Virtual power plant aggregation resource high-robustness data high-precision high-frequency acquisition system

Abstract

The invention belongs to the technical field of virtual power plant aggregation resources, and particularly relates to a virtual power plant aggregation resource high-robustness data high-precision high-frequency acquisition system which comprises a local communication module, a CPU module, an FPGA module, a high-speed AD acquisition module, a high-precision signal conditioning module, a signal switching module and a self-calibration module. The local communication module performs data interaction with a plant station through RS485, the CPU module is electrically connected with the local communication module, the high-speed AD acquisition module is electrically connected with the FPGA module, and the high-precision signal conditioning module is electrically connected with the high-speed AD acquisition module. According to the high-precision electric quantity information high-speed acquisition device with the time scale, real-time synchronous measurement of voltage and current is realized by using a high-precision GPS satellite synchronous clock, high-speed real-time data acquisition and storage are realized based on SDRAM (Synchronous Dynamic Random Access Memory) control of an FPGA (Field Programmable Gate Array), and the requirements of full-quantitative intelligent measurement, data acquisition and data analysis are met; and a data basis is provided for platform monitoring and control.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant aggregate resources, and in particular relates to a high-robustness, high-precision, and high-frequency data acquisition system for virtual power plant aggregate resources. Background Art

[0002] In the process of building a new power system, the proportion of new energy is gradually increasing, and the intermittent, random, and volatile problems of new energy power generation are becoming increasingly serious. The data collected and uploaded by the current collection system mainly meet the needs of power dispatching. Therefore, the existing collection devices and collection methods are mainly to meet the dispatching needs in terms of data collection dimensions, frequency, and data real-time. They cannot meet the needs of new power systems and power market spot transactions for massive high-accuracy, panoramic, and real-time data, and cannot provide effective support for the corresponding business. Summary of the invention

[0003] In view of the technical problem that the above-mentioned existing collection devices and collection methods are mainly aimed at meeting scheduling requirements in terms of data collection dimensions, frequency and data real-time, and cannot meet the needs of new power systems and power market spot transactions for massive high-accuracy, panoramic and real-time data, the present invention provides a high-robustness, high-precision and high-frequency data collection system for virtual power plant aggregated resources.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A high-robustness, high-precision, and high-frequency data acquisition system for aggregated resources of a virtual power plant comprises a local communication module, a CPU module, an FPGA module, a high-speed AD acquisition module, a high-precision signal conditioning module, a signal switching module, and a self-calibration module. The local communication module exchanges data with the plant station via RS485, the CPU module is electrically connected to the local communication module, the FPGA module is electrically connected to the CPU module, the high-speed AD acquisition module is electrically connected to the FPGA module, the high-precision signal conditioning module is electrically connected to the high-speed AD acquisition module, the signal switching module is electrically connected to the high-precision signal conditioning module, the self-calibration module is electrically connected to the signal switching module, and the signal switching module is electrically connected to standard voltage and current signals and transformer secondary side voltage and current signals.

[0006] The FPGA module is electrically connected to a time synchronization module, and the time synchronization module is electrically connected to a clock server or a GPS clock synchronization device in a substation via a B code.

[0007] The FPGA module is electrically connected to an SDRAM synchronous dynamic random access memory.

[0008] The high-precision signal conditioning module includes a magnetic modulation comparator, an A / D converter and electrical isolation. The magnetic modulation comparator is electrically connected to the A / D converter, the A / D converter is electrically connected to the electrical isolation, the magnetic modulation comparator is electrically connected to the signal switching module, and the electrical isolation is electrically connected to the FPGA module through a high-speed AD acquisition module.

[0009] The magnetic modulation comparator includes a current sensor, differential sampling and programmable amplification, the current sensor is electrically connected to the differential sampling, the differential sampling is electrically connected to the programmable amplification, the programmable amplification is electrically connected to the A / D converter, and the current sensor is electrically connected to the signal switching module.

[0010] The high-speed AD acquisition module adopts a 24-bit high-speed AD sampling chip.

