CN110426601A - A kind of Fault Locating Method of earth-free photovoltaic system - Google Patents
A kind of Fault Locating Method of earth-free photovoltaic system Download PDFInfo
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- CN110426601A CN110426601A CN201910779350.5A CN201910779350A CN110426601A CN 110426601 A CN110426601 A CN 110426601A CN 201910779350 A CN201910779350 A CN 201910779350A CN 110426601 A CN110426601 A CN 110426601A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- Y—GENERAL 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS 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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The invention discloses a kind of Fault Locating Methods of earth-free photovoltaic system: obtaining the mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system, and constructs mid-point voltage vector；Multiresolution analysis is carried out to extract the feature vector of each mid-point voltage signal, to obtain the feature vector of mid-point voltage vector by each mid-point voltage signal in wavelet transformation alignment voltage vector；With Classification and Identification is carried out to feature vector using the fault location neural network that the training of failure training sample is completed in advance, abort situation corresponding with the feature vector of mid-point voltage vector and fault type are exported；It include several event of failure in failure training sample, each event of failure is made of the feature vector of mid-point voltage vector, fault type and abort situation, and fault type includes pole ground fault and interpolar failure.The technical issues of prior art cannot effectively position pole ground fault is solved, classification and orientation identification can be carried out to interpolar failure and pole ground fault.
Description
Technical field
The present invention relates to signal processing, neural network and technical field of photovoltaic power generation.
Background technique
It in earth-free photovoltaic system, is difficult to detect since pole ground fault electric current is smaller, it is difficult to pass through ground fault electricity
Stream positions ground fault.Although the electric current of ground fault is smaller, if removing not in time, ground fault, which may develop, is
Short trouble generates biggish short circuit current.Ground fault can be divided into two classes: interpolar failure, pole fault to ground.Fault location is wanted
Seek the interpolar failure and pole fault to ground that can distinguish different location.
Compared to fault current, transient voltage when ground fault occurs is more readily detected, especially DC-DC inverter
Mid-point voltage, with reference to shown in Fig. 1: the second capacitor upper end of the mid-point voltage of DC-DC inverter, that is, DC-DC inverter both ends parallel connection
Voltage-to-ground；Photovoltaic array converts solar energy into electrical energy, and exports DC voltage, then realize maximum work by Buck converter
The output of rate tracing control (MPPT), Buck circuit accesses 600V DC bus through cable, will be straight finally by DC-AC inverter
Circulation is changed to three-phase alternating current.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention provides a kind of Fault Locating Method of earth-free photovoltaic system, solution
The technical issues of certainly effectively pole ground fault cannot be positioned since earth-fault current is smaller in the prior art, into one
Step can carry out classification and orientation identification to interpolar failure and pole ground fault.
In order to solve the above-mentioned technical problem, present invention employs the following technical solutions: a kind of earth-free photovoltaic system
Fault Locating Method comprising the steps of:
The mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system is obtained, and constructs mid-point voltage vector；
It is every to extract that multiresolution analysis is carried out by each mid-point voltage signal in wavelet transformation alignment voltage vector
The feature vector of a mid-point voltage signal, to obtain the feature vector of mid-point voltage vector；
Classify with the fault location neural network for using the training of failure training sample to complete in advance to feature vector
Identification exports abort situation corresponding with the feature vector of mid-point voltage vector and fault type；Include in failure training sample
Several event of failure, each event of failure are made of the feature vector of mid-point voltage vector, fault type and abort situation, and
Fault type includes pole ground fault.
Further, fault type further includes interpolar failure.
Further, mid-point voltage signal resolves into after wavelet transformation believes comprising the transient state of high frequency section and low frequency part
It ceases f (t):
In formula, N indicates to decompose total number of plies；djiIndicate i-th of detail coefficients in jth layer, j ∈ { 1,2 ..., N }；N table
Show the total number of detail coefficients in jth layer, i indicates the number of detail coefficients in jth layer；aN,iIndicate i-th of maximum of n-th layer seemingly
Right coefficient；ΦN,i(t) scaling function, Ψ are indicatedj,i(t) wavelet function is indicated.
