Electric energy quality analysis method and device

Abstract

The invention discloses an electric energy quality analysis method and device, a long-line transmission model with a power supply end, a transmission line and a user end is constructed, the user end comprises a plurality of parallel user equipments, and the method comprises the following steps: S1, obtaining a first transmission signal of the power supply end, and obtaining a first electric energy quality grade corresponding to the power supply end according to the transmission signal; s2, obtaining a second transmission signal corresponding to each user equipment, and obtaining a second power quality grade corresponding to the user side according to all the second transmission signals; s3, an interference injection model is additionally arranged on the transmission line, parameters of the transmission line are adjusted through the interference injection model, and the step S1 and the step S2 are executed again; the step S3 is executed repeatedly until the preset number of first electric energy quality grades and second electric energy quality grades are obtained; and S4, processing the preset number of first electric energy quality grades and second electric energy quality grades by using a weight method to obtain a target electric energy quality grade. Therefore, the accuracy of electric energy quality analysis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality assessment, and in particular to a power quality analysis method and device. Background Art

[0002] Power quality refers to the electrical performance requirements met during the transmission and utilization of power in the power supply system, and is an important indicator for evaluating the performance of the power system. Power quality issues mainly include voltage fluctuations, frequency fluctuations, harmonics, voltage sags, and voltage flickers. In modern power systems, the stability and quality of power quality are of great significance for ensuring the safe operation of equipment, improving production efficiency, and reducing energy waste.

[0003] At present, the analysis methods for power quality mainly include traditional field measurement methods and numerical simulation methods based on computer simulation. The field measurement method directly collects field data, such as voltage and current waveforms, by installing sensors, and then analyzes the data. The numerical simulation method uses computer software to simulate the operation status of the power system, predict power quality problems, and analyze the power quality through simulation results.

[0004] However, among the existing power quality analysis methods, the actual measurement method requires a lot of manpower and material resources, is easily affected by the external environment, and data collection is difficult; secondly, the actual measurement method can only provide point data, which is difficult to fully reflect the overall situation of power quality problems. Furthermore, the numerical simulation method requires accurate input of various parameters during the modeling process, and the calculation complexity is high, which is time-consuming and labor-intensive. Finally, the existing methods need to be further improved in terms of real-time and accuracy, especially for power quality problems in complex power systems. The existing methods are often unable to analyze more accurately. That is, the existing analysis methods for power quality include at least the following three problems:

[0005] 1) When actual power equipment is in use, factors such as load and harmonic interference often change, making it difficult to conduct timely adaptive analysis of these variables. In particular, there is a lack of power quality analysis methods for complex and changing environments. Each parameter change requires retesting, which is very cumbersome.

[0006] 2) In complex and changing environments, the accuracy of power quality analysis is insufficient;

[0007] 3) For the analysis of power quality, it is actually necessary to take into account the different situations of the three different links: power supply end, transmission line and user end. At the same time, the power quality parameters of each link are different and the power quality required in each link is also different. At present, it is difficult to have a power quality analysis method that takes into account two or even three links, and power quality analysis is often only performed on a single link. Summary of the invention

[0008] The technical problem to be solved by the present invention is: a method and device for analyzing power quality, so as to improve the accuracy of power quality analysis.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0010] A power quality analysis method is provided, which constructs a long-line transmission model having a power supply end, a transmission line and a user end, wherein the user end includes a plurality of user devices connected in parallel, and the method comprises:

[0011] S1. Acquire a first transmission signal of the power supply end, and obtain a first power quality level corresponding to the power supply end according to the transmission signal;

[0012] S2. Acquire a second transmission signal corresponding to each of the user equipments, and obtain a second power quality level corresponding to the user terminal according to all the second transmission signals;

[0013] S3, adding an interference injection model to the transmission line, adjusting the parameters of the transmission line by means of the interference injection model, and re-executing step S1 and step S2; repeating step S3 until a preset number of the first power quality level and the second power quality level are obtained;

[0014] S4. Process a preset number of the first power quality levels and the second power quality levels by using a weight method to obtain a target power quality level.

[0015] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0016] A power quality analysis device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the power quality analysis method as described above is implemented.

