DC power distribution network disturbance signal feature extraction analysis processing method based on Tsallis wavelet

Abstract

The invention discloses a Tsallis wavelet-based DC power distribution network disturbance signal feature extraction analysis processing method. The method comprises the steps of providing a Tsallis entropy theory-based DC power distribution network signal feature extraction method; a method for analyzing wavelet signals corresponding to voltage ripples and voltage sag in a time domain and an energy domain based on a Tsallis entropy theory is provided; step 2, constructing a DC power distribution network model, introducing disturbance to the AC side of the DC power distribution network model, generating DC voltage ripple and a voltage sag signal in the DC side voltage, and extracting the disturbance signal characteristics of the DC side of the DC power distribution network through the method proposed in the step 2; calculating the time entropy and the energy entropy of the disturbance signal characteristics in the time domain and the energy domain in the direct-current power distribution network according to the method proposed in the step 3; and judging and processing the voltage ripple content and the voltage sag according to the time entropy and the energy entropy. And the sensitivity and the accuracy of extracting the voltage ripple and voltage sag signal characteristics are improved.

Description

Technical Field

[0001] The invention belongs to the field of electric power systems, and in particular relates to a method for extracting, analyzing and processing characteristics of disturbance signals of a direct current distribution network based on Tsallis wavelet. Background Art

[0002] In recent years, with the rapid development of power electronic devices and distributed generation technology, DC distribution networks have gradually shown great advantages in many fields such as technology, economy, and environmental protection. DC distribution networks are a new trend in the development of distribution networks and have been widely studied. DC distribution technology has developed rapidly, but this has also brought a series of problems. Among them, the power quality problem caused by voltage ripple and voltage sag in DC distribution networks is one of the problems that need to be solved urgently. The application of a large number of power electronic devices makes the DC distribution network more stringent in terms of power quality than the AC distribution network of the same scale. As a hot issue in power quality issues, the extraction of ripple and voltage sag signal features in DC distribution networks has been widely studied. Wavelet transform, as a multi-scale signal, is widely used in transient signal extraction and classification. By detecting and extracting the transient signal features of the DC distribution network, the signal features of voltage ripple and voltage sag can be quickly and accurately extracted, and then the disturbance formation mechanism affecting the power quality in the DC distribution network can be analyzed in detail.

[0003] However, at present, the extraction of transient characteristics of power systems by multi-layer wavelet decomposition methods not only has the problem of complex calculations, but also causes energy leakage and aliasing, and reduces the accuracy of signal feature extraction. By proposing the concept of multi-scale entropy, the Shannon wavelet analysis method based on the multi-layer wavelet decomposition method combined with the Shannon entropy algorithm is also used to detect steady-state signals of power systems. However, the extraction of transient signals in power systems by this method also has the problem of inaccurate signal characteristics extraction. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of the prior art, thereby providing a method for extracting and analyzing the characteristics of disturbance signals in a DC distribution network based on Tsallis wavelet, realizing the rapid extraction of voltage ripple and voltage sag signals in a DC distribution network, and using Tsallis wavelet entropy theory to perform multi-scale analysis on the extracted signal characteristics on the basis of realizing efficient detection of DC distribution network signals. By measuring the complexity of the disturbance signal by the change of the entropy value of the signal in the wavelet transform domain, the sensitivity and accuracy of extracting the characteristics of the voltage ripple and voltage sag signal are improved, and at the same time, quantitative analysis of the disturbance signal in the DC distribution network is realized based on time entropy and energy entropy, and measures are taken to process when the disturbance exceeds the threshold, so that the DC distribution network can operate safely and stably.

[0005] A method for extracting and analyzing features of disturbance signals in a DC distribution network based on Tsallis wavelet comprises the following steps:

[0006] Step 1: Analyze the generation mechanism of voltage ripple and voltage sag signals in the DC distribution network and obtain the disturbance signal generation method;

[0007] Step 2: Propose a DC distribution network signal feature extraction method based on Tsallis entropy theory;

[0008] Step 3: Propose a method to analyze the wavelet signals corresponding to voltage ripple and voltage sag in the time domain and energy domain respectively based on Tsallis entropy theory;

[0009] Step 4: Construct a DC distribution network model, introduce disturbances on the AC side of the DC distribution network model through the disturbance signal generation method determined in step 1, generate DC voltage ripple and voltage sag signals in the DC side voltage, extract the disturbance signal characteristics on the DC side of the DC distribution network through the method proposed in step 2, and calculate the time entropy and energy entropy of the disturbance signal characteristics in the DC distribution network in the time domain and energy domain according to the method proposed in step 3;

[0010] Step 5: Determine and process the voltage ripple content and voltage sag based on time entropy and energy entropy.