[0011] The acquisition method of the high-robustness data high-precision high-frequency acquisition system of the virtual power plant aggregated resources includes the following steps:

[0012] S1. The local communication module realizes data interaction with the plant station through RS485. The collected data includes power data, load data, clock data and events. According to the requirements of different communication equipment, the corresponding collection frequency is set, and all data collection reaches minute level;

[0013] S2. A standard source is built into the metering chip, and the excitation signal for self-monitoring is transmitted to the entrance of the meter sampling circuit. The excitation signal and the load signal are sent to the ADC in the chip through the sensor and the peripheral circuit. The load signal is used to calculate the normal amount of electricity. The excitation signal is extracted and calculated by a special algorithm and then compared with the standard source signal to calculate the self-monitoring error.

[0014] S3, the clock source is provided by the clock server or GPS clock synchronization device in the substation, the sampling clocks of each monitoring node are strictly synchronized, and the sampling interval is dynamically corrected according to the frequency fluctuation of the power grid system, the sampling pulse signal required for the whole cycle sampling is provided, the analog signal is converted into a digital signal with time stamp information, and the transmission and signal processing are performed to achieve strict synchronization of the sampling signal, thereby ensuring the time correlation of the monitoring data;

[0015] S4, the signal switching module simultaneously collects the standard voltage and current signals and the transformer secondary side voltage and current signals, and outputs the mixed signals to the high-precision signal conditioning module for noise filtering and gain amplification processing;

[0016] S5. The voltage measurement front end uses a high-precision 2ppm temperature coefficient resistor-capacitor voltage divider network for signal conditioning, and the current measurement front end uses a current sensor based on a magnetic modulation comparator; based on the principle of electromagnetic induction, the theoretical error of a zero-flux current transformer is equal to zero, and there is no ratio difference or angle difference. A compensation winding is used to provide the excitation current, so that the measuring winding will not be affected by the excitation current, and there is no ratio difference or angle difference, thereby achieving high-precision measurement;

[0017] S6, FPGA combines GPS timing to strictly synchronize the acquisition of voltage and current signals, and converts analog signals into digital signals with time stamp information; high-precision GPS clock and data processing algorithms ensure the accuracy and precision of timestamps; timestamps record the specific time point when the state change occurs, allowing the system to accurately track and record the change history of the signal; digital signals with time stamp information are used to monitor the operating status of the power grid in real time, providing reliable data for fault location and analysis;

[0018] S7, FPGA module as the core controller of data acquisition, is responsible for receiving high-speed data from the analog front end and writing it into the SDRAM synchronous dynamic random access memory; at the same time, the FPGA module also implements data preprocessing and real-time analysis functions, and stores the processing results in the SDRAM synchronous dynamic random access memory again, and then reads them out from the SDRAM synchronous dynamic random access memory when the CPU module needs them and sends them out through UART.

[0019] The method for high-precision measurement in S5 is:

[0020] The magnetic potential balance equation of the current sensor of the magnetic modulation comparator is:

[0021] I1N1+I2N2=-I0N1

[0022] Among them: I1 is the primary current of the current transformer, I2 is the secondary current, I0 is the excitation current; N1 and N2 are the turns of the primary and secondary windings respectively;

[0023] When the excitation ampere-turns I0N1 is zero, I1N1 = -I2N2, that is, the change in the ampere-turns of the secondary side can fully reflect the change in the ampere-turns of the primary side, and the error is zero; I0N1 is the absolute error, and I0N1 / I1N1 is the relative error; the error of the current transformer of the magnetic modulation comparator is a complex error, which is expressed by the ratio difference f and the angle difference δ;

[0024] ε=-I0N1 / I1N1=f+jδ

[0025] Where: f = (I2N2 / I1N1) / I1×100%, δ is the angle between I2 and I1 after rotating 180° counterclockwise;

[0026] Due to the existence of I0N1, there is an angle difference δ and a ratio difference f between I2N2 and I1N1; if I0=0, the excitation magnetic potential is 0 and the error is 0; a dynamic balance electronic circuit is used to dynamically adjust it so that the core is always in a dynamic zero flux state; the magnetic potential balance equation of the zero flux current sensor is:

[0027] I1N1+I2N2+I D N D =-I0N1

[0028] Where: N D To detect the winding, I D To detect winding N D Side current;

[0029] The exciting magnetic flux generated by I1 is detected in the winding N D An induced potential is generated at both ends and added to the input end of the dynamic detection unit D. The secondary current I2 is generated through the active network G that generates the secondary current and is provided to the secondary winding. The magnetic flux generated by I2 demagnetizes the iron core, so that the iron core reaches magnetic potential balance.

