Micro-grid electric energy quality intelligent analysis method and system based on FPGA and medium

Abstract

The invention relates to the technical field of microgrids, and particularly provides an intelligent analysis method and system for the power quality of a microgrid based on an FPGA (Field Programmable Gate Array) and a medium, and the method comprises the steps: carrying out the parallel processing and analysis of multi-channel synchronous sampling data through FFT (Fast Fourier Transform), RMS calculation and wavelet decomposition; and a deep learning model is introduced to carry out intelligent identification and prediction, and the hardware parallel computing capability of the FPGA is utilized to accelerate a core algorithm including deep learning reasoning, so that real-time, accurate and prospective monitoring identification, classification, prediction and analysis of various power quality event disturbance types such as harmonic waves, inter-harmonic waves, voltage sag, transient states and the like are realized. According to the method, the accuracy and predictability of event detection are remarkably improved, and the strict requirements under the dynamic working condition of the micro-grid are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrids, and particularly relates to an intelligent analysis method, system and medium for microgrid power quality based on FPGA. Background Technique

[0002] Driven by the global energy structure transformation and the "dual carbon" goal, the application of distributed new energy sources such as photovoltaic and wind power in low-voltage microgrids is becoming increasingly widespread. These microgrid systems integrate various distributed power sources (such as photovoltaic panels, wind turbines), energy storage devices, power electronic converters, and various types of loads (including traditional linear loads and new non-linear dynamic loads). This integration brings significant advantages, such as improving power supply reliability and promoting the effective utilization of renewable energy. However, the complexity and dynamic characteristics of such systems also pose new challenges, especially in terms of power quality.

[0003] Due to the large number of power electronic devices, frequency converters, inverters, etc. contained in low-voltage microgrids, complex harmonic and interharmonic problems are caused. The existence of interharmonics not only exacerbates the electromagnetic interference in the system, but may also trigger a series of special problems, such as the lamp flicker of lighting equipment, which has a negative impact on the user's electricity consumption experience; at the same time, interharmonics of specific frequencies may interfere with wireless communication, power line carrier communication, and the specific frequency signal control logic in industrial control systems, further affecting the stability and security of the system. In addition, with the access of new dynamic loads such as electric vehicles, phenomena such as voltage sags, swells, and interruptions in microgrids have become more frequent, which poses a threat to the safe operation of the power grid.

[0004] Existing monitoring and analysis technologies have obvious deficiencies in coping with the above challenges, especially in terms of real-time response ability, disturbance identification accuracy, complex signal analysis ability, and comprehensive processing of various disturbance forms, and still cannot meet the requirements of the high-speed dynamic characteristics of microgrids. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention provides an intelligent analysis method for microgrid power quality based on FPGA, including the following steps: S1: Synchronously sample multi-channel voltage and current data, where the voltage and current data include three-phase voltage, three-phase current, neutral point-to-ground voltage, and neutral line current, and preprocess the voltage and current data to obtain preprocessed data; S2: Process the preprocessed data as follows: S2.1: Perform a fast Fourier transform on the preprocessed data to obtain the spectrum data corresponding to each channel respectively; S2.2: Calculate the voltage RMS value sequence based on the voltage data; S2.3: Decompose the preprocessed data using wavelet transform to extract transient features; S3: Conduct power quality analysis based on the output data in S2: S3.1: Conduct harmonic and interharmonic analysis based on spectral data; S3.2: Identify voltage sags, swells, and interrupt events based on the voltage RMS value sequence, and calculate short-term flicker and long-term flicker; S4: Use a deep learning model to perform anomaly detection on the power quality parameters in S1 - S3, and output the probability of power quality anomaly types according to the anomaly types. The anomaly types at least include harmonic overlimit, voltage mutation, fast transient, and interharmonic anomaly.

[0006] Furthermore, the interharmonic analysis includes the following steps: S3.1.1: Perform amplitude correction and energy correction on the spectral signals corresponding to each voltage and current data; S3.1.2: Group the spectral lines between two harmonic frequencies with an integer multiple of the fundamental frequency as the center, calculate the root mean square value of each group to obtain the effective value of the interharmonic group; S3.1.3: Identify the significant interharmonic components in the spectrum through peak search and spectral difference, and estimate their frequencies and amplitudes; S3.1.4: Calculate the interharmonic distortion rate of each interharmonic or the interharmonic distortion rate of a specific frequency band, and compare it with the known interharmonic frequencies that may cause equipment failures to evaluate the impact of interharmonics on the microgrid.

