CN110687379B - Topology-configurable non-invasive building electrical equipment monitoring and analyzing system - Google Patents
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Abstract
The topology-configurable non-invasive building electrical equipment monitoring and analyzing system comprises a user electrical data acquisition module, an electrical data processing module and an electrical data cloud server. Through the remote configuration of the power utilization cloud server, the conversion from low frequency to high frequency of the sampling frequency of the power utilization end equipment is realized, so that the data processing amount of the sampled power utilization characteristic data is increased, and the purpose of more accurately and reasonably identifying the user load is further achieved. When the problem of cost increase of computing equipment caused by increase of user requirements is solved, a data processing module group element mode formed by connecting a plurality of power utilization data processing module modes in parallel is adopted, and calculation is carried out by applying a non-invasive power utilization load identification algorithm. The upgrading of the computing power of the system can be realized under the condition of completely utilizing the hardware of the original system, the system is upgraded without secondary construction, and the transformation cost is low. The non-intrusive power load monitoring requirement for developing high performance of the smart power grid can be fully met.
Description
Technical Field
The invention relates to a system for non-invasive building electrical equipment monitoring, sampling and load identification.
Background
Today, energy saving is a challenging problem due to exponential increase of energy demand, and as people pay more attention to the problem, the emerging ubiquitous power internet of things needs to acquire detailed electricity data on the user side on a large scale, so that the solution is necessarily more non-intrusive. As the energy usage gets deeper, the stability of the power supply system becomes more important. Therefore, accurate sampling by reasonably and efficiently utilizing the electric energy monitoring technology becomes a problem to be solved urgently by researchers. The residential department occupies a certain proportion of the power consumption, but the types of the power utilization terminals are multiple, and the power utilization components are complex, so that the monitoring of the residential power utilization equipment plays a significant role in power detection.
At present, data acquisition of non-invasive electrical load monitoring equipment is mainly divided into two types, one type is low-frequency data acquisition, and the sampling frequency is generally less than or equal to 1Hz (for example, in chinese patent "a smart grid non-invasive resident load monitoring circuit and system", patent No. CN 108693417 a). The system only performs low-frequency sampling, has a simple structure and low cost, but cannot acquire harmonic information of power utilization information and switch transient process information due to the limitation of sampling frequency, so that the identification precision is relatively low. Another method for performing high-frequency data acquisition, which generally has a sampling frequency of more than 1kHz, is disclosed in chinese patents (a non-invasive load identification method and apparatus based on fusion decision, patent No. CN 107525964 a), chinese patents (a non-invasive load monitoring method and system based on current decomposition), etc., although such systems need to acquire more types of electricity consumption information to achieve more accurate identification precision of electricity consumption equipment, the solving unit in the system needs to include a corresponding judging module and selecting module in addition to higher sampling equipment cost, and a complex and higher-performance data processing device (e.g., a non-invasive load identification method and apparatus based on fusion decision, patent No. CN 107525964 a), CN 103018611B) includes its algorithm selecting module and algorithm executing module, so that the data processing amount is increased to further increase the structural complexity and the overall cost of the non-invasive electrical load monitoring device.
Disclosure of Invention
The topologically configurable non-invasive building electrical equipment monitoring and analyzing system provided by the invention realizes the conversion of the sampling frequency of electrical end equipment from low frequency to high frequency by remote configuration of an electrical cloud server, thereby improving the data processing quantity of sampled electrical characteristic data and further achieving the purpose of more accurately and reasonably identifying user load.
The invention provides a topology-configurable non-invasive building electrical equipment monitoring and analyzing system which comprises a user electrical data acquisition module, an electrical data processing module and an electrical data cloud server, wherein the user electrical data acquisition module is used for acquiring electrical data of a user;
the system comprises a user electricity consumption data acquisition module, an electricity consumption data processing module, a power system and a data acquisition processor, wherein the user electricity consumption data acquisition module is arranged on an entrance bus of a user, acquires voltage and current data of the user in real time and is in bidirectional connection with the electricity consumption data processing module through a high-speed communication mode, the high-speed communication mode is preferably a power system carrier communication mode, and the user electricity consumption data acquisition module comprises a voltage sensor, a current sensor, an AD converter, a data acquisition processor, a power supply and a communication module;
the power consumption data processing module is a data processor which can be connected in parallel, one or more power consumption data acquisition modules are configured, when one power consumption data acquisition module is configured, all installed power consumption data acquisition modules in a building are connected to the power consumption data processing module, and the power consumption data acquisition modules are configured in a low-frequency sampling mode so as to reduce the total data volume required to be processed by the data processing module. The electricity consumption data acquisition module is used for initial arrangement of the system, and the cost of the data processing module can be greatly reduced.
