Electric energy quality optimization processing method and device, computer equipment and storage medium

Abstract

The invention relates to an electric energy quality optimization processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring each electric energy quality parameter in the operation process of the power equipment, carrying out anomaly analysis on each electric energy quality parameter to obtain an anomaly analysis result, and if it is determined that the electric energy quality parameter is abnormal according to the anomaly analysis result, carrying out fault judgment and early warning processing on the electric energy quality parameter. And a fault judgment result and early warning information are obtained, the fault judgment result and the early warning information are fed back to the terminal equipment, an optimization processing strategy triggered by the terminal equipment for the fault judgment result and the early warning information is obtained, and according to the optimization processing strategy, the target adjusting equipment is adjusted, and the electric energy quality parameters are optimized. The method can improve the electric energy quality, and guarantees the stable operation of the power equipment.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing power quality. Background Art

[0002] With the development of power technology and the diversification of power equipment, power quality issues have gradually become prominent, which has a significant impact on the stable operation of the power grid and the performance of power equipment at the user end. Since power quality is not only related to the safety and reliability of power supply, but also directly affects the efficiency of industrial production and product quality, it is necessary to monitor and optimize power quality in real time to ensure the stable operation of the power grid.

[0003] In the field of power quality monitoring, traditional monitoring methods mainly rely on periodic manual detection or simple automatic monitoring systems. Among them, automatic monitoring systems usually include devices such as voltage monitors, current monitors and power analyzers, which measure and record certain parameters of power quality through these devices. However, traditional monitoring systems often use periodic sampling and cannot achieve true real-time monitoring. They are also unable to promptly detect and respond to instantaneous changes in power quality, resulting in poor power quality monitoring effects, inability to perform intelligent regulation and optimization processing in a timely manner, and prone to power equipment operation failures. Summary of the invention

[0004] Based on this, it is necessary to provide a power quality optimization processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the monitoring accuracy of power quality, timely identify the changing trends and potential problems of power quality, and ensure the stable operation of power equipment in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for optimizing power quality, comprising:

[0006] Acquire various power quality parameters during the operation of the power equipment, perform abnormal analysis on each of the power quality parameters, and obtain abnormal analysis results;

[0007] If it is determined according to the abnormality analysis result that the power quality parameter is abnormal, a fault judgment and early warning process is performed on the power quality parameter to obtain a fault judgment result and early warning information;

[0008] Feedback the fault judgment result and warning information to the terminal device, and obtain the optimization processing strategy triggered by the terminal device for the fault judgment result and warning information;

[0009] According to the optimization processing strategy, the target regulation equipment is adjusted to optimize the power quality parameters.

[0010] In a second aspect, the present application also provides a power quality optimization processing device, comprising:

[0011] The abnormality analysis module is used to obtain various power quality parameters during the operation of the power equipment, perform abnormality analysis on each of the power quality parameters, and obtain abnormality analysis results;

[0012] A fault judgment module, for performing fault judgment and early warning processing on the power quality parameter to obtain a fault judgment result and early warning information if it is determined that the power quality parameter is abnormal according to the abnormal analysis result;

[0013] An optimization processing strategy acquisition module is used to feed back the fault judgment result and warning information to the terminal device, and obtain the optimization processing strategy of the terminal device for the fault judgment result and warning information feedback;

[0014] The power quality parameter optimization module is used to adjust the target regulation device and optimize the power quality parameters according to the optimization processing strategy.

[0015] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned power quality optimization processing method are implemented.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned power quality optimization processing method are implemented.

[0017] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned power quality optimization processing method are implemented.

[0018] In the above-mentioned power quality optimization processing method, device, computer equipment, computer-readable storage medium and computer program product, by obtaining each power quality parameter during the operation of the power equipment and performing abnormal analysis on each power quality parameter to obtain the abnormal analysis result, it is possible to realize high-frequency real-time monitoring of the power quality parameters, timely discover the instantaneous change of the power quality, so as to improve the monitoring accuracy of the power quality, and timely identify the changing trend and potential problems of the power quality. Furthermore, if it is determined that the power quality parameter is abnormal according to the abnormal analysis result, the power quality parameter is subjected to fault judgment and early warning processing, and the fault judgment result and early warning information are obtained, and the fault judgment result and early warning information are fed back to the terminal device to obtain the optimization processing strategy triggered by the terminal device for the fault judgment result and the early warning information, so that the adjustment of the target regulating device and the optimization of the power quality parameters can be realized by executing the triggered optimization processing strategy, the power quality is improved, and the stable operation of the power equipment is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0020] FIG1 is a diagram showing an application environment of a method for optimizing power quality in one embodiment;

[0021] Figure 2 A schematic diagram of a flow chart of a method for optimizing power quality in one embodiment;

[0022] Figure 3 It is a flowchart of performing abnormal analysis on each power quality parameter and obtaining abnormal analysis results in one embodiment;

[0023] Figure 4 A schematic diagram of a process for adjusting a target regulating device and optimizing power quality parameters according to an optimization processing strategy in an embodiment;

[0024] Figure 5 A schematic diagram of a flow chart of adjusting a target regulating device and optimizing power quality parameters according to an optimization processing strategy in another embodiment;

[0025] Figure 6 A schematic flow chart of a method for optimizing power quality in another embodiment;

[0026] Figure 7 A schematic diagram of a flow chart of a method for optimizing power quality in another embodiment;

[0027] Figure 8 A schematic diagram of the overall processing flow of a power quality optimization processing method in one embodiment;

[0028] Fig. 9 It is a structural block diagram of a power quality optimization processing device in one embodiment;

[0029] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] The power quality optimization processing method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal device 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal device 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, portable wearable devices, and aircraft, etc. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, and projection devices. Portable wearable devices can be smart watches, smart bracelets, and head-mounted devices. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, or a server cluster composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and basic cloud computing services such as big data and artificial intelligence platforms. Among them, the terminal device 102 and the server 104 can be directly or indirectly connected through wired or wireless communication, which is not limited in the embodiments of the present application.

[0032] Among them, the terminal device 102 and the server 104 can be used alone to execute the power quality optimization processing method provided in the embodiment of the present application, and the terminal device 102 and the server 104 can also cooperate to execute the power quality optimization processing method provided in the embodiment of the present application. For example, taking the terminal device 102 and the server 104 cooperating to execute the power quality optimization processing method provided in the embodiment of the present application as an example, the server 104 obtains each power quality parameter during the operation of the power equipment, performs abnormal analysis on each power quality parameter, and obtains the abnormal analysis result. Among them, if the server 104 determines that the power quality parameter is abnormal according to the abnormal analysis result, it performs fault judgment and early warning processing on the power quality parameter to obtain the fault judgment result and early warning information. Further, the server 104 feeds back the fault judgment result and early warning information to the terminal device 102, and obtains the optimization processing strategy triggered by the terminal device 102 for the fault judgment result and the early warning information, so as to adjust the target adjustment device according to the optimization processing strategy, optimize the power quality parameters, and ensure the stable operation of the power equipment.

[0033] In an exemplary embodiment, Figure 2 As shown, a method for optimizing power quality is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps S202 to S208. Among them:

[0034] Step S202, obtaining various power quality parameters during the operation of the power equipment, performing abnormal analysis on the various power quality parameters, and obtaining abnormal analysis results.

[0035] Specifically, the server obtains multiple power quality parameters of various power equipment during operation, including power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate, and performs abnormal analysis on the collected power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate to obtain corresponding abnormal analysis results.

