CN110277834B - Power grid response building internal load monitoring method and system and storage medium - Google Patents

Power grid response building internal load monitoring method and system and storage medium Download PDF

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Publication number
CN110277834B
CN110277834B CN201910561463.8A CN201910561463A CN110277834B CN 110277834 B CN110277834 B CN 110277834B CN 201910561463 A CN201910561463 A CN 201910561463A CN 110277834 B CN110277834 B CN 110277834B
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load
data
power
time
monitoring
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CN110277834A (en
Inventor
汤延祺
张卫国
郑红娟
郑爱霞
徐石明
杨斌
李波
宋杰
陈良亮
沈宏伟
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
NARI Group Corp
NARI Nanjing Control System Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
NARI Group Corp
NARI Nanjing Control System Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00Circuit 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
    • H02J13/0006Circuit 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 for single frequency AC networks
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02BCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225Demand response systems, e.g. load shedding, peak shaving
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20End-user application control systems
    • Y04S20/222Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention discloses a method, a system and a storage medium for monitoring the internal load of a power grid response building, wherein the scheme is that a load monitor is configured at a breaker to measure the power consumption information of a group of electric loads, and a signal processing technology is used for decomposing the operation plan of a single load; connecting load monitors at each position to a cloud end, transmitting small data files containing transient modes and downsampling power data of each monitored circuit to the cloud end, realizing unsupervised load fault mode learning and building system performance prediction, and classifying various multi-scale edges in a transient load profile by utilizing the RipShetz regularity in the learning process; and finally, sending the appropriate label back to the local platform and carrying out load classification. The monitoring scheme provided by the invention improves the potential of the power monitoring equipment and reduces the workload required for deploying and debugging the monitoring solution.

