Internet of Things-Based Environmental Emergency Power Management Method and System

Abstract

The present invention provides an environmental emergency power management method and system based on the Internet of Things. The method is applied to the fault identification and fault location of a 10 kV cable distribution network, and the method includes: real-time collecting three-phase current data information of the 10 kV cable distribution network alternating current; performing data feature extraction and filtering preprocessing to obtain a three-phase alternating current current sine wave time-domain denoising signal I′ X (t) after filtering and denoising; locating the distance from the fault occurrence position to the busbar and identifying the cause of the distribution network fault; controlling the corresponding electromagnetic relay to perform overcurrent protection or tripping the circuit breaker to perform ground fault protection to achieve environmental emergency power cut-off. The present invention divides the three-phase alternating current current sine wave signal within a half cycle into N windows and selectively selects different size factors and displacement factors, thereby improving the accuracy of feature extraction and being able to perform fault location and fault cause identification according to the reconstructed three-phase alternating current current signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of power environment emergency management, and particularly relates to an environmental emergency power management method and system based on the Internet of Things. Background Art

[0002] At present, the information network system has begun to become one of the core pillars for maintaining the safe and stable operation of the power grid. However, with the continuous enhancement of the power network construction, it is gradually facing more risk sources and more security challenges. The main security risk sources in the power network come from the natural objective environment, such as some natural disasters like thunderstorms and hailstorms; or power network failures caused by factors such as the instability and defects of network devices themselves. The safe operation of the 10kV distribution network is directly related to the living standards of the people and the development of the national economy, and it is mainly overhead lines. Due to the long-term operation of overhead lines in the outdoor environment, the insulation aging phenomenon is serious, and various faults occur frequently, especially short-circuit faults and disconnection faults are the most common. After a disconnection fault occurs in the distribution network, there will be a situation where it continues to operate without obvious overcurrent, and it is difficult for the operation and maintenance personnel to detect this fault. Only after the user feedback can the fault be diagnosed and repaired, which is difficult to meet the development requirements of the current smart grid.

[0003] In the prior art, a Chinese patent with the publication number CN113067412B discloses a 5G distributed Internet of Things power detection system and monitoring method. After collecting and recording terminal data, this method aggregates and converts it into preprocessed data, and sends the preprocessed data to the management and monitoring terminal. The management and monitoring terminal calculates and displays the results based on the preprocessed data, and issues an emergency instruction to control the emergency equipment at the emergency end to perform emergency actions. However, this prior art only discloses that the management and monitoring terminal calculates the root mean square value of n preprocessed data u 1 to u n and the terminal with an out-of-tolerance deviation has a fault, but it does not clearly state how to preprocess the data collected by the terminal, and what kind of operation is performed on the collected and preprocessed data u n to identify the fault of the power grid, and it also does not clearly state what factors cause the power grid fault, and then take targeted emergency actions, such as turning on the electromagnetic relay for overcurrent protection, etc., for power grid fault emergency treatment.

[0004] The Chinese patent application document with the publication number CN114487700A discloses a distribution network fault detection remote control unit device and a remote control system, and discloses that the main control unit is wired-connected to the electronic current sensor, the electronic voltage sensor, and the secondary signal of the power-taking coil, and is used to collect phase voltage and phase current in real time. The main control unit is used to synthesize three-phase zero-sequence voltage and three-phase zero-sequence current according to the phase voltage and phase current data collected synchronously in three phases of A, B, and C, and calculate the steady-state zero-sequence active and reactive power and zero-sequence power distribution Internet of Things fault detection factor power parameters, collect the transient zero-sequence voltage and current waveforms, and perform steady-state and transient logical analysis according to the power parameters and waveforms to determine whether the line has overcurrent, short-circuit, grounding, and open-circuit faults. However, it does not disclose how to calculate the steady-state zero-sequence active and reactive power and zero-sequence power distribution Internet of Things fault detection factor power parameters, and it is not clear what the specific judgment technical indicators of the steady-state and transient logical analysis are when the line has overcurrent, short-circuit, grounding, and open-circuit faults. Summary of the Invention

[0005] In view of the above defects, the present invention provides an Internet of Things-based environmental emergency power management method and system.

[0006] The present invention provides the following technical solutions: An Internet of Things-based environmental emergency power management method, which is applied to the fault identification and fault location of a 10kV cable distribution network. The 10kV cable distribution network is set with an unearthed neutral point. The method includes the following steps:

[0007] S1: Collect the three-phase current data information of the alternating current of the 10kV cable distribution network in real time;

[0008] S2: Perform data feature extraction and filtering preprocessing to obtain the three-phase alternating current current sine wave time-domain denoised signal I′ X (t);

[0009] S3: Based on the three-phase alternating current current sine wave time-domain denoised signal I′ X (t) obtained in step S2, further locate the distance from the fault occurrence location to the bus and identify the cause of the distribution network fault;

[0010] S4: Control the corresponding electromagnetic relay to perform overcurrent protection or trip the circuit breaker to perform grounding fault protection to achieve environmental emergency power cut-off and ensure the safety of 10kV cable power distribution.

