Power quality management method and device for distributed power supply, and storage medium

Abstract

The embodiment of the invention provides a distributed power supply-oriented power quality management method and device, and a storage medium. The invention relates to the technical field of electric power systems and electric energy quality control. The method comprises the following steps: acquiring electric energy monitoring data through sensors arranged at different nodes of a power grid; respectively carrying out time domain analysis and time frequency analysis on the electric energy monitoring data to obtain an analysis result corresponding to the electric energy monitoring data; and generating an electric energy quality monitoring report based on the analysis result corresponding to the electric energy monitoring data of each sensor. The method is used for achieving the technical effect of improving the adaptability of power quality management.

Description

Technical Field

[0001] This application relates to the field of power system and power quality control technology, and in particular to a power quality management method, device and storage medium for distributed power sources. Background Technology

[0002] Distributed power sources are characterized by unstable, fluctuating, and random outputs. These characteristics introduce numerous power quality problems into traditional centralized power systems, such as voltage fluctuations, frequency instability, and harmonic interference. These issues severely impact the stable operation and security of the power grid, especially as distributed power sources increasingly dominate the grid, making its dynamic characteristics more complex and challenging traditional power systems to cope with these new challenges.

[0003] Currently, power quality management methods include power quality detection and regulation. Detection is mostly based on a single time scale, detecting long-term voltage instability or frequency drift; linear control models are used to regulate distributed power sources.

[0004] However, in existing technologies, there are various transient events in the power grid dynamics. Therefore, when faced with frequent voltage fluctuations and frequency disturbances caused by distributed power sources, power quality management cannot quickly adjust the system state and lacks adaptability to complex nonlinear dynamic behavior. Thus, existing technologies have the technical problem of poor adaptability in power quality management for distributed power sources. Summary of the Invention

[0005] This application provides a power quality management method, device, and storage medium for distributed power sources, which aims to improve the adaptability of power quality management.

[0006] In a first aspect, embodiments of this application provide a power quality management method for distributed power sources, including:

[0007] Power monitoring data is acquired by sensors installed at different nodes of the power grid; the sensors installed at different nodes have different time resolutions.

[0008] Time-domain analysis and time-frequency analysis were performed on the power monitoring data to obtain the analysis results corresponding to the power monitoring data.

[0009] Based on the analysis results corresponding to the power monitoring data of each sensor, a power quality monitoring report is generated. The power quality monitoring report includes at least the total harmonic distortion, transient voltage fluctuation amplitude, and frequency change data.

[0010] In one possible implementation, time-domain analysis and frequency-domain analysis are performed on the power monitoring data to obtain analysis results corresponding to the power monitoring data, including:

[0011] Fourier transform is performed on the power monitoring data to obtain the harmonic spectrum information in the power monitoring data;

[0012] Fast Fourier Transform is performed on the power monitoring data to obtain harmonic distortion information in the power monitoring data;

[0013] Wavelet transform is performed on the power monitoring data to obtain harmonic information from low frequency to high frequency in the power monitoring data;

[0014] The analysis results are formed based on harmonic spectrum information, harmonic distortion information, and harmonic information from low frequency to high frequency.

[0015] In one possible implementation, after generating a power quality monitoring report based on the analysis results corresponding to the power monitoring data of each sensor, the method further includes:

[0016] Based on power monitoring data, a state-space model is used to model the power grid;

[0017] Based on a predefined sliding surface function and state-space model, the voltage deviation between the actual voltage and the target voltage of nodes in the power grid, as well as the frequency deviation between the actual frequency and the target frequency, are determined.

[0018] The operating state of the reactive power compensation equipment is controlled based on voltage deviation, frequency deviation, and sliding mode control law.

[0019] In one possible implementation, the method further includes:

[0020] The control parameters of the sliding mode control law are updated based on voltage deviation, frequency deviation, and fuzzy controller.

[0021] In one possible implementation, the control parameters of the sliding mode control law are updated based on voltage deviation, frequency deviation, and the fuzzy controller, including:

[0022] Input the voltage deviation and frequency deviation into the fuzzy controller;

[0023] The fuzzy controller outputs fuzzy inference results based on pre-built fuzzy rules. The fuzzy inference results include reactive power adjustment suggestions corresponding to voltage deviation and frequency deviation.

[0024] Convert the fuzzy inference results into reactive power regulation quantities;

[0025] Update the control parameters of the sliding mode control law based on the reactive power adjustment.

[0026] In one possible implementation, the method further includes:

[0027] Time series data were constructed based on power monitoring data;

[0028] Time series data is input into the prediction model to obtain prediction results; wherein, the prediction model is a mathematical model used to predict the trend of power quality fluctuations in real time, and the prediction results include at least the predicted trends of voltage fluctuations, frequency fluctuations, and power fluctuations.

[0029] Based on the prediction results, adjust the reactive power of the reactive power compensation equipment and adjust the parameters of the sliding surface function.

[0030] In one possible implementation, the method further includes:

[0031] Establish a network flow model corresponding to the power grid; in the network flow model, nodes represent distributed power sources, load centers and energy storage units, and edges represent power transmission paths;

[0032] Based on the objective function of linear programming, the power transmission path of each distributed power source is determined.

[0033] Based on the integer programming objective function, the output power of each distributed power source is determined respectively;

[0034] The output power of the distributed power source is dynamically adjusted according to load demand.

