric quantity loss analysis step S1 specifically comprises the following steps:
b1: collecting branch lines;
b2: collecting voltage parameters of a tail-end electric energy meter;
b3: collecting current parameters;
b4: the power parameters are collected.
The working principle and beneficial effects of the technical scheme are as follows: the installation monitoring device and the sensor are arranged on a branch line of the user side energy system and a switch cabinet of a distribution room, and comprise an intelligent ammeter, a transformer, a temperature controller, a hygrothermograph and the like, and user electricity data are transmitted to a data processing unit. The data processing unit stores the data in a database on the server, and through the technology, the electric quantity parameters of the electric energy meter at the user side, including current, voltage, active power, reactive power and the like, can be monitored in real time, so that the abnormal electricity consumption can be found in time, the stability and the reliability of the system are improved, and the data transmission and communication can be realized.
As shown in the figure, in one embodiment, in the electrical quantity loss analysis step S2, the communication network transmission adopts a wireless communication technology, and the processed data is transmitted to the object pipe monitoring system through the network;
the wireless communication technology comprises the following steps: wi-Fi, zigbee, or LoRa.
The working principle and beneficial effects of the technical scheme are as follows: the design can realize data transmission between the sensor nodes and the central monitoring system.
As shown in the figure, in one embodiment, the manner of preprocessing the data in the power loss analysis step S3 further includes:
c1: the object pipe monitoring system receives data transmitted by the sensor node, and decodes and restores the data;
c2: processing and analyzing the decoded data, and calculating the electricity consumption;
and C3: and carrying out deviation analysis based on the calculated actual power consumption and the theoretical line loss consumption value, and judging whether the loss analysis has fluctuation abnormality or not.
The working principle and beneficial effects of the technical scheme are as follows: and setting data preprocessing, wherein the data preprocessing unit screens, cleans and compresses the data stored in the database. And screening out electric quantity data in a power consumption range which accords with a specified sampling frequency, clearing abnormal pulse data, filling lost data, and then compressing the data. The preprocessed data is transmitted to a data analysis unit.
The data analysis unit analyzes the preprocessed data, calculates the electric quantity loss, and outputs an analysis result to the analysis result output unit.
As shown, in one embodiment, the surge exception handling is as follows:
d1: if the deviation between the analysis result and the preset loss fluctuation value exceeds a certain value, defining the abnormal event, and immediately sending out an alarm to inform related personnel or operators;
d2: the system can trigger to automatically remind relevant operation and maintenance personnel to take corresponding measures for processing while sending out alarm notification. For example, check whether the line ammeter wiring on the user side is reversed or check whether the ammeter multiplying power is accurately debugged.
As shown, in one embodiment, the integrated loss model algorithm and model in the power loss analysis step S4 is:
e1: based on the electric quantity parameters and characteristics of the user side acquired by the object management monitoring system, establishing a mathematical model to describe the behavior and performance of the system;
e2: analyzing and processing electric quantity data by using a higher mathematical method including linear algebra, calculus and discrete mathematics;
e3: adopting the idea of genetic algorithm optimization, and designing a multi-objective optimization model by combining a plurality of influencing factors and objective functions of electric quantity loss;
e4: and continuously optimizing parameters and weights in the loss model through an iterative process of the evolutionary algorithm, and searching an optimal electric quantity loss analysis result.
The working principle and beneficial effects of the technical scheme are as follows: such an algorithm can provide a globally optimal solution taking into account the complex relationships between a number of factors.
As shown, in one embodiment, the manner of performing further mining analysis on the data in the power loss analysis step S4 is as follows:
f1: deep analysis and mining are carried out on a large amount of collected electric energy data by means of a data mining technology;
f2: and predicting and identifying the running state and power consumption abnormality of the power system by using a machine learning and artificial intelligence method.
As shown, in one embodiment, the power loss analysis step S4 uses a comprehensive loss model to decompose the power loss into different component parts, and calculates the total loss to obtain a formula:
TotalLoss＝CopperLoss+IronLoss+StrayLoss+HarmonicLoss
component power loss model: for different components including wires and transformers, the power loss can be calculated using the following formula:
CopperLoss＝I^2*R
IronLoss＝K*f^α
StrayLoss＝K*V^2
HarmonicLoss＝K*THD*V^2
optimizing an objective function: using the objective function to optimize the power loss analysis results, the following form of objective function can be considered:
Minimize:Loss＝α*CopperLoss+β*IronLoss+γ*StrayLoss+δ*HarmonicLoss
fitness function of genetic algorithm: in the genetic algorithm, the fitness function is used to evaluate the fitness of an individual, and for the power loss analysis optimization problem, the following fitness function can be used:
Fitness＝1/(Loss+ε)
loss function of deep learning model: for a deep learning based power loss model, the model may be trained using the following loss function:
Loss＝MSE(Predicted,Actual)
the working principle and beneficial effects of the technical scheme are as follows:
where I is current, R is resistance, K is constant, f is frequency, α is an index, V is voltage, and THD is total harmonic distortion.