When the data processing capacity needs to be upgraded, a data processing module group element mode formed by connecting a plurality of power consumption data processing modules in parallel is adopted and completed through a user power consumption equipment identification algorithm configured by the data processing module, and the configured user power consumption equipment identification algorithm is configured to use various non-invasive user power consumption equipment identification algorithms for operation;
the upgraded and configured data processing module adopts a data processing module group component mode, all installed power utilization data acquisition modules in the building are connected to a first data processing module, the module distributes data and corresponding processing tasks to other data processing modules of the data processing module group components for processing, the data processing modules connected in parallel with the data processing module group components are connected in an Ethernet mode, the data processing module group component mode is adopted and configured into a high-frequency sampling mode according to requirements, and the power utilization data processing module adopting the data processing module group component mode transmits the processed power utilization equipment identification result to a power utilization data cloud server through wireless communication;
the electricity consumption data cloud server is used for receiving the electricity consumption load identification result and the system log obtained by the computing module through wireless communication, and providing remote access service of electricity consumption load monitoring data for the user according to different user permissions, and the wireless communication is preferably in a 5G wireless communication mode.
The system can be upgraded and transformed under the condition of completely utilizing the hardware of the original system without secondary construction, so that the transformation cost is low.
Has the advantages that: the topology-configurable non-invasive building electrical equipment monitoring and analyzing system provided by the invention realizes the conversion of the sampling frequency of electrical end equipment from low frequency to high frequency through the remote configuration of the electrical cloud server, thereby improving the data processing amount of the sampled electrical characteristic data and further achieving the purpose of more accurately and reasonably identifying the user load. When the topology-configurable non-invasive building electrical equipment monitoring and analyzing system provided by the invention is used for solving the problem of cost increase of computing equipment caused by increase of user requirements, a data processing module group element mode formed by connecting a plurality of electrical data processing module modes in parallel is adopted, the configurable requirements are met, equipment waste caused by upgrading is reduced, the increase of engineering cost caused by secondary construction is avoided, and the requirement of developing high-performance non-invasive electrical load monitoring of an intelligent power grid can be fully met.
Description of the drawings:
fig. 1 is a topological diagram of the system configuration at low frequency sampling of the present invention.
Fig. 2 is a topological diagram of the high frequency sampling system after the upgrade configuration of the present invention.
FIG. 3 is a schematic diagram of the structure of a user electricity consumption data acquisition module of the system of the present invention.
FIG. 4 is a system data processing module workflow diagram of the present invention.
Fig. 5 is a flowchart of the first data processing module of the up-configured high frequency sampling system of the present invention.
Fig. 6 is a flow chart of the work flow of the data processing module components of the high frequency sampling system after the upgrade configuration of the present invention.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 3, the electricity consumption data acquisition module 11 includes a voltage sensor 301, a current sensor 302, an AD converter 303, a power supply 304, a data acquisition processor 305, and a communication module 306; the voltage sensor 301 and the current sensor 302 respectively select a ZMPT voltage transformer and an HCS-ES5 closed-loop Hall current transformer for non-invasively collecting voltage and current data on the electric bus 102, and set the low-frequency sampling frequency to be 0.1 Hz. The sampled analog signals are converted to digital signals by an AD converter 303 for subsequent calculations by the data acquisition processor 305, said AD converter 303 selecting an AD7193BRUZAD converter.