[0035] In one possible implementation, dynamically adjusting the output power of the distributed power source according to load demand includes:

[0036] Determine the charging and discharging status of the energy storage unit based on load demand;

[0037] The power output scheme of the distributed power source is determined based on a heuristic algorithm;

[0038] Based on the charging and discharging status of the energy storage unit and the power output scheme of the distributed power source, the output power of the distributed power source is dynamically adjusted.

[0039] Secondly, embodiments of this application provide a power quality management device for distributed power sources, comprising:

[0040] The acquisition module is used to acquire power monitoring data through sensors set at different nodes of the power grid; wherein the sensors set at different nodes have different time resolutions;

[0041] The analysis module is used to perform time-domain analysis and time-frequency analysis on the power monitoring data to obtain analysis results corresponding to the power monitoring data.

[0042] The processing module is used to generate a power quality monitoring report based on the analysis results corresponding to the power monitoring data of each sensor. The power quality monitoring report includes at least the total harmonic distortion, transient voltage fluctuation amplitude, and frequency change data.

[0043] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0044] The memory stores computer-executed instructions;

[0045] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0047] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0048] The power quality management method, device, and storage medium for distributed power sources provided in this application's embodiments acquire real-time power monitoring data by deploying sensors at different nodes of the power grid. These sensors, due to differences in deployment location and functional requirements, have different time resolutions. The collected power monitoring data is then subjected to time-domain analysis and time-frequency analysis. Time-domain analysis focuses on the characteristics of data changes over time, while time-frequency analysis is used to analyze harmonic distortion. By combining time-domain and time-frequency analysis, a comprehensive understanding of the multidimensional characteristics of power quality in the power grid can be achieved, enabling the identification of short-term transient events and the analysis of long-term harmonic distortion trends. Based on the above analysis results, the power system generates a comprehensive power quality monitoring report. The report includes at least key indicators such as total harmonic distortion, transient voltage fluctuation amplitude, and frequency variation data. Total harmonic distortion reflects the severity of harmonic pollution in the power grid, transient voltage fluctuation amplitude reveals rapid voltage changes, and frequency variation data reflects the stability of the power grid frequency. These indicators provide a quantitative basis for the power grid's operating status, thereby achieving the technical effect of improving the adaptability of power quality management. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] Figure 1 A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 1 ;

[0051] Figure 2 A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 2 ;

[0052] Figure 3 A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 3 ;

[0053] Figure 4 A schematic diagram of the power quality management device for distributed power sources provided in this application;

[0054] Figure 5 This is a hardware schematic diagram of a power quality management device for distributed power sources provided in this application.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] With the rapid development of new energy technologies, the penetration rate of distributed generation in power systems is constantly increasing. In particular, the widespread integration of intermittent renewable energy sources such as wind and solar power has brought significant challenges to the power grid. Distributed generation is characterized by volatility and randomness, which leads to numerous power quality problems in traditional centralized power systems, including voltage fluctuations, frequency drift, and harmonic distortion. These problems pose a serious threat to the stability and security of the power grid, especially when the proportion of distributed generation is high, making the nonlinear dynamic characteristics of the grid even more complex.

[0057] Most existing power quality monitoring systems are based on a single time scale and can typically only handle prolonged voltage instability or frequency drift. They struggle to capture and analyze transient events in the power grid in real time, such as millisecond-level voltage drops and harmonic fluctuations. Meanwhile, existing static compensation devices (such as Static Var Compensators (SVCs) and Static Synchronous Compensators (STATCOMs)) and filters based on linear control theory exhibit poor response speed and accuracy when dealing with dynamic power quality issues arising from distributed generation, making them ill-suited to adapting to transient changes and nonlinear fluctuations in the power grid.

[0058] Traditional power quality control strategies are mostly based on linear control models, such as PID controllers and traditional reactive power compensators. While these control strategies are effective in stabilizing the power grid in some aspects, they cannot quickly adjust the system state when faced with frequent voltage fluctuations and frequency disturbances caused by distributed generation, and lack adaptability to complex nonlinear dynamic behaviors. In addition, the adaptive capabilities of existing controllers are limited, and they cannot adjust control parameters according to real-time changes in the power grid, thus making it difficult to effectively cope with the uncertainties under conditions of high-penetration distributed generation.

[0059] In terms of power dispatch, existing load dispatch methods typically rely on fixed power dispatch strategies, lacking dynamic adjustment mechanisms and failing to dynamically allocate energy output from distributed power sources and energy storage systems based on real-time load demand. This approach fails to fully utilize the flexibility of distributed power sources and does not consider the collaborative dispatch capabilities of energy storage systems, resulting in difficulty in guaranteeing power quality and limiting grid operating efficiency when load fluctuations are significant.

