CN110119816A - A kind of load characteristic self-learning method suitable for non-intrusion type electric power monitoring - Google Patents
A kind of load characteristic self-learning method suitable for non-intrusion type electric power monitoring Download PDFInfo
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Abstract
The present invention provides a kind of load characteristic self-learning methods suitable for non-intrusion type electric power monitoring, comprising: obtains the load data sequence after load event occurs as input sample；Noise is added at random in the sample；The input layer number of noise reduction self-encoding encoder is determined according to data sample sequence length, generates input layer and output layer；It determines self-encoding encoder hidden layer neuron number and generates hidden layer；Set the training error limit of noise reduction self-encoding encoder；Initialize noise reduction self-encoding encoder interlayer mapping parameters；According to the mapping parameters sequence of calculation for the reconstructed error of list entries；Judge that reconstructed error extracts abstract characteristics of the hidden layer nodal value as load event if being less than training error limit, updates input layer and hidden layer using gradient descent algorithm if more than training error limit, the mapping parameters between hidden layer and output layer.Present invention realization has carried out global explanation to load event data and curves to realize the study of abstract characteristics to the compressed sensing of data sequence.
Description
Technical field
The invention belongs to technical field of power systems, be related to a kind of load characteristic suitable for non-intrusion type electric power monitoring from
Learning method.
Background technique
Non-intrusion type load monitoring (NILM) technology includes four big basic contents: 1) data and pretreatment acquire；2) event
Detection；3) feature extraction；4) load identifies.Its principle for collectively forming non-intrusion type load monitor system is as shown in Figure 1.System
At work, data acquisition acquires first with preprocessing module and calculates total load data (active power, reactive power, electricity
Pressure, electric current etc.), pass to event checking module；Event checking module can detect which moment load event has occurred at (bears
Lotus investment or excision)；Characteristic extracting module is according to event detection as a result, extracting load event feature after load event generation
(including steady state characteristic and transient characteristic)；Final load identification module is known according to the load event feature extracted by classification
Other algorithm carries out Classification and Identification to load event.Wherein load characteristic extraction module plays an important role in NILM, only mentions
Correct, effective load characteristic is got, further load could be carried out by load classification recognizer using these features
Identification.
Research currently for load characteristic focuses primarily upon the stable state after load switching event occurs and transient state physics is special
Sign, comprising: active, active and reactive, electric current, voltage and its residual quantity, electric current-voltage trace and higher hamonic wave feature etc..This
The characteristic quantity extracted a bit all has clear physical significance, needs artificially to go to be set when carrying out feature extraction, then lead to
It crosses and the collected electricity data of data acquisition module is calculated.When calculating these features, often it is
Based on local data's point, by taking characteristic quantity power peak as an example, it has only used a data point after load event occurs, and
This feature of active residual quantity has also only used two data points before and after certain moment.The data point of part is for corresponding load thing
Part data and curves have centainly explanatory, can reflect the substantially feature of curve, but still lack bent to load event data
The overall situation of line is explanatory.
Summary of the invention
To solve the above problems, the present invention proposes a kind of load characteristic self study side suitable for non-intrusion type electric power monitoring
Method goes study that can reflect without being manually set which specific physical features is characteristic extracting module need to extract, but independently
The abstract characteristics of load event essential characteristic, the data source of feature learning is the load thing of switching event module calibration in this method
Part moment corresponding load data sequence.
In order to achieve the above object, the invention provides the following technical scheme:
A kind of load characteristic self-learning method suitable for non-intrusion type electric power monitoring, includes the following steps:
Step 1: obtaining the load data sequence after load event occurs as input sample；
Step 2: noise is added at random in input sample；
Step 3: determining the input layer number of noise reduction self-encoding encoder according to the length of data sample sequence, and generate
Input layer and output layer；
Step 4: determining noise reduction self-encoding encoder hidden layer neuron number, and generate hidden layer；
Step 5: the training error limit of setting noise reduction self-encoding encoder；
Step 6: initialization noise reduction self-encoding encoder input layer and hidden layer, the mapping parameters of hidden layer and output interlayer, ginseng
Number includes weight and biasing；
Step 7: calculating output sequence for list entries according to the mapping parameters between input data sequence and each layer
Reconstructed error；
Step 8: the training error limit for whether being less than setting to reconstructed error judges, if reconstructed error is less than training
The limits of error then goes to step ten, goes to step nine if reconstructed error is greater than training error limit；
Step 9: input layer and hidden layer are updated using gradient descent algorithm, the mapping ginseng between hidden layer and output layer
Number；
Step 10: extracting abstract characteristics of the hidden layer nodal value as load event.
Further, output layer is identical as input layer structure in the step 3, the neuron number of output layer and input
Layer is identical.
Further, in the step 4, hidden layer neuron number is less than input layer and the specified neuron of output layer
Number.