[0030] From the current transformer magnetic potential balance equation, it can be seen that the magnetic error consists of two parts: one is the error caused by the participating magnetic potential brought by I0, and the other is the error caused by the detection winding N D Side current I D The error caused by the additional magnetic potential is:

[0031]

[0032] Where: E D N D The induced potential, I is the length of the magnetic path, S is the cross-sectional area of ​​the core, μ0 is the initial magnetic permeability of the core, R i is the input impedance of the detection unit; it can be seen that to reduce the magnetic error, one is to select a core with a higher μ0 value and a suitable number of detection winding turns. The sensor selects an ultra-microcrystalline core with a μ0 of 6*104, N D The input impedance of the detection unit should be large, E D and I2 are controlled within the required range through an active dynamic balancing network;

[0033] The current is converted into a small current after passing through the magnetic modulation comparator, and is differentially sampled through the resistor R to output a small voltage to the programmable amplifier. The signal is amplified and then transmitted to the FPGA module for digital processing through a 24-bit AD sampling chip and electrical isolation to measure the current.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The high-precision high-speed data acquisition device with time stamp of the present invention utilizes the high-precision GPS satellite synchronous clock to realize the real-time synchronous measurement of voltage and current, and realizes high-speed real-time data acquisition and storage based on FPGA SDRAM control, so as to meet the needs of full-quantity intelligent measurement, data acquisition and data analysis, and provide a data basis for platform monitoring and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0037] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0038] Figure 1 It is the overall structural diagram of the present invention;

[0039] Figure 2 This is a block diagram of the error self-monitoring principle of the present invention;

[0040] Figure 3 The schematic circuit diagram of the current sensor of the magnetic modulation comparator of the present invention;

[0041] Figure 4 The secondary equivalent circuit diagram of the current sensor of the magnetic modulation comparator of the present invention;

[0042] Figure 5 The phasor diagram of the current sensor of the magnetic modulation comparator of the present invention;

[0043] Figure 6 It is a principle block diagram of the current sensor of the magnetic modulation comparator of the present invention;

[0044] Figure 7 This is a schematic diagram of the current measurement principle of the present invention;

[0045] Figure 8 Schematic diagram of electromagnetic shielding of the magnetic modulation comparator of the present invention.

[0046] Among them: 1 is a local communication module, 2 is a CPU module, 3 is an FPGA module, 4 is a high-speed AD acquisition module, 5 is a high-precision signal conditioning module, 501 is a magnetic modulation comparator, 5011 is a current sensor, 5012 is a differential sampling, 5013 is a program-controlled amplifier, 502 is an A / D converter, 503 is electrical isolation, 6 is a signal switching module, 7 is a self-calibration module, 8 is a time synchronization module, and 9 is an SDRAM synchronous dynamic random access memory. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than to limit the claims of the present invention. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0048] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0049] Virtual power plant aggregates resources with high robustness, high precision and high frequency data acquisition system, such as Figure 1 As shown, it includes a local communication module 1, a CPU module 2, an FPGA module 3, a high-speed AD acquisition module 4, a high-precision signal conditioning module 5, a signal switching module 6 and a self-calibration module 7. The local communication module 1 exchanges data with the plant station through RS485, the CPU module 2 is electrically connected to the local communication module 1, the FPGA module 3 is electrically connected to the CPU module 2, the high-speed AD acquisition module 4 is electrically connected to the FPGA module 3, the high-precision signal conditioning module 5 is electrically connected to the high-speed AD acquisition module 4, the signal switching module 6 is electrically connected to the high-precision signal conditioning module 5, the self-calibration module 7 is electrically connected to the signal switching module 6, and the signal switching module 6 is electrically connected with the standard voltage and current signals and the transformer secondary side voltage and current signals. The FPGA module 3 is electrically connected with the time synchronization module 8, and the time synchronization module 8 is electrically connected with the clock server or GPS clock synchronization device in the substation through the B code. The FPGA module 3 is electrically connected with the SDRAM synchronous dynamic random access memory 9.

