A method for intelligent frequency regulation of photovoltaic power generation system

Abstract

The invention discloses a method for intelligent frequency regulation of a photovoltaic power generation system, and specifically relates to the technical field of photovoltaic power generation. The method calculates the deviation between the current output frequency of the photovoltaic power generation system and the frequency of a power grid, generates a preliminary regulation strategy through an adaptive PID control algorithm based on the frequency deviation, performs preliminary frequency adjustment, evaluates the stability of the frequency regulation in real-time monitoring and feedback, and divides it into stable and unstable frequency regulation according to the evaluation result. When the system is in unstable frequency regulation, the harmonic data under load changes are monitored in real time through a harmonic monitoring device, abnormal harmonic frequencies are analyzed and identified, and the regulation strategy is dynamically adjusted according to the stability of the frequency regulation and the abnormal index of the harmonic frequency in combination with fuzzy logic, thereby improving the accuracy and response speed of the regulation strategy, thereby ensuring the synchronization of the photovoltaic power generation system with the power grid frequency, reducing the problem of frequency fluctuation and harmonic resonance, and improving the stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method for intelligently adjusting the frequency of a photovoltaic power generation system. Background Art

[0002] Intelligent frequency regulation of photovoltaic power generation systems refers to real-time monitoring and adjustment of the output frequency of photovoltaic power generation systems through intelligent technology and algorithms to ensure that it is consistent with the frequency of the power grid. This regulation method collects the output power, frequency and frequency data of the photovoltaic system and the power grid, analyzes the frequency deviation and generates corresponding regulation strategies to control the output power of the photovoltaic power generation system, thereby reducing the impact of frequency fluctuations on the power grid and ensuring the stability and reliability of the power grid. However, when dynamically adjusting the output of the photovoltaic system according to the real-time monitored frequency deviation, if the frequency regulation is unstable, the system equipment will operate in frequent load changes. Long-term frequency instability will accelerate the aging of the equipment and shorten its service life. And if the system equipment operates in long-term frequent load changes, it may cause harmonic resonance problems between systems, especially in large-scale photovoltaic power generation systems. This resonance phenomenon will amplify the harmonic content in the power grid, resulting in increased harmonic distortion of the power system and affecting the normal operation of other power equipment. For example, harmonic resonance may cause motor overheating, increased noise, and even damage to power transformers. Summary of the invention

[0003] The purpose of the present invention is to provide a method for intelligently adjusting the frequency of a photovoltaic power generation system to solve the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a method for intelligently adjusting the frequency of a photovoltaic power generation system, comprising the following steps:

[0005] S1: Monitor the output frequency of the photovoltaic power generation system and the grid frequency in real time through high-precision sensors, and transmit them to the central control system through the data communication network;

[0006] S2: Preprocess the collected data, calculate the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency, generate a preliminary adjustment strategy through an adaptive PID control algorithm based on the frequency deviation, and perform preliminary frequency adjustment;

[0007] S3: monitor the regulation effect of the photovoltaic power generation system in real time and obtain feedback data. According to the output frequency fluctuation of the photovoltaic power generation system and the abnormal degree of deviation of the grid frequency from the standard in the feedback data, evaluate the stability of the frequency regulation of the photovoltaic power generation system;

[0008] S4: Based on the evaluation results, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation;

[0009] S5: When the photovoltaic power generation system is in unstable frequency regulation, the harmonic data of the photovoltaic power generation system and the power grid under load change conditions are monitored in real time through the harmonic monitoring equipment, and the monitored harmonic data are analyzed to identify abnormal harmonic frequencies in the system;

[0010] S6: According to the stability of the frequency regulation of the photovoltaic power generation system and the abnormal harmonic frequencies in the system, the regulation strategy is dynamically adjusted using fuzzy logic to improve the accuracy and response speed of the regulation strategy.

[0011] Preferably, in S3, an output frequency fluctuation index is generated according to the output frequency fluctuation of the photovoltaic power generation system in the feedback data, and the method for obtaining the output frequency fluctuation index is:

[0012] Use high-precision sensors to collect the output frequency data of the photovoltaic power generation system in real time to form time series data x(t), where t is time. De-noise and smooth the data, and perform discrete Fourier transform on the processed output frequency data. The specific transformation expression is: ;in, , is the value of the time domain signal at the nth sample point, N is the total number of signal sample points, is the kernel function of Fourier transform, where j is an imaginary unit; calculate the spectrum amplitude of the output frequency data, the spectrum amplitude Represents the intensity of the signal at each frequency component, and the calculation expression is: ;in, is the real part of X(k), It is the imaginary part of X(k), and calculates the standard deviation of the frequency amplitude, that is, calculates the output frequency fluctuation index.