[0011] Step 1 is as follows:

[0012] By analyzing the three-phase imbalance problem of the three-phase load on the AC side during the operation of the power grid through the symmetrical component method, the A phase current i a expression:

[0013]

[0014] Among them, I m1 , I m2 , I m0 They are positive sequence, negative sequence and zero sequence current amplitudes respectively; are the positive, negative and zero sequence current phase angles respectively; ω is the angular frequency of the alternating current, and t is the time;

[0015] S a , S b , S c They represent the switching functions of the three-phase converters a, b, and c respectively; the switching functions of the three-phase converters a, b, and c are defined as S a , S b , S c , when S a , S b , S c When S is equal to 1, it means that the upper bridge arm of the phase converter is turned on; a , Sb , S c When it is equal to -1, it means that the lower bridge arm of the phase converter is turned on, i a 、i b 、i c is the three-phase current, F is a coefficient that is independent of harmonics and sequence current, then the DC current i dc It can be expressed by the following formula:

[0016]

[0017] When there is negative sequence current input on the AC side, the DC side current i dc It will contain secondary ripples;

[0018] In addition, assuming that the AC contains harmonics, the AC voltage u of each phase is x and the alternating current i x Set to the following format:

[0019]

[0020] Then the DC active power P DC It can be expressed as:

[0021]

[0022] Among them, u a 、u b 、u c Represents the phase voltage of phase a, b, and c respectively, U km is the maximum value of the kth harmonic voltage, k is the number of higher harmonics; P A ,P B ,P C is the power of each phase; U m ,I m are the primary voltage and current amplitudes respectively; are the phase angles of the fundamental wave of AC voltage and AC current, θ ka ,θ kb ,θ kc , is the phase angle of the higher harmonics of each phase a, b, c, It is the power fluctuation caused by the influence of harmonics;

[0023] Since the steady-state power and fluctuation rate of the AC and DC sides are equal, the ripple content of the DC side can be calculated by the fluctuation of the AC side. The calculation method is as follows:

[0024]

[0025] in, It is power fluctuation; is the voltage fluctuation on the DC side; U dcis the DC side voltage; C dc is the DC side capacitance; h=0,-(k-1),(k-1) and h=0,-(k+1),(k+1) represent the number of voltage ripples on the DC side and their corresponding phase angles after the kth harmonic is added to the AC side. θ is the phase angle of the higher harmonics. The voltage fluctuation on the DC side can be represented by the integral plus proportion of the power fluctuation. After decomposing the integral and the harmonic order, it can be seen that when the AC power grid contains nth harmonic voltage, the DC side will contain n+1th voltage ripples.

[0026] Therefore, the three-phase imbalance, harmonics and voltage sag on the AC side of the DC distribution network will cause ripple and voltage sag in the DC distribution network. Therefore, the present invention adds three-phase imbalance, harmonics and voltage sag disturbances on the AC side of the DC distribution network, and the DC side of the distribution network will generate corresponding ripple and voltage sag signals.

[0027] The DC distribution network signal feature extraction method based on Tsallis entropy theory is as follows:

[0028] Step 2.1, signal acquisition: use Analog Devices AD736 voltage sensor to collect voltage ripple and voltage sag signals in the DC distribution network;

[0029] Step 2.2, signal preprocessing: use Texas Instruments TMS320C6713 to filter and process the noise and interference that are irrelevant to the voltage ripple and voltage sag disturbance signal characteristics in the complex high-pass signal collected in step 2.1;

[0030] Step 2.3, extract signal features: based on the Tsallis entropy theory, perform wavelet transform on the voltage ripple and voltage sag signals of the DC distribution network processed in step 2.2, and then discretize and decompose the wavelet signals of the voltage ripple and voltage sag into multi-scale wavelet components.

