Low-voltage direct-current arc hidden danger identification method based on information fusion

Abstract

The invention discloses a low-voltage direct-current arc hidden danger identification method based on information fusion, and the method comprises the steps: firstly, building an arc mathematical model, and building a direct-current micro-grid arc fault test simulation platform; an arc fault is simulated, fault branch voltage and non-fault branch voltage are collected, voltage time-domain characteristics are extracted, FFT analysis is carried out on the voltage time-domain characteristics, and the proportion of a fundamental component and each characteristic frequency band component is obtained; analyzing the characteristics of voltage offset, voltage change rate, harmonic distortion rate, oscillation frequency and the like to obtain an arc fault voltage characteristic matrix, and researching arc characteristics at different fault positions; and judging whether the system has an arc fault or not according to the DC voltage characteristics, and carrying out fault positioning according to a fault voltage harmonic distortion rate. According to the arc fault identification method, the arc fault type is analyzed from the fault branch voltage characteristic and the non-fault branch voltage characteristic, and accurate positioning is achieved.

Description

Technical Field

[0001] The invention belongs to the research field of fault arc identification in power electronic power systems, and in particular relates to a method for identifying low-voltage direct current arc hazards based on information fusion. Background Art

[0002] Arc faults are a common fault hazard problem in DC microgrid systems and have received widespread attention. Arc faults in DC busbars are often exacerbated by factors such as device aging and wire damage. However, as an important part of the connection between power output and power distribution system, the failure of DC busbar will cause equipment damage or even system disconnection. Therefore, a low-voltage DC arc hazard identification method based on information fusion is proposed, which is of great significance to maintain the stability of low-voltage DC system.

[0003] At present, the research on arc faults is divided into series faults and parallel faults according to the fault location. Among them, when a parallel arc fault occurs, the system current increases, and the DC overcurrent protection can accurately detect the fault. When a series arc fault occurs in the system, the arc voltage increases and the current decreases. Therefore, it is necessary to use information fusion technology to collect the waveforms of the voltage, current, and power of the fault branch and the non-fault branch that change with time, and extract the pre-fault voltage, current, and power time domain characteristic criteria that distinguish the normal working state and the fault arc state, so as to achieve accurate identification of the fault arc, reduce or even eliminate the harm of the fault arc, and provide a strong guarantee for the safe and stable operation of the power system. Summary of the invention

[0004] The purpose of the present invention is to provide a low-voltage DC arc hazard identification method based on information fusion. The method analyzes characteristics such as voltage amplitude and harmonic distortion rate, obtains an arc fault voltage characteristic matrix, studies the arc characteristics under different fault locations, analyzes the arc fault type from the fault branch voltage and non-fault branch voltage characteristics, and achieves precise positioning.

[0005] The present invention provides a method for identifying low-voltage DC arc hazards based on information fusion, and the analysis method specifically comprises the following steps:

[0006] Step 1: Based on the arc mathematical model, build a DC microgrid fault arc test simulation platform;

[0007] Step 2: simulate the fault arc on the DC microgrid fault arc test simulation platform, collect the waveforms of the voltage, current, and power of the fault branch and the non-fault branch changing with time, analyze the time domain characteristics of the voltage, current, and power of the fault branch and the non-fault branch before the fault, extract the time domain characteristics of the voltage after the fault and perform FFT analysis on it, and obtain its fundamental component and the proportion of each characteristic component;

[0008] Step 3: Analyze the voltage amplitude and harmonic distortion rate of the fault voltage after the fault, obtain the DC voltage characteristic matrix, and study the arc characteristics at different fault arc positions based on the DC voltage characteristic matrix. The DC voltage characteristic matrix includes the voltage offset, voltage change rate, fault voltage harmonic distortion rate, voltage oscillation frequency, and fundamental component amplitude of the fault branch and non-fault branch;

[0009] Step 4: Determine whether a fault arc occurs in the system based on the DC voltage characteristic matrix, and locate the fault based on the harmonic distortion rate of the fault voltage. The DC voltage characteristic matrix is ​​composed of the voltage offset, voltage change rate, and oscillation frequency before and after the fault arc occurs at each measuring point.