[0011] Further, step S2 includes the following steps:

[0012] S21: Construct a three-phase alternating current current sine wave signal model collected in step S1:

[0013]

[0014] where X = A, B, C; t is the t-th moment of signal acquisition; I X (t) is the sinusoidal current signal of the X-phase at the t-th moment; Amp IX (t) is the current amplitude of the X-phase at the t-th moment; ω is the fundamental frequency of the X-phase current output by the AC power grid, is the instantaneous phase of the X-phase current at the t-th moment;

[0015] S22: Perform Fourier transform on the three-phase alternating current sinusoidal current signals in the S21 step:

[0016]

[0017] where j is an imaginary number; is the three-phase alternating current sinusoidal current signal in the frequency domain;

[0018] S23: Calculate the discrete wavelet coefficients of the three-phase alternating current sinusoidal current signals

[0019] where, is the real number field, is for calculating and is the inner product calculation function of, * represents the complex conjugate of; n is the n-th window for dividing the signal of the X-phase current output by the AC power grid within half a cycle, n = 1, 2,... N; N is the total number of windows for dividing the signal of the X-phase current output by the AC power grid within half a cycle for discrete analysis; is the scale filtering function of wavelet filtering, is the displacement filtering function of the wavelet filtering model;

[0020] S24: Filter multiple discrete wavelet filtering coefficients to construct a wavelet coefficient filtering reconstruction threshold function

[0021]

[0022] where λ is the filtering threshold, is the filtered discrete wavelet coefficient; sgn(·) is the step function;

[0023] S25: Reconstruct the three-phase alternating current sinusoidal current signal in the frequency domain according to the filtered discrete wavelet coefficients

[0024] S26: For the three-phase alternating current sinusoidal current signal in the frequency domain reconstructed in the S25 step Perform an inverse Fourier transform to obtain the denoised three-phase alternating current sine wave time-domain denoised signal I′ X (t):

[0025]

[0026] Furthermore, the scale filtering function of the wavelet filtering is as follows:

[0027]

[0028] where ω n is the center frequency of the nth window that divides the signal of the X-phase current output by the AC power grid within half a cycle; T n is the sub-period of the three-phase alternating current sine wave within the nth window, T n = 2τ n , τ n is the half sub-period time slot of the three-phase alternating current sine wave within the nth window; q is a correction coefficient; the calculation formula of the correction coefficient q is as follows:

[0029]

[0030] Furthermore, the displacement filtering function of the wavelet filtering model is as follows:

[0031]

[0032] Furthermore, the distance from the location where the fault occurs to the busbar in the S3 step includes:

[0033] S301. Calculate the total current I(t) output by the 10kV distribution network cable according to the filtered result:

[0034]

[0035] S302. Calculate the energy factor E[I(t)] of the total current I(t) output by the 10kV distribution network cable and extract the instantaneous frequency ω e (t) and the envelope amplitude Amp e (t):

[0036]

[0037] S303. Calculate the moment t when the energy factor is the largest:

[0038]

[0039] s.t ω e (t) ≤ ω

[0040] Amp e (t)≥Amp IX (t);

[0041] S304, calculating the maximum time t of the energy factor calculated in step S304 max and the initial time t of the Pth monitoring point 0P The time difference Δt P , and its relationship with the initial time t of the Qth monitoring point 0Q The time difference Δt 9

[0042] Δt P =t max -t 0p ; Δt 9 =t max -t 0Q ;

[0043] S305. Calculate the distance L between the fault point and the Pth monitoring point P , and the distance between the fault point and the Qth monitoring point L Q :

[0044]

[0045] Where, v is the propagation speed of the fault wave, v = 2.95 × 10 8 m / s.

[0046] Furthermore, the identification of the cause of the distribution network fault in step S3 includes:

[0047] S311, construct the X-phase reference coordinate system Clarke transformation matrix of the AC current sine wave time domain denoising signal with the X-phase coinciding with the α-axis in the αβ stationary coordinate system, and calculate the α-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system β axis component

[0048]

[0049] in, is the α-axis component of the X-phase AC current sinusoidal wave time domain denoised signal, is the β-axis component of the X-phase AC current sinusoidal wave time domain denoised signal;

[0050] S312. Calculate the residual current I in the 10kV cable r (t):

[0051]

[0052] S313. Calculate the α-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system within a unit time Maximum current sine signal change rate m αX and the β-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system Maximum current sine signal change rate m βX and the maximum current sine signal change rate m of the residual current in the 10 kV cable r :

[0053]

[0054] S314. Further calculate the first fault judgment parameter D of the X-phase r，αX the second fault judgment parameter D of the X-phase αX，βX and the third fault judgment parameter D of the X-phase βx,αX :

[0055] D r，αX = m r / m αX , D αX,βX = m αX / m βX , D βX，αx = m βX / m αX ;

[0056] S315. According to the fault judgment parameters obtained from S314, judge that the fault type is one of single-phase ground fault, two-phase short circuit fault between phases or three-phase short circuit fault between phases

[0057] Furthermore, the specific judgment method in step S315 is as follows

[0058] S3151. When the first fault judgment parameters of phases A, B, and C are all greater than the first judgment threshold D 1 , it is a ground fault; otherwise, it is a non-ground fault; the first judgment threshold D 1 is 0.07

[0059] S3152. When it belongs to the ground fault situation, further judge whether the sum of the second fault judgment parameters of phases A, B, and C D αA，βA + D αB，βB + D αC,βC is greater than the second judgment threshold D 2 , if it is greater, it is a single-phase ground fault; otherwise, it is a two-phase ground fault; the second judgment threshold D 2 is 80

[0060] S3153. When it belongs to the non-ground fault situation, further judge whether the sum of the third fault judgment parameters of phases A, B, and C is greater than the third judgment threshold D3 , if it is greater, it is a two-phase short circuit fault between phases; otherwise, it is a three-phase short circuit fault between phases; the third judgment threshold D 3 is 6.

[0061] Furthermore, in the step S3152, when it is determined that the 10 kV cable distribution network is a grounding fault, the criteria for further identifying the grounded phase are:

[0062] When D αA，βA ≥D 5 , D αB，βB <D 5 and D αC，βC <D 5 , it is an A-phase grounding fault;

[0063] When D αB，βB ≥D 5 , D αA,βA <D 5 and D αC，βC <D 5 , it is a B-phase grounding fault;

[0064] When D αC，βC ≥D 5 , D αA,βA <D 5 and D αB，βB <D 5 , it is a C-phase grounding fault; D 5 is the fifth judgment threshold, D 5 is 10;

[0065] When D αA，βA >D aC,βC and D αA,βA >D αC,βC , it is an AB-phase grounding fault;

[0066] When D αB，βB >D αA,βA and D αC,β C>D αA,βA , it is a BC-phase grounding fault;

[0067] When D αC，βC >D αB，βB and D αA，βA >D αB，βB , it is an AC-phase grounding fault.

