CN107966600A - A kind of electricity anti-theft system and its electricity anti-theft method based on deep learning algorithm - Google Patents

A kind of electricity anti-theft system and its electricity anti-theft method based on deep learning algorithm Download PDF

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CN107966600A
CN107966600A CN201710919165.2A CN201710919165A CN107966600A CN 107966600 A CN107966600 A CN 107966600A CN 201710919165 A CN201710919165 A CN 201710919165A CN 107966600 A CN107966600 A CN 107966600A
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CN107966600B (en
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何行
夏水斌
何欢
张芹
谢玮
冉艳春
余鹤
董重重
孙秉宇
田猛
王汪兵
王先培
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State Grid Corp of China SGCC
Wuhan University WHU
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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State Grid Corp of China SGCC
Wuhan University WHU
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

A kind of electricity anti-theft system and its electricity anti-theft method based on deep learning algorithm, including the doubtful stealing line module of online recognition and off-line training deep learning mixed-media network modules mixed-media, the doubtful stealing line module of online recognition is used to identify doubtful stealing user, and off-line training deep learning mixed-media network modules mixed-media is used to determine each network parameter of deep learning network in the doubtful stealing line module of online recognition；The data of existing power information acquisition system can be efficiently used, improve the utilization rate of data；It is of low cost, it is only necessary to increase the anti-electricity-theft algoritic module based on deep learning in existing power information acquisition system, it is not necessary to increase other hardware；Identify that suspectable stealing user accuracy is higher.

Description

A kind of electricity anti-theft system and its electricity anti-theft method based on deep learning algorithm
Technical field
The present invention relates to a kind of electricity anti-theft method, electricity anti-theft system specifically based on deep learning algorithm and its anti- Stealing electricity method.
Background technology
Now, electric energy is highly important clean energy resource.But because people pursue economic interests, stealing electricity phenomenon starts It is a large amount of to grow, all cause interests loss to each area.Also, the casualties produced because of the electricity stealing of electricity filching person Also emerge in an endless stream with equipment damage, in fact, the indirect loss caused by stealing causes accident is then more huge.Thus may be used See, the significance of anti-electricity-theft work.Constantly improved with anti-theft electricity technology and the improvement of measure, electricity filching means, it is thief-proof Electric problem is, it is necessary to which the study of more personnel, and management profrssion personnel constantly go to study, crack.
Existing anti-electricity-theft means achieve certain effect really, such as by carrying out concentrated setting to electric energy meter and being tamping The measure in jail is locked, avoids user from will contacting electric energy meter；The method for packing for uniformly taking sealing can not recover after depressing, and all Seal up number and carry out record management together；The electric energy meter for forbidding reversing function using having, electric power supply plant are agreed to require producer's design during goods Kilowatt-hour meter equipped with inverse-stopping functions, prevents negative-phase sequence curent stealing.But with the development of science and technology the style of high-tech stealing is also more next More, existing anti-theft electricity technology can not take precautions against miscellaneous electricity stealing well.As intelligent grid key skill One of art, power information acquisition system is by the electric energy meter of user terminal, host computer data management system and for its communication Network forms, and has taken into account the basic function of information gathering and load monitoring.Its integration application is the inexorable trend of intelligent grid, To meet the interactive platform for having built a higher of intelligent distribution network.Implement advanced power information acquisition system, pass through Its electricity consumption data gathered analysis judges that stealing user is one of current research hotspot.However, it is since power information gathers System will gather the electricity consumption data of user every a set time point, by taking the SCADA in power information acquisition system as an example, often An electricity consumption data is gathered every 3~4s, 10000 telemetry stations will produce the data of 1.03TB every year.Therefore, power information is utilized Acquisition system identification stealing user plane faces the predicament of mass data.
Deep learning is an emerging field in machine learning, namely a popular direction in artificial intelligence. Its concept is based on neutral net, is increasing for hidden layer with the point that traditional artificial neural network distinguishes.So One can train the data of magnanimity, learn useful feature, be finally reached the classification and prediction of pinpoint accuracy.Actually It is the Feature Mapping by rearranging combination low layer, different Attribute class is represented after abstract high-rise Feature Mapping is formed Not.Compared with the method for artificial rule construct feature, using big data come learning characteristic, the abundant inherence of data can be more portrayed Information.