Further, according to the detail coefficients construction feature vector x in transient information:
X=[x1...xj...xN]T=[| | d1||...||dj||...||dN||]T；
In formula,djiIndicate i-th of detail coefficients in jth layer；djIndicate the details by jth layer
The detail coefficients vector for the jth layer that coefficient is constituted；N indicates the total number of detail coefficients in jth layer.
Further, failure training sample obtains as follows: firstly, using RTDS analogue system to different location
Interpolar failure is simulated with pole fault to ground, obtains the mid-point voltage letter of each DC-DC inverter in earth-free photovoltaic system
Number, and construct mid-point voltage vector；Then, it is carried out by each mid-point voltage signal in wavelet transformation alignment voltage vector
Multiresolution analysis is to extract the feature vector of each mid-point voltage signal, to obtain the feature vector of mid-point voltage vector；Most
Afterwards, the feature vector of mid-point voltage vector and corresponding abort situation, fault type are formed into event of failure, by each event
Barrier event forms failure training sample.
Compared with prior art, the invention has the following advantages:
1, the present invention using the mid-point voltage vector of earth-free photovoltaic system reflects pole ground fault, avoids using and is difficult to
The earth-fault current of detection reflects pole ground fault.Fixation and recognition is carried out to failure using neural network in order to realize, it is right
Mid-point voltage vector has carried out signal processing, i.e., extracts feature vector by wavelet multiresolution analysis, feature vector is inputted
The abort situation of pole ground fault can be exported using the fault location neural network that the training of failure training sample is completed.
2, the present invention in the fault type in failure training sample by increasing interpolar fault type and corresponding midpoint
Feature vector, the abort situation of voltage vector, energy Classification and Identification pole ground fault and interpolar failure, extend fault identification function
Energy.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of earth-free photovoltaic system；
Fig. 2 is the schematic illustration of wavelet transformation multiresolution analysis；
Fig. 3 is the structural schematic diagram of neural network.
Specific embodiment
The present invention is described in further detail with preferred embodiment with reference to the accompanying drawing.
A kind of Fault Locating Method of earth-free photovoltaic system comprising the steps of:
The mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system is obtained, and constructs mid-point voltage vector；
It is every to extract that multiresolution analysis is carried out by each mid-point voltage signal in wavelet transformation alignment voltage vector
The feature vector of a mid-point voltage signal, to obtain the feature vector of mid-point voltage vector；
Classify with the fault location neural network for using the training of failure training sample to complete in advance to feature vector
Identification exports abort situation corresponding with the feature vector of mid-point voltage vector and fault type；Include in failure training sample
Several event of failure, each event of failure are made of the feature vector of mid-point voltage vector, fault type and abort situation, and
Fault type includes pole ground fault and interpolar failure.
Mid-point voltage signal resolves into the transient information f (t) comprising high frequency section and low frequency part after wavelet transformation:
In formula, N indicates to decompose total number of plies；djiIndicate i-th of detail coefficients in jth layer, j ∈ { 1,2 ..., N }；N table
Show the total number of detail coefficients in jth layer, i indicates the number of detail coefficients in jth layer；aN,iIndicate i-th of maximum of n-th layer seemingly
Right coefficient；ΦN,i(t) scaling function, Ψ are indicatedj,i(t) wavelet function is indicated.
According to the detail coefficients construction feature vector x in transient information:
X=[x1...xj...xN]T=[| | d1||...||dj||...||dN||]T；
In formula,djiIndicate i-th of detail coefficients in jth layer；djIndicate the details by jth layer
The detail coefficients vector for the jth layer that coefficient is constituted；N indicates the total number of detail coefficients in jth layer.