[0036] Furthermore, the power quality parameter features corresponding to the power quality parameters, including the power quality parameter features corresponding to the voltage, current, power factor, frequency, and harmonic distortion rate, can be extracted, and the power quality trend can be predicted for the extracted power quality parameter features to obtain the power quality change trend, so as to perform abnormal analysis based on the power quality change trend and obtain the abnormal analysis result. The power quality change trend refers to the change pattern of the power quality parameter over time, which can be an increase or decrease, or a periodic fluctuation, etc., which is used to indicate that the operation status of the power system is improving or deteriorating.

[0037] In an exemplary embodiment, the abnormal analysis method adopted by the server for each power quality parameter specifically includes: 1) Time domain analysis: Time domain analysis of voltage and current signals to identify whether there are abnormal events such as voltage sag, swell, and instantaneous interruption. 2) Frequency domain analysis: Convert time domain signals such as voltage and current signals into frequency domain signals through Fourier transform (FFT) to analyze whether there are events such as harmonic anomalies and frequency deviations. 3) Waveform analysis: Analyze the shape of voltage and current waveforms to identify whether there are problems such as non-sinusoidal waveforms and waveform distortion. 4) Statistical analysis: Use statistical methods to perform descriptive statistics on the collected data, such as the mean, standard deviation, and probability distribution of each power quality parameter collected, and perform abnormal analysis on the descriptive statistical results to determine whether there are power quality parameters with values ​​that are too high or too low. 5) Correlation analysis: Analyze the correlation between different power quality parameters to identify potential causal relationships.

[0038] Furthermore, the server can also use various machine learning algorithms to perform abnormal analysis on various power quality parameters. The specific machine learning algorithms used include: (1) Supervised learning algorithms, including: A) Support vector machine (SVM): used for classification and regression tasks, suitable for the classification of power quality events; B) Decision tree: used to build classification and regression models, easy to understand and explain; C) Random forest: an integrated learning method that improves the accuracy and stability of prediction by building multiple decision trees; D) Unsupervised learning algorithms: Cluster analysis such as K-means and hierarchical clustering, used to identify patterns and anomalies in data; E) Principal component analysis (PCA): used for dimensionality reduction, reducing the number of variables in the data set while retaining the most important information. (2) Deep learning algorithms: A) Convolutional neural network (CNN): suitable for processing and analyzing time series data, such as voltage and current signals; B) Recurrent neural network (RNN): such as long short-term memory network (LSTM), suitable for processing time series data and used to predict power quality events. (3) Reinforcement learning algorithm: used to optimize the control strategy of harmonic filters and other regulating devices, and learn the best regulation strategy through interaction with the environment.

[0039] Step S204: if it is determined that the power quality parameters are abnormal according to the abnormality analysis result, fault judgment and early warning processing are performed on the power quality parameters to obtain fault judgment results and early warning information.

[0040] Specifically, if the server performs an abnormal analysis on the power quality change trend and obtains the abnormal analysis results, and determines that there is an abnormality in the power quality parameters, such as determining that there is an abnormality in one or more power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate, then fault judgment and early warning processing are performed on the abnormal power quality parameters to obtain corresponding fault judgment results and early warning information.

[0041] The power quality change trend may specifically include voltage stability, frequency stability, harmonic distortion rate, and power factor change rate. Voltage stability indicates whether the voltage is stable within the specified range, or whether there is a trend of voltage being too high or too low. Frequency stability is used to indicate whether the grid frequency remains near the rated value, or whether there is a trend of deviating from the standard frequency. Harmonic distortion is used to indicate whether the harmonic content in the voltage or current waveform is increasing to determine whether it will cause a decrease in power quality. The power factor change rate indicates whether the overall power factor of the system is changing to determine whether it will affect energy efficiency and cost.

[0042] Furthermore, an abnormal analysis is performed on the power quality change trends such as voltage stability, frequency stability, harmonic distortion rate, and power factor change rate. After the abnormal analysis results are obtained, it is determined that the power quality parameters are abnormal based on the abnormal analysis results. When it is determined that one or more power quality parameters are abnormal, fault judgment and early warning processing are performed on the power quality parameters to obtain corresponding fault judgment results and early warning information.

[0043] For example, with respect to voltage stability, if it is determined that there is an abnormality of too high or too low voltage, further fault judgment of the voltage is performed, including: 1) judging whether there is a temporary voltage sag or swell, that is, a sudden drop or rise in voltage, which may affect the operation of sensitive equipment; 2) judging whether there is a momentary interruption, that is, if the voltage suddenly drops to zero, even for a very short time, it may cause damage to certain equipment and cause abnormal equipment operation; 3) judging whether there is a current or voltage unbalanced load, that is, the current or voltage in the three-phase system is unbalanced, which may cause motor overload or equipment damage, resulting in equipment failure.

[0044] Among them, the fault judgment results for voltage stability may specifically include: determining the existence of a voltage sag or swell fault, or a momentary interruption fault, or a current or voltage unbalanced load fault, thereby generating warning information of voltage sag or swell, warning information of voltage momentary interruption, and warning information of current or voltage unbalanced load based on the corresponding fault judgment results.

[0045] Optionally, for the harmonic distortion rate, if it is determined that there is an abnormal increase in the harmonic content in the voltage or current waveform, it is further determined whether there is a fault of excessive harmonic distortion rate, that is, the increase in harmonic content is likely to cause problems such as equipment overheating, reduced efficiency or shortened life. If it is determined that there is a fault of excessive harmonic distortion rate, an early warning message of excessive harmonic distortion rate is generated.

[0046] Step S206, feeding back the fault judgment result and the warning information to the terminal device, and obtaining the optimization processing strategy triggered by the terminal device for the fault judgment result and the warning information.

[0047] Specifically, the server feeds back the fault judgment results and warning information to the terminal device for the user to view and respond in time, and the terminal device further obtains the user's response operations triggered by the fault judgment results and warning information, and obtains the optimization processing strategy corresponding to the response operation, and feeds back the optimization processing strategy to the server.

[0048] Among them, the optimization processing strategy specifically includes rule optimization strategy and adaptive optimization strategy. Among them, the rule optimization strategy means that the server can automatically trigger the control action according to the preset rules (such as voltage, current or harmonic level exceeds a specific threshold). For example, when the voltage harmonic distortion rate is detected to exceed 5%, the filter investment is automatically increased. The adaptive optimization strategy means that the server can dynamically adjust the control parameters according to real-time data and historical trends. For example, if the server detects that the harmonic level continues to rise within a specific time period, the response level of the filter can be automatically adjusted.

[0049] Step S208: According to the optimization processing strategy, the target regulation equipment is adjusted to optimize the power quality parameters.

[0050] Specifically, the server determines the target regulating device to be adjusted according to different optimization processing strategies, such as rule optimization strategy or adaptive optimization strategy, and adjusts the equipment parameters of the target regulating device, so as to optimize the power quality parameters according to the adjusted target regulating device, improve the power quality, and ensure the stable operation of the power equipment. Among them, the target regulating device may specifically include: harmonic filter, reactive power compensation device, voltage regulator, load balancer, active filter, frequency controller, protection relay, and intelligent circuit breaker, etc. According to different optimization processing strategies, the equipment parameters of different target regulating devices can be adjusted.