Compared to other methods, deep learning has very strong data learning ability and generalization ability, therefore can be used for real Now intelligent is anti-electricity-theft, more efficiently solves the problems, such as stealing.
The content of the invention
The purpose of the present invention is to the stealing problem in life to provide a kind of anti-electricity-theft side based on deep learning algorithm Method.
The technical solution adopted by the present invention is：A kind of electricity anti-theft system based on deep learning algorithm, it is characterised in that：Bag Include the doubtful stealing line module of online recognition and off-line training deep learning mixed-media network modules mixed-media, the doubtful stealing line module of online recognition For identifying doubtful stealing user, off-line training deep learning mixed-media network modules mixed-media is used to determine the doubtful stealing line module of online recognition The middle each network parameter of deep learning network.
Further, the electricity anti-theft method of the electricity anti-theft system based on deep learning algorithm, it is characterised in that：Step Suddenly include：
A, using power information acquisition system, user power utilization data, including active power W are obtainedIt is active, reactive power WIt is idle、 Valley power WValley, crest segment power WPeak value, level values power WLevel valuesAnd the data such as power；
Active power：WIt is active=(MThis month, is active-MLast month is active) m, m represent multiplying power, M represents meter reading value；
Reactive power：WIt is idle=(MThis month, is idle-MLast month is idle)·m；
Valley power：WValley=(MThis month valley-MLast month valley)·m；
Peak power：WPeak value=(MThis month peak value-MLast month peak value)·m；
Level values power：WLevel values=(MThis month level values-MLast month level values)·m；
Power factor (PF)：
B, it is necessary to active power, reactive power, flat section power, paddy section power, peak before using deep learning algorithm The normalization of the section electricity consumption data such as power and power, its value range is turned to [- 1,1], and normalization formula is：
Wherein, xmaxFor the maximum in this index value, xminFor the minimum value in this index value, xiIt is defeated for i-th Enter data, xmidRepresent median,Represent xiNumerical value after normalization；
C, according to normalized active power, reactive power, flat section power, paddy section power, crest segment power and power etc. Electricity consumption data, structure electricity consumption data matrix N × 6, wherein, N represents electricity consumption data sampling number, and 6 represent mentioned above active The quantity of the parameters such as power, reactive power, flat section power, paddy section power, crest segment power and power, using deep learning algorithm In convolutional neural networks algorithm, according to softmax classifier calculated user power utilization suspicion coefficients, in softmax graders, Softmax values calculate as follows：
Wherein, SiRepresent the softmax values of i-th of data, viRepresent i-th of data of input softmax graders, j tables Show data number, the computation model of stealing suspicion coefficient y is as follows：
Y=H (x1, x2, x3, x4, x5, x6) (4)
Wherein, H (x) represents deep learning computing
If stealing suspicion coefficient was more than or equal to 0.85 in d, this month, there is larger stealing suspicion the moon, and send warning information； If stealing suspicion coefficient was less than 0.85 in this month, it is determined that the moon is without electricity stealing.
Beneficial effects of the present invention and feature are：1. the data of existing power information acquisition system can be efficiently used, carry The utilization rate of high data；It is 2. of low cost, it is only necessary to which that increase is anti-based on deep learning in existing power information acquisition system Stealing algoritic module, it is not necessary to increase other hardware；3. identify that suspectable stealing user accuracy is higher.
Brief description of the drawings
Fig. 1 is the overview flow chart of the method for the invention；
Figure label represents respectively：The doubtful stealing line module of 1- online recognitions, 2- off-line training deep learning network moulds Block；
Embodiment
The present invention is further described below in conjunction with the accompanying drawings：
Patent of the present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
The overall system structure of electricity anti-theft system of the invention based on deep learning algorithm, as shown in Figure 1,
The electricity anti-theft system based on deep learning algorithm, including the doubtful stealing line module 1 of online recognition and offline instruction Practice deep learning mixed-media network modules mixed-media 2, the doubtful stealing line module of online recognition is used to identify doubtful stealing user, off-line training depth Learning network module is used to determine each network parameter of deep learning network in the doubtful stealing line module of online recognition.