[0017] The beneficial effects of the present invention are as follows: by performing power quality analysis on the power supply end and the user end respectively, the results of the transmission signal analysis are made more accurate; at the same time, interference injection is performed on the transmission line to simulate the environment and interference changes in actual transmission, without the need to re-layout the test every time a change occurs, thereby obtaining the overall power quality level and making the actual test easier; and using the weight method to obtain the final power quality level, taking into account the influence of the three links of transmission, effectively reflecting the overall power quality, thereby improving the accuracy of the power quality analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the steps of a power quality analysis method in an embodiment of the present invention;

[0019] Figure 2 Schematic diagram of the structure of a power quality analysis device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0021] Please refer to Figure 1 A method for analyzing power quality is provided, wherein a long-line transmission model having a power supply end, a transmission line and a user end is constructed, wherein the user end includes a plurality of user devices connected in parallel, and the method comprises:

[0022] S1. Acquire a first transmission signal of the power supply end, and obtain a first power quality level corresponding to the power supply end according to the transmission signal;

[0023] S2. Acquire a second transmission signal corresponding to each of the user equipments, and obtain a second power quality level corresponding to the user terminal according to all the second transmission signals;

[0024] S3, adding an interference injection model to the transmission line, adjusting the parameters of the transmission line by means of the interference injection model, and re-executing step S1 and step S2; repeating step S3 until a preset number of the first power quality level and the second power quality level are obtained;

[0025] S4. Process a preset number of the first power quality levels and the second power quality levels by using a weight method to obtain a target power quality level.

[0026] From the above description, it can be seen that the beneficial effects of the present invention are: by performing power quality analysis on the power supply end and the user end respectively, the results of the transmission signal analysis are made more accurate; at the same time, interference injection is performed on the transmission line to simulate the environment and interference changes in actual transmission, and there is no need to re-layout the test every time a change occurs, thereby obtaining the overall power quality level and making the actual test easier; and the weight method is used to obtain the final power quality level, taking into account the influence of the three links of transmission, effectively reflecting the overall power quality, thereby improving the accuracy of the power quality analysis.

[0027] Further, obtaining a first power quality level corresponding to the power supply end according to the first transmission signal includes:

[0028] Converting the first transmission signal into a digital signal;

[0029] Performing wavelet transform on the digital signal to obtain characteristic parameters corresponding to power quality;

[0030] The first power quality level corresponding to the power supply end is determined according to the characteristic parameter.

[0031] It can be seen from the above description that the first transmission signal is converted into a digital signal and then transformed into a wavelet transform to obtain characteristic parameters, and the first power quality level is determined based on the characteristic parameters, thereby obtaining an accurate first power quality level.

[0032] Furthermore, the wavelet transform is performed on the digital signal to obtain characteristic parameters corresponding to the power quality, including:

[0033] Performing dual-tree complex wavelet decomposition on the digital signal to obtain information entropy corresponding to at least one wavelet coefficient;

[0034] Taking each of the information entropies in the form of a feature vector as the feature parameter;

[0035] Determining the first power quality level corresponding to the power supply end according to the characteristic parameter includes:

[0036] Each of the characteristic parameters is identified by a preset classifier to obtain the first power quality level.

[0037] From the above description, it can be seen that the dual-tree complex wavelet decomposition method is used to process digital signals, which can process non-stationary signals and has high time-frequency local properties, which is very suitable for the characteristics of circuit transmission. The power quality level can be effectively obtained through the preset classifier.

[0038] Further, obtaining the second power quality level corresponding to the user terminal according to all the second transmission signals includes:

[0039] Power quality analysis is performed on all the second transmission signals through a pulse coupled neural network model to obtain a second power quality level corresponding to the user end.

[0040] From the above description, it can be seen that by using the pulse coupled neural network model to analyze the transmission signal at the user end, it is more in line with the analysis scenario of the complex power consumption situation at the user end, so that the analysis result is more accurate.

[0041] Furthermore, the pulse coupled neural network model includes a receiving part, a modulation part and a pulse part;

[0042] The performing power quality analysis on all the second transmission signals by using a pulse coupled neural network model comprises:

[0043] receiving each of the second transmission signals by the receiving section;

[0044] Modulating the second transmission signal by the modulation part using an offset and multiplication to obtain a modulated signal;

[0045] The power quality of the modulated signal is judged by the pulse portion, and the second power quality level is outputted.

[0046] From the above description, it can be seen that by receiving, modulating and judging the transmission signal in turn through the receiving part, modulation part and pulse part of the pulse coupled neural network model, the power quality can be effectively obtained and accurate analysis results can be provided.