[0051] Specifically, the adjustment methods of each target adjustment equipment include: 1) Harmonic filters are used to reduce harmonics in the power system to reduce voltage harmonics and current harmonics to an acceptable range, eliminate or reduce harmonic distortion and resonance; 2) Reactive power compensation devices include shunt capacitors, shunt reactors, and static VAR generators (SVGs), which can improve power factor, reduce line losses, and improve voltage stability by adjusting reactive power; 3) Voltage regulators include transformer tap regulators and automatic voltage regulators (AVRs), which can maintain voltage within a specified range by adjusting the transformer tap position or using an automatic voltage regulator; 4) Load balancers are used to connect loads to different phases or circuits through intelligent control, which can be Redistribute loads to reduce imbalances, improve system efficiency and equipment life; 5) Active filters, which are used to dynamically compensate for harmonics and reactive power. They detect harmonics and reactive power in the power grid in real time and generate opposite waveforms to offset them; 6) Frequency controllers, which are used to adjust when the power grid frequency deviates from the normal value. They restore the frequency to the normal range by controlling the output of the generator or the consumption of the load; 7) Protection relays: used to detect abnormal conditions in the power grid and take protective measures. They automatically disconnect the circuit to protect the equipment when abnormalities such as overload and short circuit are detected; 8) Intelligent circuit breakers, which refer to circuit breakers with monitoring and communication functions. They monitor the circuit status in real time and automatically perform disconnection operations to maintain the power quality.

[0052] In the above-mentioned power quality optimization processing method, by obtaining each power quality parameter during the operation of the power equipment and performing abnormal analysis on each power quality parameter to obtain the abnormal analysis result, it is possible to realize high-frequency real-time monitoring of the power quality parameters, timely discover the instantaneous change of the power quality, so as to improve the monitoring accuracy of the power quality, and timely identify the changing trend and potential problems of the power quality. Furthermore, if it is determined that the power quality parameters are abnormal according to the abnormal analysis results, the power quality parameters are subjected to fault judgment and early warning processing, and the fault judgment results and early warning information are obtained, and the fault judgment results and early warning information are fed back to the terminal device to obtain the optimization processing strategy triggered by the terminal device for the fault judgment results and early warning information, so that the adjustment of the target regulating equipment and the optimization of the power quality parameters can be realized by executing the triggered optimization processing strategy, the power quality is improved, and the stable operation of the power equipment is guaranteed.

[0053] In an exemplary embodiment, Figure 3 As shown, the step of performing abnormal analysis on each power quality parameter and obtaining abnormal analysis results specifically includes the following steps S302 to S306. Among them:

[0054] Step S302: performing data cleaning and normalization processing on each power quality parameter to obtain a standard power quality parameter.

[0055] Specifically, after the server collects the power quality parameters during the operation of the power equipment, it performs data cleaning through the power quality parameters to remove noise and outliers, and further standardizes or normalizes the power quality parameters without noise and outliers to obtain standard power quality parameters, so that the standard power quality parameters can be applied to the trained power quality processing model.

[0056] Step S304: extracting features of standard power quality parameters to obtain power quality parameter features.

[0057] Specifically, the server extracts features of standard power quality parameters, including voltage, current, power factor, frequency, and harmonic distortion rate, to obtain power quality parameter features corresponding to voltage, current, power factor, frequency, and harmonic distortion rate, specifically including power quality parameter features such as harmonic components, waveform parameters, and statistical indicators.

[0058] Step S306, using the trained power quality processing model, predicting the power quality trend of the power quality parameter characteristics, obtaining the power quality change trend, and performing anomaly analysis based on the power quality change trend to obtain anomaly analysis results.

[0059] Specifically, the server inputs the obtained power quality parameter characteristics such as harmonic components, waveform parameters, and statistical indicators into the trained power quality processing model, and uses the trained power quality processing model to predict the power quality trend of the power quality parameter characteristics to obtain the power quality change trend.

[0060] Among them, the power quality change trend refers to the change pattern of the power quality parameters over time, which can be an increase or decrease, or a periodic fluctuation, etc., which is used to indicate that the operating condition of the power system is improving or deteriorating. Exemplarily, the power quality change trend may specifically include voltage stability, frequency stability, harmonic distortion rate, and power factor change rate. Among them, voltage stability indicates whether the voltage is stable within the specified range, or whether there is a trend of voltage being too high or too low, frequency stability is used to indicate whether the grid frequency remains near the rated value, or whether there is a trend of deviating from the standard frequency, and harmonic distortion is used to indicate whether the harmonic content in the voltage or current waveform is increasing to determine whether it will cause a decrease in power quality, and the power factor change rate indicates whether the overall power factor of the system is changing to determine whether it will affect the efficiency and cost of energy.

[0061] Furthermore, the server uses the trained power quality processing model to predict the power quality trend of the power quality parameter characteristics. After obtaining the power quality change trend, the server performs an abnormality analysis based on the power quality change trend, such as abnormality analysis of voltage stability, frequency stability, harmonic distortion rate, and power factor change rate, to determine whether there is an abnormality and obtain an abnormality analysis result.

[0062] For example, with respect to voltage stability, if it is determined that there is an abnormality of too high or too low voltage, further fault judgment of the voltage is performed, including: 1) judging whether there is a temporary voltage sag or swell, that is, a sudden drop or rise in voltage, which may affect the operation of sensitive equipment; 2) judging whether there is a momentary interruption, that is, if the voltage suddenly drops to zero, even for a very short time, it may cause damage to certain equipment and cause abnormal equipment operation; 3) judging whether there is a current or voltage unbalanced load, that is, the current or voltage in the three-phase system is unbalanced, which may cause motor overload or equipment damage, resulting in equipment failure.

[0063] Optionally, for the harmonic distortion rate, if it is determined that there is an abnormal increase in the harmonic content in the voltage or current waveform, it is further determined whether there is a fault of excessively high harmonic distortion rate, that is, the increase in harmonic content is likely to cause problems such as equipment overheating, reduced efficiency or shortened life. At the same time, since the current or voltage imbalance in the three-phase system is likely to cause motor overload or equipment damage, it is also possible to further determine whether there is an unbalanced load (that is, current or voltage imbalance), and whether there are transient events such as surges or oscillations, that is, transient events such as surges or oscillations are likely to cause instantaneous high pressure on electrical equipment, resulting in equipment damage or failure.

[0064] In an exemplary embodiment, before using the power quality processing model to predict the power quality trend, the power quality processing model needs to be trained. Specifically, during model training, the model is trained using a supervised learning algorithm to identify and predict power quality events, and an unsupervised learning algorithm is used to discover patterns and anomalies in the data. After the power quality processing model is trained, it is necessary to evaluate the model. Specifically, the performance of the model can be evaluated through methods such as cross-validation or confusion matrix, and the model can be continuously adjusted and optimized based on real-time monitoring results and user feedback to improve the model accuracy.

[0065] In this embodiment, by performing data cleaning and normalization processing on each power quality parameter to obtain standard power quality parameters, and performing feature extraction on the standard power quality parameters to obtain power quality parameter features, the trained power quality processing model is used to perform power quality trend prediction on the power quality parameter features to obtain the power quality change trend, so that anomaly analysis can be performed based on the power quality change trend to obtain anomaly analysis results, thereby realizing high-frequency real-time monitoring of power quality parameters, timely discovering instantaneous changes in power quality, so as to improve the monitoring accuracy of power quality, and timely identifying the changing trends and potential problems of power quality, so as to solve abnormal problems in time and ensure the stable operation of power equipment.

[0066] In an exemplary embodiment, Figure 4 As shown, according to the optimization processing strategy, the step of adjusting the target regulating device and optimizing the power quality parameters specifically includes the following steps S402 to S408. Among them:

[0067] Step S402: If the optimization processing strategy is a rule optimization strategy, a first parameter optimization condition corresponding to the rule optimization strategy is obtained.

[0068] Specifically, if the optimization processing strategy is a rule optimization strategy, that is, the server can automatically trigger the control action according to the preset rules (such as voltage, current, power factor, or harmonic distortion rate exceeds a specific threshold), then the first parameter optimization condition corresponding to the rule optimization strategy is obtained.

