Power quality monitoring and analyzing method for power distribution network containing distributed energy

Abstract

The invention discloses an electric energy quality monitoring analysis method for a power distribution network containing distributed energy. The method comprises the following steps: S1, setting an optimal monitoring node; s2, collecting and processing monitoring data; performing power quality monitoring data acquisition based on the optimal power quality monitoring scheme, and processing the data; s3, electric energy quality analysis; based on the electric energy quality monitoring data, analyzing the index parameters and harmonic waves of the electric energy quality, and classifying and identifying disturbance events; s4, determining a disturbance source; the method comprises the following steps: decomposing and reconstructing a signal collected by a monitoring device to obtain high-frequency-band disturbance energy, judging the direction of a disturbance source based on positive and negative high-frequency-band disturbance energy, and positioning the disturbance source based on a genetic algorithm after determining the direction of the disturbance source; according to the invention, configuration of monitoring nodes can be optimized, monitoring reliability can be improved, electric energy quality can be analyzed, an interference source can be accurately positioned, faults can be rapidly removed, and interference accidents can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power quality monitoring and analysis, and in particular relates to a method for monitoring and analyzing power quality of a distribution network containing distributed energy. Background Art

[0002] The widespread use of photovoltaic power generation and other distributed power sources in distribution networks has a positive effect on optimizing and improving the grid architecture, but it also has a negative impact on the stable operation of the grid. Due to the randomness and intermittent nature of distributed power sources, they are prone to voltage and frequency fluctuations. The harmonic pollution caused by power electronic devices also affects the stable operation of the grid to varying degrees, has a negative impact on equipment safety and the economic operation of the grid, and puts higher requirements on the stable operation of the system.

[0003] Among them, high-quality power quality is the prerequisite for the safe operation of the power grid. The stability and improvement of the grid voltage quality can effectively improve the transmission capacity of the power system, reduce the possible loss and transfer of load in the power system, and reduce the possibility of cascading power outages in the system. Therefore, it is very important to monitor and analyze the power quality in the distribution network and locate the interference source to reduce the occurrence of interference accidents and reduce the economic losses caused by accidents.

[0004] Therefore, in order to solve the above problems, it is necessary to develop a power quality monitoring and analysis method for distribution networks containing distributed energy. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a power quality monitoring and analysis method for a distribution network containing distributed energy, optimize the configuration of monitoring nodes, improve monitoring reliability, analyze the power quality, accurately locate the interference source, facilitate rapid troubleshooting, and reduce the occurrence of interference accidents.

[0006] The object of the present invention is achieved as follows: A method for monitoring and analyzing power quality of a distribution network containing distributed energy, comprising the following steps:

[0007] S1. Optimal monitoring node setup: Based on the topological connection relationship of the distribution network, the sensitivity of each node to disturbance and the disturbance sensitivity factor are considered, and the sensitivity factors are weighted to obtain disturbance nodes that are likely to affect power quality. Monitoring devices are configured at multiple disturbance nodes, and the nodes equipped with monitoring devices are used as monitoring points to form an optimal power quality monitoring solution.

[0008] S2. Monitoring data acquisition and processing: collecting power quality monitoring data based on the optimal power quality monitoring solution and processing the data;

[0009] S3. Power quality analysis: Based on power quality monitoring data, analyze power quality parameters and harmonics, and classify and identify disturbance events;

[0010] S4. Determine the disturbance source. Decompose and reconstruct the signal collected by the monitoring device to obtain the high-frequency disturbance energy. The direction of the disturbance source is determined based on the positive or negative value of the high-frequency disturbance energy. After the direction of the disturbance source is determined, locate the disturbance source based on the genetic algorithm.

[0011] Furthermore, the monitoring device in step S1 uses a voltage sensor and a current sensor.

[0012] Furthermore, the index parameters in step S3 include voltage deviation and frequency deviation; the voltage deviation is expressed as:

[0013]

[0014] Where: U represents voltage deviation, U1 represents actual effective value of voltage, U2 represents rated value of voltage, U N Indicates the rated voltage of the object being measured;

[0015] The frequency deviation is expressed as the difference between the frequency of the measurement result and the standard fundamental signal:

[0016] f=f1-f2

[0017] Wherein: f, f1, f2 represent frequency deviation, actual frequency and rated frequency respectively, which are determined by comparing the phases of the signals.