[0013] Preferably, in S3, a grid frequency deviation index is generated according to the degree to which the actual frequency of the grid deviates from the specified standard, and the method for obtaining the grid frequency deviation index is:

[0014] Use high-precision sensors to collect the time series data m(s) of the power grid frequency in real time, where s is time. Select the wavelet basis function for signal decomposition and perform continuous wavelet transform on the power grid frequency data m(s). The specific calculation expression is: ; Where W(a,b) is the wavelet coefficient, which represents the transformation result on scale a and displacement b. a is the scale parameter, which controls the width of the wavelet function, and b is the displacement parameter, which controls the position of the wavelet function. is the complex conjugate of the wavelet basis function; extract the wavelet coefficient W(a,b) from the wavelet transform result, calculate the instantaneous frequency of the signal, and the instantaneous frequency is calculated by the phase derivative of the wavelet coefficient. The specific calculation expression is: ; Where f(s) is the instantaneous frequency, is the phase of the wavelet coefficients, is the normalization factor, and the deviation between the instantaneous frequency f(s) and the standard frequency fre is calculated. The calculation expression is: ; is the grid frequency deviation value, and the grid frequency deviation index is calculated according to the calculated grid frequency deviation value. The specific calculation expression is: ; In the formula, is the grid frequency deviation index, is the total time length of the signal.

[0015] Preferably, in S3, the stability of the frequency regulation of the photovoltaic power generation system is evaluated, specifically:

[0016] The output frequency fluctuation index and the grid frequency deviation index are normalized, and the stability coefficient of the frequency regulation of the photovoltaic power generation system is calculated through the normalized output frequency fluctuation index and the grid frequency deviation index.

[0017] Preferably, in S4, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation;

[0018] The obtained stability coefficient of the frequency regulation of the photovoltaic power generation system is compared with the preset stability coefficient reference threshold. If the stability coefficient of the frequency regulation of the photovoltaic power generation system is greater than or equal to the preset stability coefficient reference threshold, it is classified as stable frequency regulation, and a stable frequency regulation signal is generated to notify the control system to maintain the current operating state; if the stability coefficient of the frequency regulation of the photovoltaic power generation system is less than the preset stability coefficient reference threshold, it is classified as unstable frequency regulation, and an unstable frequency regulation signal is generated to notify the control system to make corresponding adjustments.

[0019] Preferably, in S5, a harmonic monitoring device is used to collect harmonic current and voltage data of the power grid in real time to form time series data E(c), where c is time, and the order of the ARMA model is determined, that is, the order p of the autoregressive part and the order q of the sliding average part, where the mathematical expression of the ARMA model is: ;in, is the observed value at time c, k is a constant term, is the coefficient of the autoregressive part, is the coefficient of the sliding average part, is the white noise error term; use historical data to fit the ARMA model and get the parameters and , use the fitted ARMA model to predict future values ​​and get the predicted value , calculate the error between the actual value and the predicted value, and calculate the harmonic frequency anomaly index based on the mean and standard deviation of the predicted error. The specific calculation expression is: ; where μ is the mean of the prediction error, σ is the standard deviation of the prediction error, is the harmonic frequency anomaly index.

[0020] Preferably, the calculated harmonic frequency anomaly index is compared with the harmonic frequency anomaly index reference threshold. If the harmonic frequency anomaly index is greater than or equal to the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as an abnormal harmonic frequency, and a system harmonic frequency anomaly signal is generated; if the harmonic frequency anomaly index is less than the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as a normal harmonic frequency, and a system harmonic frequency normal signal is generated.

[0021] Preferably, in S6, the stability coefficient of the frequency regulation of the photovoltaic power generation system and the harmonic frequency anomaly index are used as input items of the fuzzy logic, and the adjustment strength of the regulation strategy is used as the output item of the fuzzy logic;

[0022] The stability coefficient of frequency regulation and harmonic frequency anomaly index are converted into fuzzy sets, and fuzzy rules are designed based on experience and system characteristics to map fuzzy input to fuzzy output;

[0023] Use the fuzzy inference engine to calculate the output fuzzy value according to the fuzzy rules and the input fuzzy value;

[0024] Convert fuzzy output into specific adjustment strategy parameters;

[0025] The PID control parameters are adjusted dynamically according to the regulation strength output by the fuzzy logic system.