Wherein, alpha, beta, gamma and delta are weight coefficients, and the adjustment is carried out according to actual conditions.
Where ε is a small positive number to avoid dividing by zero.
Where MSE represents mean square error, predicted is the model Predicted loss value, and Actual is the Actual loss value.
The algorithm and the model can identify and classify the monitored electric quantity parameter abnormality and analyze the cause and influence of the abnormality. This helps to locate and solve problems quickly and take appropriate action to make adjustments and repairs.
As shown in the figure, in one embodiment, the pushing and displaying of the analysis result in the electricity consumption analysis step S5 is to output the analysis result to the application program, and the application program can check the detailed electricity consumption situation of the user side, and can also classify and group the users to compare the electricity consumption situations among different users, when the electricity consumption of a single user exceeds a preset threshold, the system can automatically send early warning information to the staff of the electric company, and remind the staff to perform investigation and processing.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The method for analyzing the electric quantity loss of the user side is characterized by comprising the following steps of:
s1: the system collects the voltage, current and power parameters of the user side loop metering electric energy meter in real time through a sensor or a monitoring device, processes and compresses the collected data, and then records the processed data;
s2: the collected electric quantity parameter data are transmitted to an object pipe monitoring platform or a data processing server through a communication network;
s3: in the object pipe monitoring platform or the data processing server, preprocessing the acquired data by utilizing an advanced data processing technology, which comprises but is not limited to screening, cleaning and compressing the data stored in a database, screening out electric quantity data meeting the specified sampling frequency and power consumption range, clearing abnormal pulse data, filling the lost data, and then compressing the data;
s4: the method comprises the steps of carrying out real-time monitoring and evaluation on processed data by applying a comprehensive loss model algorithm and a model, wherein the monitoring and evaluation range comprises power consumption data and power consumption deviation value data, and then carrying out further mining analysis on the data;
s5: and pushing and displaying the analysis result, and automatically reminding or distributing a work order to operation and maintenance personnel through a feedback early warning mechanism to check and treat the positioned electric quantity loss point.
2. The method for analyzing power consumption of a user side according to claim 1, wherein: the deployment mode of the sensor network in the electric quantity loss analysis step S1 is as follows:
a1: in the user side power line, a plurality of sensor nodes are arranged, including a voltage sensor, a current sensor and a power sensor,
the sensor node installation position comprises any one of the following:
a, an input end of a distribution line at the inlet side;
b, an output end of the transformer equipment;
c, an input end of the power distribution room screen cabinet;
a2: the sensor node is connected with the object pipe monitoring system in a wired or wireless mode;
the real-time acquisition of the sensor or the monitoring device in the electric quantity loss analysis step S1 specifically comprises the following steps:
b1: collecting branch lines;
b2: collecting voltage parameters of a tail-end electric energy meter;
b3: collecting current parameters;
b4: the power parameters are collected.
3. The method for analyzing power consumption of a user side according to claim 1, wherein: the communication network transmission in the electric quantity loss analysis step S2 adopts a wireless communication technology, and the processed data is transmitted to an object management monitoring system through the network;
wherein, the wireless communication technology includes: wi-Fi, zigbee, or LoRa.
4. The method for analyzing power consumption of a user side according to claim 1, wherein: the method for preprocessing the data in the electric quantity loss analysis step S3 further includes:
c1: the object pipe monitoring system receives data transmitted by the sensor node, and decodes and restores the data;
c2: processing and analyzing the decoded data, and calculating the electricity consumption;
and C3: and carrying out deviation analysis based on the calculated actual power consumption and the theoretical line loss consumption value, and judging whether the loss analysis has fluctuation abnormality or not.
5. The method for analyzing power consumption of a user according to claim 4, wherein: the fluctuation exception handling mode is as follows:
d1: if the deviation between the analysis result and the preset loss fluctuation value exceeds a certain value, defining the abnormal event, and immediately sending out an alarm to inform related personnel or operators;
d2: the system can trigger to automatically remind relevant operation and maintenance personnel to take corresponding measures for processing while sending out alarm notification. For example, check whether the line ammeter wiring on the user side is reversed or check whether the ammeter multiplying power is accurately debugged.
6. The method for analyzing power consumption of a user side according to claim 1, wherein: the comprehensive loss model algorithm and model in the electric quantity loss analysis step S4 are as follows:
e1: based on the electric quantity parameters and characteristics of the user side acquired by the object management monitoring system, establishing a mathematical model to describe the behavior and performance of the system;
e2: analyzing and processing electric quantity data by using a higher mathematical method including linear algebra, calculus and discrete mathematics;
e3: adopting the idea of genetic algorithm optimization, and designing a multi-objective optimization model by combining a plurality of influencing factors and objective functions of electric quantity loss;
e4: and continuously optimizing parameters and weights in the loss model through an iterative process of the evolutionary algorithm, and searching an optimal electric quantity loss analysis result.