[0068] Furthermore, in the step S3153, when it is determined that the 10 kV cable distribution network is a non-grounding fault, the criteria for further identifying the two short-circuited phases are:

[0069] When D βA，αA <D 6 , D βB，αB <D6 and D βC，αC ≥ D 6 When it is, it is a short - circuit between phases A and B;

[0070] When D βB，αB < D 6 、D βC，αC < D 6 and D βA，αA ≥ D 6 When it is, it is a short - circuit between phases B and C;

[0071] When D βA，αA < D 6 、D βC，αC < D 6 and D βB，αB ≥ D 6 When it is, it is a short - circuit between phases A and C; D 6 is the fifth judgment threshold, and D 6 is 15.

[0072] The present invention also provides an Internet - of - Things - based environmental emergency power management system adopting the method as described above. The system includes a current real - time acquisition module, a filtering and denoising module, a fault location and cause identification module, and an emergency execution control module;

[0073] The current real - time acquisition module is used to real - time collect three - phase current data information of the 10kV cable distribution network alternating current;

[0074] The filtering and denoising module is used to perform data feature extraction and filtering pre - processing to obtain the filtered and denoised three - phase alternating current current sine - wave time - domain denoised signal I′ X (t);

[0075] The fault location and cause identification module is used to further locate the distance from the fault occurrence location to the bus and identify the cause of the distribution network fault based on the filtered and denoised three - phase alternating current current sine - wave time - domain denoised signal I′ X (t);

[0076] The emergency execution control module is used to control the corresponding electromagnetic relay for over - current protection or trip the circuit breaker for ground - fault protection to achieve environmental emergency power cut - off and ensure the safety of 10kV cable power distribution.

[0077] The beneficial effects of the present invention are:

[0078] 1. The present invention divides the sinusoidal wave signals of three-phase alternating current collected in real time into N windows within a half cycle [0, π]. After selecting the frequency domain ranges in which the instantaneous frequency ω(t) of each window lies, different scale filtering functions and displacement filtering functions are adapted. Then, for the sinusoidal wave signals of three-phase alternating current with different frequency domain ranges of instantaneous frequency ω(t), adaptive selections of different scales and displacements are made, effectively filtering different high-frequency or low-frequency signals selectively, and realizing the control of the displacement rate according to the magnitude of the noise. This effectively improves the matching degree between the filtering moving rate of wavelet filtering between different windows and the sinusoidal wave signal ω of the three-phase alternating current collected, thereby overcoming the situation in the prior art where a single displacement factor is used for filtering rate control from the start to the end of wavelet filtering, resulting in too fast filtering when there is too much noise in some windows and the inability to effectively identify and remove the noise, and reducing the filtering efficiency when the noise is low in some windows.

[0079] 2. The present invention calculates the energy factors E[I(t)] of the sinusoidal waves at three adjacent moments through the values of three consecutive sampling points of the sinusoidal wave signals of three-phase alternating current after wavelet transform, which is simple and fast. By extracting the instantaneous frequency and envelope amplitude of the energy factor, the change of the signal can be tracked quickly. If the calculated energy value through the energy factor E[I(t)] is larger, the amplitude or frequency of the given signal changes faster. The singularity of the signal is detected, and the moment when the energy factor is the largest is the fault site. Then, the distance between two adjacent monitoring points and the distance from the bus input end can be further calculated.

[0080] 3. The present invention separately takes the sinusoidal wave signals of three-phase alternating current after wavelet transform as the reference phase and converts them into current variables in the αβ stationary coordinate system, and extracts the α-axis components of each item from the instantaneous three-phase alternating current through the transformation matrix β-axis components and seven current components including the residual current Ir(t), and further performs real-time peak calculation and takes the ratio of the parameters within each phase as the fault discrimination index. Using the proposed fault index, the fault causes of ground faults or non-ground faults, which phase or which two phases have ground faults, or which two phases have interphase short-circuit faults can be judged step by step, and then the emergency equipment can be controlled specifically to execute grid quick-break, over-current protection or tripping. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In the following, the present invention will be described in more detail based on embodiments and with reference to the drawings. Among them:

[0082] Figure 1 is a schematic flow chart of the environmental emergency power management method based on the Internet of Things provided by the present invention;

[0083] Figure 2 Schematic diagram of dividing the signal within a half cycle of the three-phase alternating current sine wave signal collected in the filtering process provided by the present invention into N windows;

[0084] Figure 3 Comparison graph of the accuracy curves of feature extraction of the original data after 10 iterations of wavelet filtering using different scale factors and displacement factors for Comparative Examples 1 - 4;

[0085] Figure 4 Schematic diagram of the envelope amplitude extracted during the fault location process of the present invention;

[0086] Figure 5 Schematic diagram of the distance between the adjacent Pth and Qth monitoring points during the fault location of the present invention

[0087] Figure 6 Graph of the change of the three-phase alternating current sine wave over time when the method provided by the present invention identifies that the distribution network has a ground fault in phase A in step S3;

[0088] Figure 7 Graph of the change of the three-phase alternating current sine wave over time when the method provided by the present invention identifies that the distribution network has a ground fault in phases BC in step S3;

[0089] Figure 8 Graph of the change of the three-phase alternating current sine wave over time when the method provided by the present invention identifies that the distribution network has an interphase short circuit fault between phases AC in step S3;

[0090] Figure 9 Graph of the change of the three-phase alternating current sine wave over time when the method provided by the present invention identifies that the distribution network has an interphase short circuit fault between phases ABC in step S3;

[0091] Figure 10 Schematic diagram of the structure of the environment emergency power management system based on the Internet of Things provided by the present invention. Detailed implementation manners