[0018] Furthermore, the harmonic analysis in step S3 is represented by the distortion rate of voltage harmonics:

[0019]

[0020] Where: THD represents the total distortion content of the voltage waveform, U h Indicates the root mean square value of the hth harmonic voltage.

[0021] Furthermore, the disturbance events in step S3 include voltage swell, voltage sag and voltage interruption disturbance events; the signal expression of the voltage swell event is expressed as:

[0022] x s (t)={1+A[u(t-t1)-u(t-t2)]}sin(ωt)

[0023] Where: A represents the amplitude of the disturbance signal, A∈[0.1,0.9]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage swell, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz;

[0024] The signal expression in the voltage sag event is:

[0025] x d (t)={1-A[u(t-t1)-u(t-t2)]}sin(ωt)

[0026] , where: A represents the amplitude of the disturbance signal, A∈[1.1,1.8]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage sag, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz;

[0027] The signal expression in the voltage interruption event is expressed as:

[0028] x i (t)={1-A[u(t-t1)-u(t-t2)]}sin(ωt)

[0029] , where: A represents the amplitude of the disturbance signal, A∈[0.9,1]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage interruption, t2-t1∈[T,9T], T is the power frequency period; π=2πf, f represents the fundamental frequency, f=50Hz.

[0030] Furthermore, in step S4, the wavelet transform is specifically used to decompose and reconstruct the monitored and collected signals. After the wavelet decomposition and reconstruction are completed, the high-frequency disturbance component is extracted, and the corresponding disturbance power is obtained through the high-frequency disturbance component. The disturbance power is integrated to obtain the high-frequency band disturbance energy.

[0031] Furthermore, the disturbance source is located based on the genetic algorithm in step S4, specifically including: ① encoding the direction of the disturbance source determined based on the positive and negative values ​​of the high-frequency disturbance energy, where the direction of the disturbance source is coded as 1 when it is forward interference and coded as 0 when it is reverse interference;

[0032] ② Construct a fitness function that represents the relationship between the line status and the direction of the disturbance source at the monitoring point, which can be expressed as:

[0033]

[0034] Where: q represents a larger integer, which is 1 in this case; n ris the number of monitoring nodes; s is the state vector of the line; n represents the dimension; r k Indicates the monitoring status of the kth monitoring node; is the monitoring function representing the disturbance direction information of the kth monitoring node, s i is the status of the i-th line in the upstream direction of the k-th monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0. j is the status of the jth line in the downstream direction of the kth monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0; A k represents the disturbance weight parameter of the kth monitoring node, E dk represents the high-frequency disturbance energy of the kth monitoring node after the disturbance is completed;

[0035] ③ Through genetic algorithms, the optimal fitness function value is obtained, the optimal solution of the population is decoded and the disturbance source is located.

[0036] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows: by analyzing the susceptibility of each node to disturbance based on the topological connection relationship of the distribution network, disturbance nodes that are likely to affect the power quality are obtained, and monitoring devices are configured at these nodes, without having to configure monitoring devices at all nodes, thereby achieving effective monitoring while reducing monitoring costs; based on the monitoring data of the monitoring device, power quality analysis and disturbance event identification are performed to quickly discover various interferences, and by performing signal analysis, decomposition and reconstruction on the monitoring data, the direction of the disturbance source is determined, and the disturbance source is quickly located based on a genetic algorithm, effectively reducing the occurrence of interference accidents and reducing the losses caused by accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0038] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0039] like Figure 1 As shown, a method for monitoring and analyzing power quality of a distribution network containing distributed energy resources includes the following steps:

[0040] S1. Setting the optimal monitoring node; based on the topological connection relationship of the distribution network, considering the sensitivity of each node to disturbance and the disturbance sensitivity factor, and weighting the sensitivity factor, obtaining the disturbance nodes that are likely to affect the power quality, configuring monitoring devices at multiple disturbance nodes, and using the nodes configured with monitoring devices as monitoring points to form an optimal power quality monitoring solution; preferably, the monitoring device uses a voltage sensor and a current sensor.

[0041] S2. Monitoring data acquisition and processing: Power quality monitoring data is acquired based on the optimal power quality monitoring solution, and the data is processed.