[0060] The power quality management method, device, and storage medium for distributed power sources provided in this application's embodiments acquire real-time power monitoring data by deploying sensors at different nodes of the power grid. These sensors, due to differences in deployment location and functional requirements, have different time resolutions. The collected power monitoring data is then subjected to time-domain analysis and time-frequency analysis. Time-domain analysis focuses on the characteristics of data changes over time, while time-frequency analysis is used to analyze harmonic distortion. By combining time-domain and time-frequency analysis, a comprehensive understanding of the multidimensional characteristics of power quality in the power grid can be achieved, enabling the identification of short-term transient events and the analysis of long-term harmonic distortion trends. Based on the above analysis results, the power system generates a comprehensive power quality monitoring report. The report includes at least key indicators such as total harmonic distortion, transient voltage fluctuation amplitude, and frequency variation data. Total harmonic distortion reflects the severity of harmonic pollution in the power grid, transient voltage fluctuation amplitude reveals rapid voltage changes, and frequency variation data reflects the stability of the power grid frequency. These indicators provide a quantitative basis for the power grid's operating status, thereby achieving the technical effect of improving the adaptability of power quality management.

[0061] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0062] Figure 1 A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0063] S101. Acquire power monitoring data through sensors installed at different nodes of the power grid.

[0064] In this embodiment, sensors located at different nodes have different time resolutions. These sensors are distributed across key locations in the power grid, such as distributed generation points, load centers, transformers, and distribution lines. Each node's sensor monitors power parameters at that location, such as voltage, current, power, and frequency. Because the power quality monitoring needs vary at different locations within the power grid, the sensor time resolutions also differ. Optionally, sensors closer to distributed generation points require higher time resolution to capture rapidly changing signals such as transient voltage fluctuations and harmonic distortion; sensors at key load nodes can employ time resolutions in the range of seconds or minutes to monitor short-term power quality changes; and sensors on distribution lines can use lower time resolutions for long-term monitoring of voltage fluctuations and frequency variations. This multi-time-resolution sensor arrangement comprehensively covers power quality issues at different time scales within the power grid, providing a rich data foundation for subsequent analysis.

[0065] S102. Perform time-domain analysis and time-frequency analysis on the power monitoring data to obtain the analysis results corresponding to the power monitoring data.

[0066] In this embodiment, time-domain analysis focuses on the characteristics of power parameters changing over time, primarily used to capture rapidly changing events within a short period. The results of time-domain analysis can reflect rapidly changing power quality issues in the power grid; time-frequency analysis is used to study the characteristics of signal changes in time and frequency, and the results of time-frequency analysis can reveal long-term harmonic distortion trends and spectral characteristics in the power grid. By combining time-domain analysis and time-frequency analysis, a comprehensive understanding of the multidimensional characteristics of power quality in the power grid can be achieved, enabling the identification of both short-term transient events and the analysis of long-term harmonic problems.

[0067] S103. Based on the analysis results corresponding to the power monitoring data of each sensor, generate a power quality monitoring report.

[0068] In this embodiment, the power quality monitoring report includes at least the total harmonic distortion, transient voltage fluctuation amplitude, and frequency variation data. The total harmonic distortion is a comprehensive assessment of the degree of harmonic pollution in the power grid, reflecting the impact of harmonic components on the fundamental signal; a higher value indicates more severe harmonic pollution. Transient voltage fluctuation amplitude describes rapid voltage changes in the power grid. Time-domain analysis extracts the fluctuation range and amplitude of the voltage signal, reflecting the stability of the power grid over a short period. Frequency variation data includes, but is not limited to, the deviation and fluctuation of the power grid frequency, used to assess the stability of the power grid frequency. Time-domain analysis extracts the frequency signal change trend, reflecting the frequency regulation capability of the power grid under load fluctuations or distributed power source access. The power quality monitoring report also includes, but is not limited to, other indicators such as power factor, reactive power variation, and three-phase imbalance, further refining the power quality assessment. Through these quantitative indicators, the power quality monitoring report provides a comprehensive reference for the power grid's operating status. Maintenance personnel can quickly locate problem areas based on the data in the report and formulate targeted improvement measures, thereby improving the operating efficiency of the power grid and the adaptability of power quality management.

[0069] The power quality management method for distributed power sources provided in this application deploys sensors at different nodes of the power grid to collect power monitoring data in real time. These sensors have different time resolutions depending on their deployment location and functional requirements. The collected power monitoring data undergoes time-domain analysis and time-frequency analysis: time-domain analysis focuses on the characteristics of data changes over time to identify short-term transient events; time-frequency analysis analyzes harmonic distortion and assesses the degree of harmonic pollution in the power grid. By combining time-domain and time-frequency analysis, a comprehensive understanding of the multidimensional characteristics of power quality in the power grid can be achieved, enabling the identification of rapidly changing transient events and the analysis of long-term harmonic distortion trends. Based on the above analysis results, the power system generates a comprehensive power quality monitoring report. This report includes several key indicators, such as total harmonic distortion, transient voltage fluctuation amplitude, and frequency variation data. The total harmonic distortion reflects the severity of harmonic pollution in the power grid, the transient voltage fluctuation amplitude reveals rapid voltage changes, and the frequency variation data reflects the stability of the power grid frequency. These quantitative indicators provide a reliable basis for assessing the power grid's operating status, helping to improve the adaptability of power quality management and the stability of power grid operation, thereby achieving more efficient and reliable power quality monitoring and control.

[0070] Figure 2 A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1Based on the examples, a power quality management method for distributed power sources is described in detail, which includes:

[0071] S201. Acquire power monitoring data through sensors installed at different nodes of the power grid.