[0092] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0093] As Figure 1As shown in the figure, it is a schematic flowchart of the environment emergency power management method based on the Internet of Things provided by the present invention. The method provided by the present invention is applied to the fault identification and fault location of a 10kV cable distribution network, and the 10kV cable distribution network is set with an unearthed neutral point. The method includes the following steps:

[0094] S1: Real-time collect the three-phase current data information of the 10kV cable distribution network alternating current; this step can be completed by a current sensor;

[0095] S2: Perform data feature extraction and filtering preprocessing to obtain the three-phase alternating current current sine wave time-domain denoised signal I′ X (t);

[0096] S2 specifically includes the following steps:

[0097] S21: Construct the three-phase alternating current current sine wave signal model collected in step S1:

[0098]

[0099] Among them, X = A, B, C; t is the t moment of signal collection; I X (t) is the X-phase current sine wave signal at the t moment; Amp IX (t) is the current amplitude of the X phase at the t moment; ω is the fundamental frequency of the X-phase current output by the AC power grid, is the instantaneous phase of the X-phase current at the t moment;

[0100] S22: Perform Fourier transform on the three-phase alternating current current sine wave signal in step S21:

[0101]

[0102] Among them, j is an imaginary number; is the Fourier transform result of the three-phase alternating current current sine wave signal I X (t), that is, the three-phase alternating current current sine wave frequency-domain signal;

[0103] S23: Calculate the discrete wavelet coefficients of the three-phase alternating current current sine wave signal

[0104] Among them, is the real number field, is for calculating and inner product calculation function, * represents Complex conjugate; n is the nth window that divides the signal of the X-phase current output by the AC power grid within half a cycle, where n = 1, 2,... N; N is the total number of windows into which the signal of the X-phase current output by the AC power grid within half a cycle is divided for discrete analysis; is the scale filtering function of wavelet filtering, and is the displacement filtering function of the wavelet filtering model;

[0105] As Figure 2 shown, [0, π] is half a cycle of the sinusoidal wave signal of the three-phase alternating current collected. Within this half cycle, the sinusoidal wave signal is divided into N windows, forming a sinusoidal wave signal segment with a total of T N subcycles. The time slot of the nth subcycle is 2τn;

[0106] The scale filtering function of wavelet filtering is as follows:

[0107]

[0108] where ω n is the center frequency of the nth window that divides the signal of the X-phase current output by the AC power grid within half a cycle; T n is the sinusoidal wave subcycle of the three-phase alternating current within the nth window, and T n = 2τ n , 2τ n is the time slot of half of the sinusoidal wave subcycle of the three-phase alternating current within the nth window; q is a correction coefficient; the correction coefficient q is a correction coefficient function related to the frequency domain signal of the sinusoidal wave of the three-phase alternating current. The calculation formula of the correction coefficient q is as follows: The scale filtering function

[0109]

[0110] determines the frequency size of wavelet filtering. The larger is, the larger the time threshold for its filtering, and the smaller the filtering scale factor. Therefore, when the frequency of the sinusoidal wave signal of the three-phase alternating current collected is low, that is, |ω| ≤ ω n -τ n , let the value of its scale filtering function be 1, then a sinusoidal wave signal of the three-phase alternating current with a lower frequency signal can be decomposed; when the frequency of the sinusoidal wave signal of the three-phase alternating current collected is high, that is, ω n -τ n ≤ |ω| ≤ ω n +τ n , at this time, the subcycle T nIn the peak region, the value of its scale filtering function is Supplemented by a correction coefficient q related to the frequency domain signal of the three-phase alternating current sine wave If the scale filtering function value is less than 1, the scale factor of the filtering is larger, and it can filter high-frequency signals with higher frequencies; furthermore, by decomposing the half cycle [0, π] of the three-phase alternating current sine wave signal into N sub-cycle windows, and according to the center frequency of different sub-cycle windows and half a sub-cycle τ n The sum or difference is used to determine whether the collected three-phase alternating current sine wave signal ω is in the high-frequency or low-frequency signal region in real time, and then determine the scale filtering function of wavelet filtering, and then perform corresponding filtering feature extraction on the high-frequency or low-frequency frequency domain it is in;

[0111] The displacement filtering function of the wavelet filtering model is as follows:

[0112]

[0113] Similarly, the function value of the displacement filtering function of the wavelet filtering model determines the displacement size of each time in the wavelet filtering process. When the real-time frequency ω within the half cycle of the collected three-phase alternating current sine wave signal is between the nth window and the (n + 1)th window divided, that is, ω n +τ n ≤|ω|≤ω n+1 -τ n+1 , let the function value of the displacement filtering function of wavelet filtering be 1, and directly move from the end of the nth window to the start of the (n + 1)th window to perform low-frequency or high-frequency filtering using the scale filtering function. When it is in the (n + 1)th window, ω n+1 -τ n+1 ≤|ω|≤ω n+1 +τ n+1 , let the function value of the displacement filtering function be When it is in the nth window, that is, ω n -τ n ≤|ω|≤ω n +τ n , let the function value of the displacement filtering function be Therefore, by determining which window the real-time frequency ω of the three-phase alternating current sine wave signal collected in real time is in during the half cycle, and then performing shifts of different displacement scales, and further effectively controlling the movement rate from the previous window to the next window, the matching degree between the filtering movement rate of wavelet filtering between different windows and the ω of the collected three-phase alternating current sine wave signal is effectively improved. Furthermore, it overcomes the situation in the prior art where a single displacement factor is always used to control the filtering rate from the start to the end of wavelet filtering, resulting in too fast filtering when there is too much noise in some windows and the inability to effectively identify and remove the noise, and in some windows with low noise, the filtering efficiency is reduced.