[0007] Preferably, the method further includes three-phase unbalance analysis, specifically including: S2.5: Perform symmetrical component decomposition on the preprocessed data to extract symmetrical components, and the symmetrical components include the amplitudes and phases of positive sequence, negative sequence, and zero sequence; S3.5: Calculate the voltage unbalance degree and current unbalance degree according to the symmetrical components.

[0008] Based on the above solution, the method further includes the triggering mechanism for power quality events: First-level trigger: Monitor the instantaneous values of voltage or current. When the instantaneous values of voltage or current change beyond the preset threshold, trigger an interrupt signal to start an anomaly response and capture transient disturbances; Second-level trigger: Based on the power quality parameters, when the logical combination conditions configured by the user are met, trigger an event record.

[0009] Furthermore, the triggering mechanism for power quality events further includes a third-level trigger: Based on the anomaly type probability output by the CNN model, when the probability of a certain anomaly pattern is significantly increased and the first-level or second-level trigger conditions are not met, perform the following operations: Adjust the threshold parameters of the first-level or second-level trigger conditions to improve the capture sensitivity for such abnormal patterns; When CNN deems the pattern suspicious or of high risk, trigger data recording for subsequent analysis or CNN model training; Generate warning information to indicate the existence of potential power quality problems or equipment abnormalities.

[0010] Preferably, the method further includes S4: performing adaptive analysis based on power quality parameters, specifically including: S4.1: Evaluate the operating conditions of the current microgrid by monitoring power quality parameters, where the power quality parameters at least include spectral entropy, RMS change rate, load information, and harmonics / interharmonics; S4.2: Determine the type of disturbance: When transient characteristics are detected, analyze wavelet data and start high-speed recording; When harmonic or interharmonic abnormalities are detected, increase the FFT analysis frequency and the analysis depth of harmonics and interharmonics; If the operating conditions are stable, perform routine detection; S4.3: Adjust the parameters of the trigger mechanism according to the operating conditions and the type of disturbance.

[0011] Based on the same inventive concept, the present invention also provides a microgrid power quality intelligent analysis system based on FPGA, which uses the above-mentioned microgrid power quality intelligent analysis method, including: A data acquisition module for collecting current and voltage data of the microgrid; A preprocessing module for receiving and processing the data of the data acquisition module; A power quality analysis module for analyzing and calculating the output data of the preprocessing module, at least including an FFT analysis unit, an RMS calculation unit, a wavelet transform feature extraction unit, and a neural network anomaly detection unit, where the FFT analysis unit, the RMS calculation unit, the wavelet transform feature extraction unit, and the neural network anomaly detection unit process in parallel; A storage module for storing the data of the data acquisition module, the preprocessing module, and the power quality analysis module.

[0012] Furthermore, the data acquisition module uses an 8-channel synchronous sampling ADC to collect three-phase voltage, three-phase current, one neutral line current, and one neutral point-to-ground voltage, and an analog filter is configured at the front end of the channel; the preprocessing module receives the ADC sampling data output by the data acquisition module, and performs digital filtering, data format conversion, decimation, denoising, and normalization on the ADC sampling data and then outputs preprocessing data, and the preprocessing data is input into the power quality analysis module.

[0013] Based on the above solution, the FFT analysis unit processes the 8-channel preprocessed data. The FFT analysis unit includes a high-precision mode and a fast update mode. The high-precision mode outputs high-resolution spectrum data, and the fast update mode outputs the spectrum amplitude and phase data corresponding to the data of each channel.

[0014] Preferably, the power quality analysis module further includes: A harmonic analysis unit that obtains the high-resolution spectrum data to obtain harmonic characteristics, where the harmonic characteristics at least include the total harmonic distortion rate of each channel and the parameters of each harmonic; An interharmonic analysis unit that obtains the high-resolution spectrum data, performs interharmonic grouping and interharmonic parameter calculation to obtain interharmonic characteristics; A voltage fluctuation and flicker calculation unit that calculates short-term flicker and long-term flicker according to the voltage RMS value sequence obtained by the RMS calculation unit.

[0015] Further, the feature matrix is input into the neural network anomaly detection unit. The feature matrix at least includes the interharmonic characteristics, harmonic characteristics, and time-domain characteristics, and outputs the probabilities of various anomaly types. The anomaly types at least include harmonic overlimit, voltage sudden change, fast transient, and interharmonic anomaly; The neural network anomaly detection unit is trained using historical power quality parameters and the corresponding anomaly types.

