Power quality responsibility division and tracing method and system based on multi-source data fusion

Abstract

The application discloses a power quality responsibility division and tracing method and system based on multi-source data fusion, which comprises the following steps: data cleaning and time alignment are performed on the multi-source data, and a multi-node data set under the same time section is constructed; power quality characteristic quantities are extracted from the multi-node data set, wherein the power quality characteristic quantities comprise harmonic distortion rate, inter-harmonic content, voltage fluctuation amplitude, flicker intensity and three-phase imbalance degree; for the selected power quality index, the correlation coefficient of the characteristic quantities between different nodes is calculated based on the power quality characteristic quantities, and then the disturbance propagation direction is judged in combination with the voltage and current phase relationship, so that the disturbance source of the power quality index is determined; and according to the disturbance propagation direction, the contribution ratio of each potential responsibility source to the exceeding of the power quality index is calculated by using an impedance-based contribution algorithm or a fluctuation-based contribution algorithm.

Description

Technical Field

[0001] This invention belongs to the field of power quality monitoring and analysis technology, and relates to a method and system for power quality responsibility allocation and traceability based on multi-source data fusion. Background Technology

[0002] In recent years, the development of new energy sources has been rapid, with large-scale grid connection of photovoltaic and wind power. With the increase in these new energy power plants, a prominent problem is substandard power quality. Excessive harmonics, voltage flicker, and three-phase imbalance can be detected at many grid connection points. The question is, who should be held responsible when problems arise? The power grid claims it's due to harmonics generated by the new energy power plants, while the power plants claim the background voltage of the grid is inherently poor, and nearby users may also be causing interference. There is no universally accepted solution, leading to disputes and hindering effective remediation efforts.

[0003] The current practice is to install a power quality monitor at the point of common coupling (PCC). It records any deviations, but this only tells you "this is bad," not "who caused it." For example, if the 5th harmonic voltage at the PCC reaches 2.5%, it could be due to high background harmonics on the grid, generated by the photovoltaic inverter, or caused by the rectifier equipment at a neighboring factory. The data from a single point is insufficient and cannot provide comprehensive information.

[0004] Some studies have attempted to trace the source of power quality issues. Some only look at the direction of harmonic currents, while others use only the transient waveform at a single point for matching. These methods either only target a single indicator or are ineffective when multiple disturbance sources exist simultaneously. Field testing personnel often encounter situations where data is collected, but the timing of equipment from different manufacturers is not aligned, resulting in mismatched waveforms and making analysis very difficult. The final report conclusions are vague, unacceptable to the other party, and unable to accurately determine the allocation of responsibility for power quality issues or trace the source of power quality problems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for power quality responsibility division and tracing based on multi-source data fusion. This method and system can perform power quality responsibility division and tracing.

[0006] To achieve the above objectives, this invention discloses a method for power quality responsibility allocation and tracing based on multi-source data fusion, comprising: Power quality monitoring terminals are installed at the exit of new energy power stations, common connection points (PCC), and adjacent power stations or load nodes. Three-phase voltage and current waveform information are collected through the power quality monitoring terminals to obtain multi-source data. Based on a unified time synchronization signal, the multi-source data is cleaned and time-aligned, and a multi-node dataset under the same time segment is constructed. Power quality features are extracted from the multi-node dataset. These power quality features include harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. For a selected power quality index, based on the power quality characteristic quantities, the correlation coefficient between the characteristic quantities of different nodes is calculated, and then the voltage and current phase relationship is combined to determine the direction of disturbance propagation, so as to determine the source of disturbance for the power quality index. Based on the direction of disturbance propagation, either an impedance-based contribution algorithm or a fluctuation-based contribution algorithm is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard.

[0007] Furthermore, the unified timing signal is a GPS pulse signal, a BeiDou pulse signal, or an IRIG-B code.

[0008] Furthermore, the correlation coefficient is either the Pearson correlation coefficient or the Spearman rank correlation coefficient. When the absolute value of the correlation coefficient is greater than 0.6, it is determined to be a strong correlation.