[0072] In this embodiment, millisecond-level sensors can be installed at the distributed power access point to monitor transient voltage fluctuations and frequency changes; second-level sensors can be deployed at critical load nodes of the power grid to monitor short-term power quality changes; minute-level sensors can be set at the distribution network and transformers to monitor long-term voltage fluctuations; and monitoring data from different time scales can be collected by connecting the sensors and the central control system through a communication network.

[0073] Specifically, power quality issues related to distributed power generation are diverse and complex, manifesting at different time scales. To achieve comprehensive power quality monitoring, the power system must be able to capture power quality changes in real time, ranging from milliseconds to minutes. This step involves installing multi-layered sensors covering key nodes and load points in the power grid, combined with a communication network for data collection and processing, ensuring comprehensive monitoring of transient, short-term, and long-term power quality fluctuations. In this embodiment, millisecond-level sensors are installed at the locations where distributed power sources connect to the grid. These sensors have a high sampling rate and can capture transient phenomena in the power grid in real time, such as voltage drops, voltage spikes, and rapid frequency fluctuations. Monitoring of transient power quality issues involves calculating the transient voltage envelope using the Hilbert transform, expressed as:

[0074]

[0075] Where Vact(t) is the instantaneous voltage; Its Hilbert transform is used. This transform can accurately capture transient voltage changes, providing data support for subsequent regulation.

[0076] Sampling frequency refers to the number of times a signal is sampled per unit of time, measured in Hertz (Hz). Time resolution, on the other hand, is the smallest interval that can be distinguished on the time axis. There is a reciprocal relationship between the two: time resolution equals the reciprocal of the sampling frequency. For example, a sampling frequency of 100Hz results in a time resolution of 1 / 100 of a second, meaning that the time interval between two consecutive samples is this value. To improve time resolution and allow the power system to capture time information more precisely, the sampling frequency needs to be increased accordingly, but factors such as actual equipment and processing capabilities must also be considered.

[0077] At critical load nodes in the power grid, such as large industrial loads and important public loads, second-level sensors with a sampling frequency of 1Hz are deployed. These sensors are primarily used to capture short-term power quality changes, including power fluctuations, frequency shifts, and voltage fluctuations. By continuously acquiring power data, the sensors perform real-time Fast Fourier Transform (FFT) calculations to analyze the spectrum, thereby analyzing short-term frequency shifts and harmonic components. The FFT formula is:

[0078]

[0079] Where X(f) ’ ) represents the frequency domain signal; x(t) is the time domain signal, used for real-time analysis of short-time harmonic distortion.

[0080] Minute-level sensors are installed at the distribution network and transformer nodes of the power grid to monitor long-term voltage imbalances and power fluctuations. These sensors have long sampling periods, such as sampling once per minute, enabling them to monitor slow voltage drift, voltage imbalances, and reactive power changes in the system. By analyzing long-term voltage change trends, the power system can promptly detect voltage fluctuations caused by load changes and make corrections in the dispatching system.

[0081] All sensors are connected to the central control system via a communication network, collecting monitoring data at multiple time scales in real time. To reduce latency and transmission burden, distributed edge computing units are used for localized processing, employing algorithms such as Fast Fourier Transform (FFT) and Wavelet Transform (WT) to perform preliminary analysis of the real-time data. Wavelet transform allows for the analysis of harmonic components in the time-frequency domain, as shown in the formula:

[0082]

[0083] in, t represents the wavelet coefficients; x(t) represents the original signal. Here, is the wavelet basis function; a is the scaling parameter; and b is the translation parameter. Using this analysis method, sensors can track and analyze power quality issues at different resolutions, transmitting the results to the central control system via a communication network to ensure the stability of the overall power grid operation.

[0084] S202. Perform Fourier transform on the power monitoring data to obtain harmonic spectrum information in the power monitoring data; perform Fast Fourier Transform on the power monitoring data to obtain harmonic distortion information in the power monitoring data; perform wavelet transform on the power monitoring data to obtain harmonic information from low frequency to high frequency in the power monitoring data; based on the harmonic spectrum information, harmonic distortion information, and harmonic information from low frequency to high frequency, form the analysis results.

[0085] In this embodiment, Fourier transform is used to convert the acquired time-domain voltage signal into a frequency-domain signal, extracting harmonic spectrum information from the system. Fourier transform effectively identifies harmonic components in the voltage signal, such as odd harmonics like the 3rd and 5th orders. The extracted spectrum information is used to further evaluate harmonic distortion in the power grid. The basic formula for Fourier transform is:

[0086]

[0087] Where X(f) ’ ) represents the frequency domain signal; x(t) is the time domain signal.

[0088] To accelerate the frequency domain analysis of voltage data, the Fast Fourier Transform (FFT) is used to process the acquired data in real time. FFT is an efficient algorithm for calculating the Fourier transform, capable of performing frequency domain analysis on large amounts of data in a short time. Its implementation is based on a divide-and-conquer strategy, decomposing the Fourier transform calculation problem of the signal into multiple small-scale transforms. The FFT results are used to identify harmonic distortion in power quality caused by nonlinear loads or other interference sources.