[0047] Further, the interference model includes a harmonic sub-model, a length attenuation sub-model and a load sub-model;

[0048] The adjusting the parameters of the transmission line by using the interference injection model comprises:

[0049] inputting harmonics for interference into the transmission line through the harmonic sub-model;

[0050] adjusting the impedance of the transmission line by means of the length attenuation sub-model;

[0051] Different loads are added to the transmission line through the load sub-model.

[0052] From the above description, it can be seen that by setting the harmonic sub-model, the length attenuation sub-model and the load sub-model, the harmonic sub-model can be used to input harmonics for interference into the transmission line, the length attenuation sub-model can be used to adjust the impedance of the transmission line, and the load sub-model can be used to add multiple different loads to the transmission line, so as to simulate the interference conditions that may exist in reality, and re-acquire the transmission signal based on the adjustment of the transmission line parameters, and then obtain the overall power quality level, which does not require re-layout and testing every time the line changes, and is more convenient.

[0053] Furthermore, the harmonic sub-model adopts a nonlinear load model or an odd harmonic model.

[0054] From the above description, it can be seen that the harmonic sub-model adopts a nonlinear load model or an odd harmonic model. Among them, the nonlinear load model can input harmonic current or harmonic voltage, and can simulate the harmonics often appearing in power electronic equipment; and the odd harmonic model is used to input homogeneous harmonics and simulate the interference of homogeneous harmonics.

[0055] Further, the length decay sub-model includes a composite noise module;

[0056] The composite noise module includes white noise, pink noise and impulse noise.

[0057] From the above description, it can be seen that by forming a composite noise module with white noise, pink noise and impulse noise, different types of noise in the power system can be simulated.

[0058] Further, the processing of a preset number of the first power quality levels and the second power quality levels by using a weight method includes:

[0059] Respectively calculating the average values ​​of a preset number of the first power quality levels and the second power quality levels to obtain a first average power quality level and a second average power quality level;

[0060] Obtaining a first weight coefficient and a second weight coefficient;

[0061] The first average power quality level and the second average power quality level are summed according to the first weight coefficient and the second weight coefficient to obtain the target power quality level.

[0062] From the above description, it can be seen that by setting the first weight coefficient and the second weight coefficient, and the first weight coefficient and the second weight coefficient can be set automatically according to demand and the overall transmission situation, thereby reflecting the impact of the user end and the power supply end on the power quality level, and can better reflect the overall power quality level.

[0063] Another embodiment of the present invention provides a power quality analysis device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of a power quality analysis method as described above when executing the computer program.

[0064] The power quality analysis method and device provided by the present invention can be applied to power quality analysis scenarios, and are described below through specific implementation methods:

[0065] Embodiment 1

[0066] Please refer to Figure 1 A power quality analysis method is provided, wherein a long-line transmission model having a power supply end, a transmission line and a user end is constructed, wherein the user end includes a plurality of user devices connected in parallel, and the method comprises:

[0067] S1. Obtain a first transmission signal from a power supply end, and obtain a first power quality level corresponding to the power supply end according to the transmission signal. The specific analysis process is as follows:

[0068] S11, converting the first transmission signal into a digital signal;

[0069] S12, perform wavelet transform on the digital signal to obtain characteristic parameters corresponding to the power quality; in this embodiment, perform dual-tree complex wavelet decomposition on the digital signal to obtain information entropy corresponding to at least one wavelet coefficient; then use each information entropy in the form of a feature vector as a characteristic parameter; wherein the dual-tree complex wavelet uses a binary tree structure for discrete wavelet transform, one tree for the real part and the other tree for the imaginary part, the real part and the imaginary part constitute a Hilbert transform pair, which differs by 90 degrees in phase in the frequency domain; filter the two trees with a filter, and then obtain information entropy of multiple wavelet coefficients, and use multiple information entropies in the form of feature vectors as characteristic parameters;

[0070] S13. Determine the first power quality level corresponding to the power supply end according to the characteristic parameters; identify each characteristic parameter through a preset classifier to obtain the first power quality level; this embodiment uses an SVM classifier for identification, thereby obtaining the first power quality level A1. For example, if there are 10 characteristic parameters, the SVM classifier can identify 10 feature vectors, and then transform them into the form of wavelet functions using the kernel function inside the SVM classifier, so that the 10 feature vectors are used as training samples for training to obtain power quality level A1. Here, the SVM classifier can use a commonly used SVM classifier for ease of use. The specific kernel function can be a Morlet wavelet function or a Mexico wavelet function. The transformation methods of these two wavelet functions are highly accurate and have been made public, so no additional details will be given here. Dual-tree complex wavelet decomposition is a complex form transformation method in wavelet transform, which can process non-stationary signals and has high time-frequency local properties, which is very consistent with the characteristics of circuit transmission.