[0016] Further, the system further includes an adaptive analysis module. The adaptive analysis module includes a working condition evaluation model that evaluates the operating conditions of the microgrid using power quality parameters; According to the working condition evaluation results, the quality analysis parameters are dynamically adjusted.

[0017] On the other hand, a computer-readable storage medium is also provided, on which there is a computer program, and the computer program includes: Instructions executable by a processor, which, when executed by the processing unit of the system-on-chip platform, execute the steps of the intelligent analysis method for the power quality of the microgrid as described above; And / or configuration data of the FPGA, which, when the configuration data is loaded into the programmable logic unit of the field programmable gate array (FPGA), execute the method steps as described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. It realizes the accurate measurement and classification of various power quality events such as harmonics, interharmonics, flicker, and transient events. At the same time, it supports CNN anomaly event prediction. Combining power quality parameters, it realizes the identification, classification, and prediction of various disturbance types, significantly improving the accuracy of anomaly detection; 2. The heterogeneous architecture of FPGA and multi-core ARM processors and the optimized software and hardware co-design significantly improve the real-time performance and data processing ability of power quality analysis, and can meet the millisecond-level monitoring requirements under the dynamic conditions of the microgrid; 3. The three-level intelligent trigger mechanism combines the fast response of hardware, the flexibility of software logic and the learning and prediction ability, can capture various power quality events more effectively, and dynamically optimize the trigger strategy to reduce missed reports and false reports; 4. The adaptive analysis strategy based on parameters such as spectral entropy and THD enables the system to dynamically adjust the analysis algorithm and its parameters according to the actual grid conditions, improving the pertinence and efficiency of the analysis. Description of the Drawings

[0019] Figure 1 It is the method flow chart of the power quality analysis of this application; Figure 2 It is the flow chart of the interharmonic analysis of this application; Figure 3 It is the flow chart of the adaptive analysis of this application; Figure 4 It is the structure diagram of the power quality analysis system of this application; Figure 5 It is the block diagram of the system hardware architecture of this application; Figure 6 It is the block diagram of the system software architecture of this application. Detailed Embodiments

[0020] The invention will be further described below in conjunction with specific embodiments.

[0021] Embodiment 1 As Figure 1 shown, this application provides a method for intelligent analysis of power quality in a microgrid based on FPGA, following the definitions and measurements of relevant parameters in international standards. The method includes the following steps: Step S1, use an 8-channel synchronous sampling ADC to collect voltage and current data. The voltage and current data include three-phase voltages (Ua, Ub, Uc), three-phase currents (Ia, Ib, Ic), neutral point-to-ground voltage (Vng) and neutral line current (In). A filter is configured at the front end of each analog input channel to prevent high-frequency signal aliasing; Preferably, a Butterworth anti-aliasing filter with a cut-off frequency of 120 kHz is selected; To improve the signal quality and facilitate subsequent processing, the voltage and current data are preprocessed by filtering, format conversion, extraction, denoising, and normalization after being processed by a filter. Specifically, to suppress out-of-band noise, digital filtering is performed, such as using a configurable 32nd-order FIR filter. To facilitate processing, the data format is unified, for example, converting 16-bit integer data to fixed-point decimal or floating-point format. The sampling rate is reduced as needed to save subsequent resources. Random noise is removed by moving average or wavelet threshold method. Normalization processing is carried out to unify the data scales of each channel and scale the data to an interval.

[0022] The preprocessed voltage and current data are obtained after the above preprocessing.

[0023] Step S2, the following processing is performed on the preprocessed voltage and current data output by S1: Step S2.1, perform FFT transformation on the preprocessed voltage and current data to obtain the spectral data corresponding to each channel respectively; Step S2.2, calculate the industrial frequency period / half-period root mean square (RMS) value sequences of the voltage and current data of each channel in parallel, and extract the RMS change rate, which can provide voltage and current fluctuation information at different time resolutions.

[0024] Step S2.3, decompose the preprocessed voltage and current data using a 4-layer Daubechies-4 mother wavelet, and extract wavelet features such as energy and significant coefficients at each scale to characterize fast transient events; The above parallel processing and analysis of multi-channel synchronous sampling data through FFT transformation, RMS calculation, and wavelet decomposition achieve real-time and high-speed processing and analysis of the acquired data.

