Electric energy quality control method under distributed energy access

Abstract

The invention relates to the technical field of electric energy quality control, and discloses an electric energy quality control method under distributed energy access, which comprises the following steps: S1, data acquisition and transmission; high-performance sensors are deployed at key nodes, sensitivity is high, stability is high, and it is ensured that data are sent to a management and control center in real time in a low-delay and low-packet-loss mode. And S2, analysis and diagnosis. The control center multi-core processor operates software fusing fast Fourier transform, wavelet transform and a deep learning algorithm, the electric energy quality is judged according to a high-standard threshold value, and an abnormal source is intelligently traced. And S3, cooperative control. When the electric energy quality does not reach the standard, the control center directs the SVG to output reactive current in a millisecond level according to a strategy library, the APF filters harmonic waves, the high-quality electric energy quality is cooperatively recovered, and energy and a power grid are balanced. The method has the advantages that data acquisition is accurate and efficient, a communication link is stable and reliable, analysis and diagnosis are intelligent and agile, and cooperative control is fine and flexible.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality control, and in particular to a method for controlling power quality under distributed energy access. Background Art

[0002] With the increasing awareness of environmental protection and the accelerated transformation of energy structure, the proportion of distributed energy such as photovoltaic power stations and wind farms in the power system is increasing. However, the intermittent and volatile nature of distributed energy and the dispersion of its access locations have brought many severe challenges to the power quality of the power grid.

[0003] On the one hand, distributed energy power generation is significantly affected by natural conditions. Photovoltaic power generation changes with sunshine intensity and cloud cover, while wind power generation fluctuates unstably according to wind speed, causing frequent fluctuations in grid voltage and frequency. In severe cases, this may cause problems such as voltage sag and flicker, affecting the normal operation of industrial production equipment. In precision electronics manufacturing workshops, a slight instability in voltage will cause a sharp increase in the defective rate. On the other hand, there are many distributed energy access points, and the access of a large number of power electronic equipment is prone to introduce harmonics. Non-linear loads aggravate the harmonic distortion of grid current, interfere with communication systems, and reduce the service life of power equipment. For example, hospital medical equipment may be misdiagnosed due to harmonic interference, threatening the life and health of patients.

[0004] Traditional power quality control methods are mostly designed for centralized power generation modes, which makes it difficult to adapt to the complex access structure of distributed energy and unable to respond to variable power quality problems in real time and accurately. Therefore, innovative, efficient and targeted power quality control methods are urgently needed. Summary of the invention

[0005] In order to solve the above-mentioned problems, the present invention proposes a method for controlling power quality under distributed energy access.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0007] Data collection and transmission: Voltage, current, harmonic and power sensors are precisely deployed at key nodes where distributed energy is connected to the power grid. These sensors can quickly capture subtle changes in power parameters with high sensitivity and stability. To ensure reliable data transmission, a hybrid wired and wireless communication link with automatic error correction and anti-interference capabilities is used. The wireless part uses a low-latency 5G private network module to make full use of its high-speed and low-latency characteristics, and the wired part uses industrial-grade optical fiber to ensure that data is transmitted to the power quality control center in real time with minimal transmission delay and extremely low packet loss rate.

[0008] Data analysis and diagnosis: Data analysis and diagnosis: The control center is equipped with a multi-core processor and built-in intelligent analysis software that integrates fast Fourier transform, wavelet transform and deep learning algorithms. It can quickly process received data, accurately analyze voltage fluctuations, locate harmonic parameters, calculate power imbalance, and quickly determine whether the power quality is abnormal based on strict power quality standards and customized high-precision thresholds, and intelligently trace the root cause of the abnormality.

[0009] Collaborative control strategy generation and execution: Once it is determined that the power quality does not meet the standard, the control center will act quickly based on the online updateable strategy library. On the one hand, precise instructions are immediately issued to the static VAR compensation device (SVG) and active power filter (APF). The SVG outputs reactive current in millisecond response according to the voltage and reactive power conditions, and the APF efficiently filters harmonics according to the reverse harmonic injection strategy. On the other hand, it interacts with the distributed energy generation equipment controller in two-way milliseconds to finely control the power generation, with an adjustment step accuracy of one thousandth of the rated power. At the same time, the energy storage system with a hybrid architecture of lithium batteries and supercapacitors is linked to switch the charging and discharging mode according to the state of charge (SOC) and the transient demand of the power grid, and works in an all-round collaborative manner to ensure that the power quality of the power grid is quickly restored to the high-quality range and maintain the dynamic balance between distributed energy and the power grid.