[0050] The high-precision signal conditioning module 5 includes a magnetic modulation comparator 501, an A / D converter 502 and an electrical isolation 503. The magnetic modulation comparator 501 is electrically connected to the A / D converter 502, the A / D converter 502 is electrically connected to the electrical isolation 503, the magnetic modulation comparator 501 is electrically connected to the signal switching module 6, and the electrical isolation 503 is electrically connected to the FPGA module 3 through the high-speed AD acquisition module 4. The magnetic modulation comparator 501 includes a current sensor 5011, a differential sampling 5012 and a program-controlled amplifier 5013. The current sensor 5011 is electrically connected to the differential sampling 5012, the differential sampling 5012 is electrically connected to the program-controlled amplifier 5013, the program-controlled amplifier 5013 is electrically connected to the A / D converter 502, and the current sensor 5011 is electrically connected to the signal switching module 6.

[0051] Further, preferably, the high-speed AD acquisition module 4 uses a 24-bit high-speed AD sampling chip.

[0052] The acquisition method of the high-robustness data high-precision high-frequency acquisition system of the virtual power plant aggregated resources includes the following steps:

[0053] Local communication module: The local communication module realizes data interaction with the plant station through RS485. The collected data includes power data, load data, clock data and events, etc. The corresponding collection frequency is set according to the requirements of different communication equipment, and all data collection is achieved at the minute level.

[0054] Self-calibration module: A standard source is built into the metering chip, and the excitation signal for self-monitoring is sent to the entrance of the meter sampling circuit (the current sampling circuit is a shunt or mutual inductor, and the voltage sampling circuit is a resistor divider string). The excitation signal and the load signal are sent to the ADC in the chip through the sensor and peripheral circuits. The load signal is used to calculate the normal amount of electricity. The excitation signal is extracted and calculated by a special algorithm and then compared with the standard source signal to calculate the self-monitoring error. Figure 2 As shown, a closed-loop system of "transmit-receive-self-monitor" is formed to achieve high-precision measurement.

[0055] Time synchronization module: In order to provide on-site measurement data more timely and accurately, the acquisition device is based on wide-area synchronous phasor measurement technology to ensure the time consistency of the output information. The clock source is provided by the clock server or GPS clock synchronization device in the substation, strictly synchronizes the sampling clock of each monitoring node, and dynamically corrects the sampling interval according to the frequency fluctuation of the power grid system, provides the sampling pulse signal required for full-cycle sampling, converts the analog signal into a digital signal with time stamp information, and performs transmission and signal processing to achieve strict synchronization of the sampling signal, thereby ensuring the time correlation of the monitoring data.

[0056] Signal switching module: The signal switching module simultaneously collects standard voltage and current signals and transformer secondary side voltage and current signals, and outputs the mixed signals to the high-precision signal conditioning module for noise filtering and gain amplification processing.

[0057] High-precision signal conditioning module: Voltage and current sampling uses effective value digital detection technology based on the space vector method. The voltage measurement front end uses a high-precision 2ppm temperature coefficient resistor-capacitor voltage divider network for signal conditioning. The current measurement front end uses a current sensor based on a magnetic modulation comparator, which can measure DC and AC currents simultaneously.

[0058] The current sensor of the magnetic modulation comparator, also known as the magnetic balance current transformer, is also based on the principle of electromagnetic induction. The theoretical error of the zero flux current transformer is equal to zero, and there is no ratio error and angle difference. In order to eliminate the influence of the excitation current on the measurement accuracy, the zero flux current transformer uses a compensation winding specifically used to provide the excitation current. In this way, the measuring winding will not be affected by the excitation current, and there will be no ratio error and angle difference, thereby achieving high-precision measurement.