[0031] The wavelet signals of voltage ripple and voltage sag are discretized and decomposed into multi-scale wavelet components, which are expressed by the following formula:

[0032]

[0033] Where S q (d) is the discretized expression of Tsallis entropy; p(i) is the modified density distribution function of random variable i, and there is q is a non-expandable parameter, where q>1 and q<1 represent the ultra-wide and sub-wide scalability of the system respectively.

[0034] Based on the Tsallis entropy theory, the wavelet signals corresponding to the voltage ripple and voltage sag are analyzed in the time domain and energy domain respectively, specifically:

[0035] Time domain: The wavelet signal corresponding to voltage ripple and voltage sag can be described as: D = {d(k), k = 1, 2, ..., N}, defining the sliding data port of the wavelet and the reconstructed signal, the window width is w∈N, the sliding factor δ∈N, N represents the full width of the signal frequency band, then the sliding port can be expressed as:

[0036] W(m,w,δ,N)={d(k),k=1+mδ,...,w+mδ}

[0037] ,m=1,2,...,(Nw) / δ

[0038] The voltage ripple and voltage sag signal characteristics are analyzed at two different scales, time and frequency. The time window and frequency window of the signal characteristics are obtained at the time and frequency scales, and the above windows are divided into m segments that do not overlap each other. m (l) is the wavelet signal corresponding to the voltage ripple and voltage sag of the power system, d(k) is the probability of being distributed in the lth time interval, and L is the number of non-overlapping intervals generated by dividing the data window on the signal. Under the transformed time scale, the time entropy W of the wavelet signal corresponding to the voltage ripple and voltage sag is TE j (m) can be defined by the following formula:

[0039]

[0040] Energy domain: is the sum of the energy of the wavelet coefficients of the signal x(t) in the time window centered at time (mδ+w / 2) at scale M. At this time, Let p m (j) = E m (j) / E m ; Then the energy entropy W of the Tsallis wavelet signal at time (mδ+w / 2) is EE M (m) can be expressed as:

[0041]

[0042] Through the above definitions, the time entropy and energy entropy of the wavelet signal corresponding to the voltage ripple and voltage sag can be obtained. The time entropy realizes the measurement of the time complexity of the voltage ripple and voltage sag, and reflects the irregularity and disorder of the voltage ripple and voltage sag signals. By comparing the time entropy values ​​of different time series, the complexity of the voltage ripple and voltage sag signals of the DC power system can be judged; the energy entropy realizes the measurement of the complexity of the energy spectrum, and reflects the energy distribution and order of the signal. By comparing the energy entropy values ​​of different energy spectra, the frequency and randomness of the voltage ripple and voltage sag signals of the DC power system can be judged.

[0043] Step 5 is as follows:

[0044] Determine whether the time entropy and energy entropy of the voltage ripple and voltage sag signals are greater than a threshold;

[0045] When the time entropy and energy entropy of the ripple signal exceed the threshold and the voltage ripple content is too high, the filter in the DC distribution network is put into use to reduce the ripple content; when the time entropy and energy entropy of the ripple signal are lower than the threshold and the voltage ripple content is within the allowable range, the filter is cut off to reduce noise and loss and avoid resonance and delay.

[0046] When the voltage sag signal time entropy and energy entropy exceed the threshold voltage sag, in order to maintain system voltage stability, the fault handling device will be activated and the load will be cut off when necessary to ensure the safe operation of the DC distribution network.

[0047] Fault handling devices include dynamic voltage restorers that can avoid power interruptions, provide compensating voltage when voltage drops, and maintain system stability, as well as uninterruptible power supplies that provide backup power when the grid is undervoltage or suddenly loses power.

[0048] The present invention detects and extracts transient characteristics of signals in a DC distribution network. Based on wavelet transform, multi-scale processing of signals is performed to highlight voltage ripple and voltage sag faults that affect the power quality of the DC distribution network, which is conducive to accurate analysis of disturbance and fault signal characteristics in the DC distribution network.

[0049] This paper analyzes the disturbance formation mechanism that causes the power quality of the DC distribution network to deteriorate. Through model construction and mathematical derivation, it is clarified that the three-phase voltage imbalance on the AC side and the voltage harmonics measured on the AC side are the main reasons affecting the power quality on the DC side. These factors may cause power quality problems such as voltage fluctuations, ripples and voltage sags, which in turn affect the stability and performance of the power system.