[0009] Furthermore, the process for determining the direction of disturbance propagation is as follows: Identify the moment when the characteristic quantity changes significantly and compare the voltage phase changes at the PCC point and the outlet of the new energy power station before and after that moment. If the voltage phase at the new energy power station leads the phase at the PCC point, the source of the disturbance is determined to be on the new energy power station side; if the phase at the PCC point leads the phase at the new energy power station side, the source of the disturbance is determined to be on the grid side.

[0010] Furthermore, the contribution rate is calculated as (U_pcc_h / Z_sys_h) / (I_user_h) × 100%, where U_pcc_h is the h-th harmonic voltage at the PCC point, Z_sys_h is the h-th harmonic impedance on the system side, and I_user_h is the h-th harmonic current injected on the user side.

[0011] Furthermore, by decomposing the voltage fluctuation at the PCC point into fluctuation components caused by each responsible source, the proportion of the effective value of each component to the total effective value of the fluctuation is calculated as the contribution.

[0012] Furthermore, the new energy power station is a photovoltaic power station, a wind farm, or an energy storage power station.

[0013] This invention discloses a power quality responsibility allocation and traceability system based on multi-source data fusion, comprising: The acquisition module is used to install power quality monitoring terminals at the exit of new energy power stations, common connection points (PCC), and adjacent power stations or load nodes. The power quality monitoring terminals collect three-phase voltage and current waveform information to obtain multi-source data. The preprocessing module is used to perform data cleaning and time alignment on the multi-source data based on a unified time synchronization signal, and to construct a multi-node dataset under the same time segment. The extraction module is used to extract power quality features from the multi-node dataset. The power quality features include harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. The determination module is used to calculate the correlation coefficient of the characteristic quantities between different nodes based on the power quality characteristic quantities for the selected power quality index, and then combine the voltage and current phase relationship to determine the direction of disturbance propagation, so as to determine the source of disturbance for the power quality index. The calculation module is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard, based on the direction of disturbance propagation, using either an impedance-based contribution algorithm or a fluctuation-based contribution algorithm.

[0014] This invention discloses a computer 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, it implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion.

[0015] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion.

[0016] The present invention has the following beneficial effects: In practical operation, the power quality responsibility allocation and tracing method and system based on multi-source data fusion described in this invention calculates the correlation coefficient between different nodes based on the power quality characteristic quantities for a selected power quality indicator. Then, it combines the voltage and current phase relationship to determine the disturbance propagation direction, thereby identifying the disturbance source of the power quality indicator. According to the disturbance propagation direction, it uses an impedance-based contribution algorithm or a fluctuation-based contribution algorithm to calculate the contribution ratio of each potential source of responsibility for the power quality indicator exceeding the standard, thereby realizing the allocation and tracing of power quality responsibility. It is highly practical. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1This is a schematic diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 A schematic diagram for propagation path identification. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0023] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0024] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0027] Example 1 refer to Figure 1 and Figure 2 The power quality responsibility allocation and tracing method based on multi-source data fusion described in this invention includes the following steps: 1) Multi-node synchronous data acquisition; Monitoring terminals should be installed at the exit of the new energy power station, the PCC point, and adjacent power stations or load nodes. The exit of the new energy power station reflects the power quality emission characteristics of the new energy itself. The PCC point is the legal interface for the division of responsibilities. Adjacent nodes are used to exclude other interference. All monitoring terminals must receive the same time source, such as GPS or Beidou, to ensure consistent collection time. It is recommended that the sampling rate be more than 256 points per cycle and that continuous recording be carried out for at least 24 hours. If the problem occurs intermittently, it is recommended to record for a week.