[0114] S24: Filter multiple discrete wavelet filtering coefficients to construct a wavelet coefficient filtering reconstruction threshold function

[0115]

[0116] where λ is the filtering threshold, is the discrete wavelet coefficient after filtering; sgn(·) is the step function;

[0117] S25: Through the wavelet coefficient filtering reconstruction threshold function of the filtering threshold function constructed in S24, obtain the discrete wavelet coefficients of the high-frequency signal when and the discrete wavelet coefficients of the low-frequency signal when When Remove the discrete wavelet coefficients whose absolute values are lower than the filtering threshold λ (that is, make the discrete wavelet coefficient after filtering According to the discrete wavelet coefficient after filtering, reconstruct the three-phase alternating current current sine wave frequency domain signal

[0118]

[0119] S26: Perform the inverse Fourier transform on the three-phase alternating current current sine wave frequency domain signal reconstructed in step S25 to obtain the three-phase alternating current current sine wave time-domain denoised signal I′ X (t):

[0120]

[0121] A large amount of fault information is contained in the transient component. Therefore, it is necessary to analyze the data of the transient three-phase alternating current in the 10 kV power grid to accurately reflect whether the distribution network providing three-phase alternating current at this moment is in a normal state or a fault state, and can effectively identify the faults or abnormalities of equipment or power systems. It can also be used to handle faults and analyze the causes of faults. The three-phase alternating current of the distribution network is a sine wave signal. When the distribution network is in a fault state, the transient signal has characteristics such as high frequency and instantaneous disconnection. Wavelet transform constructs scale filtering functions with different filtering bands and displacement filtering functions As Figure 2 shown, by dividing the X-image current sine wave signal output by the distribution network within the half cycle [0, π] into N windows, each window has a center frequency. For example, the nth window has a center frequency ω n , and the length of this window is determined by the sub-cycle T n , and then by constructing the wavelet transform coefficients of the half-cycle sine wave filter with N segmented windows in steps S21 - S25 to identify the three-phase alternating current sine wave signal within different sub-cycles T n in the window (which sub-cycle) it is in, and then effectively select the scale filtering function for filtering within this window period to effectively identify high-frequency or low-frequency noise within the belonging frequency band, and select the displacement filtering function within this window period to select the length of displacement according to the frequency band within different window periods, and then perform variable-speed filtering during the entire wavelet filtering process, perform slow filtering on the high-noise frequency band specifically to improve the denoising effect, and thus improve the accuracy of the final filtering. At the same time, perform fast filtering on the low-noise frequency band specifically to avoid the long calculation time for preprocessing and feature extraction.

[0122] S3: Based on the three-phase alternating current sine wave time-domain denoised signal I′ X (t) after filtering and denoising in step S2, further locate the distance from the fault occurrence position to the bus and identify the cause of the distribution network fault;

[0123] S4. Control the corresponding electromagnetic relay for overcurrent protection or trip the circuit breaker for ground fault protection to achieve environmental emergency power cut-off and ensure the safety of 10 kV cable power distribution.

[0124] Comparative Example 1

[0125] Use the ANN algorithm to replace the wavelet transform in steps S21 - S26 of this application to filter and denoise the three-phase alternating current of the 10 kV cable distribution network collected in real time in step S1.

[0126] Comparative Example 2

[0127] Only use a = 2 n+1 Instead of the scale filtering function Calculate the discrete wavelet coefficients of the three-phase alternating current sinusoidal wave signal in step S23

[0128]

[0129] Comparative example 3

[0130] Only use b = 2nT n Instead of the displacement filtering function Calculate the discrete wavelet coefficients of the three-phase alternating current sinusoidal wave signal in step S23

[0131]

[0132] Comparative example 4

[0133] Use a = 2 n+1 Instead of the scale filtering function At the same time, use b = 2nT n Instead of the displacement filtering function Calculate the discrete wavelet coefficients of the three-phase alternating current sinusoidal wave signal in step S23

[0134]

[0135] As Figure 3 shown, after 10 iterations, the accuracy of the three-phase alternating current sinusoidal wave denoising signal after filtering, reconstruction, and inverse transformation of the discrete wavelet coefficients constructed for each of Comparative examples 1-4 has been improved. However, the accuracy of the three-phase alternating current sinusoidal wave time-domain denoising signal obtained by feature extraction after wavelet transform filtering of the discrete wavelet coefficients with the scale filtering function and the displacement filtering function constructed in step S23 provided by this application is always the highest.

[0136] The distribution network can be divided into overhead lines and cable lines. In actual engineering, there are many factors that affect the troubleshooting and restoration time, such as too long line length, extremely harsh working environment, and very difficult manual line patrol. For this reason, in order to ensure the system safety and stable operation of the UHV DC transmission system, it is necessary to effectively locate the position where the fault occurs. As another preferred embodiment of the present invention, the distance from the position where the fault occurs to the busbar in step S3 includes:

[0137] S301. Calculate the total output current I(t) of the 10 kV distribution network cable according to the filtered result:

[0138]

[0139] S302. Calculate the energy factor E[I(t)] of the total current I(t) output by the 10 kV distribution network cable and extract the instantaneous frequency ω e (t) and envelope amplitude Amp e (t) of the total current according to the energy factor:

[0140]

[0141] S303. Calculate the moment t max at which the energy factor is maximum:

[0142]

[0143] s.t ω e (t) ≤ ω

[0144] Amp e (t) ≥ Amp IX (t);

[0145] The meaning of this step is to solve for the moment when the energy factor E[I(t)] is at its maximum under the condition that the instantaneous frequency ω e (t) of the total current extracted according to the energy factor is within the fluctuation range of the frequency ω of the three-phase alternating current sine wave signal, and as Figure 4 shown, the extracted envelope amplitude Amp e (t) can envelope all the amplitudes Amp IX (t) of the three-phase alternating current sine wave signal collected. This moment is the calculation result t max .