[0025] Step S2.4, calculate the active power and reactive power based on the preprocessed voltage and current data, and the active power and reactive power are used for load status monitoring; Step S3, perform power quality analysis based on the data output in S2: Step S3.1, extract the amplitude and phase information of each harmonic based on the spectral data, identify the fundamental wave and harmonic components according to the amplitude and phase information, and analyze the harmonics and interharmonics; According to the amplitude and phase information, evaluate the qualification of each parameter, calculate the total harmonic distortion rate (THD) of each channel, and THD represents the effective value ratio of all harmonic components relative to the fundamental wave; calculate the harmonic content rate of each harmonic to determine whether a certain harmonic exceeds the standard; judge whether it exceeds the allowable maximum value according to the amplitude.

[0026] Furthermore, through the historical data of THD and key harmonics, trend analysis and early warning are carried out.

[0027] Such asFigure 2 As shown in Figure 2 , the interharmonic analysis includes the following steps: Step S3.1.1: To compensate for the influence of the window function, amplitude correction and energy correction are performed on the 8-channel spectrum data corresponding to each voltage and current respectively. Step S3.1.2: Centered on an integer multiple of the fundamental frequency, the spectrum lines between two harmonic frequencies are grouped, and the root mean square (RMS) value of each group is calculated to obtain the effective value of the interharmonic group / sub-group. Step S3.1.3: By peak search and spectrum difference, significant interharmonic components in the spectrum data are identified, and their frequencies and amplitudes are estimated. Step S3.1.4: Calculate the interharmonic distortion rate (TIHD) of each interharmonic or the interharmonic distortion rate in a specific frequency band, and compare it with the known interharmonic frequencies that may cause equipment failures to evaluate the impact of interharmonics on the microgrid.

[0028] Step S3.2: Voltage sag, voltage swell, and voltage interruption events are identified by detecting changes in the voltage RMS value, and at the same time, the first characteristic parameters are extracted. The first characteristic parameters at least include: occurrence time, duration, depth / height, and missing voltage; the first characteristic parameters are used to identify and classify voltage transient events.

[0029] Furthermore, combined with the low-frequency voltage interharmonic information provided by the interharmonic analysis, it is used to assist in judging the cause of flicker or evaluating the severity caused by interharmonics.

[0030] Step S3.3: Digital filtering is performed on the voltage RMS value sequence to extract the voltage envelope, and short-term flicker (P_st) and long-term flicker (P_lt) are calculated. When the calculated P_st value exceeds the preset threshold, an alarm event is triggered, and relevant data is recorded for subsequent analysis and use.

[0031] Step S3.4: Based on the wavelet features extracted in S2.3, fast transient events are identified, and the second characteristic parameters are extracted. The second characteristic parameters at least include peak value, duration, and dominant oscillation frequency; the second characteristic parameters are used for accurate classification and analysis of transient events.

[0032] Voltage sag, swell, and interruption are identified by detecting changes in the voltage RMS value, while wavelet feature analysis is used to identify fast transient events and perform preliminary classification of fast transient events.

[0033] Furthermore, to detect the unbalance degree of the power quality of the microgrid, the method further includes three-phase unbalance analysis, which specifically includes: Step S2.5: Perform symmetrical component decomposition on the preprocessed voltage and current data to extract symmetrical components, where the symmetrical components include the magnitudes and phases of positive sequence, negative sequence, and zero sequence. Step S3.5: Calculate the voltage unbalance factor (VUF) and current unbalance factor (CUF) based on the symmetrical components. If it is detected that the voltage unbalance factor continuously exceeds the standard, a preliminary load adjustment strategy or energy storage unit compensation suggestion is generated according to the microgrid operation status and configuration information.

[0034] It should be noted that the detection and calculation of each power quality parameter comply with the provisions of international standards.

[0035] Furthermore, the present invention uses a deep learning model to perform intelligent analysis on power quality parameters. When the microgrid system is operating, the power quality parameters collected or calculated are integrated into a feature matrix and input into the model for learning and analysis. In this embodiment, a lightweight neural network (CNN) can be used to detect anomalies in power quality parameters and output the types of power quality anomalies and their corresponding probabilities. The anomaly types include harmonic overlimit, voltage sag, fast transient, interharmonic anomaly, and unknown anomaly, etc. In addition, the model is trained through a supervised learning method, that is, historical power quality parameters and their corresponding anomaly types are used as training data. The present invention can also adopt other models such as RNN, LSTM, and support vector machine.