[0028] 2) Data uploading and fusion processing; Each monitoring terminal transmits the collected data to the central analysis platform via the network. The central analysis platform performs data cleaning, gap filling, and time alignment to construct a cross-sectional dataset. Data that is clearly incorrect is removed; for example, a sudden voltage drop to zero followed by an instantaneous recovery is often due to sensor arcing or communication packet loss. These points need to be marked. For short-term missing data, linear interpolation between the preceding and following times is used to fill in the gaps. The most critical step is time alignment: although different devices have GPS, their internal crystal oscillators can drift, requiring the waveforms to be rearranged based on the time synchronization signal. The aligned data forms a cross-sectional dataset of multiple nodes at the same time.

[0029] 3) Feature extraction; Feature quantities are extracted from the aligned voltage and current waveforms. These feature quantities mainly include: Harmonics: Total Harmonic Distortion (THD), and the content of each harmonic from the 2nd to the 50th order. If it is an interharmonic problem, the interharmonic components should also be extracted.

[0030] Voltage fluctuations and flicker: voltage fluctuation amplitude, short-time flicker value Pst, and long-time flicker value Plt.

[0031] Three-phase imbalance: negative sequence voltage imbalance and zero sequence current imbalance.

[0032] Other parameters that can be added as needed include: frequency deviation and voltage sag / sag.

[0033] The extracted features are stored in time series, with each node and each indicator having a curve.

[0034] 4) Propagation path identification; Calculate the correlation. Compute the correlation coefficient between the time series of the PCC point and the time series of the same indicator at nodes such as the exit point of the renewable energy power station, adjacent renewable energy power stations, or load. The Pearson correlation coefficient is recommended. An absolute value of the correlation coefficient greater than 0.6 indicates a strong correlation, meaning that the two indicators show similar trends and are likely causally related.

[0035] Determining the direction. Correlation alone is insufficient, because the correlation between A and B does not necessarily mean A causes B; it could be B causing A, or both being affected by C. Determining the direction requires utilizing the phase change at the moment the disturbance occurs. Specifically: Identify the moment within the time series when the indicator suddenly increases (e.g., the 5th harmonic voltage jumps from 1.2% to 2.5%); this moment is called the disturbance initiation point. Then compare the voltage phases of the PCC point and the renewable energy plant outlet voltage within half a cycle before and after this point. If the harmonic voltage phase at the renewable energy plant outlet leads the PCC point, it indicates the disturbance is propagating outward from the renewable energy plant, and the responsibility lies with the renewable energy plant. Conversely, if the PCC point phase leads the renewable energy plant outlet phase, it indicates the disturbance is propagating in from the grid side. The direction determination for adjacent renewable energy plants or loads follows the same logic.

[0036] By combining correlation and direction, it is possible to determine which party the disturbance comes from. If there are multiple strongly correlated nodes and their directions all point to PCC, then it is considered that multiple sources of responsibility are working together.

[0037] 5) Contribution quantification; After identifying the responsible parties, it is also necessary to calculate the percentage of responsibility borne by each party, specifically: Scenario 1: Harmonic Problem. An impedance-based contribution algorithm is used. The principle is: the harmonic voltage at the PCC point is jointly generated by the system-side background harmonics and the user-side injected harmonics. If the system-side harmonic impedance is known, the voltage generated by the user-side harmonic current at the PCC point can be deduced. The system-side harmonic impedance can be estimated using the fluctuation method, i.e., without switching on the user side, the impedance is calculated using the natural fluctuations on the system side, or a small disturbance is actively injected using a switching method to measure it. After calculating the impedance, the h-th harmonic voltage generated by user i at the PCC point is U_i = I_i × Z_sys, where I_i is the h-th harmonic current injected by user i, and the user's contribution = U_i / U_pcc × 100%.

[0038] Scenario 2: Voltage Fluctuation Problem. A contribution-based algorithm based on fluctuation quantities is used. The voltage fluctuation sequence at the PCC point is decomposed into several components, each corresponding to a source of responsibility (grid background, power fluctuations at renewable energy plants, or fluctuations from adjacent renewable energy plants or loads). The decomposition method can use independent component analysis or correlation-weighted linear regression. The proportion of the squared effective value of each component to the squared effective value of the total fluctuation at the PCC point is the contribution. This method does not require knowledge of the system impedance, relying only on the statistical characteristics of the fluctuation quantities, and has good field applicability.