Description

Power grid response building internal load monitoring method and system and storage medium
Technical Field
The invention relates to the technical field of electric power system elastic load monitoring, in particular to a method and a system for monitoring internal load of a power grid response building and a storage medium.
Background art:
at present, the basic concept of demand side management has been widely accepted and agreed, but a load side regulation object mainly aims at a single air conditioning load or an interruptible industrial load, further enriches adjustable load resource types, researches an electric power elastic load quick response regulation system, and improves the state monitoring capability of an elastic load, which is the trend of gradual development of demand response services.
Aiming at an elastic load data resource control scheme, a general service system of a distributed energy management system is researched and provided, reference is provided for application layer service design of a demand response system, but the load types in a building are more, the power consumption of each load is smaller, and the efficiency and the potential of independent research on each load are lower.
Disclosure of Invention
The invention aims to provide a method, a system and a storage medium for monitoring the internal load of a power grid response building, so as to solve the defects caused in the prior art.
The scheme is realized by the following technical scheme:
in a first aspect: there is provided a method of monitoring the internal load of a power grid responsive building, the method comprising the steps of:
configuring a load monitor at the circuit breaker to measure power consumption information of the load and connecting the load monitor to a cloud;
transmitting a small data file containing a transient mode and sampling power data under a monitored circuit to a cloud end;
carrying out multi-time scale edge classification on the small data files and the sampling power data;
the cloud end processes data obtained by classifying the multi-time scale edges to obtain load indication characteristics;
and sending the load indication characteristics back to the local platform for load classification to obtain an accurate load power utilization state monitoring result.
With reference to the first aspect, further, the method for measuring the load electricity information by the load monitor includes the following steps:
each load monitor measuring a total current and a line voltage flowing to a set of electrical loads;
calculating a time-varying estimation value of the frequency content of the current of the measured line according to the total current and the line voltage, wherein at the m-th harmonic, the time-varying estimation value is as follows:
sin (m ω T) and cos (m ω T) represent the fundamental terms of the Fourier series, i (τ) is the expression of the original non-sinusoidal periodic current, τ is an integral variable, m represents a multiple of the frequency, ω is the angular frequency, T represents the time window length, T represents the time, amAnd bmRepresenting spectral envelope coefficients.
With reference to the first aspect, further, the method for transmitting the small data file and the sampled power data includes the following steps:
measuring the current on the individual circuit breakers;
creating a small file containing transient data collected around each event and calculating average power data once per second;
and periodically transmitting the transient data small file and the average power data to a remote cloud platform.
With reference to the first aspect, further, the method for multi-time scale edge classification includes the following steps:
describing these edges using the concept of lipschitz regularity, we consider the power absorbed on a given circuit as the signal f (t), if f has a singularity at t ═ v, meaning that f is not differentiable at t ═ v, and the lipschitz index feature a characterizes this singular behavior;
this concept is based on the idea that f approximates a Taylor polynomial over the interval [ v-h, v + h ]:
the absolute values of the approximation errors are as follows:
f (t) represents the power absorbed by the circuit at time t, and Pv represents the power absorbed by the circuit at time f (t) in the interval [ v-h, v + h]An approximate Taylor polynomial of (i), t represents time, m represents the derivative order of f (t), k represents the kth derivative, v represents time v, h is a minimal amount of time to represent the neighborhood of time v, fmDenotes the m-order derivative of f, u denotes [ v-h, v + h]A time variable within a range;
the above equation shows that when t tends towards v, the m-order differential of f in the v neighborhood constitutes an e (t) errorAn upper limit. If f is in the v neighborhoodLess than continuous, then pv(t) is the Taylor expansion at v; a bounded but discontinuous function at v has 0 ≦ α<1 and the value of alpha can be used to characterize the change in different singularities, such as pulsing, stepping, smooth stepping, etc.
With reference to the first aspect, further, the load indication characteristic includes a time pattern of a load transient edge sequence, a cycle number, a total energy absorbed, start and end times of a cycle, and an average steady state power at runtime.
With reference to the first aspect, further, the method for load classification includes the following steps:
firstly, positioning sudden change of a real or reactive power signal by an edge detection algorithm;
the classifier then detects the data (transient and steady state) around each edge to identify a particular on/off event;
the steady state data comprises information of active power, reactive power and other spectral envelope differences recorded before and after an event, and a single load often has repeatable steady state characteristics;
transient shape information also aids in load identification, with most loads observed in the field having repeatable transient profiles;
when an event occurs, the classifier fits the resulting transient to each example (i.e., the previously defined transient shape) using a least squares method and determines the appropriate example using the best fit metric (i.e., the 2-norm of the residual).
In a second aspect, there is provided a system for monitoring electrical network responsive building internal elastic load, comprising:
an acquisition module: the system is used for collecting the power utilization information of the load in real time;
a data transmission module: the system comprises a cloud end and a local platform, wherein the cloud end is used for transmitting data to the local platform;
a data processing module: the method is used for calculating and classifying the data.
In a third aspect, a system for monitoring electrical network responsive building internal elastic load is provided, comprising: a memory and a processor;
the memory is to store instructions;
the processor is configured to operate in accordance with the instructions-to perform the steps of the method according to any of the first aspects.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the first aspect.