[0050] The present invention uses the Tsallis wavelet analysis method to extract the characteristic time entropy and energy entropy of the signal, and successfully measures the complexity of the signal by analyzing the signal entropy value at multiple scales, thereby achieving the purpose of accurately analyzing transient signals.

[0051] Compared with the prior art, the advantages of the present invention are:

[0052] 1. A disturbance and fault detection method for DC distribution network based on Tsallis wavelet is proposed. The Tsallis wavelet entropy method can also be widely used in the transient analysis process of various power systems.

[0053] 2. The signal feature extraction method based on Tsallis wavelet entropy proposed in this invention makes up for a series of problems of traditional wavelet decomposition methods such as high computational complexity, signal leakage and stacking. Compared with the Shannon wavelet entropy method, this method can more accurately extract the signal features of voltage ripple and voltage sag in power quality. When targeting high-order harmonics, this method can reflect the harmonic injection and harmonic differences that the Shannon wavelet entropy method cannot reflect.

[0054] 3. The present invention realizes the accurate extraction and rapid analysis of the voltage ripple and voltage sag signal characteristics in the DC distribution network, and can quickly deal with the problems of excessive ripple content and voltage sag in the system, thereby avoiding serious impact on the power quality of the DC distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of the method of the present invention;

[0056] Figure 2 It is a flowchart of the DC distribution network signal feature extraction method based on Tsallis entropy theory;

[0057] Figure 3 is a structural diagram of a DC distribution network model of the present invention;

[0058] Figure 4 It is a flowchart of voltage ripple and voltage sag signal feature extraction and analysis;

[0059] Figure 5 It is a flow chart for judging and processing voltage ripple content and voltage sag based on time entropy and energy entropy. DETAILED DESCRIPTION

[0060] Figure 1 In the present invention, a method for extracting and analyzing the disturbance signal characteristics of a DC distribution network based on Tsallis wavelet is divided into the following steps:

[0061] Step 1: Generation mechanism of ripple and voltage sag signals and disturbance signal generation in DC distribution network.

[0062] During the operation of the power grid, there is a high probability that the three-phase load on the AC side will have a three-phase imbalance problem. By studying it through the symmetrical component method, we can get the A phase current i a expression.

[0063]

[0064] Among them, I m1 , I m2 , I m0 They are positive sequence, negative sequence and zero sequence current amplitudes respectively; They are the positive sequence, negative sequence and zero sequence current phase angles respectively.

[0065] S a , S b , S c They represent the switching functions of the three-phase converters a, b, and c respectively; the switching functions of the three-phase converters a, b, and c are defined as S a , S b , S c , when S a , S b , S c When S is equal to 1, it means that the upper bridge arm of the phase converter is turned on; a , S b , S c When it is equal to -1, it means that the lower bridge arm of the phase converter is turned on. a 、i b 、i c is the three-phase current, and F is a coefficient that is independent of harmonics and sequence current. Then the DC current i dc It can be expressed by the following formula.

[0066]

[0067] When there is negative sequence current input on the AC side, the DC side current i dc There will be secondary ripples in it.

[0068] In addition, the harmonics contained in AC will also become an important cause of DC harmonics. Assuming that AC contains harmonics, the AC voltage u of each phase x and the alternating current i x Set it as follows.

[0069]

[0070] Then the DC active power P DC It can be expressed as:

[0071]

[0072] Where k is the number of higher harmonics; P A ,P B ,P C is the power of each phase; U m ,I m are the voltage and current amplitudes respectively; They are the phase angles of the fundamental wave of AC voltage and AC current respectively. ka ,θ kb ,θ kc , is the phase angle of the higher harmonics of each phase a, b, c, It is the power fluctuation caused by the influence of harmonics.

[0073] Since the steady-state power quantity and fluctuation rate on the AC side and the DC side are equal, the ripple content on the DC side can be calculated through the fluctuation on the AC side.

[0074]

[0075] in, It is power fluctuation; is the voltage fluctuation on the DC side; U dc is the DC side voltage; C dc is the DC side capacitor. The DC side voltage fluctuation can be expressed by integrating the power fluctuation and adding a proportion. After decomposing the integral and harmonic order, it can be seen that when the AC power grid contains nth harmonic voltage, it will cause the DC side to contain n+1th voltage ripple.