[0059] The principle circuit of the core-through current transformer is as follows Figure 3 As shown, Figure 4 This is its secondary equivalent circuit diagram.

[0060] I1 is the primary current of the current transformer, I2 is the secondary current, and I0 is the excitation current. N1 and N2 are the turns of the primary and secondary windings respectively. Therefore, the magnetic potential balance equation of the current transformer is:

[0061] I1N1+I2N2=-I0N1

[0062] When the excitation ampere-turns I0N1 is zero, I1N1 = -I2N2, that is, the change in the secondary ampere-turns can fully reflect the change in the primary ampere-turns, and the error is zero. I0N1 is generally called the absolute error, and I0N1 / I1N1 is the relative error. The error of the current transformer is a complex error, which can be expressed by the ratio difference f and the angle difference δ.

[0063] ε=-I0N1 / I1N1=f+jδ

[0064] Where:

[0065] f=(I2N2 / I1N1) / I1×100%

[0066] δ is the angle between I2 and I1 after rotating 180° counterclockwise, such as Figure 5 shown.

[0067] It can be seen that due to the existence of I0N1, there is an angle difference δ and a ratio difference f between I2N2 and I1N1. If I0=0, the excitation magnetic potential is 0 and the error is 0. The core of the magnetic potential is in a zero flux state, and it works in the starting section (linear section) of the magnetization curve. At this time, the output waveform of the current transformer will not be distorted and maintain a good linear section. This is the zero flux principle. Therefore, if the transformer core can always be in a zero flux state, the error of the current transformer can be fundamentally eliminated. However, from the working principle of the transformer, it is impossible to achieve zero flux by the transformer itself, and it must rely on compensation or adjustment of external conditions. For this reason, a dynamic balancing electronic circuit is used to dynamically adjust it so that the core is always in a dynamic zero flux state.

[0068] Figure 6 This is the original block diagram of the zero flux current sensor. D To detect the winding, D is a dynamic detection unit, and G is an active network that generates secondary current. The magnetic potential balance equation of this circuit is:

[0069] I1N1+I2N2+I D N D =-I0N1

[0070] The exciting magnetic flux generated by I1 is D The induced potential is generated at both ends and added to the input end of the dynamic detection unit D. The secondary current I2 is generated through G and provided to the secondary winding. The magnetic flux generated by I2 demagnetizes the iron core, making the iron core reach magnetic potential balance. Therefore, in an ideal state, the secondary winding current I2 of the sensor is fully supplied by the active network G, and no current is taken from the induced potential. D High-speed dynamic detection N D The potential difference between the two ends, when the potential difference is small enough (approximately zero allowable value), the magnetic flux in the core is approximately zero flux. If the detected value deviates from the allowable value, G will automatically adjust at high speed. Such high-speed tracking adjustment can keep the core close to zero flux at all times, and the sensor can achieve higher accuracy.

[0071] The error of zero flux current transformer includes three parts: capacitive error, magnetic error and sensitivity error of detection and adjustment electronic circuit. The so-called capacitive error refers to the measurement error caused by the capacitive leakage current between the coils on each side and the coils. For power frequency signals, when N2<1000, this error can be controlled within 105.

[0072] After the current sensor is adjusted dynamically at high speed, I0 approaches 0, the core approaches zero flux, and the magnetic error is very small. But in fact, the completely zero flux state cannot be achieved. There must be a little weak flux in the core to make G output I2, which makes the magnetic error still exist. From the current transformer magnetic potential balance equation, it can be seen that the magnetic error is mainly composed of two parts: one is the error caused by the participating magnetic potential brought by I0, and the other is the error caused by the detection winding I D The error caused by the additional magnetic potential is:

[0073]

[0074] Where: E D N D The induced potential, I is the length of the magnetic path, S is the cross-sectional area of ​​the core, μ0 is the initial magnetic permeability of the core, R i It is the input impedance of the detection unit. It can be seen that to reduce the magnetic error, one should choose an iron core with a higher μ0 value and a suitable number of detection winding turns. The sensor chooses an ultra-microcrystalline iron core with a μ0 of 6*104, N D The input impedance of the detection unit should be large, E D and I2 can be controlled within the required range through an active dynamic balancing network. In addition, highly conductive and highly magnetic materials are used for shielding to eliminate electromagnetic field interference.