[0010] Preferably, the sensor has an automatic calibration function, which can dynamically calibrate the measurement accuracy based on the ambient temperature in the range of -20°C to 60°C, the humidity in the range of 20% to 90% relative humidity, and the power grid frequency fluctuation amplitude within ±0.5Hz. The calibration period is intelligently adjusted according to the interference change rate to ensure that the data acquisition error rate does not exceed ±0.5%, thereby providing a reliable data basis for subsequent precise control.

[0011] Preferably, the optical fiber used in the wired and wireless hybrid communication network is single-mode optical fiber with a transmission bandwidth of at least 10 Gbps. The 5G private network module supports the 5G NRS standalone architecture, which can achieve an air interface transmission delay of less than 1 millisecond. The network is equipped with triple data encryption nodes, using the Advanced Encryption Standard 256-bit encryption algorithm and the cyclic redundancy check method to ensure that the data transmission packet loss rate is less than 0.01%, and the transmission process is safe and reliable.

[0012] Preferably, the intelligent analysis software integrating multiple algorithms has self-learning ability, and uses at least 1000 groups of big data samples covering different distributed energy output scenarios, various load characteristics and various interference factor combinations to update algorithm weights and model structures every week. The learning frequency can be flexibly adjusted from once a month to three times a day according to the complexity of the power quality problem, thereby continuously improving the recognition speed of new power quality interference characteristics and shortening the judgment response time by at least 30%.

[0013] Preferably, the static VAR compensation device uses silicon carbide power devices, has a response time of less than 2 milliseconds, can achieve a step change of reactive current from 0 to the rated value within 0.1 milliseconds, and the compensation amount adjustment resolution reaches 0.1% of the rated reactive power. It can seamlessly adapt to the dynamic changes in the reactive power demand of the power grid and quickly smooth out voltage fluctuations.

[0014] Preferably, the active power filter uses adaptive harmonic detection technology based on instantaneous reactive power theory to track the change trajectory of harmonics from 2nd to 100th order in real time, with a filtering efficiency of more than 97% for each harmonic, and can automatically optimize the filtering parameters every 10 milliseconds according to the harmonic spectrum characteristics, thereby minimizing the total harmonic distortion rate of the power grid to less than 3%.

[0015] Preferably, in the hybrid architecture of lithium batteries and supercapacitors of the energy storage system, the lithium batteries are responsible for providing large-capacity energy storage with a capacity of not less than 100 kWh, and the supercapacitors are used to support instantaneous high-power charging and discharging with an instantaneous discharge power of up to 500 kW. The charge state monitoring system accurately feedbacks the energy storage level in real time with a monitoring accuracy of ±1%, and intelligently switches the charging and discharging modes according to the real-time working conditions of the power grid, flexibly supporting the power quality regulation of the power grid.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] Accurate and efficient data collection: The sensor has high sensitivity and stability, and can keenly capture subtle changes in power parameters. It can be dynamically calibrated under complex environmental conditions, such as different temperature and humidity ranges and grid frequency fluctuation ranges. The calibration cycle can be intelligently adjusted to ensure that the collection error rate is as low as ±0.5%, providing a solid data foundation for subsequent control. Compared with traditional fixed calibration or insufficiently accurate sensors, it greatly improves data reliability.

[0018] The communication link is stable and reliable: the wired network uses high-bandwidth single-mode optical fiber, and the wireless network uses low-latency 5G private network modules. The integrated architecture not only achieves extremely small transmission delay and extremely low packet loss rate, but also sets up multiple encryption verification nodes. The data transmission is secure and private, effectively avoiding external interference and information leakage, which is far superior to traditional communication methods that are susceptible to interference and have limited bandwidth.

[0019] Intelligent and agile analysis and diagnosis: The intelligent analysis software that integrates multiple advanced algorithms has powerful self-learning capabilities. It is regularly updated based on massive and diverse big data samples, and can flexibly adjust the learning frequency. It can quickly lock in new power quality interference, significantly shorten the judgment response time by at least 30%, and accurately trace the root cause of the anomaly. Traditional methods rely more on experience and find it difficult to respond to complex and changeable interference in a timely manner.

[0020] Collaborative control is precise and flexible: The static VAR compensation device responds extremely quickly, with a reactive adjustment resolution of 0.1%. The active power filter has a 2-100th harmonic filtering rate of over 97% and rapid parameter optimization. Distributed energy generation is precisely regulated, and the energy storage system is intelligently charged and discharged. All links work closely together to ensure the stability of power quality in all aspects and in real time, reduce the probability of equipment failure and downtime, and improve energy utilization and distribution efficiency. It is difficult to achieve such a diverse, precise and dynamic collaborative control effect with traditional means. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a principle flow chart of a method for controlling power quality under distributed energy access according to the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below with reference to the accompanying drawings.

[0023] Example 1

[0024] Small photovoltaic power stations connected to urban distribution networks

[0025] A small photovoltaic power station with an installed capacity of 500kW was newly built on the edge of a town and connected to the local 10kV distribution network.