7. The method for analyzing power consumption of a user side according to claim 1, wherein: the manner of further mining and analyzing the data in the electric quantity loss analysis step S4 is as follows:
f1: deep analysis and mining are carried out on a large amount of collected electric energy data by means of a data mining technology;
f2: and predicting and identifying the running state and power consumption abnormality of the power system by using a machine learning and artificial intelligence method.
8. The method for analyzing power consumption of a user side according to claim 1, wherein: in the electric quantity loss analysis step S4, an integrated loss model is used to decompose the electric quantity loss into different components, and calculate the total loss to obtain a formula expression:
totaloss = CopperLoss + IronLoss + StrayLoss + harmonic loss of power model of assembly: for different components including wires and transformers, the power loss can be calculated using the following formula:
CopperLoss＝I^2*R
IronLoss＝K*f^α
StrayLoss＝K*V^2
HarmonicLoss＝K*THD*V^2
optimizing an objective function: using the objective function to optimize the power loss analysis results, the following form of objective function can be considered:
Minimize:Loss＝α*CopperLoss+β*IronLoss+γ*StrayLoss+δ*HarmonicLoss
fitness function of genetic algorithm: in the genetic algorithm, the fitness function is used to evaluate the fitness of an individual, and for the power loss analysis optimization problem, the following fitness function can be used:
Fitness＝1/(Loss+ε)
loss function of deep learning model: for a deep learning based power loss model, the model may be trained using the following loss function:
Loss＝MSE(Predicted,Actual)。
9. the method for analyzing power consumption of a user side according to claim 1, wherein: the pushing and displaying of the analysis result in the electricity consumption analysis step S5 is that the analysis result is output to an application program, the application program can check the detailed electricity consumption condition of a user side, the users can be classified and grouped to compare the electricity consumption conditions among different users, and when the electricity consumption of a single user exceeds a preset threshold value, the system can automatically send early warning information to staff of an electric company and remind the staff of investigation and processing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310828592.5A CN116865442A (en) | 2023-07-07 | 2023-07-07 | User side electric quantity loss analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310828592.5A CN116865442A (en) | 2023-07-07 | 2023-07-07 | User side electric quantity loss analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116865442A true CN116865442A (en) | 2023-10-10 |
Family
ID=88221095
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310828592.5A Pending CN116865442A (en) | 2023-07-07 | 2023-07-07 | User side electric quantity loss analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116865442A (en) |
-
2023
- 2023-07-07 CN CN202310828592.5A patent/CN116865442A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106655522B (en) | A kind of main station system suitable for electric grid secondary equipment operation management | |
| CN110518880B (en) | Photovoltaic power station state diagnosis method and device | |
| CN103872782B (en) | A kind of power quality data integrated service system | |
| CN103454516B (en) | Intelligent transformer substation secondary equipment health state diagnostic method | |
| CN107680368A (en) | A kind of metering device on-line monitoring and intelligent diagnosing method based on gathered data | |
| CN111191878A (en) | Abnormal analysis based station area and electric energy meter state evaluation method and system | |
| CN104730458B (en) | Generator excited system state monitoring method | |
| CN105548744A (en) | Substation equipment fault identification method based on operation-detection large data and system thereof | |
| CN110349048B (en) | Substation multidimensional data operation interactive control platform and fault handling method | |
| CN114137916A (en) | Supervision and control system for circuit board production based on data analysis | |
| CN104820884A (en) | Power network dispatching real-time data inspection method combined with characteristics of power system | |
| CN117060409B (en) | Automatic detection and analysis method and system for power line running state | |
| CN116937575A (en) | Energy monitoring management system for grid system | |
| CN105184521A (en) | Method, device and system for evaluating risk of power grid operation mode with equipment health state | |
| CN111669123A (en) | Method and device for fault diagnosis of photovoltaic string | |
| CN117078017A (en) | Intelligent decision analysis system for monitoring power grid equipment | |
| CN117390944A (en) | Substation operation condition simulation system | |
| CN112905670A (en) | Electric energy meter system for indoor power failure fault study and judgment and indoor power failure fault study and judgment method | |
| CN110750760B (en) | Abnormal theoretical line loss detection method based on situation awareness and control diagram | |
| CN110310048B (en) | Distribution network planning overall process evaluation method and device | |
| CN116865442A (en) | User side electric quantity loss analysis method | |
| CN111582626A (en) | Power grid planning adaptability method based on big data | |
| KR102411915B1 (en) | System and method for froviding real time monitering and ai diagnosing abnormality sign for facilities and equipments | |
| CN113055454A (en) | Centralized processing method and system for monitoring data of main transformer equipment | |
| CN1665088A (en) | Digital diagrammatic view switch apparatus system |
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 |