[0010] The present invention has the following beneficial effects:

[0011] (1) The present invention collects the fault branch voltage and the non-fault branch voltage to obtain the fundamental component and the proportion of each characteristic frequency band component; analyzes the characteristics such as voltage amplitude and harmonic distortion rate to obtain the arc fault voltage characteristic matrix for identifying arc fault hazards;

[0012] (2) The present invention discloses a method for identifying low-voltage DC arc hazards based on information fusion, which analyzes the time domain characteristics of voltage, current and power before the fault, measures the time domain characteristics of the arc fault by the load power change, voltage change rate and voltage offset, and realizes accurate positioning of the arc fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is the topology diagram of the DC microgrid multi-machine parallel system;

[0014] Figure 2 This is the DC voltage simulation waveform when a 30V series fault arc occurs at point S1;

[0015] Figure 3 This is the DC voltage simulation waveform when a 30V series fault arc occurs at point S2;

[0016] Figure 4 This is the DC voltage waveform spectrum diagram when a 30V series fault arc occurs at point S2;

[0017] Figure 5 This is the DC voltage simulation waveform when a 20V series fault arc occurs at point S2;

[0018] Figure 6 To change L line The DC voltage simulation waveform under the 20V series fault arc at parameter time point S2;

[0019] Figure 7 The DC voltage simulation waveform when a 20V series fault arc occurs at point S3;

[0020] Figure 8 This is the DC voltage simulation waveform when a 30V series fault arc occurs at point S4. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments described are only part of the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the protection scope of the present invention.

[0022] The present invention aims to propose a method for identifying low-voltage DC arc hazards based on information fusion, so as to solve the problem that a circuit breaker cannot operate due to a series arc fault due to a reduction in current. Figure 1 The figure shows a topological diagram of a DC microgrid multi-machine parallel system provided by an embodiment of the present invention, which includes multiple series arc fault points. The research on arc fault identification at home and abroad is mainly based on the analysis of current and voltage characteristics. It is difficult to identify and locate series arc faults based on a single feature. The present invention collects the fault branch voltage and the non-fault branch voltage to obtain the fundamental component and the proportion of each characteristic frequency band component; analyzes the characteristics such as voltage amplitude and harmonic distortion rate, and obtains the arc fault voltage characteristic matrix for identifying fault arc hazards.

[0023] A method for identifying low-voltage DC arc hazards based on information fusion specifically comprises the following steps:

[0024] Step 1: Based on the arc mathematical model, build a DC microgrid fault arc test simulation platform;

[0025] Step 2: Based on the DC microgrid fault arc test simulation platform built in step 1, simulate the fault arc, collect the waveforms of the voltage, current, and power of the fault branch and the non-fault branch changing with time, analyze the time domain characteristics of the voltage, current, and power of the fault branch and the non-fault branch before the fault, extract the time domain characteristics of the voltage after the fault and perform fast Fourier transform (FFT) analysis on it, and obtain its fundamental component and the proportion of each characteristic component; the characteristic component refers to the component at the oscillation frequency;

[0026] Step 3: According to step 2, the fault arcs at different positions are simulated, the voltage amplitude and the harmonic distortion rate of the fault voltage after the fault are analyzed, the DC voltage characteristic matrix is ​​obtained, and the arc characteristics at different fault arc positions are studied based on the DC voltage characteristic matrix;

[0027] Step 4: determine whether a fault arc occurs in the system based on the DC voltage characteristic matrix obtained in step 3, and locate the fault based on the harmonic distortion rate of the fault voltage.

[0028] In step 1, the arc mathematical model is:

[0029] (1)

[0030] In the formula, g represents the arc conductivity per unit length, τ is the arc time constant, u represents the arc voltage, and U c is the voltage gradient of the arc.

[0031] A DC microgrid fault arc test simulation platform based on the Cassie arc model is established. The simulation platform includes an inverter, a combiner box, and a DC / DC converter. The fault arc is located at the front end of the inverter, the front end of the combiner box, and the front end of the DC / DC converter.

[0032] In step 2, the time domain characteristics of the post-fault voltage are extracted and subjected to fast Fourier transform (FFT) analysis to obtain the fundamental component and the proportion of each characteristic component, including:

[0033] The time domain characteristics of the fault voltage are measured by the output power change, voltage change rate and voltage offset after the fault. The voltage offset represents the difference between the voltage at any time after the fault and the steady-state voltage before the fault; the voltage change rate represents the ratio of the voltage change at any time to time, that is, the slope of the voltage change curve at any time.

[0034] Output power change:

[0035] (2)

[0036] In the formula, Indicates the power change, Indicates the output voltage before the fault. Indicates the output current before the fault. Indicates the output voltage after a fault. Indicates the output current after a fault.

[0037] For arc faults at different locations, the dynamic characteristics of system voltage and current before and after the fault are also different. Therefore, the load power change can also be identified as:

[0038] (3)

[0039] in, Indicates the output voltage offset, δ Indicates the output current offset.

[0040] Voltage change rate:

[0041] (4)

[0042] Integrated voltage offset and voltage change rate , if the maximum voltage offset is greater than its threshold, , or the voltage change rate is greater than its threshold, , it can be determined that the system has failed, where is the voltage offset threshold, is the voltage change rate threshold.

[0043] The time domain characteristics of the voltage before the fault are extracted and FFT analysis is performed on them to obtain the fundamental component and the proportion of each characteristic frequency band component, and to determine the magnitude of the fault arc voltage.