[0036] Embodiment 2 Based on Embodiment 1, in order to ensure the timely response to various power quality events and effective data capture, this embodiment adds a triggering mechanism for power quality events, including: (1) First-level trigger: Monitor the instantaneous values of voltage or current. When the change in the instantaneous value of voltage or current exceeds a preset threshold, an interrupt signal is immediately generated to start the anomaly response mechanism and capture transient disturbances, such as impacts, fast overvoltages, and the initial stage of voltage sags. On the one hand, after the interrupt signal is generated, event marking and response are performed. On the other hand, the data before and after the triggering moment is automatically latched into the buffer to provide high-accuracy original waveforms for subsequent analysis.

[0037] The first-level trigger is a direct hardware trigger. The instantaneous values of line voltage and current are directly monitored through 8-channel analog comparators. In this embodiment, when the instantaneous value of voltage exceeds 120% of the rated voltage peak or is lower than 80% of the rated voltage peak, a hardware interrupt signal is immediately generated. The response time of this triggering operation is the shortest, enabling a rapid response to power quality events.

[0038] (2)Secondary triggering, based on power quality parameters, at least including RMS sequence values, THD, voltage flicker values, transient characteristic energy (energy distribution at different wavelet scales in wavelet transform), possible interharmonic disturbances, and interharmonic group amplitudes. When these parameter indicators meet the user's preset logical combination conditions (such as combination conditions like THD exceeding the limit, P_st exceeding the limit, transient energy increasing, interharmonic amplitude exceeding the standard, etc.), event recording is triggered.

[0039] The secondary triggering is a software logic trigger. It configures the logical condition combination for the calculated power quality parameters and uses a priority queue scheduling to ensure that the event judgment and triggering delay are controlled within less than 50µs, and records the detailed parameters of the power quality event.

[0040] (3)Tertiary triggering, which realizes the prediction of anomaly probability through a CNN model. Since the training data of the CNN model contains rich harmonic, interharmonic, and various combined disturbance characteristics, its prediction can reflect potential early faults or complex disturbance risks that are not easily captured by fixed thresholds.

[0041] Based on the anomaly type probability output by the CNN model, when the probability of predicting a certain anomaly pattern significantly increases and does not meet the primary or secondary triggering conditions, the following operations can be performed: Adjust the threshold parameters of the primary or secondary triggering conditions to improve the capture sensitivity for this type of anomaly pattern; At this time, the CNN considers the pattern suspicious or has a high risk, triggers data recording for subsequent analysis or CNN model training; Generate a warning message to prompt the maintenance personnel of potential power quality problems or specific equipment anomalies.

[0042] Through the tertiary triggering, the predictability and accuracy of power quality analysis are improved, false alarms are reduced, and at the same time, by updating the CNN model and triggering logic, it can continuously adapt to new anomaly patterns and evolution characteristics emerging in the microgrid.

[0043] Through the collaborative work of the above three triggering mechanisms, it is possible to ensure a rapid response to power quality events while accurately capturing power quality events, achieving anomaly warning and data recording, thereby realizing comprehensive and efficient power quality event monitoring and data management.

[0044] Example 3 Based on Example 2, in order to improve the accuracy of power quality analysis, the method further includes step S4: performing adaptive analysis based on power quality parameters, such as Figure 3 shown, specifically including: Step S4.1: Evaluate the current operating condition of the microgrid by monitoring power quality parameters. The operating conditions include stable, slightly fluctuating, severely disturbed, and suspected fault. The power quality parameters at least include spectral entropy, RMS change rate, load information, and harmonics / interharmonics; Spectral entropy is an index that quantifies the uniformity or complexity of the signal power spectrum distribution. A lower spectral entropy value indicates that the signal energy is mainly concentrated in a few frequency components (such as a pure fundamental wave signal), while a higher spectral entropy value indicates that the signal energy is more dispersedly distributed in multiple frequency components (such as a distorted signal containing rich harmonics, interharmonics, or noise). Therefore, by monitoring the change of spectral entropy, the overall condition of power quality or the occurrence of disturbances can be effectively reflected, providing an important basis for condition assessment.

[0045] Step S4.2: Make adjustments according to the condition assessment result: When transient characteristics are detected, preferentially analyze wavelet data and start high-speed recording; When abnormal harmonics or interharmonics are detected, increase the FFT analysis frequency and the analysis depth of harmonics and interharmonics; when continuous interharmonics are detected, the FFT configuration with higher resolution can be switched, or the execution frequency of interharmonics can be increased; If the detected condition is stable, focus on conventional detection; Step S4.3: Adjust the FFT analysis window or RMS calculation window according to the disturbance characteristics, and configure the window length according to the demand to balance the resolution and response speed; Step S4.4: Adjust the parameters of the trigger mechanism, such as the thresholds of THD and P_st, according to the condition and disturbance type.