[0069] Among them, the first parameter optimization condition can specifically be a parameter value of a certain power quality parameter, which reaches a second parameter threshold value corresponding to the power quality parameter. For example, when there is a parameter value of any power quality parameter among voltage, current, power factor, or harmonic distortion rate, which reaches the second parameter threshold value corresponding to the corresponding power quality parameter, for example, the parameter value of voltage reaches the second parameter threshold value preset for voltage, or the parameter value of current reaches the second parameter threshold value preset for current, etc.

[0070] Step S404: When the parameter value of any power quality parameter reaches a second parameter threshold corresponding to the power quality parameter, it is determined that the first parameter optimization condition is currently satisfied.

[0071] Specifically, if the parameter value of any power quality parameter among voltage, current, power factor, frequency, and harmonic distortion rate reaches a second parameter threshold corresponding to the power quality parameter, it is determined that the first parameter optimization condition is currently met.

[0072] Exemplarily, for example, when the parameter value of the voltage reaches a second parameter threshold value preset for the voltage, or the parameter value of the current reaches a second parameter threshold value preset for the current, or the parameter value of the power factor reaches a second parameter threshold value preset for the power factor, or the parameter value of the harmonic distortion rate reaches a second parameter threshold value preset for the harmonic distortion rate, it is determined that the first parameter optimization condition is currently met.

[0073] Step S406: Determine the first target regulating device to be adjusted according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold.

[0074] Specifically, according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold, for example, the power quality parameter whose parameter value reaches the second parameter threshold is voltage, or current, or power factor, or harmonic distortion rate, the first target adjustment device to be adjusted is determined. Among them, the target adjustment device may specifically include: harmonic filter, reactive power compensation device, voltage regulator, load balancer, active filter, frequency controller, protection relay, and intelligent circuit breaker, etc., then according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold, one or more devices among harmonic filter, reactive power compensation device, voltage regulator, load balancer, active filter, frequency controller, protection relay, and intelligent circuit breaker may be determined as the first target adjustment device.

[0075] Exemplarily, if the power quality parameter whose parameter value reaches the second parameter threshold is voltage, then based on the rule optimization strategy and the power quality parameter "voltage", the regulating device involved in "voltage" adjustment, such as a voltage regulator (including a transformer tap regulator, an automatic voltage regulator, etc.), can be determined as the first target regulating device.

[0076] Optionally, if the power quality parameter whose parameter value reaches the second parameter threshold is power factor, then the regulating device involved in "power factor" adjustment, such as reactive power compensation device (including shunt capacitor, shunt reactor, and static VAR generator (SVG), etc.), can be determined as the first target regulating device. For another example, if the power quality parameter whose parameter value reaches the second parameter threshold is harmonic distortion rate, then the regulating device involved in "harmonic distortion rate" adjustment, such as harmonic filter or active filter, can be determined as the first target regulating device.

[0077] Step S408, executing the rule optimization strategy, adjusting the device parameters of the first target regulating device, and optimizing the power quality parameters according to the adjusted first target regulating device.

[0078] Specifically, the server adjusts the equipment parameters of the first target regulating device by executing the rule optimization strategy, such as adjusting the equipment parameters of the harmonic filter, reactive power compensation device, voltage regulator, load balancer, active filter, frequency controller, protection relay, and intelligent circuit breaker, so as to optimize the power quality parameters according to the adjusted first target regulating device.

[0079] Exemplarily, if the power quality parameter whose parameter value reaches the second parameter threshold is voltage, then according to the rule optimization strategy and the power quality parameter "voltage", the regulating device involved in "voltage" adjustment, such as a voltage regulator (including a transformer tap regulator, an automatic voltage regulator, etc.), can be determined as the first target regulating device, and the voltage can be maintained within a specified range by adjusting the first target regulating device, such as adjusting the transformer tap position or using an automatic voltage regulator.

[0080] Optionally, if the power quality parameter whose parameter value reaches the second parameter threshold is power factor, then the regulating device involved in the "power factor" adjustment, such as the reactive power compensation device (including parallel capacitors, parallel reactors, and static VAR generators (SVG)), etc.), can be determined as the first target regulating device, and the first target regulating device can be adjusted, such as adjusting the reactive power through the reactive power compensation device, to improve the power factor, reduce line losses, and improve voltage stability.

[0081] For another example, if the power quality parameter whose parameter value reaches the second parameter threshold is the harmonic distortion rate, then the regulating device involved in adjusting the "harmonic distortion rate", such as a harmonic filter or an active filter, can be determined as the first target regulating device, and the first target regulating device can be adjusted, such as by real-time detection of harmonics and reactive power in the power grid, adjusting the input of the active filter to generate an opposite waveform for offsetting, or adjusting the input or response level of the harmonic filter to reduce voltage harmonics and current harmonics to an acceptable range, thereby eliminating or reducing harmonic distortion and resonance.

[0082] In this embodiment, when the optimization processing strategy is a rule optimization strategy, a first parameter optimization condition corresponding to the rule optimization strategy is obtained, and when the parameter value of any power quality parameter reaches the second parameter threshold corresponding to the power quality parameter, it is determined that the first parameter optimization condition is currently satisfied, so as to determine the first target regulation device to be adjusted according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold, and execute the rule optimization strategy to adjust the device parameters of the first target regulation device, so as to optimize the power quality parameters according to the adjusted first target regulation device, so that the matched optimization processing strategy can be flexibly selected according to the actual scenario, so as to realize the adjustment of the target regulation device and the optimization of the power quality parameters, improve the power quality, and ensure the stable operation of the power equipment.

[0083] In an exemplary embodiment, Figure 5 As shown, according to the optimization processing strategy, the target regulating device is adjusted to optimize the power quality parameters, specifically including the following steps S502 to S508. Among them:

[0084] Step S502: If the optimization processing strategy is an adaptive optimization strategy, a second parameter optimization condition corresponding to the adaptive optimization strategy is obtained.

[0085] Specifically, if the optimization processing strategy is an adaptive optimization strategy, that is, the server can dynamically adjust the control parameters according to real-time data and historical trends, then the second parameter optimization condition corresponding to the adaptive optimization strategy is obtained. Among them, the second parameter optimization condition can be a power quality change trend corresponding to a certain power quality parameter, and the target change trend corresponding to the power quality parameter is achieved. For example, if the server detects that the harmonic distortion rate continues to rise in a specific time period, it indicates that the second parameter optimization condition is currently met, and the server can automatically adjust the response level of the filter to optimize the power quality parameter.

[0086] For example, the power quality change trend may specifically include voltage stability, frequency stability, harmonic distortion rate, and power factor change rate. Among them, voltage stability indicates whether the voltage is stable within the specified range, or whether there is a trend of voltage being too high or too low, frequency stability is used to indicate whether the grid frequency remains near the rated value, or whether there is a trend of deviating from the standard frequency, and harmonic distortion is used to indicate whether the harmonic content in the voltage or current waveform is increasing to determine whether it will cause a decrease in power quality, and the power factor change rate indicates whether the overall power factor of the system is changing to determine whether it will affect the efficiency and cost of energy.

[0087] Specifically, the second parameter optimization condition can be any power quality change trend among voltage stability, frequency stability, harmonic distortion rate, and power factor change rate, so as to achieve the target change trend corresponding to the corresponding power quality parameter, such as a continuous increase in harmonic distortion rate or a continuous decrease in power factor change rate.

[0088] Step S504: When the power quality change trend corresponding to any power quality parameter reaches the target change trend corresponding to the power quality parameter, it is determined that the second parameter optimization condition is currently satisfied.

[0089] Specifically, if any power quality change trend of voltage stability, frequency stability, harmonic distortion rate, and power factor change rate reaches the target change trend corresponding to the corresponding power quality parameter, such as the harmonic distortion rate continues to increase, or the power factor change rate continues to decrease, it indicates that the second parameter optimization condition is currently met.