[0044] In step three, the voltage amplitude and the harmonic distortion rate of the fault voltage are analyzed to obtain the DC voltage characteristic matrix. In the study of arc characteristics at different fault locations based on the DC voltage characteristic matrix, the harmonic distortion rate of the fault voltage is expressed as:

[0045] (5)

[0046] In the formula, Indicates DC voltage The harmonic distortion rate, is DC voltage No. The harmonic components, is DC voltage The fundamental component of the DC voltage The order of .

[0047] The DC voltage characteristic matrix includes the voltage offset, voltage change rate, voltage harmonic distortion rate, voltage oscillation frequency, and fundamental component amplitude of the fault branch and the non-fault branch.

[0048] In step 4, it is determined whether a fault arc occurs in the system based on the DC voltage characteristics, and the fault is located based on the harmonic distortion rate of the fault voltage. The specific contents are as follows:

[0049] a. Obtain DC voltage characteristic matrix;

[0050] b. Fault arc identification based on DC voltage characteristic matrix;

[0051] The DC voltage characteristic refers to the DC voltage effective value V dcRMS1,2,3 :

[0052] (6)

[0053] In the formula, Represents the output voltage across the capacitor, represents one oscillation period, represents the DC steady-state voltage component, Represents the DC transient voltage component.

[0054] Fault arc identification based on DC voltage characteristic matrix includes:

[0055] Calculate the accumulated energy generated by the DC transient component and use it in conjunction with the DC voltage harmonic distortion rate to identify system oscillations:

[0056] (7)

[0057] In the formula, t0 represents the starting time of the arc. Indicates the energy accumulation caused by DC transient components.

[0058] c. According to the fault arc identification results, the fault arc is located by integrating the voltage offset, voltage change rate, fault voltage harmonic distortion rate, and oscillation frequency of the fault branch and non-fault branch voltages after the fault;

[0059] d. Based on the fault arc location results, the proposed low-voltage DC arc hazard identification method based on information fusion is verified. The verification process includes building a DC microgrid multi-machine parallel system simulation model in MATLAB, simulating fault arcs of different intensities at different locations in the DC microgrid multi-machine parallel system simulation model, collecting the output voltage waveforms of the fault branch and non-fault branch, using the time-frequency analysis method to process the fault voltage signal, extracting the output voltage characteristic parameters, and performing data analysis.

[0060] Figure 2 The DC voltage simulation waveform of a 30V series fault arc at S1 is given; Figure 2 It can be seen that when a fault arc occurs at S1, the system output DC voltage fluctuates by only 6-7V and returns to a steady state after a cycle of about 0.1s. The system has good stability.

[0061] Figure 3 The DC voltage simulation waveform of a 30V series fault arc at S2 is given, where L line1 =1.5mH, L line2 =1mH,L line3 =1.5mH, other parameters are the same; from Figure 3 It can be seen that a fault arc occurs at S2, and the system output DC voltage oscillates at a super-synchronous frequency of 134 Hz. The oscillation frequency of each branch is the same and there is a short delay. dc2 V dc1 Delay is about 2.4ms, V dc3 V dc1 The delay is about 4.1ms, and the output voltage V dc1 The spectrum is shown as Figure 4 As shown. Figure 4 It can be seen that different L lineThe total harmonic distortion rate of the DC voltage output of the non-fault branch is similar, but V dc2 V dc3 The oscillation deviation is different, L line The larger the DC voltage deviation, the greater the DC voltage deviation.

[0062] In order to study the influence of fault arc strength on DC voltage oscillation characteristics, Figure 5 The DC voltage simulation waveform of a 20V series fault arc at S2 is given. It can be seen from the figure that the voltage oscillation deviation is relatively Figure 3 The oscillation frequency is almost unchanged, but the fault arc occurs at 0.5s, and the oscillation occurs about 0.07s later. There is a certain delay. Therefore, the strength of the arc can be judged by the delay time and voltage offset.

[0063] In order to study the influence of system parameters on DC voltage oscillation characteristics, Figure 6 Given L line1 =L line2 =L line3 =1mH, C1=C2= C3=1mF, DC voltage simulation waveform under 20V series fault arc occurs at S2, for comparison Figure 5 and Figure 6 It can be found that reducing L line1 and L line3 After that, V dc1 and V dc2 The voltage oscillation offset of each branch increases, and the output voltage oscillation frequency of each branch increases, and V dc2 The increase is more significant. In addition, V dc1 With V dc2 and V dc3 There is still a certain time lag, but V dc2 and V dc3 Under this condition, the same frequency oscillation occurs.