Referring to fig. 2, signal can be divided into different frequency range by wavelet transformation multiresolution analysis.If the sample rate of signal is fs, then
The frequency range of the frequency spectrum of signal is [0, fs/2].By once decomposing, high frequency section D1, frequency range is [fs/4, fs/
2], low frequency part A1, [0, fs/4].Later, the decomposition of complete pair signals A1, and so on.According to each thin in each layer
Section coefficient can construct the measure feature vector x of each mid-point voltage signal.
Failure training sample obtains as follows: firstly, using RTDS analogue system to the interpolar failure of different location
It is simulated with pole fault to ground, obtains the mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system, and construct
Mid-point voltage vector；Then, differentiate is carried out by each mid-point voltage signal in wavelet transformation alignment voltage vector to divide more
Analysis is to extract the feature vector of each mid-point voltage signal, to obtain the feature vector of mid-point voltage vector；Finally, by midpoint
The feature vector of voltage vector and corresponding abort situation, fault type form event of failure, by each event of failure group
At failure training sample.
Composition of proportions training sample and test sample by failure training sample by 70%, 30%.Training sample completes mind
Training through network each neuron weight and threshold value, training algorithm choose trainlm.Referring to Fig. 3, neural network includes output
Layer, hidden layer and output layer.Wherein, output quantity is characterized vector x, and output quantity is the failure of different location.Neural metwork training
After the completion, then the effect of input test Sample neural metwork training, into the neural network by test as fault location
Neural network.
Claims (5)
1. a kind of Fault Locating Method of earth-free photovoltaic system, which is characterized in that comprise the steps of:
The mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system is obtained, and constructs mid-point voltage vector；
Multiresolution analysis is carried out by each mid-point voltage signal in wavelet transformation alignment voltage vector to extract in each
The feature vector of point voltage signal, to obtain the feature vector of mid-point voltage vector；
Classification and Identification is carried out to feature vector with the fault location neural network for using the training of failure training sample to complete in advance,
Export abort situation corresponding with the feature vector of mid-point voltage vector and fault type；Include several events in failure training sample
Barrier event, each event of failure is made of the feature vector of mid-point voltage vector, fault type and abort situation, and failure classes
Type includes pole ground fault.
2. the Fault Locating Method of earth-free photovoltaic system according to claim 1, it is characterised in that: failure training sample
In fault type further include interpolar failure.
3. the Fault Locating Method of earth-free photovoltaic system according to claim 1, it is characterised in that: mid-point voltage signal
The transient information f (t) comprising high frequency section and low frequency part is resolved into after wavelet transformation:
In formula, N indicates to decompose total number of plies；djiIndicate i-th of detail coefficients in jth layer, j ∈ { 1,2 ..., N }；N indicates jth
The total number of detail coefficients in layer, i indicate the number of detail coefficients in jth layer；aN,iIndicate i-th of maximum likelihood system of n-th layer
Number；ΦN,i(t) scaling function, Ψ are indicatedj,i(t) wavelet function is indicated.
4. the Fault Locating Method of earth-free photovoltaic system according to claim 3, it is characterised in that: according to transient information
In detail coefficients construction feature vector x:
X=[x1...xj...xN]T=[| | d1||...||dj||...||dN||]T；
In formula,djiIndicate i-th of detail coefficients in jth layer；djIndicate the detail coefficients by jth layer
The detail coefficients vector of the jth layer of composition；N indicates the total number of detail coefficients in jth layer.
5. the Fault Locating Method of earth-free photovoltaic system according to claim 2, it is characterised in that: failure training sample
It obtains as follows: firstly, simulated using interpolar failure of the RTDS analogue system to different location with pole fault to ground,
The mid-point voltage signal of each DC-DC inverter in earth-free photovoltaic system is obtained, and constructs mid-point voltage vector；Then, lead to
The each mid-point voltage signal crossed in wavelet transformation alignment voltage vector carries out multiresolution analysis to extract each mid-point voltage
The feature vector of signal, to obtain the feature vector of mid-point voltage vector；Finally, by the feature vector of mid-point voltage vector and
Corresponding abort situation, fault type form event of failure, form failure training sample by each event of failure.
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