[0090] Step S506, determining a second target regulating device to be adjusted according to the adaptive optimization strategy and the power quality parameters whose power quality variation trend reaches the target variation trend.

[0091] Specifically, according to the adaptive optimization strategy and the power quality parameters whose power quality change trend reaches the target change trend, such as the power quality parameters whose power quality change trend reaches the target change trend are voltage, current, power factor, or harmonic distortion rate, the second target regulation device to be adjusted is determined.

[0092] Among them, the target regulation equipment may specifically include: harmonic filters, reactive power compensation devices, voltage regulators, load balancers, active filters, frequency controllers, protection relays, and intelligent circuit breakers, etc., and one or more devices among the harmonic filters, reactive power compensation devices, voltage regulators, load balancers, active filters, frequency controllers, protection relays, and intelligent circuit breakers may be determined as the second target regulation equipment according to the adaptive optimization strategy and the power quality parameters that achieve the target change trend of the power quality change trend.

[0093] Exemplarily, if the changing trend of voltage stability reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "voltage" whose changing trend of power quality reaches the target changing trend, the regulating device involved in "voltage" adjustment, such as voltage regulator (including transformer tap regulator, automatic voltage regulator, etc.), can be determined as the second target regulating device.

[0094] Optionally, if the changing trend of frequency stability reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "frequency" whose changing trend of power quality reaches the target changing trend, the regulating device involving "frequency" adjustment, such as a frequency controller or a load balancer, etc., can be determined as the second target regulating device. For another example, if the changing trend of harmonic distortion rate reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "harmonic distortion rate" whose changing trend of power quality reaches the target changing trend, the regulating device involving "harmonic distortion rate" adjustment, such as a harmonic filter or an active filter, can be determined as the second target regulating device.

[0095] Step S508, executing the adaptive optimization strategy, adjusting the device parameters of the second target regulating device, and optimizing the power quality parameters according to the adjusted second target regulating device.

[0096] Specifically, the server adjusts the equipment parameters of the second target regulation equipment by executing an adaptive optimization strategy, such as adjusting the equipment parameters of harmonic filters, reactive power compensation devices, voltage regulators, load balancers, active filters, frequency controllers, protection relays, and intelligent circuit breakers, so as to optimize the power quality parameters according to the adjusted second target regulation equipment.

[0097] Exemplarily, if the changing trend of voltage stability reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "voltage" whose changing trend of power quality reaches the target changing trend, the regulating device involved in "voltage" adjustment, such as a voltage regulator (including a transformer tap regulator, an automatic voltage regulator, etc.), can be determined as the second target regulating device, and the voltage can be maintained within a specified range by adjusting the second target regulating device, such as adjusting the transformer tap position, or using an automatic voltage regulator.

[0098] Optionally, if the changing trend of the frequency stability reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "frequency" at which the power quality changing trend reaches the target changing trend, the regulating device involved in "frequency" adjustment, such as a frequency controller or a load balancer, can be determined as a second target regulating device, and the second target regulating device can be adjusted, such as by adjusting the load balancer, controlling the load to be connected to different phases or circuits, and redistributing the load to reduce imbalance, improve system efficiency and equipment life, or adjusting the frequency controller to adjust when the grid frequency deviates from the normal value, that is, by controlling the output of the generator or the consumption of the load to restore the frequency to the normal range.

[0099] For another example, if the changing trend of the harmonic distortion rate reaches the target changing trend, then according to the adaptive optimization strategy and the power quality parameter "harmonic distortion rate" whose changing trend reaches the target changing trend, the regulating device involved in adjusting the "harmonic distortion rate", such as a harmonic filter or an active filter, can be determined as the second target regulating device, and the second target regulating device can be adjusted, such as by real-time detection of harmonics and reactive power in the power grid, adjusting the input of the active filter to generate an opposite waveform for offsetting, or adjusting the input or response level of the harmonic filter to reduce the voltage harmonics and current harmonics to an acceptable range, thereby eliminating or reducing harmonic distortion and resonance.

[0100] In this embodiment, if the optimization processing strategy is an adaptive optimization strategy, the second parameter optimization condition corresponding to the adaptive optimization strategy is obtained, and when the power quality change trend corresponding to any power quality parameter reaches the target change trend corresponding to the power quality parameter, it is determined that the second parameter optimization condition is currently satisfied, so as to determine the second target regulation device to be adjusted according to the adaptive optimization strategy and the power quality parameter that reaches the target change trend according to the power quality change trend, and execute the adaptive optimization strategy to adjust the device parameters of the second target regulation device, so as to optimize the power quality parameters according to the adjusted second target regulation device, so that the matched optimization processing strategy can be flexibly selected according to the actual scenario, so as to realize the adjustment of the target regulation device and the optimization of the power quality parameters, improve the power quality, and ensure the stable operation of the power equipment.

[0101] In an exemplary embodiment, Figure 6 As shown, a method for optimizing power quality is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps S602 to S606. Among them:

[0102] Step S602: Determine sensors and data acquisition devices that match each power quality parameter.

[0103] Specifically, the power quality parameters include voltage, current, power factor, frequency, and harmonic distortion rate, etc., and it is necessary to determine the sensors and data acquisition equipment that match the voltage, current, power factor, frequency, and harmonic distortion rate, etc. respectively.

[0104] Among them, sensors are used to monitor power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate in real time, while data acquisition equipment is used to collect and obtain the specific values ​​of each parameter detected by the sensor in real time.

[0105] Exemplarily, the sensors specifically include: 1. Voltage sensor: used to measure the voltage level of the power grid. For example: electromagnetic voltage transformer (VT), capacitor voltage divider. 2. Current sensor: used to measure the current in the power grid. For example: electromagnetic current transformer (CT), Hall effect sensor. 3. Power sensor: used to measure active power, reactive power and apparent power. For example: three-phase power sensor, wattmeter. 4. Frequency sensor: used to monitor the frequency change of the power grid. Usually integrated in the multifunctional power quality monitoring equipment. 5. Harmonic analyzer: used to measure and analyze the harmonic content of voltage and current. For example: Fourier transform analyzer. 6. Power quality analyzer: used to monitor and record power quality parameters, such as voltage sag, swell, instantaneous interruption, etc., usually with data recording and analysis functions.

[0106] Optionally, the data acquisition equipment specifically includes: 1. Voltage acquisition equipment: used to connect with the voltage sensor, collect and obtain the monitoring data of the voltage sensor in real time, such as obtaining the voltage data monitored by the electromagnetic voltage transformer (VT) and the capacitor voltage divider. 2. Current acquisition equipment: used to connect with the current sensor, collect and obtain the monitoring data of the current sensor in real time, such as obtaining the current data monitored by the electromagnetic current transformer (CT) and the Hall effect sensor. 3. Power acquisition equipment: used to connect with the power sensor, collect and obtain the monitoring data of the power sensor in real time, such as active power, reactive power and apparent power. 4. Frequency acquisition equipment: used to connect with the frequency sensor, collect and obtain the monitoring data of the frequency sensor in real time, such as the frequency change of the power grid. 5. Harmonic acquisition equipment: used to connect with the harmonic analyzer, collect and obtain the monitoring data of the harmonic analyzer in real time, such as obtaining the harmonic content of the voltage and current analyzed by the Fourier transform analyzer.

[0107] Step S604, obtaining first parameter thresholds set for each power quality parameter, and configuring parameters of sensors and data acquisition devices respectively matching each power quality parameter in turn according to each first parameter threshold, to obtain configured sensors and data acquisition devices.