[0042] S3. Power quality analysis: Based on power quality monitoring data, analyze power quality index parameters and harmonics, and classify and identify disturbance events.

[0043] Preferably, the index parameters in step S3 include voltage deviation and frequency deviation; the voltage deviation is expressed as:

[0044]

[0045] Where: U represents voltage deviation, U1 represents actual effective value of voltage, U2 represents rated value of voltage, U N Indicates the rated voltage of the object being measured;

[0046] The frequency deviation is expressed as the difference between the frequency of the measurement result and the standard fundamental signal:

[0047] f=f1-f2

[0048] Wherein: f, f1, f2 represent frequency deviation, actual frequency and rated frequency respectively, which are determined by comparing the phases of the signals.

[0049] Preferably, the harmonic analysis in step S3 is represented by the distortion rate of voltage harmonics:

[0050]

[0051] Where: THD represents the total distortion content of the voltage waveform, U h Indicates the root mean square value of the hth harmonic voltage.

[0052] Preferably, the disturbance events in step S3 include voltage swell, voltage sag and voltage interruption disturbance events; the signal expression of the voltage swell event is expressed as:

[0053] x s (t)={1+A[u(t-t1)-u(t-t2)]}sin(ωt)

[0054] , where: A represents the amplitude of the disturbance signal, A∈[0.1,0.9]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage swell, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz;

[0055] The signal expression in the voltage sag event is:

[0056] x d (t)={1-A[u(t-t1)-u(t-t2)]}sin(ωt)

[0057] , where: A represents the amplitude of the disturbance signal, A∈[1.1,1.8]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage sag, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz;

[0058] The signal expression in the voltage interruption event is expressed as:

[0059] x i (t)={1-A[u(t-t1)-u(t-t2)]}sin(ωt)

[0060] Where: A represents the amplitude of the disturbance signal, A∈[0.9,1]; u(t) represents the unit step function, t1 and t2 represent the start and end times of the voltage interruption, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz.

[0061] S4. Determine the disturbance source. Decompose and reconstruct the signal collected by the monitoring device to obtain the high-frequency disturbance energy. The direction of the disturbance source is determined based on the positive or negative value of the high-frequency disturbance energy. After the direction of the disturbance source is determined, locate the disturbance source based on the genetic algorithm.

[0062] Preferably, in step S4, wavelet transform is specifically used to decompose and reconstruct the monitored and collected signals. After the wavelet decomposition and reconstruction are completed, the high-frequency disturbance component is extracted, and the corresponding disturbance power is obtained through the high-frequency disturbance component. The disturbance power is integrated to obtain the high-frequency band disturbance energy.

[0063] Preferably, the disturbance source is located based on a genetic algorithm in step S4, specifically including: ① encoding the direction of the disturbance source determined based on the positive and negative signs of the high-frequency band disturbance energy, encoding the disturbance source direction as 1 when it is forward interference and encoding it as 0 when it is reverse interference.

[0064] ② Construct a fitness function that represents the relationship between the line status and the direction of the disturbance source at the monitoring point, which can be expressed as:

[0065]

[0066] Where: q represents a larger integer, which is 1 in this case; n r is the number of monitoring nodes; s is the state vector of the line; n represents the dimension; r k Indicates the monitoring status of the kth monitoring node; is the monitoring function representing the disturbance direction information of the kth monitoring node, s i is the status of the i-th line in the upstream direction of the k-th monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0. j is the status of the jth line in the downstream direction of the kth monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0; A k represents the disturbance weight parameter of the kth monitoring node, E dk It represents the high-frequency disturbance energy of the kth monitoring node after the disturbance is completed.