[0146] S304. Calculate the time difference Δt max between the moment t 0P when the energy factor is maximum obtained in step S304 and the initial moment t P of the Pth monitoring point, and its time difference Δt 0Q with the initial moment t Q of the Qth monitoring point

[0147] P Δt max = t 0P - t Q ; Δt max = t 0Q - t

[0148] S305. Calculate the distance L from the fault point to the Pth monitoring point P, and the distance between the fault point and the distance L of the Qth monitoring point Q :

[0149]

[0150] Among them, v is the propagation speed of the fault wave, v = 2.95×10 8 m / s.

[0151] Furthermore, as Figure 5 shown, the distance between the fault point and the initial position of the bus input terminal can be determined according to the distance L P0 of the Pth monitoring point from the initial position of the bus input terminal or according to the distance L Q0 of the Qth monitoring point from the initial position of the bus input terminal, and then the position of the fault point can be located. The specific positioning formula is: L P0 +L P or L Q0 -L Q is the distance between the fault point and the initial position of the bus input terminal.

[0152] By calculating the energy factors E[I(t)] of the sine waves at three adjacent moments through the values of three consecutive sampling points, it is simple and fast. By extracting the instantaneous frequency and envelope amplitude of the energy factor, the change of the signal can be quickly tracked. If the energy value calculated by the energy factor E[I(t)] is larger, the amplitude or frequency of the given signal changes faster. By detecting the singularity of the signal, the moment when the energy factor is the largest is the fault site.

[0153] In order to clarify whether the fault occurring at the fault point of the 10kV distribution network is a ground fault or a non-ground fault, whether the ground fault is a single-phase ground fault or a two-phase ground fault, and whether the non-ground fault is a two-phase interphase short circuit or a three-phase interphase short circuit, it is necessary to perform phase-mode conversion of the three-phase alternating current current sine wave signal after denoising in the αβ stationary coordinate system, and continue to analyze the situation of the three-phase alternating current in the stationary coordinate system to further analyze the cause of the fault. Therefore, as another preferred embodiment of the present invention, the identification of the cause of the distribution network fault in step S3 includes:

[0154] S311. Construct a Clark transformation matrix of the X-phase reference coordinate system for the time-domain denoised signal of the alternating current current sine wave with the X-phase coinciding with the α-axis in the αβ stationary coordinate system, perform Clark transformation on each phase current in the three-phase alternating current, and calculate the α-axis component β-axis component

[0155]

[0156] That is, when the A-phase coincides with the α-axis in the αβ stationary coordinate system, the Clark transformation matrix of the A-phase reference coordinate system As follows:

[0157]

[0158] When the B-phase coincides with the α-axis in the αβ stationary coordinate system, the Clarke transformation matrix of the B-phase reference coordinate system is as follows:

[0159]

[0160] When the C-phase coincides with the α-axis in the αβ stationary coordinate system, the Clarke transformation matrix of the C-phase reference coordinate system is as follows:

[0161]

[0162] Where, is the α-axis component of the time-domain denoised signal of the sinusoidal wave of the X-phase alternating current, is the β-axis component of the time-domain denoised signal of the sinusoidal wave of the X-phase alternating current;

[0163] S312. Calculate the residual current I r (t) in the 10 kV cable:

[0164]

[0165] S313. Calculate the maximum current sine signal change rate m of the α-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system, αX the maximum current sine signal change rate m of the β-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system, βX and the maximum current sine signal change rate m r of the residual current in the 10 kV cable:

[0166]

[0167] S314. Further calculate the first fault judgment parameter D r,αX of the X-phase, the second fault judgment parameter D αX，βX of the X-phase, and the third fault judgment parameter D βX,αX of the X-phase:

[0168] D r,αX = m r / m αX D αX,βX = m αX / m βX D βX，αX = m βX / m αX ;

[0169] S315. Based on the fault judgment parameter calculated in S314, determine that the fault type is one of single-phase grounding fault, two-phase interphase short circuit fault, or three-phase interphase short circuit fault.

[0170] The specific judgment method in step S315 is as follows:

[0171] S3151. When the first fault judgment parameters of phase A, phase B, and phase C are all greater than the first judgment threshold D 1 it is a grounding fault; otherwise, it is a non-grounding fault; that is, judge whether D r,αA > D 1 , D r,αB > D 1 and D r,αC > D 1 If satisfied, it is a grounding fault; D 1 is 0.07;

[0172] S3152. When it belongs to the grounding fault situation, further judge whether the sum of the second fault judgment parameters of phase A, phase B, and phase C, D αA,βA + D αB,βB + D αC,βC is greater than the second judgment threshold D 2 (that is, judge whether D αA，βA + D αB,βB + D αC,βC > D 2 ), if greater, it is a single-phase grounding fault; otherwise, it is a two-phase grounding fault; the second judgment threshold D 2 is 80;

[0173] S3153. When it belongs to the non-grounding fault situation, further judge whether the sum of the third fault judgment parameters of phase A, phase B, and phase C is greater than the third judgment threshold D 3 , if greater, it is a two-phase interphase short circuit fault; otherwise, it is a three-phase interphase short circuit fault; the third judgment threshold D 3 is 6.

[0174] In step S3152, when it is judged that the 10kV cable distribution network is a grounding fault, the standard for further identifying the grounding phase is:

[0175] When D αA,βA ≥ D 5 , D αB，βB < D 5 and D αC,βC < D 5 it is a phase A grounding fault;

[0176] When D αB，βB ≥ D 5 , D αA,βA < Dx and DαC,βC <D 5 When it is, it is a ground fault of phase B;

[0177] When D αC,βC ≥D 5 、D αA，βA <D 5 and D αB，βB <D 5 When it is, it is a ground fault of phase C; D 5 is the fifth judgment threshold, D 5 is 10;

[0178] When D αA,βA >D αC，βC and D αA，βA >D αC，βC When it is, it is a ground fault of phases AB;

[0179] When D αB,βB >D αA,βA and D αC,βC >D αA，βA When it is, it is a ground fault of phases BC;

[0180] When D αC,βC >D αB,βB and D αA,βA >D αB，βB When it is, it is a ground fault of phases AC.