[0108] Specifically, by obtaining the first parameter threshold set for each power quality parameter, such as the first parameter threshold set for voltage, current, power factor, frequency, and harmonic distortion rate, and according to the first parameter threshold set for voltage, current, power factor, frequency, and harmonic distortion rate, parameters of sensors and data acquisition devices that match each power quality parameter are configured in turn, such as voltage sensors, current sensors, power sensors, frequency sensors, harmonic analyzers, power quality analyzers, voltage acquisition devices, current acquisition devices, power acquisition devices, frequency acquisition devices, and harmonic acquisition devices, etc., to obtain configured sensors and data acquisition devices.

[0109] Exemplarily, the parameters of the sensor and data acquisition equipment are configured, including: (1) Range setting: according to the maximum and minimum voltage and current values ​​of the power grid, set the measurement range of the sensor, and set the measurement accuracy of the sensor, such as ±0.5% of the reading. (2) Sampling frequency: set the frequency of data acquisition, such as 1000 times per second. Example: If the voltage range of the power grid is 90-270V, the sensor range can be set to 0-300V, the accuracy is ±0.5%, and the sampling frequency is 1000Hz. (3) Harmonic analysis parameters, including harmonic order, that is, setting the harmonic order to be analyzed, such as 2nd to 50th harmonics, and harmonic distortion rate calculation method, that is, selecting the method for calculating harmonic distortion rate, such as IEEE standard or IEC standard. Example: Analyze 2nd to 50th harmonics and use IEEE 519 standard to calculate harmonic distortion rate.

[0110] Step S606, calling the configured sensors and data acquisition equipment to respectively monitor and collect various power quality parameters during the operation of the power equipment.

[0111] Specifically, by calling configured sensors, such as voltage sensors, current sensors, power sensors, frequency sensors, harmonic analyzers and power quality analyzers, power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate are monitored in real time, and by calling configured data acquisition devices, including voltage acquisition devices, current acquisition devices, power acquisition devices, frequency acquisition devices, and harmonic acquisition devices, etc., the monitoring data of corresponding sensors, including various power quality parameters during the operation of power equipment, are collected and obtained in real time, thereby obtaining various power quality parameters during the operation of power equipment, including voltage, current, power factor, frequency, and harmonic distortion rate, etc.

[0112] In the above-mentioned power quality optimization processing method, by determining the sensors and data acquisition devices that match each power quality parameter, the first parameter thresholds set for each power quality parameter are obtained, and according to each first parameter threshold, the sensors and data acquisition devices that match each power quality parameter are configured in turn to obtain the configured sensors and data acquisition devices, so that the configured sensors and data acquisition devices can be called to monitor and collect each power quality parameter during the operation of the power equipment, so as to realize comprehensive real-time monitoring of the power quality parameters, timely discover the instantaneous changes in the power quality, so as to improve the monitoring accuracy of the power quality, and timely identify the changing trends and potential problems of the power quality, so as to solve abnormal problems in time, improve the power quality, and ensure the stable operation of the power equipment.

[0113] In an exemplary embodiment, Figure 7As shown, a method for optimizing power quality is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps S701 to S718. Among them:

[0114] Step S701, determining sensors and data acquisition devices that match each power quality parameter.

[0115] Step S702, obtaining first parameter thresholds set for respective power quality parameters, and configuring parameters of sensors and data acquisition devices respectively matching respective power quality parameters in turn according to respective first parameter thresholds, to obtain configured sensors and data acquisition devices.

[0116] Step S703, calling the configured sensors and data acquisition equipment to respectively monitor and collect various power quality parameters during the operation of the power equipment.

[0117] Step S704, acquiring various power quality parameters during the operation of the power equipment, performing data cleaning and normalization processing on the various power quality parameters, and obtaining standard power quality parameters.

[0118] Step S705: extracting features of standard power quality parameters to obtain power quality parameter features.

[0119] Step S706, using the trained power quality processing model, predicting the power quality trend of the power quality parameter characteristics, obtaining the power quality change trend, and performing anomaly analysis based on the power quality change trend to obtain anomaly analysis results.

[0120] Step S707: If it is determined that the power quality parameters are abnormal based on the abnormality analysis results, fault judgment and early warning processing are performed on the power quality parameters to obtain fault judgment results and early warning information.

[0121] Step S708, feeding back the fault judgment result and warning information to the terminal device, and obtaining the optimization processing strategy triggered by the terminal device for the fault judgment result and warning information.

[0122] After executing step S708, step S709 is executed. If the optimization processing strategy is a rule optimization strategy, a first parameter optimization condition corresponding to the rule optimization strategy is obtained.

[0123] Step S710: When the parameter value of any power quality parameter reaches a second parameter threshold corresponding to the power quality parameter, it is determined that the first parameter optimization condition is currently satisfied.

[0124] Step S711, determining a first target regulating device to be adjusted according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold.

[0125] Step S712, executing the rule optimization strategy, adjusting the device parameters of the first target regulation device, and optimizing the power quality parameters according to the adjusted first target regulation device.

[0126] After executing step S708, step S713 is executed. If the optimization processing strategy is an adaptive optimization strategy, a second parameter optimization condition corresponding to the adaptive optimization strategy is obtained.

[0127] Step S714: When the power quality change trend corresponding to any power quality parameter reaches the target change trend corresponding to the power quality parameter, it is determined that the second parameter optimization condition is currently satisfied.

[0128] Step S715, determining a second target regulating device to be adjusted according to the adaptive optimization strategy and the power quality parameters whose power quality variation trend reaches the target variation trend.

[0129] Step S716, executing the adaptive optimization strategy, adjusting the device parameters of the second target regulating device, and optimizing the power quality parameters according to the adjusted second target regulating device.

[0130] After executing step S712 or step S716, step S717 is executed to generate a power quality analysis report based on the fault judgment result, the warning information and the optimized power quality parameters.

[0131] Step S718, displaying the power quality analysis report, and responding to the processing operation triggered by the power quality analysis report.

[0132] In an exemplary embodiment, Figure 8 As shown, an overall processing flow of a power quality optimization processing method is provided, referring to Figure 8 It can be seen that the overall processing flow of the power quality optimization processing method includes:

[0133] 1. Initialize the system: Initialize the system, including configuring system parameters and checking system status.

[0134] Among them, the system parameters include: 1. Sensor configuration parameters: (1) Range setting: Set the sensor's measurement range according to the maximum and minimum voltage and current values ​​of the power grid. Accuracy: Set the sensor's measurement accuracy, such as ±0.5% of the reading. (2) Sampling frequency: Set the frequency of data acquisition, such as 1000 times per second. Example: If the voltage range of the power grid is 90-270V, the sensor's range can be set to 0-300V, the accuracy can be set to ±0.5%, and the sampling frequency can be set to 1000Hz. 2. Data acquisition system parameters: (1) Data buffer size: Set the size of the internal buffer of the data acquisition system to store the collected data. (2) Data transmission protocol: Select a data transmission protocol, such as Modbus, DNP3, or IEC 61850. Example: Set the data buffer to 100MB and use the Modbus protocol for data transmission. 3. Communication network parameters: (1) Communication frequency: Set the communication frequency between the sensor and the central processing unit. (2) Data encryption: Select a data encryption algorithm, such as AES or RSA, to protect the security of data transmission. Example: Set the communication frequency to 10 times per second and use the AES-256 encryption algorithm. 4. Alarm and notification parameters: (1) Alarm threshold: Set the alarm threshold for power quality parameters, such as voltage over- or under-voltage. (2) Notification method: Select the alarm notification method, such as email, SMS, or app push. Example: Set the voltage alarm threshold to ±10% and the notification method to email and SMS. 5. Harmonic analysis parameters: (1) Harmonic order: Set the harmonic order to be analyzed, such as 2nd to 50th harmonics. (2) Harmonic distortion calculation method: Select the method for calculating harmonic distortion, such as IEEE standard or IEC standard. Example: Analyze 2nd to 50th harmonics and calculate harmonic distortion using IEEE 519 standard. 6. Device control parameters: (1) Control strategy: Set the control strategy for harmonic filters and other regulating devices, such as threshold-based control or adaptive control. (2) Response time: Set the time it takes for the device to respond to abnormal conditions. Example: Set the control strategy to threshold-based control and the response time to 5 seconds.