[0071] In an optional implementation, power quality analysis of the power supply end is achieved by setting up a first analysis system at the power supply end. Specifically: the first analysis system includes a signal collector, an ADC converter and a CPU; the signal collector is used to collect signals transmitted from the power supply end, and the collected signals are converted into digital signals via the ADC converter and then transmitted to the CPU, and the CPU then performs wavelet transform on the digital signals to obtain characteristic parameters of the power quality, and then determines the first power quality level A1 corresponding to the power supply end based on the characteristic parameters.

[0072] It should be noted that wavelet transform is a signal processing technology used to decompose signals into wavelet functions of different scales so as to analyze the signals in the time domain and frequency domain; wavelet transform can help capture instantaneous changes and frequency characteristics in the signal, and has the ability to provide more comprehensive information at different time scales and frequencies. SVM classifier is an instrument based on supervised learning model, which can be used for pattern recognition, classification and regression analysis. Here, wavelet transform is used for signal processing and feature parameter extraction. The extracted feature parameters can be used as input data for training SVM classifier. This combination can make full use of the feature extraction ability of wavelet transform and the classification performance of SVM, so as to perform classification more simply, that is, to identify the power quality level.

[0073] S2. Obtain the second transmission signal corresponding to each user equipment, and obtain the second power quality level corresponding to the user terminal according to all the second transmission signals; the specific analysis process is as follows:

[0074] S21. Perform power quality analysis on all second transmission signals through a pulse coupled neural network model (PCNN) to obtain a second power quality level corresponding to the user end; wherein the pulse coupled neural network model includes a receiving part, a modulating part and a pulse part; each second transmission signal is received through the receiving part, that is, the receiving part is equivalent to the input end of a neuron, for receiving the transmitted signal; the modulating part modulates the second transmission signal using an offset and multiplication to obtain a modulated signal, which is equivalent to adjusting the strength and form of the signal for subsequent processing; the power quality of the modulated signal is judged through the pulse part, and the second power quality level A2 is obtained by output.

[0075] Specifically, for each neuron in the PCNN, the receiving part is provided with an information threshold. If the received transmitted signal is less than the threshold, the neuron will not carry out the next information transmission. Once the received transmitted signal reaches the threshold, the neuron will ignite to generate a potential, thereby integrating information with other neurons and transmitting it to the corresponding modulation part through two or more channels. The modulation part adds an offset to the signal of each channel, multiplies and modulates different channels, improves the signal expression, and obtains an easy-to-process modulated signal. The pulse part is composed of a threshold regulator, a comparator and a pulse generator. The threshold regulator is used to adjust the ignition threshold at the output end of the neuron to ensure that a pulse can be generated; the comparator is used to compare the adjustment signal with the ignition threshold adjusted by the threshold regulator to determine whether a pulse is generated, that is, to determine whether the power quality is qualified; the pulse generator generates a pulse signal according to the comparison result of the comparator. Among them, the setting of the above threshold corresponds to the level of power quality. There can be multiple levels of power quality, and there can be multiple thresholds correspondingly, so that the pulse generator can also output a corresponding pulse signal under the corresponding threshold, and this pulse signal also indicates the level of power quality.

[0076] In an optional implementation, a second analysis system is set at the user end, and the second analysis system includes a multi-terminal monitor and a user power supervision platform; the multi-terminal monitor is a signal acquisition instrument, which is set between the transmission line and the input end of the user end. The multi-terminal monitor can connect to multiple user devices, thereby collecting the signal corresponding to each user device and transmitting it to the user power supervision platform for processing; the user power supervision platform uses the PCNN model to perform power quality analysis on the signal transmitted by the multi-terminal monitor and determine the power quality level A2 corresponding to the user end.

[0077] It should be noted that the PCNN model is a pulse coupled neural network model, which is a self-supervised learning network model that can extract effective information from complex data without prior training. This feature makes the PCNN model more efficient and flexible in real-time evaluation; on the user side, not only is the number of devices large and the environment changeable, but the specifications of the devices used by users may also change constantly, so the use of the PCNN model can evaluate the power quality in real time and quickly respond to changes in various data in the power grid, which is in line with the actual use scenarios of the user side. Correspondingly, on the power supply side, the equipment and lines are generally relatively stable, so the test and analysis of the power quality level can be completed using relatively simple wavelet transforms and SVM classifiers.