[0026] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0027] 1. The present invention uses intelligent technology and algorithms to monitor and adjust the output frequency of the photovoltaic power generation system in real time to ensure that it is consistent with the grid frequency, thereby significantly improving the stability and response speed of the system. Preliminary frequency adjustment is performed using high-precision sensors and adaptive PID control algorithms, real-time feedback is used to evaluate the adjustment effect, and fuzzy logic is combined to dynamically adjust the control parameters, effectively reducing the impact of frequency fluctuations on the grid and ensuring the stability and reliability of the grid.

[0028] 2. The present invention monitors and analyzes harmonic data in real time, identifies and processes abnormal harmonic frequencies, further optimizes the adjustment strategy, prevents harmonic resonance problems, avoids equipment overheating, increased noise, and damage to power transformers, extends equipment life, and improves power quality. This method not only improves the operating efficiency of the photovoltaic power generation system, but also ensures the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0030] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the method for intelligently adjusting the frequency of a photovoltaic power generation system described in this embodiment includes the following steps:

[0033] S1: Monitor the output frequency of the photovoltaic power generation system and the grid frequency in real time through high-precision sensors, and transmit them to the central control system through the data communication network;

[0034] S2: Preprocess the collected data, calculate the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency, generate a preliminary adjustment strategy through an adaptive PID control algorithm based on the frequency deviation, and perform preliminary frequency adjustment;

[0035] S3: monitor the regulation effect of the photovoltaic power generation system in real time and obtain feedback data. According to the output frequency fluctuation of the photovoltaic power generation system and the abnormal degree of deviation of the grid frequency from the standard in the feedback data, evaluate the stability of the frequency regulation of the photovoltaic power generation system;

[0036] S4: Based on the evaluation results, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation;

[0037] S5: When the photovoltaic power generation system is in unstable frequency regulation, the harmonic data of the photovoltaic power generation system and the power grid under load change conditions are monitored in real time through the harmonic monitoring equipment, and the monitored harmonic data are analyzed to identify abnormal harmonic frequencies in the system;

[0038] S6: According to the stability of the frequency regulation of the photovoltaic power generation system and the abnormal harmonic frequencies in the system, the regulation strategy is dynamically adjusted using fuzzy logic to improve the accuracy and response speed of the regulation strategy.

[0039] Among them, in S1, the output frequency of the photovoltaic power generation system and the grid frequency are monitored in real time through high-precision sensors, and transmitted to the central control system through the data communication network.

[0040] High-precision sensors monitor the output frequency of photovoltaic power generation systems and grid frequency in real time, which means using precision sensor equipment to continuously measure the output frequency of photovoltaic power generation systems and grid frequency. These sensors can capture frequency changes in the power system and provide high-resolution and high-accuracy frequency data.

[0041] The working principle of high-precision sensors includes: voltage sensors and current sensors are installed at key locations of photovoltaic power generation systems and power grids, which can sense and measure the voltage and current output by the system. These sensors calculate the corresponding frequency values ​​by measuring the change cycle of voltage and current. High-precision sensors can detect small frequency changes to ensure the accuracy and reliability of measurement results. Sensors can continuously monitor the frequency of photovoltaic power generation systems and power grids and provide the latest frequency data in real time. This real-time performance is essential for quickly responding to frequency changes and maintaining system stability. High-precision sensors can detect small frequency fluctuations and provide accurate data to support the optimization and adjustment of frequency regulation algorithms.

[0042] The frequency data collected by the sensor is transmitted to the central control system through a data communication network. This network can be wired (such as a fiber optic network) or wireless (such as Wi-Fi, cellular network). The data communication network needs to have high bandwidth and low latency to ensure that the frequency data can be transmitted to the central control system in a very short time to achieve real-time monitoring and adjustment.

[0043] S2: Preprocess the collected data, calculate the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency, and generate a preliminary adjustment strategy based on the frequency deviation through the adaptive PID control algorithm to perform preliminary frequency adjustment.

[0044] The central control system receives the transmitted frequency data, processes and analyzes it, and uses this data to calculate the deviation between the output frequency of the photovoltaic power generation system and the grid frequency.

[0045] Before analyzing the collected data, the raw data is cleaned, filtered and converted to improve data quality and reliability. The specific steps include:

[0046] Data cleaning: Remove noise and outliers. For example, a sensor may occasionally produce erroneous readings, which need to be identified and excluded.

[0047] Data filtering: Use filtering algorithms (such as low-pass filters) to smooth data, reduce short-term fluctuations, and preserve long-term trends.

[0048] Data calibration: Calibrate sensor data to ensure its accuracy and consistency.