[0039] 6) Output a responsibility allocation report; Finally, a report should be generated, specifying: which indicator exceeded the standard, the period of exceeding the standard, the name and percentage of contribution of each responsible party (pie chart or bar chart), and a propagation path diagram (arrows indicate the direction of disturbance). Remediation recommendations should be specific, such as "Install a 5th harmonic filter on the photovoltaic power station side" or "Optimize reactive power control parameters on the wind farm side." The report format should be easy to communicate with the power grid company, renewable energy plant owners, and users.

[0040] Example 2 The power quality responsibility allocation and traceability system based on multi-source data fusion described in this invention includes: The acquisition module is used to install power quality monitoring terminals at the exit of new energy power stations, common connection points (PCC), and adjacent power stations or load nodes. The power quality monitoring terminals collect three-phase voltage and current waveform information to obtain multi-source data. The preprocessing module is used to perform data cleaning and time alignment on the multi-source data based on a unified time synchronization signal, and to construct a multi-node dataset under the same time segment. The extraction module is used to extract power quality features from the multi-node dataset. The power quality features include harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. The determination module is used to calculate the correlation coefficient of the characteristic quantities between different nodes based on the power quality characteristic quantities for the selected power quality index, and then combine the voltage and current phase relationship to determine the direction of disturbance propagation, so as to determine the source of disturbance for the power quality index. The calculation module is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard, based on the direction of disturbance propagation, using either an impedance-based contribution algorithm or a fluctuation-based contribution algorithm.

[0041] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0042] Example 3 A 10MW photovoltaic power station in a certain area is connected to the power grid via a 10kV line. A machine shop and a public transformer are also connected to the power supply point (PCC). Power quality testing revealed that during periods of good sunlight in the daytime, the 5th harmonic voltage distortion rate at the PCC reached a maximum of 2.8%, exceeding the national standard limit of 2.0%. The photovoltaic power station claims it's not their problem and suspects it's caused by the factory's frequency converter, while the factory denies any involvement.

[0043] According to the method of this invention, monitoring terminals were installed at three points: the photovoltaic power station outlet (point A), the PCC point (point B), and the factory inlet side (point C). All terminals were time-synchronized with GPS, with a sampling rate of 256 points per cycle, and data was continuously collected for 72 hours.

[0044] 1) Data alignment and initial observation; After exporting the data, time alignment was performed first. The timestamps of the waveform files from the three devices were basically consistent, but there were slight deviations of tens of microseconds. Interpolation and resampling were used to align them to the same time axis. Then, the curve of the 5th harmonic voltage changing with time was plotted. A clear pattern was found: the 5th harmonic voltage at point B increased between 9:00 AM and 4:00 PM every day, with the peak occurring between 12:00 PM and 2:00 PM. This period coincides with the peak output of photovoltaic power generation. The harmonic content dropped below 0.5% at night. The factory's production hours are from 8:00 AM to 8:00 PM, but the harmonics did not increase at night while the factory was still operating. Initially, the correlation with photovoltaic power generation appears to be stronger.

[0045] 2) Correlation calculation; The correlation between the 5th harmonic voltage at point B and the 5th harmonic current at point A was calculated using the Pearson correlation coefficient. With a time series length of 72 hours and one point per minute, the result was 0.83, indicating a strong correlation. The correlation between the 5th harmonic voltage at point B and the 5th harmonic current at point C was then calculated, yielding a result of 0.29, indicating a weak correlation. This suggests that the harmonic voltage at point PCC primarily follows the changes in the harmonic current of the photovoltaic power station and is not significantly related to the factory itself.