[0089] To accurately capture the harmonic components in transient signals, wavelet transform is used to perform time-frequency domain analysis on the acquired voltage signal. The expression for wavelet transform is:

[0090]

[0091] in, t represents the wavelet coefficients; x(t) represents the original signal. Here, is the wavelet basis function; a is the scaling parameter; and b is the translation parameter. Wavelet transform can decompose harmonic components in different frequency bands, extracting harmonic information from low to high frequencies in the system. Through wavelet transform, the system can capture the high-frequency components and harmonic distortion information contained in instantaneous voltage fluctuations, information that is often difficult to capture using Fourier transform.

[0092] Based on harmonic spectrum information, harmonic distortion information, and harmonic information from low to high frequencies, an analysis result is generated. This result includes the total harmonic distortion, the amplitude of transient voltage fluctuations, and frequency variations. The power quality monitoring report provides real-time data support for grid managers to assess the stability of power quality and the operational status after distributed power generation is connected.

[0093] S203. Based on the analysis results corresponding to the power monitoring data of each sensor, generate a power quality monitoring report.

[0094] S204. Based on power monitoring data, a state-space model is used to model the power grid; based on a predefined sliding mode surface function and state-space model, the voltage deviation between the actual voltage and the target voltage of the nodes in the power grid, as well as the frequency deviation between the actual frequency and the target frequency, are determined; based on the voltage deviation, frequency deviation, and sliding mode control law, the working state of the reactive power compensation equipment is controlled.

[0095] In this embodiment, a state-space model is used to model the power grid, thereby establishing state equations describing the dynamic changes in voltage and frequency. The state equations are as follows:

[0096]

[0097] in, Let x(t) be the power grid state vector, u(t) be the control input, and f(x(t),u(t),t) be the nonlinear dynamic function.

[0098] The predefined sliding surface function is shown in the following equation:

[0099]

[0100] Where s(x) is the sliding surface function; V ref Reference voltage; V act (t) represents the actual voltage value of the system; λ represents the sliding mode control gain.

[0101] The sliding mode control law is shown in the following equation:

[0102]

[0103] Where k is the control gain; sign(s(x)) is the sign function, which is 1 when s(x)>0 and -1 when s(x)<0.

[0104] In one possible implementation, the control parameters of the sliding mode control law can be updated based on voltage deviation, frequency deviation, and a fuzzy controller. Introducing fuzzy control can further improve system performance, especially when dealing with complex nonlinearities and uncertainties. For example, adjusting key parameters in the sliding mode control, such as the gain value, through fuzzy logic can improve the overall system performance.

[0105] Specifically, the voltage deviation and frequency deviation are input into the fuzzy controller; the fuzzy controller outputs fuzzy inference results based on pre-built fuzzy rules, including reactive power adjustment suggestions corresponding to the voltage deviation and frequency deviation; the fuzzy inference results are converted into reactive power adjustment quantities; and the control parameters of the sliding mode control law are updated based on the reactive power adjustment quantities.

[0106] Optionally, voltage deviation and frequency deviation are input into the fuzzy controller, defining the input variables as voltage deviation and frequency deviation. A fuzzy rule base is constructed, and reactive power regulation output is generated based on voltage deviation and rate of change. The input is fuzzified by a fuzzy inference system, and a fuzzy regulation signal is output in combination with the fuzzy rule base. The gradient descent algorithm is used to adjust the weights in the fuzzy rule base to optimize the rules. A defuzzification module is designed to convert the fuzzy output into a precise reactive power regulation quantity. After converting the fuzzy inference result into a reactive power regulation quantity, the control parameters of the sliding mode control law are updated according to the regulation quantity, thereby realizing the dynamic regulation of voltage deviation and frequency deviation.

[0107] A fuzzy logic controller combined with a sliding mode controller is used to adaptively adjust control parameters under complex nonlinear power grid conditions. By constructing a fuzzy rule base, the system dynamically generates reactive power adjustment output based on voltage and frequency deviations. The rule base includes multiple "if-then" rules, such as: if the voltage deviation is large and the rate of change is positive, then increase the reactive power output; if the voltage deviation is small and the rate of change is negative, then decrease the reactive power output.

[0108] Optionally, a gradient descent algorithm is used to dynamically optimize the weights of the fuzzy rule base. The weight update formula is:

[0109]

[0110] Among them, w ij (t) represents the weights in the fuzzy rule base; η is the learning rate; and J is the error loss function.

[0111] The fuzzy inference system generates a fuzzy adjustment signal based on the input voltage deviation and rate of change, according to a fuzzy rule base, for adjusting reactive power output. The system uses a defuzzification module to convert the fuzzy output into a precise reactive power adjustment value, ensuring that the adjustment signal suits the actual operating requirements of the reactive power compensation equipment, thus achieving precise control of voltage and frequency.

[0112] One possible implementation involves using a deep learning model to predict power quality issues and adaptively adjusting the parameters of the sliding surface function based on the prediction results. Specifically, this method includes: constructing time-series data based on power monitoring data; inputting the time-series data into the prediction model to obtain prediction results; wherein the prediction model is a mathematical model used to predict power quality fluctuation trends in real time, and the prediction results include at least voltage fluctuation prediction trends, frequency fluctuation prediction trends, and power fluctuation prediction trends; and adjusting the reactive power of the reactive power compensation equipment and the parameters of the sliding surface function based on the prediction results.