[0046] Embodiment 4 Based on the microgrid power quality intelligent analysis method of the above embodiment, this embodiment provides a microgrid power quality intelligent analysis system based on FPGA, as Figure 4 shown, including: A data acquisition module, which uses an 8-channel synchronous sampling ADC to collect three-phase voltages, three-phase currents, one neutral line current, and one neutral point-to-ground voltage. An analog filter is configured at the front end of the channel; A preprocessing module, which receives the ADC sampling data output by the data acquisition module, and performs digital filtering, data format conversion, decimation, denoising, and normalization on the ADC sampling data and then outputs preprocessed data. The preprocessed data is input to the power quality analysis module; The power quality analysis module is used to analyze and calculate the output data of the preprocessing module, and at least includes an FFT analysis unit, an RMS calculation unit, a wavelet transform feature extraction unit, and a neural network anomaly detection unit; the FFT analysis unit processes the 8-channel preprocessed data. The FFT analysis unit includes a high-precision mode and a fast update mode. The high-precision mode uses a long data window and outputs high-frequency spectrum data for accurate harmonic and interharmonic analysis; the fast update mode uses a short data window to achieve millisecond-level fast update of the spectrum, which is used for dynamic monitoring and fast event response, and outputs the spectrum amplitude and phase data corresponding to the data of each channel.

[0047] The RMS calculation unit calculates the power frequency period or half-period root mean square (RMS) value sequence of the voltage and current of each channel in parallel; uses the Daubechies-4 (db4) mother wavelet to implement 4-layer wavelet packet decomposition of the signal and extract fast transient features; Input the feature matrix into the neural network anomaly detection unit. The feature matrix at least includes the interharmonic features, harmonic features, and time-domain features, and outputs the probabilities of various anomaly types. The anomaly types at least include harmonic overlimit, voltage sudden change, fast transient, and interharmonic anomaly; use the historical power quality parameters and the corresponding anomaly types to train the neural network anomaly detection unit.

[0048] The storage module includes a high-speed cache (DDR4) and a non-volatile storage (Flash), which are used to store the data of the data acquisition module, the preprocessing module, and the power quality analysis module.

[0049] Furthermore, the power quality analysis module further includes: a harmonic analysis unit, which obtains the high-resolution spectrum data to obtain harmonic features. The harmonic features at least include the total harmonic distortion rate of each channel and the parameters of each harmonic; An interharmonic analysis unit, which obtains the high-resolution spectrum data, performs interharmonic grouping and interharmonic parameter calculation to obtain interharmonic features; A voltage fluctuation and flicker calculation unit, which calculates short-term flicker and long-term flicker according to the voltage RMS value sequence obtained by the RMS calculation unit; A symmetrical component calculation unit, which is used to calculate voltage unbalance and current unbalance.

[0050] Furthermore, the power quality analysis system further includes an adaptive analysis module. The adaptive analysis module includes a working condition evaluation model, which uses power quality parameters to evaluate the operating conditions of the microgrid; according to the working condition evaluation results, dynamically adjusts the quality analysis parameters.

[0051] Such as Figure 5 And Figure 6As shown, the power quality analysis system and method in this application are based on a computing architecture that coordinates a multi-core ARM processor (PS side) and an FPGA (PL side). The analysis of power quality is achieved through task division between the PL side and the PS side. Among them, the PS side is responsible for executing tasks such as complex system management, advanced algorithm processing, AI inference control, and human-computer interaction, while the PL side undertakes high-speed data acquisition, parallel signal preprocessing, real-time spectrum analysis, and key hardware acceleration tasks, thus forming an efficient heterogeneous computing architecture.

[0052] Specifically, the PL side mainly realizes the following functions: high-speed ADC interface control, 8-channel synchronous sampling data caching, real-time RMS value calculation, wavelet transform and FFT analysis, dual-mode spectrum analysis (high-precision mode and fast update mode), CNN inference accelerator, and a primary hardware trigger mechanism, etc.; parallel processing of multiple computing units, and its core goal is to provide low-latency and high-throughput data stream processing capabilities, and provide high-quality basic feature data for the PS side.