Further, in the step 6, the mapping function between input layer and hidden layer is defined as:
Y=fθ(X')=S (WX'+b) (1)
S (X) is the activation primitive of noise reduction self-encoding encoder in formula (1), and θ is coding parameter, is made of weight W and biasing b；
Mapping function between hidden layer and output layer is defined as:
Z=fθ'(Y)=S (W'Y+b') (2)
θ ' is decoding parametric in formula (2), is made of weight W' and biasing b'.
Further, in the step 7, the calculation formula of the reconstructed error are as follows:
Wherein, l is the input layer number of self-encoding encoder.
Compared with prior art, the invention has the advantages that and the utility model has the advantages that
The present invention utilizes noise reduction self-encoding encoder model, carries out coding further decoding to the load data sequence of input, to realize
To the compressed sensing of data sequence, to realize the study of abstract characteristics.The data source of feature self study is in the method for the present invention
Load event data and curves are realized the overall situation by the load event moment corresponding load data sequence of switching event module calibration
It explains.
Detailed description of the invention
Fig. 1 is the basic schematic diagram for being non-intrusion type load monitor system.
Fig. 2 is self-encoding encoder structure chart.
Fig. 3 is the flow chart of the load characteristic self-learning method suitable for non-intrusion type electric power monitoring.
Specific embodiment
Technical solution provided by the invention is described in detail below with reference to specific embodiment, it should be understood that following specific
Embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
The present invention utilizes noise reduction self-encoding encoder model realization, and self-encoding encoder is a kind of special neural network, i.e., output with
Input identical, model passes through training adjusting parameter, so that input restores former as much as possible by way of feature coding further decoding
The input signal come, these are the abstract characteristics for indicating input signal through the transformed numerical value of feature coding, general self-editing
Code device structure is as shown in Figure 2.
Using the different corresponding time serieses of load switching event as the input of self-encoding encoder, with a certain specific load
For switching event sample, it is assumed that corresponding sample size is k, then sample set is x={ x(1),x(2)...x(k), any one
Sample x(i)It is the time series that length is l, i.e. x(i)It is l dimensional vector, the input layer number for designing self-encoding encoder is l,
The neuron number for designing intermediate hidden layers is m, since self-encoding encoder uses the reconstruct of back-propagation algorithm optimization input data
Error, even if target exports y(i)→x(i), force neural network to remove the compression expression of study input data, i.e., must be tieed up from m
Hidden neuron activity vector α(i)∈RmIn reconstruct x(i).If the arbitrary sample in sample set is completely random, than
Such as the x of each input(i)It is all the independent identically distributed Gaussian random variable completely irrelevant with other input variables, this study
Process would become hard to carry out, but if the sample data of input all implies some specific structures, then this algorithm can be sent out
Correlation between existing input sample data.After network training, each input sample x(i)Corresponding hidden layer activity
Vector α(i)Abstract characteristics vector after being equivalent to dimensionality reduction (study).Some random noises are added in the input of self-encoding encoder, this
When self-encoding encoder will obtain the ability that abstract characteristics are extracted from the input data being disturbed, the at this time robustness of self-encoding encoder
Enhancing.
A kind of load characteristic self-learning method suitable for non-intrusion type electric power monitoring proposed by the present invention, process is as schemed
Shown in 3, comprising the following steps:
Step 1: obtaining the load data sequence after load event occurs as input sample:
With active power data instance, the initial time of load event and corresponding has been demarcated by incident Detection Algorithm
Stable state and transient process, using from initial time to the active power data sequence of steady-state process as the input sample of self-encoding encoder
This.
Step 2: noise is added at random in input sample:
The step for purpose be that input sample X is made to become noisy sample X ', to simulate the disturbance pair that may occur at random
The influence of self-encoding encoder feature learning ability, if self-encoding encoder can have very list entries in the presence of noise
Small reconstructed error, then it is assumed that the robustness of its feature learning ability is enhanced.For the self-encoding encoder of noise is artificially added
Referred to as noise reduction self-encoding encoder.
Step 3: determining the input layer number of noise reduction self-encoding encoder according to the length of data sample sequence, and generate
Input layer and output layer:
If the sequence length of the input sample X ' after adding and making an uproar is l, the neuron number for setting input layer is also l, i.e.,
There is the relationship mapped one by one in input sample sequence and input layer.According to the principle of self-encoding encoder feature learning, need to use up
It may reduce to the reconstructed error of input data sequence, therefore output layer should keep structure identical with input layer, i.e. output layer
Neuron number is identical as input layer.
Step 4: determining the hidden layer neuron number of noise reduction self-encoding encoder and generating hidden layer:
Have determined that input layer and the specified neuron number of output layer are l in step 3, for hidden layer neuron
The selection of number k should follow the principle of k < l, this is to meet the pumping that high-dimensional input vector is compressed into more low dimensional
As feature vector, to realize that the compression to data characteristics is extracted.
Step 5: the training error limit of setting noise reduction self-encoding encoder.
Training error limit may be considered the upper limit of the received reconstructed error of energy.
Step 6: initialization noise reduction self-encoding encoder input layer and hidden layer, the mapping parameters of hidden layer and output interlayer, ginseng
Number includes weight and biasing.