[0075] The current is converted into a small current after passing through the magnetic modulation comparator, and then differentially sampled through the resistor R to output a small voltage to the programmable amplifier. The signal is amplified and then transmitted to the FPGA module through a 24-bit AD sampling chip and electrical isolation for digital processing, and then the current is measured. Figure 7 shown.

[0076] The accuracy of the magnetic modulation comparator determines the accuracy of current measurement, and its errors mainly include magnetic error and capacitive error. There are a large number of electromagnetic interference sources inside the accuracy detection device of the smart energy meter, which is a great challenge to the comparator's ability to resist electromagnetic interference. Double-layer shielding technology is adopted, with the main shielding using high magnetic flux density material (FeSiAl) and the inner shielding using high magnetic permeability material (Permalloy) for shielding. First, install the comparator core in a Permalloy shielding box for separate shielding, and then place the inner shielding box and the test circuit in the main shielding box, such as Figure 8 As shown in the figure, capacitive error is caused by the parasitic capacitance and potential difference between winding turns, between windings, and between windings and ground, which causes corresponding leakage current between them. Electrostatic shielding can be used to reduce the error.

[0077] High-speed AD acquisition module: The device uses a 24-bit high-speed AD sampling chip. Because it can read very small signal changes, it has higher accuracy and resolution than conventional 12-bit or 16-bit ADCs; it has a high-speed sampling rate and can read rapidly changing signals; it has a low distortion rate and can keep the signal accurate and clear.

[0078] FPGA module: FPGA module has the advantages of high speed, programmability, parallel processing, etc., and is very suitable for high-speed data acquisition systems. This device selects FPGA module as the control center of the system, which is responsible for controlling the sampling timing, data storage, data transmission and other tasks of ADC. The FPGA module strictly synchronizes the acquisition of voltage and current signals in combination with GPS timing, and converts the analog signal into a digital signal with time stamp information. The high-precision GPS clock and data processing algorithm ensure the accuracy and precision of the timestamp. The timestamp records the specific time point when the state change occurs, allowing the system to accurately track and record the change history of the signal. Digital signals with time stamp information (real-time recording and analysis of parameters such as voltage and current) can be used to monitor the operating status of the power grid in real time, providing reliable data for fault location and analysis.

[0079] FPGA+SDRAM high-speed real-time data acquisition: FPGA realizes real-time processing and transmission of high-speed data. Based on the high speed and large capacity of SDRAM, it realizes the caching and storage of large amounts of data. FPGA+SDRAM realizes high-speed real-time data acquisition. As the core controller of data acquisition, FPGA is responsible for receiving high-speed data from the analog front end and writing it to SDRAM. At the same time, FPGA also realizes data preprocessing and real-time analysis functions, and stores the processing results in SDRAM again, and then reads them out from SDRAM and sends them out through UART when the CPU needs them.

[0080] CPU module: The core processing CPU module performs high-precision power calibration, high-precision power quality monitoring, fault diagnosis, etc., providing support and guarantee for building a metering business system for the new power system.

[0081] This embodiment is a device that can communicate bidirectionally with user power loads, distributed energy systems, etc., and has intelligent functions such as second-level data collection, multi-time scale regulation, flexible and precise control, flexible load management, user-friendly interaction, and local frequency autonomy. It can meet the edge business needs of new load management systems.

[0082] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.