The data acquisition processor 305 performs low-frequency sampling, preprocesses the information of the voltage and the current of the bus acquired by the voltage sensor 301 and the current sensor 302 into power utilization characteristic data of active power and reactive power, and transmits the information to the communication module 306;
the communication module 306 is connected with the electricity consumption data processing module 12 in a two-way manner by adopting a high-speed communication mode of the local area communication network 101;
the power supply 304 is used for powering the data acquisition processor 305;
the electricity consumption data processing module 12 is a user processing module based on a Cortex-a15 chip, has low energy consumption and high calculation efficiency, is used for receiving the electricity consumption characteristic data and the bus position information sent by the electricity consumption data acquisition module 11, performs user electricity consumption equipment identification by using a non-invasive electricity consumption identification algorithm configured by the electricity consumption data processing module, sends warning information when the calculation capacity is judged to be insufficient, and finally sends the user equipment identification result to the electricity consumption cloud server 13 in a wireless communication network mode through the communication module 306; when the users are all low frequency acquisitions,the sampling frequency is 0.1Hz, and the algorithm implementation frequency of the data processing module is 0.1Hz。
The electricity consumption data cloud server 13 receives the electricity consumption load identification result and the system log obtained by the computing module, and can provide remote access service of the electricity consumption load monitoring data for the user according to different user permissions.
The work flow of the electricity consumption data processing module 12 is shown in fig. 4, and specifically as follows:
proceeding to step 500, begin;
step 505, the power utilization data processing module 12 receives the power utilization characteristic data sent by each user data acquisition module 11 and marks corresponding sampling bus position information on the data;
proceeding to step 510, the electricity consumption data processing module 12 checks whether the processing module meets the calculation amount requirement; if yes, go to step 515, if no, go to step 520,525,530 and then go to step 515;
proceed to step 540 and end.
Embodiment 2 as shown in fig. 2, a topology-configurable non-intrusive monitoring and analyzing system for building electrical equipment comprises a plurality of user electrical data acquisition modules 2201, a first electrical data processing module 2203 of the user electrical data processing modules, electrical data processing module components 2204 and an electrical data cloud server 2206;
the plurality of power consumption data acquisition modules 2201 are respectively connected with a power distribution network 2202, and acquire voltage and current data of a user in real time on a user bus of the user, wherein the power consumption data acquisition modules 2201 of the user are connected with a first power consumption data processing module 2203 in a high-speed communication mode of a local area communication network, and the high-speed communication mode is a power system carrier communication mode;
the constitution of the electricity consumption data acquisition module 2201 is the same as that of embodiment 1, and the data acquisition processor 305 of the electricity consumption data acquisition module 2201 needs to meet the requirements of high-frequency and low-frequency sampling at the same time, a TMS320F28335DSP chip is selected for high-performance data preprocessing, bus voltage and current information acquired by the voltage sensor 301 and the current sensor 302 are preprocessed into important characteristic data of active power, reactive power, effective voltage and effective current, when the frequency of high-frequency sampling is set to be 3.2kHz, that is, about 64 data are acquired in each alternating current power supply period, and instantaneous voltage and current data are acquired by preprocessing the acquired high-frequency voltage and current signals.
The first electrical data processing module 2203 is a user processing module based on a Cortex-a15 chip, can utilize the user processing module 12 in embodiment 1 to perform program upgrading improvement, is low in energy consumption and high in computational efficiency, receives the electrical characteristic data and the corresponding bus position information sent by the user electrical characteristic data acquisition module 2201 through a local area network, and distributes the electrical characteristic data and the corresponding bus position information to the electrical data processing modules connected in parallel with each other of the electrical data processing module components 2204 for processing;
the electricity consumption data processing module component 2204 comprises a plurality of electricity consumption data processing modules which are connected in parallel, and the first electricity consumption data processing module 2203 is connected with the electricity consumption data processing module component 2204 through an Ethernet 2207;
the parallel electricity consumption data processing modules of the electricity consumption data processing module components 2204 are high-performance data computing processors based on Cortex-A9 chips, carry out load identification on the electricity consumption equipment through a configured non-invasive user electricity consumption equipment identification algorithm, and then transmit information to the user data cloud server 2206 through the wireless communication network 2205; and before the data processing module identifies the electric equipment, the data processing module implements fast Fourier transform, and the implementation frequency of an equipment identification algorithm is 10 Hz.
The electric equipment identification algorithm can be configured to apply various electric equipment identification algorithms. In the embodiment, the hidden Markov model is used for carrying out algorithm part operation, and the decoding problem in the hidden Markov model is solved, and the Viterbi algorithm is selected by the load decomposition algorithm.