The electricity anti-theft method of the electricity anti-theft system based on deep learning algorithm, includes the following steps：
A, using power information acquisition system, user power utilization data, including active power W are obtainedIt is active, reactive power WIt is idle、 Valley power WValley, crest segment power WPeak value, level values power WLevel valuesAnd the data such as power；
Active power：WIt is active=(MThis month, is active-MLast month is active) m, m represent multiplying power, M represents meter reading value；
Reactive power：WIt is idle=(MThis month, is idle-MLast month is idle)·m；
Valley power：WValley=(MThis month valley-MLast month valley)·m；
Peak power：WPeak value=(MThis month peak value-MLast month peak value)·m；
Level values power：WLevel values=(MThis month level values-MLast month level values)·m；
Power factor (PF)：
B, it is necessary to active power, reactive power, flat section power, paddy section power, peak before using deep learning algorithm The normalization of the section electricity consumption data such as power and power, its value range is turned to [- 1,1], and normalization formula is：
Wherein, xmaxFor the maximum in this index value, xminFor the minimum value in this index value, xiIt is defeated for i-th Enter data, xmidRepresent median,Represent xiNumerical value after normalization；
C, according to normalized active power, reactive power, flat section power, paddy section power, crest segment power and power etc. Electricity consumption data, structure electricity consumption data matrix N × 6, wherein, N represents electricity consumption data sampling number, and 6 represent mentioned above active The quantity of the parameters such as power, reactive power, flat section power, paddy section power, crest segment power and power, using deep learning algorithm In convolutional neural networks algorithm, according to softmax classifier calculated user power utilization suspicion coefficients, in softmax graders, Softmax values calculate as follows：
Wherein, SiRepresent the softmax values of i-th of data, viRepresent i-th of data of input softmax graders, j tables Show data number, the computation model of stealing suspicion coefficient y is as follows：
Y=H (x1, x2, x3, x4, x5, x6) (0≤y≤1) (4)
Wherein, H (x) represents deep learning computing
If stealing suspicion coefficient was more than or equal to 0.85 in d, this month, there is larger stealing suspicion the moon, and send warning information； If stealing suspicion coefficient was less than 0.85 in this month, it is determined that the moon is without electricity stealing.
The step of off-line training deep learning mixed-media network modules mixed-media specific implementation, includes：
A. user power utilization data are chosen, input convolutional neural networks；
B. the reality output of convolutional neural networks is calculated；
C. the reality output of convolutional neural networks and the difference of ideal value are calculated；
D. adjustment matrix is propagated according to minimization error approach direction；
E. training is completed, and calculates user power utilization suspicion coefficient；
During using convolutional neural networks, the specific implementation of the above process includes the following steps：
Step1. from selected training group, a certain number of samples are randomly selected in meter reading data is normalized as training sample This collection；
Step2. initialize, by each weights vij, wjkAnd threshold θk,Be arranged to it is sufficiently small close to 0 it is random Value, and the initial value of the parameter such as control parameter ε and learning rate α, v are setijThe weights of expression output unit i to implicit unit j, wjkRepresent implicit unit j to the weights of output unit k, θkWithThe threshold value of output unit k and the threshold of implicit unit j are represented respectively Value；
Step3. any one input pattern X is chosen from training group to be input in network, and specify its target export to Measure D；
Step4. (5) are utilized to calculate intermediate layer output vector H=(h0, h1..., hL), recycle formula (6) to calculate Reality output vector Y；
In formula, L represents the network number in network intermediate layer, and k represents output unit, ykRepresent the output of output unit k, hjTable Show the output valve of intermediate layer j；.