[0064] Figure 7 The DC voltage simulation waveform of a 20V series fault arc at S3 is given, where L line1 =L line2 =L line3 =1mH, where C1=C2=1mF, C3=2mF. Figure 5 In comparison, the harmonic distortion rate of the output voltage at the fault branch port is significantly reduced, which is different from V dc1 and V dc2 , increase the capacitor C3 so that the voltage V dc3 The oscillation amplitude is significantly reduced.

[0065] Figure 8The DC voltage simulation waveform under the 30V series fault arc at S4 is given. It can be seen from the figure that the series arc only causes the voltage to produce a voltage offset of about 2.2V, and it recovers to the steady-state value after about 0.04s. The system has good dynamic characteristics. Comprehensively comparing the system output DC voltage offset, voltage change rate, oscillation frequency and voltage hysteresis characteristics of each branch when fault arcs occur at different locations, the system fault arc identification and positioning can be realized.

Claims

1. A method for identifying low-voltage DC arc hazards based on information fusion, characterized in that: The following steps are involved: Step 1: Based on the arc mathematical model, build a DC microgrid fault arc test simulation platform; Step 2: Based on the DC microgrid fault arc test simulation platform built in step 1, simulate the fault arc, collect the waveforms of the voltage, current, and power of the fault branch and the non-fault branch changing with time, analyze the time domain characteristics of the voltage, current, and power of the fault branch and the non-fault branch before the fault, extract the time domain characteristics of the voltage after the fault and perform FFT analysis on it, and obtain its fundamental component and the proportion of each characteristic component; Step three, according to the fault arc occurring at different positions simulated in step two, analyze the voltage amplitude and the harmonic distortion rate of the fault voltage after the fault, obtain the DC voltage characteristic matrix, and study the arc characteristics at different fault arc positions based on the DC voltage characteristic matrix. The DC voltage characteristic matrix includes the voltage offset of the fault branch and the non-fault branch, the voltage change rate, the harmonic distortion rate of the fault voltage, the voltage oscillation frequency, and the amplitude of the fundamental component; Step 4: determine whether a fault arc occurs in the system based on the DC voltage characteristic matrix obtained in step 3, and locate the fault based on the harmonic distortion rate of the fault voltage.

2. A method for identifying low-voltage DC arc hazards based on information fusion according to claim 1, characterized in that: In the step 1, The arc mathematical model is: (1) In the formula, g represents the arc conductivity per unit length, τ is the arc time constant, u represents the arc voltage, and U c is the voltage gradient of the arc; A DC microgrid fault arc test simulation platform based on an arc mathematical model is established. The fault arc is located at the front end of the inverter, the front end of the combiner box, and the front end of the DC / DC converter.

3. The method for identifying low-voltage DC arc hazards based on information fusion according to claim 1 is characterized in that: In the step 2, an arc fault is simulated, and the waveforms of voltage, current, and power of the fault branch and the non-fault branch changing with time are collected, and the time domain characteristics of the voltage, current, and power before the fault are analyzed. The time domain characteristics of the fault arc are measured by the output power change, voltage change rate, and voltage offset after the fault; Output power change: (2) In the formula, Indicates the power change, Indicates the output voltage before the fault. Indicates the output current before the fault. Indicates the output voltage after a fault. Indicates the output current after a fault; For fault arcs at different locations, the output power variation is identified as: (3) Among them, δ Indicates the output voltage offset, δ Indicates the output current offset.

4. The method for identifying low-voltage DC arc hazards based on information fusion according to claim 1 is characterized in that: In the step three, the voltage amplitude and the harmonic distortion rate of the fault voltage are analyzed to obtain a DC voltage characteristic matrix, and arc characteristics at different fault locations are studied based on the DC voltage characteristic matrix; Fault voltage harmonic distortion rate: (4) Where, THD_V dc Indicates DC voltage V dc Total harmonic distortion rate, V dcn is DC voltage The nth order harmonic component, V dc0 is DC voltage The fundamental component of the DC voltage The order of .

5. The method for identifying low-voltage DC arc hazards based on information fusion according to claim 1 is characterized in that: In the step 4, judging whether a fault arc occurs in the system according to the DC voltage characteristic matrix and locating the fault according to the harmonic distortion rate of the fault voltage includes: a. Obtain DC voltage characteristics; b. Fault arc identification based on DC voltage characteristic matrix; c. Integrate information such as voltage offset, voltage change rate, fault voltage harmonic distortion rate, and oscillation frequency of the fault branch and non-fault branch voltages after the fault to locate the fault arc; d. Verify the proposed low-voltage DC arc hazard identification method based on information fusion. The verification process includes building a DC microgrid multi-machine parallel system simulation model in MATLAB, simulating fault arcs of different intensities occurring at different locations in the DC microgrid multi-machine parallel system simulation model, collecting the output voltage waveforms of the fault branch and non-fault branch, using time-frequency analysis method to process the fault voltage signal, extracting the output voltage characteristic parameters, and performing data analysis.

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