[0076] Therefore, the three-phase imbalance, harmonics and voltage sag on the AC side of the DC distribution network will cause ripple and voltage sag in the DC distribution network. Therefore, the present invention adds three-phase imbalance, harmonics and voltage sag disturbances on the AC side of the DC distribution network, and the DC side of the distribution network will generate corresponding ripple and voltage sag signals.

[0077] Step 2: Propose a DC distribution network signal feature extraction method based on Tsallis entropy theory.

[0078] Transient signals in power systems are usually short in duration and have concentrated energy. Transient signals contain a large amount of ripple and interference, which will affect the reliability and accuracy of signal extraction. In addition, mutations and jumps in transient signals will also seriously affect the stability of the power system. Therefore, it is extremely important to extract more important signal features from a large number of interference signals. In DC distribution networks, voltage ripple and voltage sag are the main factors affecting power quality. The relevant research on the extraction of voltage ripple and voltage sag fault signal features has broad prospects.

[0079] Wavelet transform can analyze signal characteristics at multiple scales. Wavelet analysis based on Tsallis entropy theory can analyze transient signals of power systems at both time and energy scales, avoiding a series of shortcomings of traditional wavelet analysis methods such as complex calculations and signal stacking, and therefore has broad application prospects. This method is a new decomposition standard for disturbance signals, which can effectively measure fault disturbance signals such as voltage ripple and voltage sag in DC power systems. This method discretizes the wavelet signals of voltage ripple and voltage sag into multi-scale wavelet components, which can be expressed by the following formula:

[0080]

[0081] Where S q (d) is the discretized expression of Tsallis entropy; p(i) is the modified density distribution function of random variable i, and there is q is a non-expandable parameter. q>1 and q<1 represent the ultra-wide and sub-wide scalability of the system respectively. For different systems, the selected q value is also different. In order to make this method have good scalability, the q value in this system is 2.

[0082] like Figure 2 ,The process of extracting the characteristics of voltage ripple and voltage sag signals of DC distribution network based on Tsallis entropy theory can be summarized as the following steps:

[0083] (1) Signal acquisition: Use high-precision signal sampling equipment such as sensors or signal collectors to collect various transient signal characteristics of the power system to obtain high-fidelity signals. Extensive extraction of various disturbance signal characteristics of the DC distribution network is the basis for extracting voltage ripple and voltage sag faults. The present invention uses Analog Devices AD736 voltage sensor to collect voltage ripple and voltage sag signals in the DC distribution network.

[0084] (2) Signal preprocessing: The collected power system signal is likely to contain noise and interference that are irrelevant to the voltage ripple and voltage sag disturbance signal characteristics. Therefore, the signal needs to be filtered. The present invention uses Texas Instruments TMS320C6713 to filter and process complex high-pass signals.

[0085] (3) Based on the Tsallis entropy theory, the signal characteristics of the voltage ripple and voltage sag in the DC distribution network are transformed by wavelet transform, and the voltage ripple and voltage sag signals are discretized and decomposed into multi-scale wavelet components through the above formula.

[0086] Step 3: Propose a wavelet component analysis method corresponding to voltage ripple and voltage sag in the time domain and energy domain respectively based on Tsallis entropy theory;

[0087] Time domain: The wavelet signal corresponding to voltage ripple and voltage sag can be described as: D = {d(k), k = 1, 2, ..., N}, defining the sliding data port of the wavelet and the reconstructed signal, the window width is w∈N, the sliding factor δ∈N, then the sliding port can be expressed as:

[0088] W(m,w,δ,N)={d(k),k=1+mδ,...,w+mδ}

[0089] ,m=1,2,...,(Nw) / δ

[0090] The voltage ripple and voltage sag signal characteristics are analyzed at two different scales, time and frequency. At the time and frequency scales, the time window and frequency window of the signal characteristics are obtained, and the above windows are divided into m segments that do not overlap each other. m (l) is the wavelet signal corresponding to the voltage ripple and voltage sag fault of the power system, and the probability that d(k) is distributed in the lth time interval. Under the transformed time scale, the time entropy of the wavelet signal of the voltage ripple and voltage sag fault can be defined by the following formula:

[0091]