[0046] 3) Direction determination; Consider a typical disturbance event: At 12:05 PM one day, the 5th harmonic voltage at point B jumps from 1.6% to 2.7% within 30 seconds. Extract the waveforms of one cycle before and after this moment, perform a Fast Fourier Transform (FFT), and extract the phase of the 5th harmonic. The phase of the 5th harmonic voltage at point A is -42 degrees, and the phase at point B is -58 degrees. The phase at point A leads that at point B by approximately 16 degrees. Based on the direction criterion, the side that leads is the source of the disturbance. Therefore, the source of the disturbance is on the photovoltaic power station side; that is, the harmonics emitted by the photovoltaic power station flow into the grid, causing the voltage at point PCC to rise.

[0047] 4) Contribution calculation; First, the 5th harmonic impedance on the system side is estimated using the fluctuation method. A time period at night when neither the factory nor the photovoltaic system is operating significantly (2 AM to 4 AM) is selected, during which the 5th harmonic at point PCC is mainly due to grid background noise. Regression analysis using data from this period estimates the 5th harmonic impedance on the system side to be approximately 0.75Ω. During the day, when the photovoltaic system is operating at full capacity, the average 5th harmonic current at point A is 14.2A, generating a harmonic voltage of 14.2 × 0.75 = 10.65V at point PCC. The measured 5th harmonic voltage at point B is 12.8V. The photovoltaic power station's contribution = 10.65 / 12.8 = 83.2%. The remaining 16.8% is attributed to grid background harmonics and a small contribution from the factory. The factory's 5th harmonic current is only 2.1A, contributing approximately 1.6V, accounting for 12.5%; the grid background contributes approximately 0.55V, accounting for 4.3%.

[0048] 5) Report conclusions; The final report concluded that the primary responsibility for the 5th harmonic exceedance at the PCC point lay with the photovoltaic power plant, contributing 83%; the secondary responsibility lay with the factory, contributing 13%; and the grid background contributed 4%. Recommendations for remediation: On the photovoltaic power plant side, check the harmonic filtering parameters of the inverter, or install a 5th harmonic active filter on the AC side. On the factory side, it is recommended to check whether a reactor is installed at the inverter input.

[0049] Example 4 A wind farm with an installed capacity of 50MW is connected to the power grid via a 110kV line. Near the same PCC point is a large steel plant. The operation of the electric arc furnace causes voltage flicker. Recently, the short-term flicker value Pst at the PCC point has frequently exceeded 1.0, reaching as high as 1.3. The wind farm believes this is caused by the steel plant, while the steel plant claims it's due to fluctuations in wind power output.

[0050] Voltage data were simultaneously collected at three locations: the wind farm booster station outlet (point A), PCC point (point B), and the steel plant incoming line side (point C). The data was recorded continuously for one week, with a sampling rate of 256 points per cycle. The active power of the wind farm and the active power of the steel plant were also recorded.

[0051] 1) Feature extraction; The flicker value Pst was extracted from the voltage waveform, with one value every 10 minutes, totaling 1008 points per week. After plotting three curves, it was found that the Pst fluctuation at point B showed some synchronization with the wind power fluctuation at point A, but was also related to the steel mill's operating hours at point C. The steel mill typically operates from 8:00 AM to 8:00 PM during the day, with the electric arc furnace completing a smelting cycle every 40 minutes, causing the flicker value to rise periodically. Wind power, however, varies with wind speed and does not have a fixed period.

[0052] 2) Correlation and direction analysis; The correlation coefficient between Pst at point B and Pst at point A is 0.68, and the correlation coefficient with Pst at point C is 0.59. Both are greater than 0.5, indicating that both wind power and the steel plant contribute to the flicker at point PCC. Further analysis of the disturbance direction: A typical event was selected: at 3 PM one afternoon, the wind speed suddenly dropped from 6 m / s to 3 m / s, and the wind power output dropped from 40 MW to 12 MW within 5 minutes. Simultaneously, Pst at point B increased from 0.9 to 1.2. The voltage phase changes during this process were compared. The fundamental voltage phase at point A led by approximately 8 degrees during the power decrease, while the phase change at point B lagged behind that at point A by approximately 2 seconds. This indicates that the disturbance was transmitted from the wind farm side to the grid.