The invention has the advantages that: the power grid response building internal load monitoring method is characterized in that a load monitor is configured at a breaker to measure power utilization information of a group of electric loads, and a signal processing technology is used for decomposing an operation plan of a single load; connecting load monitors at each position to a cloud end, transmitting small data files containing transient modes and downsampling power data of each monitored circuit to the cloud end, realizing unsupervised load fault mode learning and building system performance prediction, and classifying various multi-scale edges in a transient load profile by utilizing the RipShetz regularity in the learning process; and finally, sending the appropriate label back to the local platform and carrying out load classification.
Compared with the existing general service system of the distributed energy management system, the monitoring scheme provided by the invention can collect the electricity utilization information of all loads in the building, and further analyze the elastic load characteristics under more sufficient data information and monitor the electricity utilization state of the elastic load.
Drawings
FIG. 1 is a schematic flow chart of a method for monitoring the internal load of a power grid response building according to the present invention;
FIG. 2 is a schematic diagram of an unsupervised training implementation of the present invention in which a load monitor is connected to a cloud;
fig. 3 is a schematic view of load state monitoring in the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in fig. 1 to 3, a method for monitoring the internal load of a power grid response building, as shown in fig. 1, comprises the following steps:
step one, configuring a load monitor at each breaker to detect load operation on a single circuit, connecting the load monitors at various places to a cloud end, creating a distributed load monitor network, and training by using the aggregated data of the distributed load monitor network to obtain a useful load mode, as shown in fig. 2;
step two, each non-intrusive load monitor measures the total current and line voltage flowing to a group of electric loads, a preprocessor based on software calculates the time-varying estimated value of the frequency content of the current of the measured line, and at m-th harmonic, the time-varying estimated value is as follows:
sin (m ω T) and cos (m ω T) represent the fundamental terms of the Fourier series, i (τ) is the expression of the original non-sinusoidal periodic current, τ is an integral variable, m represents a multiple of the frequency, ω is the angular frequency, T represents the time window length, T represents the time, amAnd bmRepresenting spectral envelope coefficients.
This is a Fourier series analysis equation calculated over a moving window of length t, coefficient am(t) and bm(t) contains time-local information about the frequency content of i (t). Assuming that the fundamental terms sin (m ω t) and cos (m ω t) are synchronous with the line voltage, the spectral envelope coefficients have a useful physical explanation of the active, reactive and harmonic power. The preprocessor calculates spectral envelope curves of 120HZ samples and uses the envelope curves to detect switching events on various circuits;
considering that the main loads such as air conditioners and electric appliances have their own dedicated circuits, the event detection threshold is easily adjusted so that all relevant loads are detected. Events are located using a simple FIR filter, since the system does not initially know what load is installed on each breaker, and therefore cannot classify any event;
step three, a small embedded computer locally measures the current on several individual circuit breakers, a local processing station creates a small file containing transient data collected around each event and calculates the average power data once per second, the data and small transient event files are periodically transmitted to a remote, cloud-based platform. The cloud unpacks the files and performs feature extraction to identify the load on each circuit by a known pattern.
When checking a load profile, there are not only significant and useful patterns on the daily scale, but also useful transient patterns for the same load occurring on the sub-second, second and minute scales, the cloud platform can deploy appropriate computing resources, training using a generic pattern on these different time scales;
and step four, classifying various multi-time scale edges in the transient load profile as a key element in the training process. To describe these edges, we have adopted the concept of Liphoz regularization. We consider the power absorbed on a given circuit as signal f (t), if f has a singularity at t ═ v, meaning that f is not differentiable at t ═ v, and the lipschitz index signature a characterizes this singular behavior. This concept is based on the idea that f approximates a Taylor polynomial over the interval [ v-h, v + h ]:
the absolute values of the approximation errors are as follows:
f (t) represents the power absorbed by the circuit at time t, and Pv represents the power absorbed by the circuit at time f (t) in the interval [ v-h, v + h]An approximate Taylor polynomial of (i), t represents time, m represents the derivative order of f (t), k represents the kth derivative, v represents time v, h is a minimal amount of time to represent the neighborhood of time v, fmDenotes the m-order derivative of f, u denotes [ v-h, v + h]Time variable within a range.
The above equation shows that when t tends towards v, the m-order differential of f in the v neighborhood constitutes the upper bound of the e (t) error. If f is in the v neighborhoodLess than continuous, then pv(t) is the Taylor expansion at v. A bounded but discontinuous function at v has 0 ≦ α<1 and the value of alpha can be used to characterize the change in different singularities, such as pulsing, stepping, smooth stepping, etc.
The above equation shows that when t tends towards v, the m-order differential of f in the v neighborhood constitutes the upper bound of the e (t) error. If f is in the v neighborhoodLess than continuous, then pv(t) is the Taylor expansion at v. A bounded but discontinuous function at v has 0 ≦ α<1 and the value of alpha can be used to characterize the change in different singularities, such as pulsing, stepping, smooth stepping, etc.
Since wavelet amplitude cross-scale attenuation is related to the rischz law of the signal, and the power signal is a discrete time series, discrete wavelet transforms can be used to describe various edges of the transient power signal.
And fifthly, obtaining relevant load indication characteristics through cloud processing, wherein the relevant load indication characteristics comprise a time mode of a transient edge sequence, the number of load circulation times in one day, the starting time and the ending time of the load circulation, the average steady-state power during operation, the total energy absorbed in one day and the like. These features are combined in a multi-dimensional feature vector machine and sent back to the local platform for classification and performance tracking. Once the process begins, the local unit continues to communicate with the cloud system to help monitor for abnormal behavior. Fig. 3 shows the relationship between hvac energy and outside air temperature over two different time periods for a given site. In general, the heating, ventilating and air conditioning energy and the temperature are in a piecewise linear relationship, wherein the slope is negative at low temperature and positive and slow at high temperature. The data on the left side of fig. 3, which has no strong correlation with temperature, shows that the compressor in the hvac heat pump system has failed, while the graph on the right side shows the behavior after the repair.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.