[0092] Energy domain: is the energy sum of the wavelet coefficients of the signal x(t) in the time window centered at time (mδ+w / 2) at scale M. At this time, Let p m (j) = E m (j) / E m ; Then the energy entropy of the Tsallis wavelet signal at time (mδ+w / 2) can be expressed as:

[0093]

[0094] Through the above definitions, the time entropy and energy entropy of the wavelet signal corresponding to the voltage ripple and voltage sag can be obtained. The time entropy measures the time complexity of the voltage ripple and voltage sag, and reflects the irregularity and disorder of the voltage ripple and voltage sag signals. By comparing the time entropy values ​​of different time series, the complexity of the voltage ripple and voltage sag signals of the DC power system can be determined. The energy entropy measures the complexity of the energy spectrum, reflecting the energy distribution and order of the signal. By comparing the energy entropy values ​​of different energy spectra, the frequency and randomness of the voltage ripple and voltage sag signals of the DC power system can be determined.

[0095] like Figure 4 , Step 4: Build Figure 3In the DC distribution network model shown in the figure, the introduction of disturbance on the AC side of the DC distribution network by the disturbance signal generation method determined in step one will generate voltage ripple and voltage sag signals on the DC side of the distribution network, and the addition of disturbance on the AC side of the distribution network system will generate DC ripple and voltage sag signals in the DC side voltage. The method of step two is used to extract the disturbance signal characteristics on the DC side of the DC distribution network and perform preprocessing. According to the method proposed in step three, the voltage ripple signal characteristics in the DC distribution network are analyzed at multiple scales in the time domain and the energy domain, and the ripple content contained in the distribution network signal and the influence of the voltage sag are quantitatively measured by time entropy and energy entropy.

[0096] Step 5: Determine and process the voltage ripple content and voltage sag based on time entropy and energy entropy.

[0097] Excessive voltage ripple content in the DC distribution network will seriously affect the stable operation of the system, causing a series of serious consequences such as decreased performance of electrical equipment, noise interference, shortened life of power components, and increased system heat generation.

[0098] In addition, compared with excessive voltage ripple content, voltage sag may even directly lead to the decoupling of the DC distribution network. Therefore, when the voltage ripple and voltage sag signal content is too high, corresponding measures must be taken.

[0099] When the voltage ripple content is too high, the GRM40 series filters in the DC distribution network are put into use to reduce the ripple content. When the ripple content is within the allowable range, the filter is cut off to reduce noise and loss, and avoid resonance and delay. When the voltage drops too much, in order to maintain the stability of the system voltage, it is necessary to add an action device to handle the fault, and cut off the load when necessary to ensure the safe operation of the DC distribution network. In addition, to avoid power outages, voltage compensation devices such as dynamic voltage restorers (DVRs) can be installed in the DC distribution network to provide compensation voltage when the voltage drops and maintain system stability; in addition, uninterruptible power supplies (UPS) can also be installed to ensure the power supply of critical loads and provide backup power when the grid is undervoltage or suddenly loses power.

[0100] like Figure 5 According to the time entropy and energy entropy, the judgment and processing of voltage ripple content and voltage sag can be divided into the following steps:

[0101] (1) Determine whether the time entropy and energy entropy of the voltage ripple and voltage sag signals are greater than a threshold;

[0102] (2) When the time entropy and energy entropy of the ripple signal exceed the threshold and the voltage ripple content is too high, the filter in the DC distribution network is put into use to reduce the ripple content; when the time entropy and energy entropy of the ripple signal are lower than the threshold and the voltage ripple content is within the allowable range, the filter is cut off to reduce noise and loss and avoid the occurrence of resonance and delay;

[0103] (3) When the time entropy and energy entropy of the voltage sag signal exceed the threshold voltage sag, in order to maintain the system voltage stability, the fault handling device will be activated and the load will be cut off if necessary to ensure the safe operation of the DC distribution network.