[0053] Regarding the steel mill, at the moment the electric arc furnace ignites (power suddenly jumps from 5MW to 25MW), the voltage fluctuation at point C leads the phase of point B by approximately 0.5 seconds, indicating that the disturbance from the steel mill also propagates towards point PCC. Since both directions point towards point PCC, it suggests that both wind power and the steel mill are sources of responsibility.

[0054] 3) Contribution quantification; The fluctuation decomposition method is used, instead of the impedance method (because flicker is difficult to calculate using impedance). The Pst time series at point B is used as the dependent variable, and the Pst at points A and C are used as independent variables, for multiple linear regression. Considering the potential interaction between wind power and steel plant fluctuations, the two independent variables are first orthogonalized. Regression results: The regression coefficient at point A is 0.52, the coefficient at point C is 0.38, and the intercept term is 0.08 (representing background grid fluctuations). The contribution ratios of each variable were normalized by the square of the regression coefficients: Wind power contribution = 0.52² / (0.52²+0.38²+0.08²) = 0.2704 / (0.2704+0.1444+0.0064) = 0.2704 / 0.4212 = 64.2%; Steel plant contribution = 0.1444 / 0.4212 = 34.3%; Grid background contribution = 0.0064 / 0.4212 = 1.5%.

[0055] Another more intuitive method is to directly compare the Pst increments caused by each source of responsibility: during periods when wind power fluctuates alone while steel mills remain stable, the ratio of the Pst increment at point B to that at point A is approximately 0.7; during periods when steel mills initiate an arc alone while wind power remains stable, the ratio is approximately 0.5. Overall, wind power contributes approximately 60%-65%, and steel mills contribute approximately 30%-35%, consistent with the regression results.

[0056] 4) Report conclusions; Conclusion: The primary responsibility for excessive voltage flicker at the PCC point lies with the wind farm, contributing 64%; the secondary responsibility lies with the steel plant, contributing 34%; the contribution from the grid background is minimal. Recommendations: For the wind farm, optimize reactive power control strategies to reduce the impact of power fluctuations on voltage; consider configuring SVG (Static Var Compensator) devices. For the steel plant, install Static Var Compensators (SVCs) on the electric arc furnace power supply lines.

[0057] Example 5 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion. For example, it includes: installing power quality monitoring terminals at the exit of new energy power plants, the point of common coupling (PCC), and adjacent power plants or load nodes; collecting three-phase voltage and current waveform information through the power quality monitoring terminals to obtain multi-source data; and performing data cleaning and time alignment on the multi-source data based on a unified time synchronization signal, and constructing data under the same time segment. The system uses a multi-node dataset; it extracts power quality features from the dataset, including harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance; for a selected power quality index, it calculates the correlation coefficient between features of different nodes based on the power quality features, and then combines the voltage and current phase relationship to determine the disturbance propagation direction, thereby identifying the disturbance source of the power quality index; based on the disturbance propagation direction, it uses an impedance-based contribution algorithm or a fluctuation-based contribution algorithm to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device; the processor, network interface, and memory are interconnected via an internal bus, which can be an industry standard architecture bus, a peripheral component interconnection standard bus, an extended industry standard architecture bus, etc., and the bus can be divided into address bus, data bus, control bus, etc. The memory is used to store programs, specifically, the programs may include program code, and the program code includes computer operation instructions. Memory can include main memory and non-volatile memory, and provides instructions and data to the processor.