[0078] S3, add an interference injection model to the transmission line, adjust the parameters of the transmission line through the interference injection model, and re-execute step S1 and step S2; repeat step S3 until a preset number of first power quality levels and second power quality levels are obtained. That is, multiple first power quality levels A1 and multiple second power quality levels A2 are obtained. Among them, the interference model includes a harmonic sub-model, a length attenuation sub-model and a load sub-model, specifically:

[0079] S31. The harmonic sub-model inputs harmonics for interference into the transmission line to simulate various types of harmonic interference that often occur in actual transmission. For example, the harmonic sub-model adopts a nonlinear load model or an odd harmonic model. The nonlinear load model can input harmonic currents or harmonic voltages. These two types of harmonics are also common harmonics in power electronic equipment. The odd harmonic model is used to input homogeneous harmonics. Homogeneous harmonics are very common harmonics in actual transmission and are also one of the important factors affecting power quality.

[0080] S32, the length attenuation sub-model is used to simulate the attenuation problem of the transmission line. The attenuation problem may be caused by the impedance change caused by the change of transmission length, resulting in signal attenuation, or it may be caused by the skin effect and the attenuation problem caused by the loss of transmission medium, all of which may lead to changes in power quality; for example, the length attenuation sub-model includes a composite noise module; and the composite noise module includes white noise, pink noise and pulse noise; white noise is a random noise with the same energy density at all frequencies. In the power system, white noise may come from various random factors, such as thermal noise and shot noise; pink noise is a noise with a relatively uniform frequency distribution, and its energy density gradually decreases with the increase of frequency. Pink noise is more common in nature, such as wind and rain. In the power system, pink noise may come from some nonlinear elements in the power grid or electromagnetic radiation of the transmission line; pulse noise is a noise pulse that appears suddenly and lasts for a short time. Pulse noise may be caused by various transient processes in the power system, such as switching operation, short circuit fault, etc. Pulse noise has a great impact on the stability of the power grid and the power quality.

[0081] S33. The load sub-model adds different loads on the transmission line; wherein, the load sub-model contains multiple specific circuits, each of which is a hybrid model combining multiple electronic devices such as capacitors, resistors, inductors, field effect transistors, integrated circuits and chips, and is mainly connected in parallel to the transmission circuit. The specific circuit of the load sub-model needs to be simulated according to the actual equipment used, and each specific circuit is an equivalent circuit of a corresponding actual equipment; for example, if a rechargeable battery is to be connected, a specific circuit for the rechargeable battery is set up; the added load may introduce new noise and may also affect the impedance of the transmission line. The load sub-model can effectively simulate the impact of various external devices on the power quality in actual transmission.

[0082] S4. Process a preset number of first power quality levels and second power quality levels using a weight method to obtain a target power quality level. Specifically:

[0083] Respectively calculate the means of a preset number of first power quality levels A1 and second power quality levels A2 to obtain a first average power quality level B1 and a second average power quality level B2; obtain a first weight coefficient p and a second weight coefficient q; sum the first average power quality level and the second average power quality level according to the first weight coefficient and the second weight coefficient to obtain a target power quality A3, the specific formula is: A3 = q*B1+q*B2.

[0084] The weight method can be used to effectively solve the overall power quality level, and the two weight coefficients can be set by the tester according to the needs and the overall transmission situation; for example, in the first scenario, there are fewer devices on the power supply end and the power supply and power quality levels remain stable. The number of devices used on the user end far exceeds that of the devices on the power supply end. At the same time, the devices on the user end are constantly changing, and the corresponding power quality level on the user end is also changing frequently. At this time, the first weight coefficient p can be lowered and the second weight coefficient q can be increased, so as to more accurately reflect the overall power quality level; in the second scenario, the number of devices used by the power supply end and the user end are similar. At the same time, the equipment at both ends is often replaced, and the equipment changes are high. At this time, the first weight coefficient p and the second weight coefficient q can be set to 0.5, which can better reflect the overall power quality level.

[0085] In addition, it should be noted that since the mean value B1 of the power quality level at the power supply end and the power quality level B2 at the user end are obtained, and the overall power quality level A3 is calculated, the tester can take into account both local and overall situations. When it is necessary to perform power quality analysis on a certain transmission link separately, the first weight coefficient p can be directly set to 0 or the second weight coefficient q can be set to 0. The result obtained in this way reflects the power quality level of the power supply end or the power quality level of the user end.