[0026] During the S1 data collection and transmission stage, special sensors are installed at the photovoltaic power station grid connection point, multiple feeder key branches and other locations. The ambient temperature is usually between -10℃ and 40℃, the humidity is between 30% and 80%, and the grid frequency fluctuates very little. The sensor automatic calibration function is fine-tuned according to this working condition, and the calibration cycle is about once every 2 hours to ensure that the error rate of the collected data is stable at ±0.3%. The data is transmitted to the control center via the 5G private network module and optical fiber dual channels with a delay of less than 0.5 milliseconds.

[0027] In the S2 stage, the multi-core processor of the control center quickly analyzes the data. The rapid movement of clouds at noon in spring and summer causes sudden changes in power generation. The software accurately captures voltage fluctuations of ±3%, locates the 5th and 7th harmonics slightly exceeding the standard, and determines that the abnormal power quality is caused by the sudden change in photovoltaic output. The analysis is completed within 10 milliseconds.

[0028] In the S3 stage, SVG contains silicon carbide power devices, with a response time of 0.5 milliseconds, and fine-tunes reactive power compensation. The APF updates the filtering parameters every 5 milliseconds, filters out harmonics and reduces THD to 2%. The photovoltaic power step is set to 0.5kW. The energy storage system has a lithium battery with 200 kWh of energy storage and a supercapacitor with 200 kW of discharge support. It can flexibly charge and discharge according to the SOC, restore power quality within seconds, and ensure stable electricity consumption for surrounding residents and small businesses.

[0029] Example 2

[0030] Coastal wind farms connected to regional transmission grid

[0031] A large-scale wind farm along the coast has a total installed capacity of 100MW and is connected to the 220kV regional transmission network.

[0032] At S1, in the harsh environment of salt spray and strong wind at sea, the sensor accurately collects data at -15℃ to 50℃, humidity 60% to 95%, and frequency fluctuation within ±0.3Hz. Calibration is frequent, once every hour. With the help of high-bandwidth single-mode optical fiber (25Gbps) and enhanced 5G modules, the packet loss rate is close to zero and the data transmission delay is about 0.8 milliseconds.

[0033] In S2, the wind turbine cluster was impacted by gusts of wind, and the control center instantly detected voltage flicker and multi-frequency harmonics. The deep learning algorithm combined with historical wave and wind condition big data samples (2,000 sets updated weekly) identified the root cause of the abnormality as the wind turbine cluster coordination disorder within half a second, and quickly determined that the power quality was deteriorating.

[0034] When S3 is started, SVG responds in 1.5 milliseconds and dynamically balances reactive power. APF strongly tracks 2-100th harmonics with a filtration rate of 98%, optimizing parameters based on the spectrum in 8 milliseconds. The wind turbine pitch angle is regulated, and the power step is finely adjusted in 50kW. The 800 kWh lithium battery with energy storage is paired with a 400 kW supercapacitor, and SOC monitoring is accurate, supporting the power grid to survive transient shocks, maintain power quality, and ensure reliable power supply to industrial clusters.

[0035] Example 3

[0036] Distributed Energy Hybrid Community Microgrid

[0037] A smart community contains distributed energy sources such as rooftop photovoltaics and small gas turbines, which are formed into a microgrid that interacts with a 380V low-voltage distribution network.

[0038] S1, sensors are deployed in distribution boxes and energy conversion nodes in the community. The ambient temperature and humidity are stable, the frequency fluctuates by ±0.2Hz, the calibration cycle is 3 hours, and the data is sent to the control center via 10Gbps optical fiber and 5G lightweight modules with low latency and zero packet loss.

[0039] S2, the switching of residents' electricity consumption during peak and valley periods causes sudden load changes. The software uses wavelet transform to sensitively locate the voltage drop of 5% and the breeding of low-order harmonics. The fusion algorithm is based on daily load big data (500 groups updated daily), quickly analyzes the causes, and locks the abnormal points of power quality in 20 milliseconds.

[0040] S3, SVG compensates for reactive power in milliseconds, APF efficiently clears harmonics, and controls THD to 1.5%; fine-tunes distributed energy output with a step size of 0.1kW; stores 50 kWh lithium batteries and 100 kW supercapacitors, intelligently charges and discharges according to real-time working conditions, optimizes the power quality of community microgrids, guarantees diverse power demands such as smart homes and electric vehicle charging, and improves residents' power comfort and equipment operating life.