[0049] Frequency deviation is the difference between the output frequency of the photovoltaic power generation system and the grid frequency. From the preprocessed data set, the output frequency of the photovoltaic power generation system and the real-time frequency of the grid are extracted. Frequency deviation = photovoltaic power generation system output frequency - grid frequency. The deviation value reflects the difference between the current system frequency and the grid frequency.

[0050] The adaptive PID control algorithm can automatically adjust the PID parameters according to the real-time frequency deviation, so that the control system can maintain the best performance under different working conditions. Calculate the control quantity: Proportional control (P): proportional to the current deviation, providing fast response. Integral control (I): the sum of the accumulated deviations, eliminating long-term steady-state errors. Differential control (D): the rate of change of deviations, predicting system trends, and suppressing over-response. The final control signal is generated by combining the three parts of P, I, and D.

[0051] According to the control signal generated by the adaptive PID control algorithm, the photovoltaic power generation system is preliminarily adjusted in frequency, for example, the output frequency of the photovoltaic system is adjusted by changing the input voltage or current of the inverter.

[0052] By changing the input voltage or current of the inverter, the output power and frequency of the photovoltaic system can be directly affected. Specifically, when the input voltage or current of the inverter increases, the output power of the inverter will also increase accordingly, thereby increasing the output frequency of the photovoltaic system; conversely, when the input voltage or current decreases, the output power of the inverter will decrease, causing the output frequency of the photovoltaic system to decrease. By accurately adjusting the input parameters of the inverter, the output frequency of the photovoltaic system can be dynamically controlled, thereby ensuring its synchronization with the grid frequency, reducing frequency deviation, and improving the stability and response speed of the system.

[0053] S3: Monitor the regulation effect of the photovoltaic power generation system in real time and obtain feedback data. According to the output frequency fluctuation of the photovoltaic power generation system and the abnormal degree of deviation of the grid frequency from the standard in the feedback data, evaluate the stability of the frequency regulation of the photovoltaic power generation system.

[0054] Monitor the regulation effect of the photovoltaic power generation system in real time and obtain feedback data. Continuously observe the output frequency and grid frequency of the photovoltaic power generation system through high-precision sensors and monitoring systems, record the changes in various parameters of the system during the frequency regulation process, and transmit these data back to the central control system for analysis to evaluate the effectiveness and stability of the regulation strategy, thereby providing a basis for further optimizing the regulation strategy.

[0055] The output frequency fluctuation index is generated according to the output frequency fluctuation of the photovoltaic power generation system in the feedback data. The method for obtaining the output frequency fluctuation index is:

[0056] Use high-precision sensors to collect the output frequency data of the photovoltaic power generation system in real time to form time series data x(t), where t is time. Perform denoising and smoothing on the data, such as applying a moving average filter or a median filter, and perform discrete Fourier transform on the processed output frequency data. The specific transformation expression is: ;in, , is the value of the time domain signal at the nth sample point, N is the total number of signal sample points, is the kernel function of Fourier transform, where j is an imaginary unit; calculate the spectrum amplitude of the output frequency data, the spectrum amplitude Represents the intensity of the signal at each frequency component, and the calculation expression is: ;in, is the real part of X(k), It is the imaginary part of X(k), and calculates the standard deviation of the frequency amplitude, that is, calculates the output frequency fluctuation index.

[0057] The larger the output frequency fluctuation index, the worse the stability of the photovoltaic power generation system's frequency regulation. A high fluctuation index indicates that the system has significant frequency fluctuations during the frequency regulation process, which may be due to the frequency regulation algorithm being inaccurate, or the system over- or under-reacting to load changes, resulting in frequent frequency fluctuations. Severe frequency fluctuations will not only affect the stability of the power grid, but also cause additional stress on photovoltaic power generation equipment, shortening the life of the equipment.

[0058] The frequency fluctuation index reflects the degree of change of the system frequency within a certain period of time. If the fluctuation index is high, it means that the frequency deviates from the target value many times and with a large amplitude, indicating that the system's frequency regulation ability is insufficient. This situation may make it difficult to maintain the grid frequency in a stable state, affect the power quality, and increase the risk of equipment failure. In order to improve system stability, it is necessary to optimize the frequency regulation algorithm, improve the response speed and accuracy, and reduce frequency fluctuations.

[0059] Therefore, by monitoring and analyzing the output frequency fluctuation index, the regulation performance of the photovoltaic power generation system can be evaluated. Reducing the fluctuation index is an important indicator for improving system stability, which needs to be achieved by improving control strategies and regulation algorithms. The ultimate goal is to keep the frequency fluctuation within the minimum range to ensure the safe and stable operation of the system and power grid.