The steps of load decomposition mainly comprise:
1. firstly, the hidden Markov algorithm parameters need to be estimated, namely, the learning problem in the algorithm, the sequence of the observed values is obtained through known sampling, and then the optimal solution of the parameter matrix is obtained, the active power sequence is selected in the embodiment,
，
is composed oftSampling an active power value output by the electric equipment at the moment; is provided with
Is composed oftImplicit state values of a power consumer at a moment in time, describing its operating state at a certain moment in time, constituting a sequence
；
2. Is provided withNAn independent electric device, a hidden Markov chain is sharedNThe layer Markov chain is formed, the chains are not influenced mutually, and the total observed value at a certain time corresponds to the second observed valuemThe relationship of the layer Markov chain is
A set of parameters for a hidden Markov chain,
including the main parameters
Probability matrix for initial state of electric equipment
，
The state transition matrix represents the probability of the state of the powered device changing from the i state to the j state, and is characterized by the following,
and is
Is an outputThe probability matrix is the probability of the observation value corresponding to the state value;
3.
the parameter set conforms to a Gaussian probability distribution, so it iterates many times using the expectation-maximization algorithm to make a function
Convergence to obtain the optimal solution with the parameter set to maximize the expectation
And obtaining three parameter matrixes corresponding to the optimal solution
4. Knowing an optimal parameter set, solving the state set problem through the measured observation matrix, and selecting a Viterbi algorithm to decode the problem, so that the joint probability of a state sequence and an observation sequence is maximum, and the optimization function of the corresponding Viterbi algorithm is as follows:
The work flow of the first electrical data processing module 2203 is as shown in fig. 5, and specifically as follows:
proceeding to step 100, begin;
in step 105, the first electrical data processing module 2203 receives the electrical characteristic data sent by all the data acquisition modules 2201 and marks corresponding sampling bus position information on the data;
in step 110, the first electrical data processing module 2203 calculates and distributes the electrical characteristic data processing tasks of the plurality of parallel data processing modules according to the data receiving rate and the connection configuration of the plurality of parallel data processing modules of the electrical characteristic data processing module group 2204;
step 115 is performed, the first electrical data processing module 2203 checks whether the electrical characteristic data processing task of each data processing module of the electrical characteristic data processing module component 2204 meets the calculation amount requirement, if so, step 120 is performed, if not, step 125,130,135 is performed, and then step 120 is performed;
in step 120, the first electricity data processing module 2203 sends the electricity characteristic data and the corresponding bus position information to the data processing modules of the electricity characteristic data processing module components 2204 for data processing;
proceeding to step 125, the first electricity data processing module 2203 sends warning information to the electricity data cloud server 2206;
in step 130, the first electrical data processing module 2203 sends the electrical characteristic data to the data acquisition module 2201 to perform down-sampling to a sampling frequency that can meet the processing requirement,
in step 135, the first power consumption data processing module 2203 receives the power consumption characteristic data after frequency reduction and the corresponding bus position information;
in step 140, the first electrical data processing module 2203 receives the summary of the electrical load identification result of the electrical characteristic data processing module component 2204;
proceeding to step 150, the first electricity data processing module 2203 sends warning information that the data calculation task is not completed to the electricity consumption data cloud server 2206;
155, and finishing.
The work flow of the electric characteristic data processing module component 2204 is shown in the attached figure 6:
proceeding to step 200, begin;
step 205 is performed, a plurality of parallel data processing modules of the electricity utilization characteristic data processing module component 2204 receive the electricity utilization data sent by the first electricity utilization data processing module 2203 and mark the position information of the electricity utilization data;
step 210 is carried out, the power utilization characteristic data processing module component 2204 applies the configured non-invasive power utilization load monitoring algorithm to obtain a power utilization load identification result;
step 215 is performed, the power utilization characteristic data processing module component 2204 sends power utilization bus position information and a power utilization load identification result to the power utilization data cloud server 2206, and sends a summary of the power utilization load identification result to the first power utilization data processing module 2203;
proceed to step 220 and end.
The power utilization cloud server 2206 receives power utilization load identification results and system warning and log information acquired by the parallel data processing modules of the power utilization characteristic data processing module component 2204 of the user through wireless network communication, when the power utilization cloud server 2206 receives warning that data calculation of the first power utilization data processing module 2203 is not completed or the calculation capacity is insufficient, troubleshooting or configuration upgrading is carried out, the power utilization cloud server 2206 can expand functions of an upper computer thereof, and remote access service of power utilization load monitoring data is provided for the user according to different user permissions; and the upper computer selects a client developed based on an Android environment.