[0113] Specifically, the collected power monitoring data, including power quality data such as voltage, frequency, and power fluctuations, is preprocessed and constructed into time series data, which is then input into a Long Short-Term Memory (LSTM) model for training. LSTM models have the advantage of processing time series data and can capture the long- and short-term dependencies of power quality changes during grid operation. The input data sequence is in the form X = [x1, x2, ... x...]. T ]; where represents x T This represents the voltage, frequency, or power fluctuation value at time t. The LSTM model adjusts for and learns the changing trends in power quality through memory units, such as input gates, forget gates, and output gates.

[0114] The trained LSTM model is used to predict future power quality parameters in real time, including voltage fluctuation prediction trends, frequency fluctuation prediction trends, and power fluctuation prediction trends. The LSTM model can generate predicted values ​​for power quality parameters at multiple future moments based on the current input data and the model's memory state. Through this prediction, the system can identify potential voltage fluctuations, frequency deviations, and abnormal power fluctuations in advance.

[0115] Based on the prediction results of the LSTM model, the system can adjust the operating parameters of reactive power compensation equipment and dynamic filters in advance before voltage, frequency, and power fluctuations occur. The adjustment of the reactive power compensation equipment ensures that the reactive power supply of the power grid meets demand, while the dynamic filter is used to compensate for harmonic currents and reduce the impact of harmonic distortion on power quality. The adjusted parameters can include the reactive power compensation amount, the filter frequency, and the compensation speed.

[0116] The predicted voltage and frequency fluctuation trends are used to adjust the sliding surface and reactive power output of the sliding mode controller in real time. The sliding mode controller controls the reactive power output by adjusting the sliding surface, ensuring that voltage and frequency deviations quickly return to the set reference values. The core of the sliding mode controller is the sliding surface function, calculated using the following formula:

[0117]

[0118] Where s(x) is the sliding surface function; V ref Reference voltage; V act (t) represents the actual voltage value of the system; λ represents the sliding mode control gain.

[0119] Based on the prediction results, the parameters of the sliding mode controller can be dynamically adjusted to optimize reactive power output and maintain stable power quality.

[0120] Figure 3A flowchart illustrating the power quality management method for distributed power sources provided in this application. Figure 3 ,like Figure 3 As shown, in this embodiment... Figure 1 Based on the embodiments, a detailed explanation is provided on adjusting the output power for distributed power sources, the method including:

[0121] S301. Establish a network flow model corresponding to the power grid; in the network flow model, nodes represent distributed power sources, load centers, and energy storage units, and edges represent power transmission paths.

[0122] In this embodiment, the energy flow of the distributed power source is modeled using a network flow model. Nodes represent distributed power sources, load centers, and energy storage units, and edges represent power transmission paths. The goal of the model is to maximize load demand satisfaction and optimize energy flow transmission efficiency. The energy balance of each node can be represented as:

[0123]

[0124] Among them, P in,i Let P be the input power of node i. out,j Let be the output power of node j.

[0125] S302. Based on the linear programming objective function, determine the power transmission path for each distributed power source.

[0126] In this embodiment, to optimize the energy flow of distributed power sources, a linear programming method is used to determine the optimal power transmission path. The optimization objective is to minimize transmission loss or power allocation deviation, and the linear programming objective function can be defined as:

[0127]

[0128] Among them, C ij P represents the power transmission loss factor from node i to node j. ij Let be the power to be transmitted. By solving this linear programming problem, the optimal power allocation scheme on each path can be obtained.

[0129] S303. Based on the integer programming objective function, determine the output power of each distributed power source.

[0130] In this embodiment, integer programming is used to calculate the power output of the distributed generation to ensure that the system can balance load demand and power quality requirements. The integer programming objective function is based on the output power scheduling of the distributed generation and takes into account the load demand and the constraints of the distributed generation, and is optimized as follows:

[0131]

[0132] Integer programming can ensure that the power output is a discrete value and meets the power requirements of the actual system.

[0133] S304. Determine the charging and discharging state of the energy storage unit based on load demand; determine the power output scheme of the distributed power source based on heuristic algorithms.

[0134] In this embodiment, the output power of the distributed power source is dynamically adjusted according to the real-time demand on the load side. Load demand is fed back in real time through a monitoring system in the power grid. The system automatically adjusts the output power of each distributed power source based on load changes to ensure a balance between load demand and supply, avoiding power surplus or shortage. To further balance the fluctuations in grid load and distributed power sources, this step describes in detail the coordinated scheduling of distributed energy storage systems (such as battery storage) and distributed power sources. By combining the charging and discharging characteristics of the energy storage system with the generation characteristics of the distributed power source, stable system operation is ensured during load fluctuations.

[0135] The state of charge / discharge of the energy storage system is calculated based on real-time load demand. The energy state of the energy storage system is expressed by the following formula:

[0136]

[0137] Where E(t) is the energy state of the energy storage system at time t; P charge (t) represents the charging power; P discharge (t) represents the discharge power; Δt represents the time interval. The system dynamically adjusts the charging and discharging state of the energy storage system according to load demand to ensure the power balance of the power grid.

[0138] One possible implementation involves using heuristic algorithms, such as genetic algorithms or particle swarm optimization, to calculate the optimal output power of the distributed power source. Heuristic algorithms can find approximate optimal solutions under complex multi-constraint conditions, with the optimization objective being to minimize power loss and maximize system efficiency. During the search process, the heuristic algorithm iterates and updates the solution to obtain the optimal power output scheme for the distributed power source.