Claims

1. A method for extracting and analyzing the characteristics of disturbance signals in a DC distribution network based on Tsallis wavelet, characterized in that: The following steps are involved: Step 1: Analyze the generation mechanism of voltage ripple and voltage sag signals in the DC distribution network and obtain the disturbance signal generation method; Step 2: Propose a DC distribution network signal feature extraction method based on Tsallis entropy theory; Step 3: Propose a method to analyze the wavelet signals corresponding to voltage ripple and voltage sag in the time domain and energy domain respectively based on Tsallis entropy theory; Step 4: Construct a DC distribution network model, introduce disturbances on the AC side of the DC distribution network model through the disturbance signal generation method determined in step 1, generate DC voltage ripple and voltage sag signals in the DC side voltage, extract the disturbance signal characteristics on the DC side of the DC distribution network through the method proposed in step 2, and calculate the time entropy and energy entropy of the disturbance signal characteristics in the DC distribution network in the time domain and energy domain according to the method proposed in step 3; Step 5: Determine and process the voltage ripple content and voltage sag based on time entropy and energy entropy.

2. According to claim 1, a method for extracting and analyzing the characteristics of disturbance signals in a DC distribution network based on Tsallis wavelet is characterized in that: Step 1 is as follows: By analyzing the three-phase imbalance problem of the three-phase load on the AC side during the operation of the power grid through the symmetrical component method, the A phase current i a expression: Among them, I m1 , I m2 , I m0 They are positive sequence, negative sequence and zero sequence current amplitudes respectively; are the positive, negative and zero sequence current phase angles respectively; ω is the angular frequency of the alternating current, and t is the time; S a , S b , S c They represent the switching functions of the three-phase converters a, b, and c respectively; the switching functions of the three-phase converters a, b, and c are defined as S a , S b , S c , when S a , S b , S c When S is equal to 1, it means that the upper bridge arm of the phase converter is turned on; a , S b , S c When it is equal to -1, it means that the lower bridge arm of the phase converter is turned on, i a 、i b 、i c is the three-phase current, F is a coefficient that is independent of harmonics and sequence current, then the DC current i dc It can be expressed by the following formula: When there is negative sequence current input on the AC side, the DC side current i dc It will contain secondary ripples; In addition, assuming that the AC contains harmonics, the AC voltage u of each phase is x and the alternating current i x Set to the following format: Then the DC active power P DC It can be expressed as: Among them, u a 、u b 、u c Represents the phase voltage of phase a, b, and c respectively, U km is the maximum value of the kth harmonic voltage, k is the number of higher harmonics; P A ,P B ,P C is the power of each phase; U m ,I m are the primary voltage and current amplitudes respectively; are the phase angles of the AC voltage fundamental wave and the AC current fundamental wave, θ ka ,θ kb ,θ kc , is the phase angle of the higher harmonics of each phase a, b, c, It is the power fluctuation caused by the influence of harmonics; Since the steady-state power and fluctuation rate of the AC and DC sides are equal, the ripple content of the DC side can be calculated by the fluctuation of the AC side. The calculation method is as follows: in, It is power fluctuation; is the voltage fluctuation on the DC side; U dc is the DC side voltage; C dc is the DC side capacitance; h=0,-(k-1),(k-1) and h=0,-(k+1),(k+1) represent the number of voltage ripples on the DC side and their corresponding phase angles after the kth harmonic is added to the AC side. θ is the phase angle of the higher harmonics. The voltage fluctuation on the DC side can be represented by the integral plus proportion of the power fluctuation. After decomposing the integral and the harmonic order, it can be seen that when the AC power grid contains nth harmonic voltage, the DC side will contain n+1th voltage ripples. Therefore, the three-phase imbalance, harmonics and voltage sag on the AC side of the DC distribution network will cause ripple and voltage sag in the DC distribution network. Therefore, the present invention adds three-phase imbalance, harmonics and voltage sag disturbances on the AC side of the DC distribution network, and the DC side of the distribution network will generate corresponding ripple and voltage sag signals.

3. The method for extracting and analyzing the disturbance signal characteristics of a DC distribution network based on Tsallis wavelet according to claim 1 is characterized in that: The DC distribution network signal feature extraction method based on Tsallis entropy theory is as follows: Step 2.1, signal acquisition: use Analog Devices AD736 voltage sensor to collect voltage ripple and voltage sag signals in the DC distribution network; Step 2.2, signal preprocessing: use Texas Instruments TMS320C6713 to filter and process the noise and interference that are irrelevant to the voltage ripple and voltage sag disturbance signal characteristics in the complex high-pass signal collected in step 2.1; Step 2.3, extract signal features: based on the Tsallis entropy theory, perform wavelet transform on the voltage ripple and voltage sag signals of the DC distribution network processed in step 2.2, and then discretize and decompose the wavelet signals of the voltage ripple and voltage sag into multi-scale wavelet components.