When the requirement of a certain family user is increased and the requirement of upgrading the sampling frequency of the user side is provided, the step of increasing the frequency of the sampling device is as follows:
b) firstly, a certain user puts forward a sampling frequency upgrading requirement to a power grid end;
c) after the grid end confirms, the power grid end sends the information of upgrading the sampling frequency of a certain user and the bus position information of the corresponding user to the first electric data processing module 2203 through accessing the electric cloud server 2206 and wireless network communication;
d) after receiving the information, the first power data processing module 2203 increases the number of high-frequency sampling users in a judgment program, and sends sampling frequency upgrading information to the power data acquisition module 2201 of the corresponding user through power system carrier communication;
e) after receiving the signal with the increased sampling frequency through the communication module 306, the power consumption data acquisition module 2201 further changes the sampling frequency of the user terminal through the data acquisition processor 305 without upgrading the hardware of the user terminal device. The configurable requirement is met, meanwhile, the secondary construction problem is avoided, the cost is reduced, and the equipment waste is reduced.
Claims (1)
1. A topology-configurable non-invasive building electrical equipment monitoring and analyzing system is characterized by comprising a user electrical data acquisition module, an electrical data processing module and an electrical data cloud server; the user electricity consumption data acquisition module is arranged on an entrance bus of a user, acquires voltage and current data of the user in real time, and is in bidirectional connection with the electricity consumption data processing module in a high-speed communication mode, and comprises a voltage sensor, a current sensor, an AD converter, a data acquisition processor, a power supply and a communication module; the power utilization data processing module is a data processor which can be connected in parallel, one or more power utilization data processing modules are configured, when one power utilization data acquisition module is configured, all installed power utilization data acquisition modules in the building are connected to the power utilization data processing module, and the power utilization data acquisition modules are configured in a low-frequency sampling mode; when the data processing capacity needs to be upgraded, a data processing module group element mode formed by connecting a plurality of power consumption data processing modules in parallel is adopted, and the data processing module group element mode is completed by changing a calculation flow configured by the data processing module, and the configured user power equipment identification algorithm can be configured and applied to various non-invasive user power equipment identification algorithms to perform algorithm part operation; the electricity utilization data processing module adopts a data processing module group component mode after the configuration is upgraded, all installed electricity utilization data acquisition modules in the building are connected to a first data processing module, then the module distributes data and corresponding processing tasks to other data processing modules of the data processing module group components for processing, the data processing modules connected in parallel of the data processing module group components are connected in an Ethernet mode, the data processing module group component mode is adopted and configured into a high-frequency sampling mode according to requirements, and the electricity utilization data processing module adopting the data processing module group component mode transmits processed electricity utilization equipment identification results to an electricity utilization data cloud server through wireless communication; the power consumption data cloud server is used for receiving the power consumption load identification result and the system log obtained by the computing module through wireless communication and providing remote access service of power consumption load monitoring data for the user according to different user permissions;
the wireless communication selects a 5G wireless communication mode.
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| CN106685703A (en) * | 2016-12-13 | 2017-05-17 | 浙江工业大学 | Data acquisition and visual monitoring intelligent system |
| CN110133393A (en) * | 2018-09-13 | 2019-08-16 | 华南理工大学 | A kind of electricity consumption monitoring system and method based on non-intruding monitor technology |
| CN110288113A (en) * | 2019-03-19 | 2019-09-27 | 浙江工业大学 | A kind of non-intrusion type load intelligent identifying system |
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| WO2015179632A1 (en) * | 2014-05-22 | 2015-11-26 | Scheffler Lee J | Methods and systems for neural and cognitive processing |
| CN104536420A (en) * | 2014-12-29 | 2015-04-22 | 芜湖市高科电子有限公司 | Building monitoring system based on the Internet of things |
| CN106153114A (en) * | 2016-08-09 | 2016-11-23 | 安徽喜悦信息科技有限公司 | A kind of remotely control intelligent building monitoring system |
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| CN110133393A (en) * | 2018-09-13 | 2019-08-16 | 华南理工大学 | A kind of electricity consumption monitoring system and method based on non-intruding monitor technology |
| CN110288113A (en) * | 2019-03-19 | 2019-09-27 | 浙江工业大学 | A kind of non-intrusion type load intelligent identifying system |
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