Claims (8)

1. A method for monitoring the internal load of a power grid responsive building, the method comprising the steps of:
configuring a load monitor at the circuit breaker to measure power consumption information of the load and connecting the load monitor to a cloud;
transmitting a small data file containing a transient mode and sampling power data under a monitored circuit to a cloud end;
carrying out multi-time scale edge classification on the small data files and the sampling power data;
the cloud end processes data obtained by classifying the multi-time scale edges to obtain load indication characteristics;
sending the load indication characteristics back to the local platform for load classification to obtain an accurate load power consumption state monitoring result;
the method for multi-time scale edge classification comprises the following steps:
checking a mode of the time-varying estimation value;
edges describing the small data file and the sampled power data;
considering the power absorbed on a given circuit as signal f (t), if f has a singularity at t ═ v, it means that f is not differentiable at t ═ v;
based on the view that f approximates a Taylor polynomial over the interval [ v-h, v + h ]:
the absolute values of the approximation errors are as follows:
pv(t) denotes f (t) in the interval [ v-h, v + h]An approximate Taylor polynomial of (i), t represents time, m represents the derivative order of f (t), k represents the kth derivative, v represents time v, h is a minimal amount of time to represent the neighborhood of time v, fmDenotes the m-order derivative of f, u denotes [ v-h, v + h]A time variable within a range;
the above equation shows that when t tends towards v, the m-order differential of f in the v neighborhood constitutes the upper bound of the e (t) error if f in the v neighborhood isLess than continuous, then pv(t) is the Taylor expansion at v; a bounded but discontinuous function at v has 0 ≦ α<1, and the value of a can be used to characterize the change in different singularities, including pulsing, stepping, and smooth stepping.
2. A method for monitoring the internal load of a grid responsive building as claimed in claim 1, wherein: the method for measuring the load electricity utilization information by the load monitor comprises the following steps:
each load monitor measuring the total current and line voltage flowing to a set of electrical loads;
calculating a time-varying estimation value of the frequency content of the current of the measured line according to the total current and the line voltage, wherein at the m-th harmonic, the time-varying estimation value is as follows:
sin (m ω T) and cos (m ω T) represent the fundamental terms of the Fourier series, i (τ) is the expression of the original non-sinusoidal periodic current, τ is an integral variable, m represents a multiple of the frequency, ω is the angular frequency, T represents the time window length, T represents the time, amAnd bmRepresenting spectral envelope coefficients.
3. A method for monitoring the internal load of a grid responsive building as claimed in claim 1, wherein: the transmission method of the small data file and the sampling power data comprises the following steps:
measuring the current on the individual circuit breakers;
creating a small data file containing the collected data around each event and calculating the average power data once per second;
and periodically transmitting the small data files and the average power data to the cloud.
4. A method for monitoring the internal load of a grid responsive building as claimed in claim 1, wherein: the load indicating features include time patterns of load transient edge sequences, cycle times, total energy absorbed, start and end times of cycles, and average steady state power at runtime.
5. A method for monitoring the internal load of a grid responsive building as claimed in claim 1, wherein: the method for classifying the load comprises the following steps:
firstly, positioning sudden change of a real or reactive power signal;
subsequently detecting data around each edge to identify a particular on/off event, the data including transient data and steady state data;
the steady state data comprises information of active power, reactive power and other spectrum envelope differences recorded before and after an event, and a single load often has repeatable steady state characteristics;
transient data also facilitates load identification, with most loads observed in the field having repeatable transient profiles;
when an event occurs, the resulting transients are fitted to each example using a least squares method and the best fit metric is used to determine the appropriate example.
6. A grid responsive building internal elastic load monitoring system, wherein a grid responsive building internal load monitoring method according to claim 1 is adopted, and the method comprises the following steps:
an acquisition module: the system is used for collecting the power utilization information of the load in real time;
a data transmission module: the system comprises a cloud end and a local platform, wherein the cloud end is used for transmitting data to the local platform;
a data processing module: the method is used for calculating and classifying the data.
7. A grid responsive building internal elastic load monitoring system, comprising: a memory and a processor;
the memory is to store instructions;
the processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 5.
CN201910561463.8A 2019-06-26 2019-06-26 Power grid response building internal load monitoring method and system and storage medium Active CN110277834B (en)

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