[0135] 2. Configure sensors and data acquisition systems: Configure corresponding sensors and data acquisition equipment according to the power quality parameters that need to be monitored. Among them, sensors are used to monitor power quality parameters such as voltage, current, power factor, frequency, harmonic distortion rate, etc. in real time, and data acquisition equipment is used to collect and obtain the specific values ​​of each parameter detected by the sensor in real time.

[0136] 3. Real-time monitoring of power quality parameters: The sensor monitors power quality parameters such as voltage, current, power factor, frequency, and harmonic distortion rate in real time.

[0137] 4. Data transmission to the central processing unit: The monitored data is transmitted to the central processing unit through the communication network for analysis.

[0138] 5. Data analysis and machine learning: Use big data analysis and machine learning algorithms to conduct in-depth analysis of data to identify changing trends and potential problems in power quality.

[0139] 1) The analytical process of using big data analysis and machine learning algorithms to conduct in-depth analysis of data, including: (1) Data preprocessing: Clean the data to remove noise and outliers. Standardize or normalize the data to make it suitable for machine learning models. (2) Feature extraction: Extract useful features from the raw data, such as harmonic components, waveform parameters, statistical indicators, etc. (3) Model training: Use supervised learning algorithms to train models to identify and predict power quality events. Use unsupervised learning algorithms to discover patterns and anomalies in the data. (4) Model evaluation: Evaluate the performance of the model through methods such as cross-validation and confusion matrix. (5) Result interpretation: Explain the model's prediction results and provide insights for fault diagnosis and power quality analysis. (6) Real-time monitoring and prediction: Apply the trained model to real-time data to monitor and predict power quality events. (7) Adaptive learning: Continuously adjust and optimize the model based on real-time monitoring results and user feedback. (8) Intelligent control: Use the model's prediction results to automatically adjust harmonic filters and other regulation equipment to optimize power quality.

[0140] 2) Trend of power quality: It indicates the change pattern of power quality parameters over time, which can be an increase or decrease, or periodic fluctuation, etc., which is used to indicate that the operating condition of the power system is improving or deteriorating.

[0141] 3) Potential problems: Indicates abnormal or adverse conditions in the monitoring data that may cause power quality problems, including: A. Voltage sag or swell: A sudden drop or rise in voltage may affect the operation of sensitive equipment. B. Momentary interruption: A sudden drop in voltage to zero, even for a short time, may cause damage to some equipment. C. Excessive harmonic distortion: Increased harmonic content may cause equipment to overheat, reduce efficiency or shorten life. D. Unbalanced load: Unbalanced current or voltage in a three-phase system may cause motor overload or equipment damage. E. Transient events: Such as surges or oscillations, may cause instantaneous high stress on electrical equipment.

[0142] 6. Detect abnormalities: Determine whether the analysis results show abnormalities in the power quality.

[0143] 7. Fault warning and judgment: If an abnormality is detected, a fault warning and judgment will be performed to determine the possible problems and causes.

[0144] 8. Send fault judgment results and warning information: send fault judgment results and warning information to users.

[0145] 9. Obtain optimization processing strategies: Obtain optimization processing strategies triggered by fault judgment results and warning information of terminal devices.

[0146] 10. Adjust harmonic filters and other regulating devices: According to the optimization processing strategy, adjust the harmonic filters and other regulating devices to optimize the power quality.

[0147] Among them, the optimization processing strategy allows the system to automatically adjust harmonic filters and other regulating devices according to preset rules and algorithms without direct user intervention. Among them, (1) Rule-based optimization: The system automatically triggers control actions based on preset rules (such as voltage, current or harmonic levels exceeding certain thresholds). For example, when the voltage harmonic distortion rate is detected to exceed 5%, the filter input is automatically increased. (2) Adaptive optimization: Control parameters can be dynamically adjusted based on real-time data and historical trends. For example, if the system detects that the harmonic level continues to rise over a specific time period, it may automatically adjust the filter's response level.

[0148] 11. Optimize harmonic distortion rate: Optimize the harmonic distortion rate of the power grid by adjusting the target regulation equipment to ensure that the power quality meets the standards.

[0149] Among them, determining whether the power quality meets the standards usually involves multiple parameters and indicators, among which harmonic distortion is an important indicator for measuring power quality. For example, the IEC 61000-3-6 standard specifies the limits of harmonic distortion for different types of equipment and power grids. For low-voltage power supply systems, the limits of the voltage total harmonic distortion (THDv) are usually between 5% and 8%, while the limits of the current total harmonic distortion (THDi) are usually between 10% and 20%.

[0150] 12. Update monitoring strategy: Update monitoring strategy and control algorithm based on monitoring results and user feedback.

[0151] 13. Adaptive learning and optimization: When no abnormality is detected, adaptive learning is performed to optimize monitoring and control strategies.

[0152] 14. Generate power quality report: Generate power quality report for users to view and analyze.

[0153] 15. Display of real-time data and historical trends: The user interface displays real-time monitoring data and historical trends to facilitate user monitoring and analysis.

[0154] 16. Remote monitoring and control: Monitor and control operations through remote access to the system.

[0155] 17. Security and privacy protection: Ensure the security of data transmission and storage, and use encryption technology to protect the privacy of user data and system operations.

[0156] In the above-mentioned power quality optimization processing method, by obtaining each power quality parameter during the operation of the power equipment and performing abnormal analysis on each power quality parameter to obtain the abnormal analysis result, it is possible to realize high-frequency real-time monitoring of the power quality parameters, timely discover the instantaneous change of the power quality, so as to improve the monitoring accuracy of the power quality, and timely identify the changing trend and potential problems of the power quality. Furthermore, if it is determined that the power quality parameters are abnormal according to the abnormal analysis results, the power quality parameters are subjected to fault judgment and early warning processing, and the fault judgment results and early warning information are obtained, and the fault judgment results and early warning information are fed back to the terminal device to obtain the optimization processing strategy triggered by the terminal device for the fault judgment results and early warning information, so that the adjustment of the target regulating equipment and the optimization of the power quality parameters can be realized by executing the triggered optimization processing strategy, the power quality is improved, and the stable operation of the power equipment is guaranteed.

[0157] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0158] Based on the same inventive concept, the embodiment of the present application also provides a power quality optimization processing device for implementing the power quality optimization processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power quality optimization processing device embodiments provided below can refer to the limitations of the power quality optimization processing method above, and will not be repeated here.

[0159] In an exemplary embodiment, Fig. 9 As shown, a power quality optimization processing device is provided, including: an abnormality analysis module 902, a fault judgment module 904, an optimization processing strategy acquisition module 906 and a power quality parameter optimization module 908, wherein:

[0160] The abnormality analysis module 902 is used to obtain various power quality parameters during the operation of the power equipment, perform abnormality analysis on each power quality parameter, and obtain abnormality analysis results;

[0161] The fault judgment module 904 is used to perform fault judgment and early warning processing on the power quality parameters if it is determined that the power quality parameters are abnormal according to the abnormality analysis results, and obtain the fault judgment result and early warning information;

[0162] The optimization processing strategy acquisition module 906 is used to feed back the fault judgment result and warning information to the terminal device, and obtain the optimization processing strategy of the terminal device for the fault judgment result and warning information feedback;

[0163] The power quality parameter optimization module 908 is used to adjust the target regulation equipment and optimize the power quality parameters according to the optimization processing strategy.