Mapping function between input layer and hidden layer can be with is defined as:
Y=fθ(X')=S (WX'+b) (1)
S (X) is the activation primitive of noise reduction self-encoding encoder in formula (1), and θ is coding parameter, is made of weight W and biasing b；
Mapping function between hidden layer and output layer can be with is defined as:
Z=fθ'(Y)=S (W'Y+b') (2)
θ ' is decoding parametric in formula (2), is made of weight W' and biasing b'；
Step 7: calculating output sequence for list entries according to the mapping parameters between input data sequence and each layer
Reconstructed error.
The calculation formula of reconstructed error are as follows:
Pay attention in formula (3), output sequence Z is calculated on the basis of the list entries X' for the processing that has already passed through plus make an uproar
Arrive, but participate in reconstructed error calculate be still original list entries X.
Step 8: the training error limit for whether being less than setting to reconstructed error judges, if reconstructed error is less than training
The limits of error then thinks that noise reduction self-encoding encoder model has learnt to have good explanatory abstract characteristics to input data, turns step
Rapid ten.
If reconstructed error is limited greater than training error, then it is assumed that noise reduction self-encoding encoder model not yet arrive to input data by study
With good explanatory abstract characteristics, nine are gone to step.
Step 9: input layer and hidden layer are updated using gradient descent algorithm, the mapping ginseng between hidden layer and output layer
Number.
Step 10 extracts abstract characteristics of the hidden layer nodal value as load event.
The technical means disclosed in the embodiments of the present invention is not limited only to technological means disclosed in above embodiment, further includes
Technical solution consisting of any combination of the above technical features.It should be pointed out that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (5)
1. a kind of load characteristic self-learning method suitable for non-intrusion type electric power monitoring, which comprises the steps of:
Step 1: obtaining the load data sequence after load event occurs as input sample；
Step 2: noise is added at random in input sample；
Step 3: determining the input layer number of noise reduction self-encoding encoder according to the length of data sample sequence, and generate input
Layer and output layer；
Step 4: determining noise reduction self-encoding encoder hidden layer neuron number, and generate hidden layer；
Step 5: the training error limit of setting noise reduction self-encoding encoder；
Step 6: initialization noise reduction self-encoding encoder input layer and hidden layer, the mapping parameters of hidden layer and output interlayer, parameter packet
Include weight and biasing；
Step 7: calculating reconstruct of the output sequence for list entries according to the mapping parameters between input data sequence and each layer
Error；
Step 8: the training error limit for whether being less than setting to reconstructed error judges, if reconstructed error is less than training error
Limit then goes to step ten, goes to step nine if reconstructed error is greater than training error limit；
Step 9: input layer and hidden layer are updated using gradient descent algorithm, the mapping parameters between hidden layer and output layer；
Step 10: extracting abstract characteristics of the hidden layer nodal value as load event.
2. the load characteristic self-learning method according to claim 1 suitable for non-intrusion type electric power monitoring, feature exist
In: output layer is identical as input layer structure in the step 3, and the neuron number of output layer is identical as input layer.
3. the load characteristic self-learning method according to claim 1 suitable for non-intrusion type electric power monitoring, feature exist
In: in the step 4, hidden layer neuron number is less than input layer and the specified neuron number of output layer.
4. the load characteristic self-learning method according to claim 1 suitable for non-intrusion type electric power monitoring, feature exist
In: the mapping function in the step 6, between input layer and hidden layer is defined as:
Y=fθ(X')=S (WX'+b) (1)
S (X) is the activation primitive of noise reduction self-encoding encoder in formula (1), and θ is coding parameter, is made of weight W and biasing b；
Mapping function between hidden layer and output layer is defined as:
Z=fθ'(Y)=S (W'Y+b') (2)
θ ' is decoding parametric in formula (2), is made of weight W' and biasing b'.
5. the load characteristic self-learning method according to claim 1 suitable for non-intrusion type electric power monitoring, feature exist
In: in the step 7, the calculation formula of the reconstructed error are as follows:
Wherein, l is the input layer number of self-encoding encoder.
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| CN114910742A (en) * | 2022-05-05 | 2022-08-16 | 湖南腾河智慧能源科技有限公司 | Single-phase fault grounding monitoring method and system, electronic equipment and storage medium |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325234A (en) * | 2019-12-29 | 2020-06-23 | 杭州拓深科技有限公司 | Method for screening key features in non-invasive load identification |
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| CN114069853B (en) * | 2021-11-10 | 2024-04-02 | 天津大学 | Multi-energy charge data online compression and reconstruction method based on segmented symbol representation |
| CN114910742A (en) * | 2022-05-05 | 2022-08-16 | 湖南腾河智慧能源科技有限公司 | Single-phase fault grounding monitoring method and system, electronic equipment and storage medium |
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| CN115201615A (en) * | 2022-09-15 | 2022-10-18 | 之江实验室 | Non-invasive load monitoring method and device based on physical constraint neural network |
| CN115201615B (en) * | 2022-09-15 | 2022-12-20 | 之江实验室 | Non-invasive load monitoring method and device based on physical constraint neural network |
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