4. The method for extracting and analyzing the disturbance signal characteristics of a DC distribution network based on Tsallis wavelet according to claim 3 is characterized in that: The wavelet signals of voltage ripple and voltage sag are discretized and decomposed into multi-scale wavelet components, which are expressed by the following formula: Where S q (d) is the discretized expression of Tsallis entropy; p(i) is the modified density distribution function of random variable i, and there is q is a non-expandable parameter, where q>1 and q<1 represent the ultra-wide and sub-wide scalability of the system respectively.

5. The method for extracting and analyzing the disturbance signal characteristics of a DC distribution network based on Tsallis wavelet according to claim 1 is characterized in that: Based on the Tsallis entropy theory, the wavelet signals corresponding to the voltage ripple and voltage sag are analyzed in the time domain and energy domain respectively, specifically: Time domain: The wavelet signal corresponding to voltage ripple and voltage sag can be described as: D = {d(k), k = 1, 2, ..., N}, defining the sliding data port of the wavelet and the reconstructed signal, the window width is w∈N, the sliding factor δ∈N, N represents the full width of the signal frequency band, then the sliding port can be expressed as: W(m,w,δ,N)={d(k),k=1+mδ,...,w+mδ},m=1,2,...,(Nw) / δ The voltage ripple and voltage sag signal characteristics are analyzed at two different scales, time and frequency. The time window and frequency window of the signal characteristics are obtained at the time and frequency scales, and the above windows are divided into m segments that do not overlap each other. m (l) is the wavelet signal corresponding to the voltage ripple and voltage sag of the power system, d(k) is the probability of being distributed in the lth time interval, and L is the number of non-overlapping intervals generated by dividing the data window on the signal. Under the transformed time scale, the time entropy W of the wavelet signal corresponding to the voltage ripple and voltage sag is TE j (m) can be defined by the following formula: Energy domain: is the sum of the energy of the wavelet coefficients of the signal x(t) in the time window centered at time (mδ+w / 2) at scale M. At this time, Let p m (j) = E m (j) / E m ; Then the energy entropy W of the Tsallis wavelet signal at time (mδ+w / 2) is EE M (m) can be expressed as: Through the above definitions, the time entropy and energy entropy of the wavelet signal corresponding to the voltage ripple and voltage sag can be obtained. The time entropy realizes the measurement of the time complexity of the voltage ripple and voltage sag, and reflects the irregularity and disorder of the voltage ripple and voltage sag signals. By comparing the time entropy values ​​of different time series, the complexity of the voltage ripple and voltage sag signals of the DC power system can be judged; the energy entropy realizes the measurement of the complexity of the energy spectrum, and reflects the energy distribution and order of the signal. By comparing the energy entropy values ​​of different energy spectra, the frequency and randomness of the voltage ripple and voltage sag signals of the DC power system can be judged.

6. The method for extracting and analyzing the disturbance signal characteristics of a DC distribution network based on Tsallis wavelet according to claim 1 is characterized in that: Step 5 is as follows: Determine whether the time entropy and energy entropy of the voltage ripple and voltage sag signals are greater than a threshold; When the time entropy and energy entropy of the ripple signal exceed the threshold and the voltage ripple content is too high, the filter in the DC distribution network is put into use to reduce the ripple content; when the time entropy and energy entropy of the ripple signal are lower than the threshold and the voltage ripple content is within the allowable range, the filter is cut off to reduce noise and loss and avoid resonance and delay. When the voltage sag signal time entropy and energy entropy exceed the threshold voltage sag, in order to maintain system voltage stability, the fault handling device will be activated and the load will be cut off when necessary to ensure the safe operation of the DC distribution network.

7. The method for extracting and analyzing the characteristics of disturbance signals in a DC distribution network based on Tsallis wavelet according to claim 6 is characterized in that: Fault handling devices include dynamic voltage restorers that can avoid power interruptions, provide compensating voltage when voltage drops, and maintain system stability, as well as uninterruptible power supplies that provide backup power when the grid is undervoltage or suddenly loses power.

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