[0058] Example 6 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion. For example, it includes: installing power quality monitoring terminals at the exit of new energy power plants, the point of common coupling (PCC), and adjacent power plants or load nodes; collecting three-phase voltage and current waveform information through the power quality monitoring terminals to obtain multi-source data; and performing data cleaning and time alignment on the multi-source data based on a unified time signal, and constructing a multi-node dataset at the same time segment. Power quality features are extracted from the multi-node dataset, including harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. For a selected power quality index, the correlation coefficient between features of different nodes is calculated based on the power quality features, and the disturbance propagation direction is determined by combining the voltage and current phase relationship to identify the disturbance source of the power quality index. According to the disturbance propagation direction, an impedance-based contribution algorithm or a fluctuation-based contribution algorithm is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0059] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0064] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0065] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for power quality responsibility allocation and tracing based on multi-source data fusion, characterized in that, include: Power quality monitoring terminals are installed at the exit of new energy power stations, common connection points (PCC), and adjacent power stations or load nodes. Three-phase voltage and current waveform information are collected through the power quality monitoring terminals to obtain multi-source data. Based on a unified time synchronization signal, the multi-source data is cleaned and time-aligned, and a multi-node dataset under the same time segment is constructed. Power quality features are extracted from the multi-node dataset. These power quality features include harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. For a selected power quality index, based on the power quality characteristic quantities, the correlation coefficient between the characteristic quantities of different nodes is calculated, and then the voltage and current phase relationship is combined to determine the direction of disturbance propagation, so as to determine the source of disturbance for the power quality index. Based on the direction of disturbance propagation, either an impedance-based contribution algorithm or a fluctuation-based contribution algorithm is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard.

2. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, The unified timing signal is a GPS pulse signal, a BeiDou pulse signal, or an IRIG-B code.

3. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, The correlation coefficient is either the Pearson correlation coefficient or the Spearman rank correlation coefficient. When the absolute value of the correlation coefficient is greater than 0.6, it is considered a strong correlation.

4. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, The process for determining the direction of disturbance propagation is as follows: Identify the moment when the characteristic quantity changes significantly and compare the voltage phase changes at the PCC point and the outlet of the new energy power station before and after that moment. If the voltage phase at the new energy power station leads the phase at the PCC point, the source of the disturbance is determined to be on the new energy power station side; if the phase at the PCC point leads the phase at the new energy power station side, the source of the disturbance is determined to be on the grid side.

5. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, Contribution = (U_pcc_h / Z_sys_h) / (I_user_h) × 100%, where U_pcc_h is the h-th harmonic voltage at PCC point, Z_sys_h is the h-th harmonic impedance on the system side, and I_user_h is the h-th harmonic current injected on the user side.

6. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, By decomposing the voltage fluctuation at the PCC point into fluctuation components caused by each responsible source, the proportion of the effective value of each component to the total effective value of the fluctuation is calculated as the contribution.

7. The method for power quality responsibility allocation and tracing based on multi-source data fusion according to claim 1, characterized in that, The new energy power stations mentioned are photovoltaic power stations, wind farms, or energy storage power stations.

8. A power quality responsibility allocation and traceability system based on multi-source data fusion, characterized in that, include: The acquisition module is used to install power quality monitoring terminals at the exit of new energy power stations, common connection points (PCC), and adjacent power stations or load nodes. The power quality monitoring terminals collect three-phase voltage and current waveform information to obtain multi-source data. The preprocessing module is used to perform data cleaning and time alignment on the multi-source data based on a unified time synchronization signal, and to construct a multi-node dataset under the same time segment. The extraction module is used to extract power quality features from the multi-node dataset. The power quality features include harmonic distortion rate, interharmonic content, voltage fluctuation amplitude, flicker intensity, and three-phase imbalance. The determination module is used to calculate the correlation coefficient of the characteristic quantities between different nodes based on the power quality characteristic quantities for the selected power quality index, and then combine the voltage and current phase relationship to determine the direction of disturbance propagation, so as to determine the source of disturbance for the power quality index. The calculation module is used to calculate the contribution ratio of each potential source of responsibility for the power quality index exceeding the standard, based on the direction of disturbance propagation, using either an impedance-based contribution algorithm or a fluctuation-based contribution algorithm.

9. A computer 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, it implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power quality responsibility allocation and tracing method based on multi-source data fusion as described in any one of claims 1-7.

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