[0060] Among them, the degree of abnormality of the grid frequency deviation from the standard refers to the degree to which the actual frequency of the grid deviates from its prescribed standard (usually 50 Hz or 60 Hz) and its impact on the operation of the power system. Specifically, this deviation can reflect problems such as supply and demand imbalance, load changes, or system failures in the power grid. The greater the frequency deviation, the more severe the fluctuation of the grid frequency and the worse the stability of the power system, which may lead to power equipment failure, malfunction of protection devices, and even large-scale power outages. By evaluating the degree of abnormality of frequency deviation, timely measures can be taken to restore the stability of the grid frequency and ensure the reliable operation of the power system.

[0061] The grid frequency deviation index is generated according to the degree to which the actual frequency of the grid deviates from the specified standard. The method for obtaining the grid frequency deviation index is as follows:

[0062] Use high-precision sensors to collect the time series data m(s) of the power grid frequency in real time, where s is time. Select the wavelet basis function for signal decomposition and perform continuous wavelet transform on the power grid frequency data m(s). The specific calculation expression is: ; Where W(a,b) is the wavelet coefficient, which represents the transformation result on scale a and displacement b. a is the scale parameter, which controls the width of the wavelet function, and b is the displacement parameter, which controls the position of the wavelet function. is the complex conjugate of the wavelet basis function; extract the wavelet coefficient W(a,b) from the wavelet transform result. The wavelet coefficient reflects the characteristics of the signal at different scales and times. Analyze the amplitude and phase information of the wavelet coefficient and calculate the instantaneous frequency of the signal. The instantaneous frequency is calculated by the phase derivative of the wavelet coefficient. The specific calculation expression is: ; In the formula, f(s) is the instantaneous frequency, which means the frequency of the signal at time s, is the phase of the wavelet coefficients, is the normalization factor, and the deviation between the instantaneous frequency f(s) and the standard frequency fre is calculated. The calculation expression is: ; is the grid frequency deviation value, and the grid frequency deviation index is calculated according to the calculated grid frequency deviation value. The specific calculation expression is: ; In the formula, is the grid frequency deviation index, is the total time length of the signal.

[0063] The larger the grid frequency deviation index is, the worse the stability of the photovoltaic power generation system frequency regulation is. This indicates that the photovoltaic power generation system cannot effectively keep the output frequency consistent with the grid frequency during the frequency regulation process, resulting in frequent and large frequency fluctuations. Severe frequency fluctuations will have a negative impact on the overall stability of the grid, increase the system's operating risks, and may cause failures or damage to power equipment.

[0064] A high frequency deviation index reflects the system's inability to respond to frequency fluctuations, which may be due to the control algorithm being inaccurate or the response speed being slow, resulting in unsatisfactory frequency regulation. Frequency fluctuations not only affect the power quality of the grid, but also cause additional stress on the components of the photovoltaic power generation system itself, shortening its service life. In addition, frequent frequency fluctuations may trigger the grid's protection mechanism, resulting in power supply interruptions.

[0065] Therefore, by monitoring the grid frequency deviation index, the performance of the photovoltaic power generation system frequency regulation can be evaluated. Reducing the frequency deviation index is an important goal to improve system stability, which needs to be achieved by optimizing the control strategy and improving the accuracy of the regulation algorithm to ensure that the system can operate smoothly and reduce the negative impact on grid stability.

[0066] The output frequency fluctuation index and the grid frequency deviation index are normalized, and the stability coefficient of the frequency regulation of the photovoltaic power generation system is calculated through the normalized output frequency fluctuation index and the grid frequency deviation index.

[0067] For example, the present invention can use the following formula to calculate the stability coefficient of the frequency regulation of the photovoltaic power generation system, and the calculation expression is: ; In the formula, is the stability coefficient of frequency regulation of photovoltaic power generation system, is the output frequency fluctuation index, is the grid frequency deviation index, is the proportional coefficient of the output frequency fluctuation index and the grid frequency deviation index, and 0.

[0068] S4: Based on the evaluation results, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation.

[0069] The obtained stability coefficient of the frequency regulation of the photovoltaic power generation system is compared with the preset stability coefficient reference threshold. If the stability coefficient of the frequency regulation of the photovoltaic power generation system is greater than or equal to the preset stability coefficient reference threshold, it means that the stability of the frequency regulation of the photovoltaic power generation system is higher, and it is divided into stable frequency regulation, and a stable frequency regulation signal is generated to notify the control system to maintain the current operating state; if the stability coefficient of the frequency regulation of the photovoltaic power generation system is less than the preset stability coefficient reference threshold, it means that the stability of the frequency regulation of the photovoltaic power generation system is lower, and it is divided into unstable frequency regulation, and an unstable frequency regulation signal is generated to notify the control system to make corresponding adjustments.