[0139] S305. Based on the charging and discharging status of the energy storage unit and the power output scheme of the distributed power source, dynamically adjust the output power of the distributed power source.

[0140] In this embodiment, the system dynamically allocates energy output from distributed generation and energy storage systems based on load demand. When load demand increases, the energy storage system is prioritized to release energy; when load decreases, the energy storage system absorbs excess energy. Simultaneously, the output power of the distributed generation is adjusted according to actual demand to ensure load balance and power stability across the entire system. The output power of the energy storage system and the generation power of the distributed generation are coordinated through a joint dispatching mechanism. This joint dispatching strategy dynamically adjusts the output ratio of the energy storage system and the distributed generation based on the real-time power demand and power quality requirements of the power grid, ensuring a balance between energy supply and demand in the grid. Furthermore, the system automatically optimizes the use of energy storage devices to ensure the charge-discharge cycle efficiency of the energy storage system.

[0141] The power quality management method for distributed power sources provided in this application acquires power monitoring data through sensors installed at different nodes of the power grid; performs time-domain analysis and time-frequency analysis on the power monitoring data to obtain analysis results corresponding to the power monitoring data; and generates a power quality monitoring report based on the analysis results corresponding to the power monitoring data from each sensor. This method has the following beneficial effects:

[0142] Through a multi-timescale power quality monitoring network, power quality issues such as voltage and frequency fluctuations and harmonic distortion in the power grid after distributed power sources are connected can be captured within different time ranges such as milliseconds, seconds, and minutes. Compared with existing single-timescale monitoring systems, multi-level monitoring networks can comprehensively cover various complex power quality events, ensuring accurate monitoring of transient events and long-term trends in the power grid, and providing more efficient data support for subsequent regulation.

[0143] The nonlinear dynamic power quality controller based on sliding mode control and adaptive fuzzy control can better cope with the complex nonlinear characteristics brought about by the integration of distributed power sources. Compared with the traditional linear control strategy, the sliding mode controller has good robustness and can quickly adjust voltage and frequency deviations, while the fuzzy controller adaptively adjusts the reactive power output to ensure that the system remains stable in uncertain and rapidly changing environments, achieving a better dynamic response effect than existing technologies.

[0144] The intelligent scheduling platform based on energy routing optimizes the energy output and scheduling of distributed power sources and energy storage systems through linear programming, integer programming, and heuristic algorithms. Compared with existing fixed power scheduling methods, it dynamically allocates power according to real-time load demand, ensuring that the power quality remains balanced and stable even when the load fluctuates. This significantly improves the system's flexibility and operating efficiency, and effectively enhances the stability and controllability of distributed power sources in the power grid.

[0145] Figure 4 The schematic diagram of the power quality management device for distributed power sources provided in this application is as follows: Figure 4 As shown, the power quality management 40 for distributed power sources provided in this embodiment includes:

[0146] The acquisition module 401 is used to acquire power monitoring data through sensors set at different nodes of the power grid; wherein the sensors set at different nodes have different time resolutions.

[0147] Analysis module 402 is used to perform time-domain analysis and time-frequency analysis on the power monitoring data to obtain analysis results corresponding to the power monitoring data;

[0148] The processing module 403 is used to generate a power quality monitoring report based on the analysis results corresponding to the power monitoring data of each sensor. The power quality monitoring report includes at least the total harmonic distortion, transient voltage fluctuation amplitude, and frequency change data.

[0149] In one possible implementation, the analysis module 402 is further configured to:

[0150] Fourier transform is performed on the power monitoring data to obtain the harmonic spectrum information in the power monitoring data;

[0151] Fast Fourier Transform is performed on the power monitoring data to obtain harmonic distortion information in the power monitoring data;

[0152] Wavelet transform is performed on the power monitoring data to obtain harmonic information from low frequency to high frequency in the power monitoring data;

[0153] The analysis results are formed based on harmonic spectrum information, harmonic distortion information, and harmonic information from low frequency to high frequency.

[0154] In one possible implementation, the processing module 403 is further configured to:

[0155] Based on power monitoring data, a state-space model is used to model the power grid;

[0156] Based on a predefined sliding surface function and state-space model, the voltage deviation between the actual voltage and the target voltage of nodes in the power grid, as well as the frequency deviation between the actual frequency and the target frequency, are determined.

[0157] The operating state of the reactive power compensation equipment is controlled based on voltage deviation, frequency deviation, and sliding mode control law.

[0158] In one possible implementation, the processing module 403 is further configured to:

[0159] The control parameters of the sliding mode control law are updated based on voltage deviation, frequency deviation, and fuzzy controller.

[0160] In one possible implementation, the processing module 403 is further configured to:

[0161] Input the voltage deviation and frequency deviation into the fuzzy controller;

[0162] The fuzzy controller outputs fuzzy inference results based on pre-built fuzzy rules. The fuzzy inference results include reactive power adjustment suggestions corresponding to voltage deviation and frequency deviation.

[0163] Convert the fuzzy inference results into reactive power regulation quantities;

[0164] Update the control parameters of the sliding mode control law based on the reactive power adjustment.