Step5. the element y in output vector is calculatedkWith the element d in preferable object vectorkDifference, calculate M Output error item：
δk=(dk-yk)yk(1-yk) (7)
Wherein, M represents the unit number of output layer；
Step6. the error term of the implicit unit in intermediate layer is calculated, common L is a：
Step7. the adjustment amount Δ w of each connection weight is calculated successivelyjk(n) and Δ vjk(n), n represents input layer unit Number, Δ vijRepresent output unit i to the weighed value adjusting amount of implicit unit j, Δ wjkRepresent the power of implicit unit j to output unit k It is worth adjustment amount；
Δwjk(n)=(α/(1+L)) * (Δ wjk(n-1)+1)*δk*hj
Δvjk(n)=(α/(1+L)) * (Δ vjk(n-1)+1)*δk*hj (9)
With the adjustment amount of threshold value, Δ θkWithThe threshold value of output unit k and the adjusting thresholds of implicit unit j are represented respectively Amount：
Δθk(n)=(α/(1+L)) * (Δ θk(n-1)+1)*δk
Δφj(n)=(α/(1+L)) * Δs φj(n-1)+1)*δj (10)
Adjust weights：
wjk(n+1)=wjk(n)+Δwjk(n)
vjk(n+1)=vjk(n)+Δvjk(n) (11)
Adjust threshold value：
θk(n+1)=θk(n)+Δθk(n)
φj(n+1)=φj(n)+Δφj(n) (12)
After Step8.k repeats 1 to M, judge whether deliberated index meets required precision：E≤ε, wherein E are overall errors Function, and
If not satisfied, being returned to the 3rd step, continue to calculate iteration, carried out in next step if meeting；
Step9. after training, weights and threshold value that training is obtained preserve in a network, it is believed that stablize available net Network grader has been formed, and need to be such as trained again, then directly invokes existing weights and threshold value is trained, without again Carry out initialization operation.
The method of two module cooperative work is as follows：First, off-line training deep learning mixed-media network modules mixed-media is according to the use of input Electric data, determine the network parameter in convolutional neural networks；Then, the doubtful stealing line module of online recognition is according to definite net Network parameter, doubtful stealing user is determined using softmax graders；Finally, regularly updated and rolled up according to the electricity consumption data of storage Network parameter in product neutral net, adjusts relevant parameter, ensures convolution audit net in the doubtful stealing line module of online recognition The real-time of network parameter.
The basic principle and main feature and advantages of the present invention of the present invention has been shown and described above.The technology of the industry Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this The structural relation and principle of invention, without departing from the spirit and scope of the present invention, the present invention also have various change and Improve, these changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended power Sharp claim and its equivalent thereof.

Claims (3)

  1. A kind of 1. electricity anti-theft system based on deep learning algorithm, it is characterised in that：Including the doubtful stealing user mould of online recognition Block (1) and off-line training deep learning mixed-media network modules mixed-media (2), the doubtful stealing line module of online recognition are used to identify that doubtful stealing is used Family, off-line training deep learning mixed-media network modules mixed-media are used to determine that deep learning network is each in the doubtful stealing line module of online recognition Network parameter.
  2. 2. the electricity anti-theft method of the electricity anti-theft system according to claim 1 based on deep learning algorithm, it is characterised in that： Step includes：
    A, using power information acquisition system, user power utilization data, including active power W are obtainedIt is active, reactive power WIt is idle, valley Power WValley, crest segment power WPeak value, level values power WLevel valuesAnd the data such as power；
    Active power：WIt is active=(MThis month, is active-MLast month is active) m, m represent multiplying power, M represents meter reading value；
    Reactive power：WIt is idle=(MThis month, is idle-MLast month is idle)·m；
    Valley power：WValley=(MThis month valley-MLast month valley)·m；
    Peak power：WPeak value=(MThis month peak value-MLast month peak value)·m；
    Level values power：WLevel values=(MThis month level values-MLast month level values)·m；
    Power factor (PF)：
    B, it is necessary to active power, reactive power, flat section power, paddy section power, crest segment work(before using deep learning algorithm The normalization of the electricity consumption data such as rate and power, its value range is turned to [- 1,1], and normalization formula is：
    <mrow> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> <mn>2</mn> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> </mrow> <mrow> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, xmaxFor the maximum in this index value, xminFor the minimum value in this index value, xiFor i-th of input number According to xmidRepresent median,Represent xiNumerical value after normalization；
    C, according to electricity consumptions such as normalized active power, reactive power, flat section power, paddy section power, crest segment power and power Data, structure electricity consumption data matrix N × 6, wherein, N expression electricity consumption data sampling numbers, 6 expressions active power mentioned above, The quantity of the parameters such as reactive power, flat section power, paddy section power, crest segment power and power, using in deep learning algorithm Convolutional neural networks algorithm, according to softmax classifier calculated user power utilization suspicion coefficients, in softmax graders, Softmax values calculate as follows：
    Wherein, SiRepresent the softmax values of i-th of data, viRepresent i-th of data of input softmax graders, j represents number According to numbering, the computation model of stealing suspicion coefficient y is as follows：
    Y=H (x1, x2, x3, x4, x5, x6) (4)
    Wherein, H (x) represents deep learning computing
    If stealing suspicion coefficient was more than or equal to 0.85 in d, this month, there is larger stealing suspicion the moon, and send warning information；If should The moon, stealing suspicion coefficient was less than 0.85, it is determined that the moon is without electricity stealing.