[0053] The PS side runs an asymmetric multi-processing (AMP) architecture. Some cores run RTOS to ensure hard real-time communication and event response with the FPGA, and the remaining cores run the Linux operating system, which is responsible for executing functions such as IEC standard-compliant harmonic / interharmonic post-processing algorithms, flicker calculation, spectrum entropy analysis, deep learning model management, adaptive analysis strategy decision-making, coordination of the three-level intelligent trigger mechanism, network communication protocol stack, data storage, and Web interface. In addition, the PS side also adjusts parameters and switches modes for the functional modules on the PL side through a dynamic configuration mechanism to achieve the optimal utilization of system resources and intelligent closed-loop control.

[0054] In addition, the power quality analysis method according to the present invention can be recorded in a computer-readable recording medium. Specifically, according to the present invention, when the program is executed on an embedded processor of an MPSoC platform (including a multi-core ARM processor running Linux and RTOS), or when the configuration code is loaded into the programmable logic unit of the FPGA, the system can execute the steps of the intelligent power quality analysis method of the microgrid as described above.

[0055] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains at least one executable instruction for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0056] Generally speaking, various example embodiments of the present invention may be implemented in hardware or a dedicated circuit, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, a microprocessor, or other computing devices. When aspects of the embodiments of the present invention are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, devices, systems, technologies, or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, a dedicated circuit or logic, general hardware or a controller or other computing devices, or some combination thereof.

[0057] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0058] Although the specific implementation manners of the present invention are described above, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. An intelligent analysis method for power quality of microgrid based on FPGA, characterized in that, Including: S1: Synchronously sample voltage and current data in multiple channels. The voltage and current data include three-phase voltage, three-phase current, neutral point-to-ground voltage, and neutral line current. Preprocess the voltage and current data to obtain preprocessed data. S2: Process the preprocessed data as follows: S2.1: Perform a fast Fourier transform on the preprocessed data to obtain spectral data corresponding to each channel. S2.2: Calculate the voltage RMS value sequence based on the voltage data. S2.3: Decompose the preprocessed data using wavelet transform to extract transient features. S3: Perform power quality analysis based on the output data in S2: S3.1: Perform harmonic and interharmonic analysis based on the spectral data. S3.2: Identify voltage sags, swells, and interrupt events based on the voltage RMS value sequence, and calculate short-term flicker and long-term flicker. S4: Use a deep learning model to perform anomaly detection on the power quality parameters in S1 - S3, and output the power quality anomaly type and probability. The anomaly types at least include harmonic overlimit, voltage sudden change, fast transient, and interharmonic anomaly.

2. The intelligent analysis method for power quality of microgrid based on FPGA according to claim 1, characterized in that, The interharmonic analysis includes the following steps: S3.1.1: Perform amplitude correction and energy correction on the spectral signals corresponding to the voltage and current data respectively. S3.1.2: Centering on integer multiples of the fundamental frequency, group the spectral lines between two harmonic frequencies, calculate the root mean square value of each group, and obtain the effective value of the interharmonic group. S3.1.3: Identify significant interharmonic components in the spectrum through peak search and spectral difference, and estimate their frequencies and amplitudes. S3.1.4: Calculate the interharmonic distortion rate of each interharmonic or the interharmonic distortion rate of a specific frequency band, and compare it with the known interharmonic frequencies that may cause equipment failures to evaluate the impact of interharmonics on the microgrid.

3. The intelligent power quality analysis method for microgrid based on FPGA according to claim 1, wherein The method also includes three-phase unbalance analysis, specifically including: Perform symmetrical component decomposition on the preprocessed data to extract symmetrical components. The symmetrical components include the amplitudes and phases of positive sequence, negative sequence, and zero sequence. Calculate the voltage unbalance degree and current unbalance degree according to the symmetrical components.

4. A method for intelligent analysis of microgrid power quality based on FPGA according to claim 1, characterized in that, The method also includes the triggering mechanism for power quality events: First-level trigger: Monitor the instantaneous value of voltage or current. When the change in the instantaneous value of voltage or current exceeds a preset threshold, trigger an interrupt signal to start an abnormal response and capture transient disturbances. Second-level trigger: Based on the power quality parameters, when the logical combination conditions configured by the user are met, trigger an event record.

5. The intelligent analysis method for power quality of microgrid based on FPGA according to claim 4, characterized in that, The triggering mechanism for power quality events also includes a third-level trigger: Based on the anomaly type probability output by the CNN model, when the probability of predicting a certain abnormal pattern significantly increases and the first-level or second-level trigger conditions are not met, perform the following operations: Adjust the threshold parameters of the first-level or second-level trigger conditions to improve the capture sensitivity for this type of abnormal pattern. At this time, the CNN considers the pattern suspicious or of high risk, triggers data recording for subsequent analysis or CNN model training. Generate a warning message to indicate the existence of potential power quality problems or equipment anomalies.