[0181] In step S3153, when it is determined that the 10 kV cable distribution network is a non-ground fault, the criteria for further identifying the two short-circuited phases are:

[0182] When D βA，αA <D 6 、D βB，αB <D 6 and D βC，αC ≥D 6 When it is, it is a phase-to-phase short circuit between phases AB;

[0183] When D βB，αB <D 6 、D βC，αC <D 6 and D βA，αA ≥D 6 When it is, it is a phase-to-phase short circuit between phases BC;

[0184] When D βA，αA <D 6 、D βC，αC <D 6 and D βB，αB ≥D 6 When it is, it is a phase-to-phase short circuit between phases AC; D 6 is the fifth judgment threshold, D 6 is 15.

[0185] As shown Figures 6 - 9 , they are respectively the three-phase current variation curve diagrams during A-phase ground fault (single-phase ground fault embodiment), BC-phase ground fault (two-phase ground fault embodiment), AC two-phase interphase short circuit fault, and ABC three-phase interphase short circuit fault.

[0186] In the present invention, by using the ABC three-phase alternating current separately as the reference phase and converting it into the current variables in the αβ stationary coordinate system, the α-axis component of each item is extracted from the instantaneous three-phase alternating current through the transformation matrix β-axis component and the residual current Ir(t), a total of seven current components. After preprocessing the original collected three-phase alternating current sine wave signal through wavelet transform filtering, feature extraction, and reconstruction and restoration, D r,αA , D r，αB , D r，αC , D αA,βA , D αB,βB , D αC,βC , D βA，αA , D βB，αB和 D βC，αC Nine fault discrimination indexes are calculated. By using the proposed fault indexes, the ground fault or non-ground fault, the fault cause of which phase or which two phases have ground faults, or which two phases have interphase short circuit faults can be judged step by step, and then the emergency equipment can be controlled targeted to execute the grid quick break, overcurrent protection or tripping.

[0187] The present invention also provides an Internet of Things-based environmental emergency power management system adopting the above method. As shown Figure 10 , the system includes a current real-time acquisition module, a filtering and denoising module, a fault location and cause identification module, and an emergency execution control module;

[0188] The current real-time acquisition module is used to collect the three-phase current data information of the 10kV cable distribution network alternating current in real time;

[0189] The filtering and denoising module is used to perform data feature extraction and filtering preprocessing to obtain the filtered and denoised three-phase alternating current sine wave time-domain denoised signal I′ X (t);

[0190] The fault location and cause identification module is used to further locate the distance from the fault occurrence location to the bus and identify the cause of the distribution network fault based on the filtered and denoised three-phase alternating current sine wave time-domain denoised signal I′ X (t);

[0191] An emergency execution control module is used to control the corresponding electromagnetic relay for overcurrent protection or trip the circuit breaker for ground fault protection, so as to achieve emergency power cut-off in the environment and ensure the safety of 10kV cable power distribution.

[0192] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variant thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, device, article or method including the element.

[0193] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0194] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An environmental emergency power management method based on the Internet of Things, the method is applied to fault identification and fault location of a 10kV cable distribution network, the 10kV cable distribution network is set with an ungrounded neutral point, and is characterized in that: The method comprises the following steps: S1: Real-time collection of 10kV cable distribution network AC three-phase current data information; S2: Extract data features and filter preprocessing to obtain the three-phase AC current sinusoidal wave time domain denoised signal I′ after filtering and denoising X (t); S3: The three-phase AC current sinusoidal wave time-domain denoised signal I′ after filtering and denoising based on the S2 step X (t), further locate the distance between the fault location and the busbar and identify the cause of the distribution network fault; S4, control the corresponding electromagnetic relay to perform overcurrent protection or trip the circuit breaker to perform ground fault protection, so as to realize environmental emergency power cut-off and ensure the safety of 10kV cable power distribution; The S2 step includes the following steps: S21: Construct a three-phase alternating current sinusoidal wave signal model collected in step S1: Where X = A, B, C; t is the time of signal acquisition; I X (t) is the current sinusoidal wave signal of phase X at time t; Amp IX (t) is the current amplitude of phase X at time t; ω is the fundamental frequency of the output current of phase X of the AC power grid, is the instantaneous phase of the X-phase current at time t; S22: Perform Fourier transform on the three-phase alternating current sinusoidal wave signal of step S21: Where, j is an imaginary number; It is the three-phase alternating current sinusoidal wave frequency domain signal; S23: Calculate the discrete wavelet coefficients of the three-phase AC current sine wave signal in, is the field of real numbers, For calculation and The inner product calculation function of The complex conjugate of ; n is the nth window into which the signal of the AC power grid output X-phase current within a half cycle is divided, n=1,2,…N; N is the total number of N windows into which the signal of the AC power grid output X-phase current within a half cycle is divided for discrete analysis; is the scale filter function of wavelet filtering, is the displacement filter function of the wavelet filter model; S24: Filter multiple discrete wavelet filter coefficients to construct a wavelet coefficient filter reconstruction threshold function Where λ is the filtering threshold, is the discrete wavelet coefficient after filtering; sgn(·) is the step function; S25: Reconstruct the three-phase AC current sinusoidal frequency domain signal based on the filtered discrete wavelet coefficients S26: reconstructing the three-phase alternating current sinusoidal wave frequency domain signal of step S25 Perform inverse Fourier transform to obtain the three-phase AC current sinusoidal wave time domain denoised signal I′ after filtering and denoising X (t): The scale filter function of the wavelet filter as follows: Among them, ω n is the center frequency of the nth window that divides the signal of the AC power grid output X-phase current within half a cycle; T n is the three-phase AC current sine wave sub-period in the nth window, T n =2τ n , τ n is the half three-phase alternating current sine wave sub-cycle time slot in the nth window; q is the correction coefficient; the calculation formula of the correction coefficient q is as follows: The displacement filter function of the wavelet filter model as follows: The distance between the fault location and the busbar in step S3 includes: S301. Calculate the total output current I(t) of the 10kV distribution network cable based on the filtered value: S302, calculating the energy factor E[I(t)] of the total current I(t) output by the 10 kV distribution network cable and the instantaneous frequency ω of the total current extracted according to the energy factor e (t) and envelope amplitude Amp e (t): S303, calculate the maximum time t of the energy factor: s.t ω e (t)≤ω Amp e (t)≥Amp IX (t); S304, calculating the maximum time t of the energy factor calculated in step S304 max and the initial time t of the Pth monitoring point 0P The time difference Δt P , and its relationship with the initial time t of the Qth monitoring point 0Q The time difference Δt Q Δt P =t max -t 0P ;Δt Q =t max -t 0Q ; S305. Calculate the distance L between the fault point and the Pth monitoring point P , and the distance between the fault point and the Qth monitoring point L Q : Where, v is the propagation speed of the fault wave, v = 2.95 × 10 8 m / s; The identification of the cause of the distribution network fault in step S3 includes: S311, construct the X-phase reference coordinate system Clarke transformation matrix of the AC current sine wave time domain denoising signal with the X-phase coinciding with the α-axis in the αβ stationary coordinate system, and calculate the α-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system β axis component in, is the α-axis component of the X-phase AC current sinusoidal wave time domain denoised signal, is the β-axis component of the X-phase AC current sinusoidal wave time domain denoised signal; S312. Calculate the residual current I in the 10kV cable r (t): S313, calculate the α-axis component of the three-phase current of the X-phase in the αβ stationary coordinate system per unit time Maximum current sinusoidal signal change rate m αX , the β-axis component of the three-phase current of phase X in the αβ stationary coordinate system Maximum current sinusoidal signal change rate m βX And the maximum current sinusoidal signal change rate of residual current in 10kV cable m r : S314, further calculating the first fault judgment parameter D of the X phase r,αX , X phase second fault judgment parameter D αX,βX and X phase third fault judgment parameter D βX,αx : D r,αX =m r / m αX ,D αX,βX =m αX / m βX ,D βX,αX =m βX / m αX ; S315. According to the fault judgment parameter calculated in S314, determine whether the fault type is a single-phase grounding fault, a two-phase interphase short circuit fault, or a three-phase interphase short circuit fault.