[0070] S5: When the photovoltaic power generation system is in unstable frequency regulation, the harmonic data of the photovoltaic power generation system and the power grid under load change conditions are monitored in real time through harmonic monitoring equipment, and the monitored harmonic data are analyzed to identify abnormal harmonic frequencies in the system.

[0071] Harmonic monitoring equipment is installed at key nodes of photovoltaic power generation systems and power grids to monitor harmonic current and voltage in real time. Harmonic monitoring equipment can capture various harmonic components in the power system and record harmonic data.

[0072] Load changes refer to changes in the switching and operating status of electrical equipment in the power grid, which can cause fluctuations in current and voltage in the power system. These fluctuations may cause harmonic resonance or other power quality problems. Under load change conditions, harmonic monitoring equipment can capture the harmonic current and voltage generated in the power grid due to load changes. These data are very important for analyzing the dynamic response characteristics of the system.

[0073] Harmonic current and voltage data of the power grid are collected in real time using harmonic monitoring equipment to form time series data E(c), where c is time. The order of the ARMA model is determined, that is, the order p of the autoregressive part and the order q of the moving average part. The mathematical expression of the ARMA model is: ;in, is the observed value at time c, k is a constant term, is the coefficient of the autoregressive part, is the coefficient of the sliding average part, is the white noise error term; use historical data to fit the ARMA model and get the parameters and , use the fitted ARMA model to predict future values ​​and get the predicted value , calculate the error between the actual value and the predicted value, and calculate the harmonic frequency anomaly index based on the mean and standard deviation of the predicted error. The specific calculation expression is: ; where μ is the mean of the prediction error, σ is the standard deviation of the prediction error, is the harmonic frequency anomaly index.

[0074] The calculated harmonic frequency anomaly index is compared with the harmonic frequency anomaly index reference threshold. If the harmonic frequency anomaly index is greater than or equal to the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as an abnormal harmonic frequency, and a system harmonic frequency anomaly signal is generated; if the harmonic frequency anomaly index is less than the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as a normal harmonic frequency, and a system harmonic frequency normal signal is generated.

[0075] In this embodiment, the output frequency and grid frequency of the photovoltaic power generation system are monitored in real time by high-precision sensors, and transmitted to the central control system through a data communication network; the collected data is preprocessed, the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency is calculated, and a preliminary adjustment strategy is generated by an adaptive PID control algorithm according to the frequency deviation, and a preliminary frequency adjustment is performed; the adjustment effect of the photovoltaic power generation system is monitored in real time, and feedback data is obtained, and the stability of the frequency adjustment of the photovoltaic power generation system is evaluated according to the output frequency fluctuation of the photovoltaic power generation system and the abnormal degree of deviation of the grid frequency from the standard in the feedback data; according to the evaluation results, the stability of the frequency adjustment of the photovoltaic power generation system is divided into stable frequency adjustment and unstable frequency adjustment; when the photovoltaic power generation system is in unstable frequency adjustment, the harmonic data of the photovoltaic power generation system and the grid under load change conditions are monitored in real time by a harmonic monitoring device, and the monitored harmonic data are analyzed to identify the abnormal harmonic frequency in the system. This process ensures the synchronization and stability of the photovoltaic power generation system and the grid frequency, optimizes the system response speed and adjustment accuracy, improves the reliability of power supply, and reduces equipment loss and failure risks through real-time detection and adjustment.

[0076] S6: According to the stability of the frequency regulation of the photovoltaic power generation system and the abnormal harmonic frequencies in the system, the regulation strategy is dynamically adjusted using fuzzy logic to improve the accuracy and response speed of the regulation strategy.

[0077] The stability coefficient LK and harmonic frequency anomaly index QD of the photovoltaic power generation system frequency regulation are used as the input items of fuzzy logic, and the adjustment strength of the regulation strategy is used as the output item of fuzzy logic;

[0078] Stability coefficient of frequency regulation (LK): Convert LK to a fuzzy set, such as "low", "medium", "high".

[0079] Harmonic frequency anomaly index (QD): Convert QD to fuzzy sets, such as "normal", "slightly abnormal", "severely abnormal".

[0080] According to experience and system characteristics, fuzzy rules are designed to map fuzzy input to fuzzy output. For example: if LK is "high" and QD is "normal", the adjustment strength is "low".