  3. 3. the electricity anti-theft system according to claim 1 based on deep learning algorithm, it is characterised in that：The off-line training The step of deep learning mixed-media network modules mixed-media implements includes：
    A. user power utilization data are chosen, input convolutional neural networks；
    B. the reality output of convolutional neural networks is calculated；
    C. the reality output of convolutional neural networks and the difference of ideal value are calculated；
    D. adjustment matrix is propagated according to minimization error approach direction；
    E. training is completed, and calculates user power utilization suspicion coefficient；
    During using convolutional neural networks, the specific implementation of the above process includes the following steps：
    Step1. from selected training group, a certain number of samples are randomly selected in meter reading data is normalized as training sample Collection；
    Step2. initialize, by each weights vij, wjkAnd threshold θk,Be arranged to it is sufficiently small close to 0 random value, and set Put the initial value of the parameter such as control parameter ε and learning rate α, vijRepresent output unit i to the weights of implicit unit j, wjkRepresent hidden J containing unit is to the weights of output unit k, θkWithThe threshold value of output unit k and the threshold value of implicit unit j are represented respectively；
    Step3. any one input pattern X is chosen from training group to be input in network, and specifies its target output vector D；
    Step4. (5) are utilized to calculate intermediate layer output vector H=(h0,h1,…,hL), recycle formula (6) to calculate reality Output vector Y；
    In formula, L represents the network number in network intermediate layer, and k represents output unit, ykRepresent the output of output unit k, hjIn expression The output valve of interbed j；
    Step5. the element y in output vector is calculatedkWith the element d in preferable object vectorkDifference, calculate M output Error term：
    δk=(dk-yk)yk(1-yk) (7)
    Wherein, M represents the unit number of output layer；
    Step6. the error term of the implicit unit in intermediate layer is calculated, common L is a：
    Step7. the adjustment amount Δ w of each connection weight is calculated successivelyjk(n) and Δ vjk(n), n represents input layer unit number, Δ vijRepresent output unit i to the weighed value adjusting amount of implicit unit j, Δ wjkRepresent the weights tune of implicit unit j to output unit k Whole amount；
    Δwjk(n)=(α/(1+L)) * (Δ wjk(n-1)+1)*δk*hj
    Δvjk(n)=(α/(1+L)) * (Δ vjk(n-1)+1)*δk*hj (9)
    With the adjustment amount of threshold value, Δ θkWithThe threshold value of output unit k and the adjusting thresholds amount of implicit unit j are represented respectively：
    Δθk(n)=(α/(1+L)) * (Δ θk(n-1)+1)*δk
    Δφj(n)=(α/(1+L)) * (Δ φj(n-1)+1)*δj (10)
    Adjust weights：
    wjk(n+1)=wjk(n)+Δwjk(n)
    vjk(n+1)=vjk(n)+Δvjk(n) (11)
    Adjust threshold value：
    θk(n+1)=θk(n)+Δθk(n)
    φj(n+1)=φj(n)+Δφj(n) (12)
    After Step8.k repeats 1 to M, judge whether deliberated index meets required precision：E≤ε, wherein E are overall error letters Number, and
    If not satisfied, being returned to the 3rd step, continue to calculate iteration, carried out in next step if meeting；
    Step9. after training, weights and threshold value that training is obtained preserve in a network, it is believed that stablize available network point Class device has been formed, and need to be such as trained again, then directly invokes existing weights and threshold value is trained, without carrying out again Initialization operation.
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CN113687176A (en) * 2021-10-25 2021-11-23 广东电网有限责任公司湛江供电局 Deep neural network-based power consumption abnormity detection method and system

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