2. The environmental emergency power management method based on the Internet of Things according to claim 1 is characterized in that: The specific determination method in step S315 is: S3151, when the first fault judgment parameters of phase A, phase B and phase C are all greater than the first judgment threshold D1, it is a ground fault; otherwise, it is a non-ground fault; the first judgment threshold D1 is 0.07; S3152: When it is a ground fault, further determine the sum of the second fault judgment parameters D of phase A, phase B and phase C. αA,βA +D αB,βB +D αC,βC Is it greater than a second judgment threshold D2? If so, it is a single-phase grounding fault; otherwise, it is a two-phase grounding fault; the second judgment threshold D2 is 80; S3153. When it is a non-ground fault, further determine whether the sum of the third fault judgment parameters of phase A, phase B and phase C is greater than the third judgment threshold D3. If so, it is a two-phase short circuit fault; otherwise, it is a three-phase short circuit fault; the third judgment threshold D3 is 6.

3. The environmental emergency power management method based on the Internet of Things according to claim 2 is characterized in that: In the step S3152, when it is determined that the 10kV cable distribution network is a ground fault, the standard for further identifying the ground phase is: When D αA,βA ≥ D5, D αB,βB < D5 and D αC,βC < D5, it is a ground fault of phase A; When D αB,βB ≥ D5, D αA,βA < D5 and D αC,βC < D5, it is a phase B ground fault; When D αC,βC ≥ D5, D αA,βA < D5 and D αB,βB < D5, it is a ground fault of phase C; D5 is the fifth judgment threshold, and D5 is 10; When D αA,βA >D αC,βC And D αA,βA >D αC,βC When , it is AB phase grounding fault; When D αβ,βB >D αA,βA And D αC,βC >D αA,βA When , it is a BC phase grounding fault; When D αC,βC >D αB,βB And D αA,βA >D αB,βB When , it is an AC phase grounding fault.

4. The environmental emergency power management method based on the Internet of Things according to claim 2 is characterized in that: In the step S3153, when it is determined that the 10kV cable distribution network is a non-ground fault, the standard for further identifying the short-circuited two phases is: When D βA,αA <D6, D βB,αB <D6 and D βC,αC ≥ D6, it is a short circuit between phases A and B; When D βB,αB <D6, D βC,αC <D6 and D βA,αA ≥ D6, it is a short circuit between phases BC; When D βA,αA <D6, D βC,αC <D6 and D βB,αB ≥ D6, it is a short circuit between phase A and phase C; D6 is the fifth judgment threshold, and D6 is 15.

5. An environmental emergency power management system based on the Internet of Things, using the method according to any one of claims 1 to 4, characterized in that: The system includes a current real-time acquisition module, a filtering and denoising module, a fault location and cause identification module, and an emergency execution control module; The current real-time acquisition module is used to collect the three-phase current data information of the AC power of the 10kV cable distribution network in real time; The filtering and denoising module is used to extract data features and perform filtering preprocessing to obtain the three-phase AC current sinusoidal wave time domain denoised signal I' after filtering and denoising. X (t); The fault location and cause identification module is used to filter and de-noise the three-phase AC current sinusoidal wave time domain de-noised signal I' based on the de-noising module X (t), further locate the distance between the fault location and the busbar and identify the cause of the distribution network fault; The emergency execution control module is used to control the corresponding electromagnetic relay to perform overcurrent protection or trip the circuit breaker to perform ground fault protection, so as to achieve environmental emergency power cut-off and ensure the safety of 10kV cable power distribution.

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