[0081] If LK is "Medium" and QD is "Slightly Abnormal", adjust the intensity to "Medium".

[0082] If LK is "Low" and QD is "Severe Abnormal", adjust the intensity to "High".

[0083] Use the fuzzy inference engine to calculate the output fuzzy value based on the fuzzy rules and the input fuzzy value.

[0084] Convert the fuzzy output into specific regulation strategy parameters (such as the adjustment range of PID control parameters).

[0085] Commonly used defuzzification methods include the central average method and the maximum membership method.

[0086] According to the regulation strength of the fuzzy logic system output, the PID control parameters (proportional coefficient p, integral coefficient i, differential coefficient d) are adjusted dynamically. The greater the regulation strength, the greater the adjustment range of the PID control parameters, so as to respond more quickly to frequency deviation and harmonic abnormalities.

[0087] Monitor the regulation effect in real time and obtain feedback data. Update the stability coefficient (LK) and harmonic frequency anomaly index (QD) of frequency regulation based on the new feedback data. Continuously optimize the fuzzy logic system and control strategy to improve the regulation accuracy and response speed of the system.

[0088] In this embodiment, according to the stability coefficient and harmonic frequency anomaly index of the frequency regulation of the photovoltaic power generation system, the regulation strategy is dynamically adjusted through fuzzy logic to improve the accuracy and response speed of the regulation strategy. A fuzzy inference engine is used to calculate the output fuzzy value according to the fuzzy value, and it is converted into a specific PID control parameter adjustment amplitude through a defuzzification method. The greater the adjustment intensity, the greater the adjustment amplitude of the PID control parameter, so as to respond to frequency deviation and harmonic anomalies more quickly. The regulation effect is monitored in real time, feedback data is obtained, and the fuzzy logic system and control strategy are continuously optimized according to the new data to improve the regulation accuracy and response speed of the system. By dynamically adjusting the regulation strategy through fuzzy logic, the photovoltaic power generation system can respond to frequency changes and harmonic anomalies more accurately and efficiently, and improve the overall performance and stability of the system.

[0089] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for intelligently adjusting the frequency of a photovoltaic power generation system, characterized in that: The steps include: S1: Monitor the output frequency of the photovoltaic power generation system and the grid frequency in real time through high-precision sensors, and transmit them to the central control system through the data communication network; S2: Preprocess the collected data, calculate the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency, generate a preliminary adjustment strategy through an adaptive PID control algorithm based on the frequency deviation, and perform preliminary frequency adjustment; S3: monitor the regulation effect of the photovoltaic power generation system in real time and obtain feedback data. According to the output frequency fluctuation of the photovoltaic power generation system and the abnormal degree of deviation of the grid frequency from the standard in the feedback data, evaluate the stability of the frequency regulation of the photovoltaic power generation system; The step S3 specifically includes: generating an output frequency fluctuation index according to the output frequency fluctuation of the photovoltaic power generation system in the feedback data, and the method for obtaining the output frequency fluctuation index is: Use high-precision sensors to collect the output frequency data of the photovoltaic power generation system in real time to form time series data x(t), where t is time. De-noise and smooth the data, and perform discrete Fourier transform on the processed output frequency data. The specific transformation expression is: Where X(k) is the value of the frequency domain signal at the kth frequency point, x(n) is the value of the time domain signal at the nth sample point, N is the total number of signal sample points, e-j2πft / N is the kernel function of Fourier transform, where j is an imaginary unit; calculate the spectrum amplitude of the output frequency data, the spectrum amplitude |X(k)| represents the intensity of the signal at each frequency component, and the calculation expression is: Where Re(X(k)) 2 is the real part of X(k), IM(X(k)) 2 It is the imaginary part of X(k), and calculates the standard deviation of the frequency amplitude, that is, calculates the output frequency fluctuation index; The grid frequency deviation index is generated according to the degree to which the actual frequency of the grid deviates from the specified standard. The method for obtaining the grid frequency deviation index is as follows: Use high-precision sensors to collect the time series data m(s) of the power grid frequency in real time, where s is time. Select the wavelet basis function for signal decomposition and perform continuous wavelet transform on the power grid frequency data m(s). The specific calculation expression is: Among them, W(a,b) is the wavelet coefficient, which represents the transformation result on scale a and displacement b. a is the scale parameter, which controls the width of the wavelet function. b is the displacement parameter, which controls the position of the wavelet function. ψ* is the complex conjugate of the wavelet basis function. The wavelet coefficient W(a,b) is extracted from the wavelet transform result, and the instantaneous frequency of the signal is calculated. The instantaneous frequency is calculated by the phase derivative of the wavelet coefficient. The specific calculation expression is: Where f(s) is the instantaneous frequency, arg(W(a,s) is the phase of the wavelet coefficient, is the normalization factor, and the deviation between the instantaneous frequency f(s) and the standard frequency fre is calculated. The calculation expression is: Δf(s) = |f(s)-fre|; Δf(s) is the grid frequency deviation value. The grid frequency deviation index is calculated according to the calculated grid frequency deviation value. The specific calculation expression is: Where AS is the grid frequency deviation index, T is the total time length of the signal; The output frequency fluctuation index and the grid frequency deviation index are normalized, and the stability coefficient of the frequency regulation of the photovoltaic power generation system is calculated by the normalized output frequency fluctuation index and the grid frequency deviation index; S4: Based on the evaluation results, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation; S5: When the photovoltaic power generation system is in unstable frequency regulation, the harmonic data of the photovoltaic power generation system and the power grid under load change conditions are monitored in real time through the harmonic monitoring equipment, and the monitored harmonic data are analyzed to identify abnormal harmonic frequencies in the system; S6: According to the stability of the frequency regulation of the photovoltaic power generation system and the abnormal harmonic frequencies in the system, the regulation strategy is dynamically adjusted using fuzzy logic to improve the accuracy and response speed of the regulation strategy.