[0041] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for controlling power quality under distributed energy access, characterized in that: The following steps are involved: S1: Data collection and transmission: Voltage, current, harmonic and power sensors are accurately deployed at key nodes where distributed energy is connected to the power grid. The sensors are highly sensitive and stable, and can quickly capture subtle changes in power parameters. A hybrid wired and wireless communication link with automatic error correction and anti-interference capabilities is used. The wireless part uses a low-latency 5G private network module, and the wired part uses industrial-grade optical fiber to ensure that data is transmitted to the power quality control center in real time with minimal transmission delay and extremely low packet loss rate. S2: Data analysis and diagnosis: The control center is equipped with a multi-core processor and built-in intelligent analysis software that integrates fast Fourier transform, wavelet transform and deep learning algorithms. It can quickly process received data, accurately analyze voltage fluctuations, locate harmonic parameters, calculate power imbalance, and quickly determine whether the power quality is abnormal based on strict power quality standards and customized high-precision thresholds, and intelligently trace the root cause of the abnormality. S3: Collaborative control strategy generation and execution: Once it is determined that the power quality does not meet the standard, the control center acts according to the online updateable strategy library and immediately sends instructions to the static VAR compensation device (SVG) and the active power filter (APF). The SVG outputs reactive current in millisecond response according to the voltage and reactive power conditions, and the APF efficiently filters out harmonics according to the reverse harmonic injection strategy. At the same time, it interacts with the distributed energy generation equipment controller in two-way milliseconds to finely control the power generation power with an adjustment step accuracy of one thousandth of the rated power. It links with the energy storage system with a hybrid architecture of lithium batteries and supercapacitors to switch the charging and discharging mode according to the state of charge and transient demand of the power grid, and collaboratively restores the power quality to a high-quality range to balance the relationship between energy and the power grid.

2. The method for controlling power quality under distributed energy access according to claim 1 is characterized in that: In the S1 data collection and transmission step, the sensor has an automatic calibration function, which can dynamically calibrate the measurement accuracy based on the ambient temperature in the range of -20°C to 60°C, the humidity in the range of 20% to 90% relative humidity, and the grid frequency fluctuation amplitude within ±0.5Hz. The calibration cycle is intelligently adjusted according to the interference change rate to ensure that the data collection error rate does not exceed ±0.5%, providing a reliable data basis for subsequent precise control.

3. The method for controlling power quality under distributed energy access according to claim 1 is characterized in that: In step S1, the optical fiber used in the wired and wireless hybrid communication network is single-mode optical fiber with a transmission bandwidth of at least 10 Gbps. The 5G private network module supports the 5G NRS standalone architecture, which can achieve an air interface transmission delay of less than 1 millisecond. The network is equipped with triple data encryption nodes, using the Advanced Encryption Standard 256-bit encryption algorithm and cyclic redundancy check method to ensure that the data transmission packet loss rate is less than 0.01%, and the transmission process is safe and reliable.

4. The method for controlling power quality under distributed energy access according to claim 1, characterized in that: In the S2 data analysis and diagnosis step, the intelligent analysis software that integrates multiple algorithms has self-learning capabilities, and uses at least 1,000 sets of big data samples covering different distributed energy output scenarios, various load characteristics, and multiple interference factor combinations to update algorithm weights and model structures every week. The learning frequency can be flexibly adjusted from once a month to three times a day according to the complexity of the power quality problem, continuously improving the recognition speed of new power quality interference characteristics and shortening the judgment response time by at least 30%.

5. The method for controlling power quality under distributed energy access according to claim 1, characterized in that: In the S3 collaborative control strategy generation and execution step, the static VAR compensation device uses silicon carbide power devices with a response time of less than 2 milliseconds. It can achieve a step change of reactive current from 0 to the rated value within 0.1 milliseconds. The compensation amount adjustment resolution reaches 0.1% of the rated reactive power. It can seamlessly adapt to the dynamic changes in the reactive power demand of the power grid and quickly smooth out voltage fluctuations.

6. The method for controlling power quality under distributed energy access according to claim 1, characterized in that: In step S3, the active power filter uses adaptive harmonic detection technology based on instantaneous reactive power theory to track the change trajectory of harmonics from 2nd to 100th order in real time, with a filtering efficiency of more than 97% for each harmonic. It can also automatically optimize the filtering parameters every 10 milliseconds according to the harmonic spectrum characteristics, thereby minimizing the total harmonic distortion rate of the power grid to below 3%.

7. The method for controlling power quality under distributed energy access according to claim 1, characterized in that: In step S3, in the hybrid architecture of lithium batteries and supercapacitors of the energy storage system, the lithium batteries are responsible for providing large-capacity energy storage with a capacity of not less than 100 kWh, and the supercapacitors are used to support instantaneous high-power charging and discharging, with an instantaneous discharge power of up to 500 kW. The charge state monitoring system provides real-time and accurate feedback on the energy storage level with a monitoring accuracy of ±1%. It intelligently switches the charging and discharging modes according to the real-time working conditions of the power grid, and flexibly supports the power quality regulation of the power grid.

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