[0067] ③ Through genetic algorithms, the optimal fitness function value is obtained, the optimal solution of the population is decoded and the disturbance source is located.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring and analyzing power quality of a distribution network containing distributed energy, characterized by: The following steps are involved: S1. Optimal monitoring node setup: Based on the topological connection relationship of the distribution network, the sensitivity of each node to disturbance and the disturbance sensitivity factor are considered, and the sensitivity factors are weighted to obtain disturbance nodes that are likely to affect power quality. Monitoring devices are configured at multiple disturbance nodes, and the nodes equipped with monitoring devices are used as monitoring points to form an optimal power quality monitoring solution. S2, monitoring data collection and processing; Collect power quality monitoring data based on the optimal power quality monitoring solution and process the data; S3. Power quality analysis; Based on power quality monitoring data, analyze power quality index parameters and harmonics, and classify and identify disturbance events; S4, disturbance source determination; The signals collected by the monitoring device are decomposed and reconstructed to obtain the high-frequency disturbance energy. The direction of the disturbance source is determined based on the positive and negative values ​​of the high-frequency disturbance energy. After the direction of the disturbance source is determined, the disturbance source is located based on the genetic algorithm.

2. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, characterized in that: In step S1, the monitoring device uses a voltage sensor and a current sensor.

3. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, wherein: The index parameters in step S3 include voltage deviation and frequency deviation; the voltage deviation is expressed as: Where: U represents voltage deviation, U1 represents actual effective value of voltage, U2 represents rated value of voltage, U N Indicates the rated voltage of the object being measured; The frequency deviation is expressed as the difference between the frequency of the measurement result and the standard fundamental signal: f=f1-f2 Wherein: f, f1, f2 represent frequency deviation, actual frequency and rated frequency respectively, which are determined by comparing the phases of the signals.

4. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, wherein: The harmonic analysis in step S3 is represented by the distortion rate of voltage harmonics: Where: THD represents the total distortion content of the voltage waveform, U h Indicates the root mean square value of the hth harmonic voltage.

5. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, characterized in that: The disturbance events in step S3 include voltage swell, voltage sag and voltage interruption disturbance events; the signal expression of the voltage swell event is as follows: x s (t)={1+A[u(t-t1)-u(t-t2)]}sin(ωt) Where: A represents the amplitude of the disturbance signal, A∈[0.1,0.9]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage swell, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz; The signal expression in the voltage sag event is: x d (t)={1-A[u(t-t1)-u(t-t2)]}sin(πt) Where: A represents the amplitude of the disturbance signal, A∈[1.1,1.8]; u(t) represents the unit step function, t1 and t2 represent the start and end time of the voltage sag, t2-t1∈[T,9T], T is the power frequency period; ω=2πf, f represents the fundamental frequency, f=50Hz; The signal expression in the voltage interruption event is expressed as: x i (t)={1-A[u(t-t1)-u(t-t2)]}sin(ωt) Where: A represents the amplitude of the disturbance signal, A∈[0.9,1]; u(t) represents the unit step function, t1 and t2 represent the start and end times of the voltage interruption, respectively, t2-t1∈[T,9T], T is the power frequency period; π=2πf, f represents the fundamental frequency, f=50Hz.

6. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, characterized in that: In step S4, the signal collected by monitoring is decomposed and reconstructed by wavelet transform. After the wavelet decomposition and reconstruction are completed, the high-frequency disturbance component is extracted, and the corresponding disturbance power is obtained through the high-frequency disturbance component. The disturbance power is integrated to obtain the high-frequency disturbance energy.

7. The method for monitoring and analyzing power quality of a distribution network containing distributed energy according to claim 1, characterized in that: The step S4 locates the disturbance source based on the genetic algorithm, specifically including: ① Encode the direction of the disturbance source determined by the positive and negative signs of the high-frequency disturbance energy. When the disturbance source direction is positive interference, it is coded as 1, and when it is negative interference, it is coded as 0. ② Construct a fitness function that represents the relationship between the line status and the direction of the disturbance source at the monitoring point, which can be expressed as: Where: q represents a larger integer, which is 1 in this case; n r is the number of monitoring nodes; s is the state vector of the line; n represents the dimension; r k Indicates the monitoring status of the kth monitoring node; is the monitoring function representing the disturbance direction information of the kth monitoring node, s i is the status of the i-th line in the upstream direction of the k-th monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0. j is the status of the jth line in the downstream direction of the kth monitoring node. It takes the value 1 when there is a disturbance source, otherwise it takes the value 0; A k represents the disturbance weight parameter of the kth monitoring node, E dk represents the high-frequency disturbance energy of the kth monitoring node after the disturbance is completed; ③ Through genetic algorithms, the optimal fitness function value is obtained, the optimal solution of the population is decoded and the disturbance source is located.

Images