[0086] Embodiment 2

[0087] Please refer to Figure 2 , a power quality analysis device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in a power quality analysis method as described in Example 1 is implemented.

[0088] In summary, the present invention provides a power quality analysis method and device, which analyzes the power quality of the power supply end and the user end by setting up two analysis systems, especially using the PCNN model at the user end to meet the complex power consumption of the user end, so that the results of the transmission signal analysis are more accurate; at the same time, interference injection is performed on the transmission line to simulate the environment and interference changes in actual transmission, without the need to re-layout the test every time a change occurs, thereby obtaining the overall power quality level and making the actual test easier; and the weight method is used to obtain the final power quality level, taking into account the influence of the three links of transmission, effectively reflecting the overall power quality, thereby improving the accuracy of power quality analysis.

[0089] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for analyzing power quality, characterized in that: A long-line transmission model is constructed with a power supply end, a transmission line and a user end, wherein the user end includes a plurality of user devices connected in parallel. The method includes: S1. Acquire a first transmission signal of the power supply end, and obtain a first power quality level corresponding to the power supply end according to the transmission signal; S2. Acquire a second transmission signal corresponding to each of the user equipments, and obtain a second power quality level corresponding to the user terminal according to all the second transmission signals; S3, adding an interference injection model to the transmission line, adjusting the parameters of the transmission line by means of the interference injection model, and re-executing step S1 and step S2; repeating step S3 until a preset number of the first power quality level and the second power quality level are obtained; S4. Process a preset number of the first power quality levels and the second power quality levels by using a weight method to obtain a target power quality level.

2. A method for analyzing power quality according to claim 1, characterized in that: The obtaining, according to the first transmission signal, a first power quality level corresponding to the power supply end comprises: Converting the first transmission signal into a digital signal; Performing wavelet transform on the digital signal to obtain characteristic parameters corresponding to power quality; The first power quality level corresponding to the power supply end is determined according to the characteristic parameter.

3. A power quality analysis method according to claim 2, characterized in that: The wavelet transform of the digital signal to obtain characteristic parameters corresponding to the power quality includes: Performing dual-tree complex wavelet decomposition on the digital signal to obtain information entropy corresponding to at least one wavelet coefficient; Taking each of the information entropies in the form of a feature vector as the feature parameter; Determining the first power quality level corresponding to the power supply end according to the characteristic parameter includes: Each of the characteristic parameters is identified by a preset classifier to obtain the first power quality level.

4. A method for analyzing power quality according to claim 1, characterized in that: The obtaining, according to all the second transmission signals, the second power quality level corresponding to the user terminal comprises: Power quality analysis is performed on all the second transmission signals through a pulse coupled neural network model to obtain a second power quality level corresponding to the user end.

5. A method for analyzing power quality according to claim 4, characterized in that: The pulse coupled neural network model includes a receiving part, a modulation part and a pulse part; The performing power quality analysis on all the second transmission signals by using a pulse coupled neural network model comprises: receiving each of the second transmission signals by the receiving section; Modulating the second transmission signal by the modulation part using an offset and multiplication to obtain a modulated signal; The power quality of the modulated signal is judged by the pulse portion, and the second power quality level is outputted.

6. A method for analyzing power quality according to claim 1, characterized in that: The interference model includes a harmonic sub-model, a length attenuation sub-model and a load sub-model; The adjusting the parameters of the transmission line by using the interference injection model comprises: inputting harmonics for interference into the transmission line through the harmonic sub-model; adjusting the impedance of the transmission line by means of the length attenuation sub-model; Different loads are added to the transmission line through the load sub-model.

7. A method for analyzing power quality according to claim 6, characterized in that: The harmonic sub-model adopts a nonlinear load model or an odd harmonic model.

8. A method for analyzing power quality according to claim 6, characterized in that: The length decay sub-model includes a composite noise module; The composite noise module includes white noise, pink noise and impulse noise.

9. The power quality analysis method according to claim 1, characterized in that: The processing of a preset number of the first power quality levels and the second power quality levels by using the weight method includes: Respectively calculating the average values ​​of a preset number of the first power quality levels and the second power quality levels to obtain a first average power quality level and a second average power quality level; Obtaining a first weight coefficient and a second weight coefficient; The first average power quality level and the second average power quality level are summed according to the first weight coefficient and the second weight coefficient to obtain the target power quality level.

10. A power quality analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the power quality analysis method according to any one of claims 1 to 9 is implemented.

Images