[0165] In one possible implementation, the processing module 403 is further configured to:

[0166] Time series data were constructed based on power monitoring data;

[0167] Time series data is input into the prediction model to obtain prediction results; wherein, the prediction model is a mathematical model used to predict the trend of power quality fluctuations in real time, and the prediction results include at least the predicted trends of voltage fluctuations, frequency fluctuations, and power fluctuations.

[0168] Based on the prediction results, adjust the reactive power of the reactive power compensation equipment and adjust the parameters of the sliding surface function.

[0169] In one possible implementation, the processing module 403 is further configured to:

[0170] Establish a network flow model corresponding to the power grid; in the network flow model, nodes represent distributed power sources, load centers and energy storage units, and edges represent power transmission paths;

[0171] Based on the objective function of linear programming, the power transmission path of each distributed power source is determined.

[0172] Based on the integer programming objective function, the output power of each distributed power source is determined respectively;

[0173] The output power of the distributed power source is dynamically adjusted according to load demand.

[0174] In one possible implementation, the processing module 403 is further configured to:

[0175] Determine the charging and discharging status of the energy storage unit based on load demand;

[0176] The power output scheme of the distributed power source is determined based on a heuristic algorithm;

[0177] Based on the charging and discharging status of the energy storage unit and the power output scheme of the distributed power source, the output power of the distributed power source is dynamically adjusted.

[0178] The power quality management device for distributed power sources provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0179] Figure 5 This is a hardware schematic diagram of the power quality management device for distributed power sources provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0180] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0181] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0182] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0183] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0185] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0186] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0187] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0189] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, methods, or units, and may be electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0192] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0194] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A power quality management method for distributed power sources, characterized in that, The method includes: Power monitoring data is acquired by sensors installed at different nodes of the power grid; wherein the sensors installed at different nodes have different time resolutions. Time-domain analysis and time-frequency analysis are performed on the power monitoring data to obtain analysis results corresponding to the power monitoring data; Based on the analysis results corresponding to the power monitoring data of each of the sensors, a power quality monitoring report is generated. The power quality monitoring report includes at least the total harmonic distortion, transient voltage fluctuation amplitude, and frequency change data.

2. The method according to claim 1, characterized in that, The power monitoring data is subjected to time-domain analysis and frequency-domain analysis respectively to obtain analysis results corresponding to the power monitoring data, including: Perform a Fourier transform on the power monitoring data to obtain the harmonic spectrum information in the power monitoring data; Perform a Fast Fourier Transform on the power monitoring data to obtain harmonic distortion information in the power monitoring data; Wavelet transform is performed on the power monitoring data to obtain harmonic information from low frequency to high frequency in the power monitoring data; The analysis results are formed based on the harmonic spectrum information, the harmonic distortion information, and the harmonic information from low frequency to high frequency.

3. The method according to claim 1, characterized in that, After generating a power quality monitoring report based on the analysis results corresponding to the power monitoring data from each of the sensors, the method further includes: Based on the power monitoring data, the power grid is modeled using a state-space model; Based on the predefined sliding surface function and the state space model, the voltage deviation between the actual voltage and the target voltage of the nodes in the power grid, as well as the frequency deviation between the actual frequency and the target frequency, are determined. The operating state of the reactive power compensation device is controlled based on the voltage deviation, the frequency deviation, and the sliding mode control law.

4. The method according to claim 3, characterized in that, The method further includes: The control parameters of the sliding mode control law are updated based on the voltage deviation, the frequency deviation, and the fuzzy controller.

5. The method according to claim 4, characterized in that, The step of updating the control parameters of the sliding mode control law based on the voltage deviation, the frequency deviation, and the fuzzy controller includes: The voltage deviation and the frequency deviation are input into the fuzzy controller; The fuzzy controller outputs fuzzy inference results based on pre-built fuzzy rules, and the fuzzy inference results include reactive power adjustment suggestions corresponding to the voltage deviation and the frequency deviation. The fuzzy inference result is converted into a reactive power adjustment amount; The control parameters of the sliding mode control law are updated based on the reactive power adjustment.

6. The method according to claim 5, characterized in that, The method further includes: Based on the power monitoring data, time series data is constructed; The time series data is input into the prediction model to obtain the prediction results; wherein, the prediction model is a mathematical model used to predict the power quality fluctuation trend in real time, and the prediction results include at least the voltage fluctuation prediction trend, the frequency fluctuation prediction trend, and the power fluctuation prediction trend. Based on the prediction results, the reactive power of the reactive power compensation device is adjusted, and the parameters of the sliding surface function are also adjusted.

7. The method according to claim 1, characterized in that, The method further includes: Establish a network flow model corresponding to the power grid; the nodes in the network flow model represent the distributed power sources, load centers and energy storage units, and the edges in the network flow model represent power transmission paths; Based on the linear programming objective function, the power transmission path of each of the distributed power sources is determined respectively; Based on the integer programming objective function, the output power of each of the distributed power sources is determined respectively; The output power of the distributed power source is dynamically adjusted according to load demand.

8. The method according to claim 7, characterized in that, The step of dynamically adjusting the output power of the distributed power source according to load demand includes: Based on the load demand, determine the charging and discharging state of the energy storage unit; The power output scheme of the distributed power source is determined based on a heuristic algorithm; The output power of the distributed power source is dynamically adjusted based on the charging and discharging state of the energy storage unit and the power output scheme of the distributed power source.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

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