2. A method for intelligently adjusting the frequency of a photovoltaic power generation system according to claim 1, characterized in that: In S4, the stability of the frequency regulation of the photovoltaic power generation system is divided into stable frequency regulation and unstable frequency regulation; The obtained stability coefficient of the frequency regulation of the photovoltaic power generation system is compared with the preset stability coefficient reference threshold. If the stability coefficient of the frequency regulation of the photovoltaic power generation system is greater than or equal to the preset stability coefficient reference threshold, it is classified as stable frequency regulation, and a stable frequency regulation signal is generated to notify the control system to maintain the current operating state; if the stability coefficient of the frequency regulation of the photovoltaic power generation system is less than the preset stability coefficient reference threshold, it is classified as unstable frequency regulation, and an unstable frequency regulation signal is generated to notify the control system to make corresponding adjustments.

3. The method for intelligently adjusting the frequency of a photovoltaic power generation system according to claim 1, characterized in that: In S5, the harmonic current and voltage data of the power grid are collected in real time using harmonic monitoring equipment to form time series data E(c), where c is time, and the order of the ARMA model is determined, that is, the order p of the autoregressive part and the order q of the moving average part. The mathematical expression of the ARMA model is: Among them, E c is the observed value at time c, k is a constant term, φ i is the coefficient of the autoregressive part, θ j is the coefficient of the sliding average part, s c is the white noise error term; use historical data to fit the ARMA model and get the parameter φ i and θ j , use the fitted ARMA model to predict future values ​​and get the predicted value WE c , calculate the error between the actual value and the predicted value, and calculate the harmonic frequency anomaly index based on the mean and standard deviation of the predicted error. The specific calculation expression is: Where μ is the mean of the prediction error, σ is the standard deviation of the prediction error, and QD is the harmonic frequency anomaly index.

4. A method for intelligently adjusting the frequency of a photovoltaic power generation system according to claim 3, characterized in that: The calculated harmonic frequency anomaly index is compared with the harmonic frequency anomaly index reference threshold. If the harmonic frequency anomaly index is greater than or equal to the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as an abnormal harmonic frequency, and a system harmonic frequency anomaly signal is generated; if the harmonic frequency anomaly index is less than the harmonic frequency anomaly index reference threshold, the harmonic frequency in the system is marked as a normal harmonic frequency, and a system harmonic frequency normal signal is generated.

5. A method for intelligently adjusting the frequency of a photovoltaic power generation system according to claim 4, characterized in that: In S6, the stability coefficient and harmonic frequency anomaly index of the photovoltaic power generation system frequency regulation are used as input items of fuzzy logic, and the adjustment strength of the regulation strategy is used as the output item of fuzzy logic; The stability coefficient of frequency regulation and harmonic frequency anomaly index are converted into fuzzy sets, and fuzzy rules are designed based on experience and system characteristics to map fuzzy input to fuzzy output; Use the fuzzy inference engine to calculate the output fuzzy value according to the fuzzy rules and the input fuzzy value; Convert fuzzy output into specific adjustment strategy parameters; The PID control parameters are adjusted dynamically according to the regulation strength output by the fuzzy logic system.

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