[0164] In the above-mentioned power quality optimization processing device, by obtaining various power quality parameters during the operation of the power equipment and performing abnormal analysis on each power quality parameter to obtain abnormal analysis results, it is possible to realize high-frequency real-time monitoring of the power quality parameters, timely discover instantaneous changes in power quality, so as to improve the monitoring accuracy of power quality, and timely identify the changing trends and potential problems of power quality. Furthermore, if it is determined that the power quality parameters are abnormal according to the abnormal analysis results, the power quality parameters are subjected to fault judgment and early warning processing, and the fault judgment results and early warning information are obtained, and the fault judgment results and early warning information are fed back to the terminal device to obtain the optimization processing strategy triggered by the terminal device for the fault judgment results and early warning information, so that the adjustment of the target regulating equipment and the optimization of the power quality parameters can be achieved by executing the triggered optimization processing strategy, thereby improving the power quality and ensuring the stable operation of the power equipment.

[0165] In an exemplary embodiment, a power quality optimization processing device is provided, further comprising:

[0166] The device determination module is used to determine the sensors and data acquisition devices that match each power quality parameter respectively; the parameter configuration module is used to obtain the first parameter thresholds set for each power quality parameter, and according to each first parameter threshold, sequentially configure the parameters of the sensors and data acquisition devices that match each power quality parameter respectively, to obtain the configured sensors and data acquisition devices; the power quality parameter monitoring module is used to call the configured sensors and data acquisition devices to monitor and collect each power quality parameter during the operation of the power equipment.

[0167] In an exemplary embodiment, the abnormality analysis module is also used to: perform data cleaning and normalization processing on each power quality parameter to obtain standard power quality parameters; perform feature extraction on the standard power quality parameters to obtain power quality parameter features; use the trained power quality processing model to perform power quality trend prediction on the power quality parameter features to obtain the power quality change trend, and perform abnormality analysis based on the power quality change trend to obtain abnormality analysis results.

[0168] In an exemplary embodiment, the power quality parameter optimization module is also used to: if the optimization processing strategy is a rule optimization strategy, obtain a first parameter optimization condition corresponding to the rule optimization strategy; when the parameter value of any power quality parameter reaches a second parameter threshold corresponding to the power quality parameter, determine that the first parameter optimization condition is currently satisfied; determine a first target regulation device to be adjusted according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold; execute the rule optimization strategy, adjust the device parameters of the first target regulation device, and optimize the power quality parameters according to the adjusted first target regulation device.

[0169] In an exemplary embodiment, the power quality parameter optimization module is also used to: if the optimization processing strategy is an adaptive optimization strategy, obtain the second parameter optimization condition corresponding to the adaptive optimization strategy; when the power quality change trend corresponding to any power quality parameter reaches the target change trend corresponding to the power quality parameter, determine that the second parameter optimization condition is currently satisfied; determine the second target regulation device to be adjusted according to the adaptive optimization strategy and the power quality parameter whose power quality change trend reaches the target change trend; execute the adaptive optimization strategy, adjust the device parameters of the second target regulation device, and optimize the power quality parameters according to the adjusted second target regulation device.

[0170] In an exemplary embodiment, a power quality optimization processing device is provided, further comprising:

[0171] The power quality analysis report generation module is used to generate a power quality analysis report based on the fault judgment results, warning information and optimized power quality parameters; the power quality analysis report display module is used to display the power quality analysis report and respond to the processing operations triggered by the power quality analysis report.

[0172] Each module in the above-mentioned power quality optimization processing device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0173] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal device. The computer device is described as a server. The internal structure diagram thereof may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as power quality parameters, abnormal analysis results, fault judgment results, early warning information, and optimization processing strategies. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power quality optimization processing method is implemented.

[0174] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0175] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0176] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0177] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0178] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0180] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0181] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing power quality, characterized in that: The method comprises: Acquire various power quality parameters during the operation of the power equipment, perform abnormal analysis on each of the power quality parameters, and obtain abnormal analysis results; If it is determined according to the abnormality analysis result that the power quality parameter is abnormal, a fault judgment and early warning process is performed on the power quality parameter to obtain a fault judgment result and early warning information; Feedback the fault judgment result and warning information to the terminal device, and obtain the optimization processing strategy triggered by the terminal device for the fault judgment result and warning information; According to the optimization processing strategy, the target regulation equipment is adjusted to optimize the power quality parameters.

2. The method according to claim 1, characterized in that The method further comprises: Determining sensors and data acquisition devices that match each of the power quality parameters; Obtaining first parameter thresholds set for each of the power quality parameters, and performing parameter configuration on sensors and data acquisition devices respectively matching each of the power quality parameters according to each of the first parameter thresholds, to obtain configured sensors and data acquisition devices; The configured sensors and data acquisition equipment are called to respectively monitor and collect the power quality parameters during the operation of the power equipment.

3. The method according to claim 1, characterized in that The performing abnormal analysis on each of the power quality parameters to obtain abnormal analysis results includes: Performing data cleaning and normalization processing on each of the power quality parameters to obtain standard power quality parameters; Extracting features of the standard power quality parameters to obtain power quality parameter features; The trained power quality processing model is used to predict the power quality trend of the power quality parameter characteristics to obtain the power quality change trend, and an abnormality analysis is performed based on the power quality change trend to obtain an abnormality analysis result.

4. The method according to any one of claims 1 to 3, characterized in that: The step of adjusting the target regulating device according to the optimization processing strategy to optimize the power quality parameters includes: If the optimization processing strategy is a rule optimization strategy, obtaining a first parameter optimization condition corresponding to the rule optimization strategy; When the parameter value of any of the power quality parameters reaches a second parameter threshold corresponding to the power quality parameter, determining that the first parameter optimization condition is currently satisfied; Determining a first target regulating device to be adjusted according to the rule optimization strategy and the power quality parameter whose parameter value reaches the second parameter threshold; The rule optimization strategy is executed to adjust the device parameters of the first target regulation device, and the power quality parameters are optimized according to the adjusted first target regulation device.

5. The method according to any one of claims 1 to 3, characterized in that: The step of adjusting the target regulating device according to the optimization processing strategy to optimize the power quality parameters includes: If the optimization processing strategy is an adaptive optimization strategy, obtaining a second parameter optimization condition corresponding to the adaptive optimization strategy; When the power quality change trend corresponding to any of the power quality parameters reaches the target change trend corresponding to the power quality parameter, determining that the second parameter optimization condition is currently satisfied; Determining a second target regulating device to be adjusted according to the adaptive optimization strategy and the power quality parameter whose power quality change trend reaches the target change trend; The adaptive optimization strategy is executed to adjust the device parameters of the second target regulation device, and the power quality parameters are optimized according to the adjusted second target regulation device.

6. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Generate a power quality analysis report based on the fault judgment result, warning information and optimized power quality parameters; The power quality analysis report is displayed, and a processing operation triggered in response to the power quality analysis report is responded to.

7. A power quality optimization processing device, characterized in that: The device comprises: The abnormality analysis module is used to obtain various power quality parameters during the operation of the power equipment, perform abnormality analysis on each of the power quality parameters, and obtain abnormality analysis results; A fault judgment module, for performing fault judgment and early warning processing on the power quality parameter to obtain a fault judgment result and early warning information if it is determined that the power quality parameter is abnormal according to the abnormal analysis result; An optimization processing strategy acquisition module is used to feed back the fault judgment result and warning information to the terminal device, and obtain the optimization processing strategy of the terminal device for the fault judgment result and warning information feedback; The power quality parameter optimization module is used to adjust the target regulation device and optimize the power quality parameters according to the optimization processing strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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