6. The intelligent power quality analysis method for microgrid based on FPGA according to claim 1, wherein The method also includes S4: Perform adaptive analysis based on the power quality parameters, specifically including: S4.1: Evaluate the current operating condition of the microgrid by monitoring power quality parameters, where the power quality parameters at least include spectral entropy, RMS change rate, load information, and harmonics / inter - harmonics; S4.2: Conduct disturbance type judgment: When transient characteristics are detected, analyze wavelet data and start high - speed recording; When abnormal harmonics or inter - harmonics are detected, increase the FFT analysis frequency and the analysis depth of harmonics and inter - harmonics; If the operating condition is stable, conduct routine detection; S4.3: Adjust the parameters of the trigger mechanism according to the operating condition and disturbance type.

7. An intelligent power quality analysis system for a microgrid based on FPGA, characterized in that, The system is configured to execute the method as claimed in claim 1, including: A data acquisition module for collecting current and voltage data of the microgrid; A pre - processing module for receiving and processing the data of the data acquisition module; A power quality analysis module for analyzing and calculating the output data of the pre - processing module, at least including an FFT analysis unit, an RMS calculation unit, a wavelet transform feature extraction unit, and a neural network anomaly detection unit, where the FFT analysis unit, the RMS calculation unit, the wavelet transform feature extraction unit, and the neural network anomaly detection unit process in parallel; A storage module for storing the data of the data acquisition module, the pre - processing module, and the power quality analysis module.

8. An intelligent power quality analysis system for a microgrid based on FPGA according to claim 7, characterized in that, The data acquisition module uses an 8 - channel synchronous sampling ADC to collect three - phase voltages, three - phase currents, one neutral - line current, and one neutral - point - to - ground voltage, and an analog filter is configured at the front end of the channel; The pre - processing module receives the ADC sampling data output by the data acquisition module, and after digital filtering, data format conversion, decimation, denoising, and normalization of the ADC sampling data, outputs pre - processed data, and the pre - processed data is input into the power quality analysis module.

9. An intelligent power quality analysis system for a microgrid based on FPGA according to claim 8, characterized in that, The FFT analysis unit processes the 8 - channel pre - processed data. The FFT analysis unit includes a high - precision mode and a fast - update mode. The high - precision mode outputs high - resolution spectrum data, and the fast - update mode outputs spectrum amplitude and phase data corresponding to each channel's data.

10. An intelligent power quality analysis system for a microgrid based on FPGA according to claim 9, characterized in that, The power quality analysis module further includes: A harmonics analysis unit for obtaining the high - resolution spectrum data to obtain harmonics characteristics, where the harmonics characteristics at least include the total harmonic distortion rate of each channel and the parameters of each harmonic; An inter - harmonics analysis unit for obtaining the high - resolution spectrum data, conducting inter - harmonics grouping and inter - harmonics parameter calculation to obtain inter - harmonics characteristics; A voltage fluctuation and flicker calculation unit for calculating short - term flicker and long - term flicker according to the voltage RMS value sequence obtained by the RMS calculation unit.

11. An intelligent power quality analysis system for a microgrid based on FPGA according to claim 10, characterized in that, Input the feature matrix into the neural network anomaly detection unit, where the feature matrix at least includes the inter - harmonics characteristics, the harmonics characteristics, and the time - domain characteristics, and outputs the probabilities of multiple anomaly types, where the anomaly types at least include harmonic over - limit, voltage sudden change, fast transient, and inter - harmonics anomaly; train the neural network anomaly detection unit using historical power quality parameters and the corresponding anomaly types.

12. An intelligent analysis system for power quality of microgrid based on FPGA according to claim 10, characterized in that, It further includes an adaptive analysis module, where the adaptive analysis module includes an operating condition evaluation model for evaluating the operating condition of the microgrid using power quality parameters; dynamically adjust the quality analysis parameters according to the operating condition evaluation result.

13. A computer-readable storage medium storing a computer program, characterized in that, The computer program includes: Instructions executable by a processor, which, when executed by a processing unit of a system-on-chip platform, perform the steps of the intelligent power quality analysis method for a microgrid according to any one of claims 1 to 6; And / or configuration data of an FPGA, which, when the configuration data is loaded into a programmable logic unit of a field-programmable gate array (FPGA), perform the method steps according to any one of claims 1 to 6.

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