Claims

1. Virtual power plant aggregated resources high robustness data high precision high frequency acquisition system, characterized by: The invention comprises a local communication module (1), a CPU module (2), an FPGA module (3), a high-speed AD acquisition module (4), a high-precision signal conditioning module (5), a signal switching module (6) and a self-calibration module (7); the local communication module (1) performs data exchange with a plant station via RS485; the CPU module (2) is electrically connected to the local communication module (1); the FPGA module (3) is electrically connected to the CPU module (2); the high-speed AD acquisition module (4) is electrically connected to the FPGA module (3); the high-precision signal conditioning module (5) is electrically connected to the high-speed AD acquisition module (4); the signal switching module (6) is electrically connected to the high-precision signal conditioning module (5); the self-calibration module (7) is electrically connected to the signal switching module (6); and the signal switching module (6) is electrically connected to a standard voltage and current signal and a transformer secondary side voltage and current signal.

2. According to claim 1, the high-robustness data, high-precision and high-frequency acquisition system for aggregated resources of virtual power plants is characterized by: The FPGA module (3) is electrically connected to a time synchronization module (8), and the time synchronization module (8) is electrically connected to a clock server or a GPS clock synchronization device in a substation via a B code.

3. The high-robustness, high-precision, high-frequency data acquisition system for virtual power plant aggregated resources according to claim 1 is characterized by: The FPGA module (3) is electrically connected to an SDRAM synchronous dynamic random access memory (9).

4. The high-robustness, high-precision, high-frequency data acquisition system for virtual power plant aggregated resources according to claim 1 is characterized by: The high-precision signal conditioning module (5) comprises a magnetic modulation comparator (501), an A / D converter (502) and an electrical isolation (503); the magnetic modulation comparator (501) is electrically connected to the A / D converter (502); the A / D converter (502) is electrically connected to the electrical isolation (503); the magnetic modulation comparator (501) is electrically connected to a signal switching module (6); and the electrical isolation (503) is electrically connected to an FPGA module (3) via a high-speed AD acquisition module (4).

5. The high-robustness, high-precision, high-frequency data acquisition system for virtual power plant aggregated resources according to claim 4 is characterized by: The magnetic modulation comparator (501) comprises a current sensor (5011), a differential sampling (5012) and a programmable amplifier (5013); the current sensor (5011) is electrically connected to the differential sampling (5012); the differential sampling (5012) is electrically connected to the programmable amplifier (5013); the programmable amplifier (5013) is electrically connected to an A / D converter (502); and the current sensor (5011) is electrically connected to a signal switching module (6).

6. The high-robustness, high-precision, high-frequency data acquisition system for virtual power plant aggregated resources according to claim 1 is characterized by: The high-speed AD acquisition module (4) adopts a 24-bit high-speed AD sampling chip.

7. The acquisition method of the high-robustness data, high-precision and high-frequency acquisition system of the virtual power plant aggregated resources according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. The local communication module realizes data interaction with the plant station through RS485. The collected data includes power data, load data, clock data and events. According to the requirements of different communication equipment, the corresponding collection frequency is set, and all data collection reaches minute level; S2. A standard source is built into the metering chip, and the excitation signal for self-monitoring is transmitted to the entrance of the meter sampling circuit. The excitation signal and the load signal are sent to the ADC in the chip through the sensor and the peripheral circuit. The load signal is used to calculate the normal amount of electricity. The excitation signal is extracted and calculated by a special algorithm and then compared with the standard source signal to calculate the self-monitoring error. S3, the clock source is provided by the clock server or GPS clock synchronization device in the substation, the sampling clocks of each monitoring node are strictly synchronized, and the sampling interval is dynamically corrected according to the frequency fluctuation of the power grid system, the sampling pulse signal required for the whole cycle sampling is provided, the analog signal is converted into a digital signal with time stamp information, and the transmission and signal processing are performed to achieve strict synchronization of the sampling signal, thereby ensuring the time correlation of the monitoring data; S4, the signal switching module simultaneously collects the standard voltage and current signals and the transformer secondary side voltage and current signals, and outputs the mixed signals to the high-precision signal conditioning module for noise filtering and gain amplification processing; S5. The voltage measurement front end uses a high-precision 2ppm temperature coefficient resistor-capacitor voltage divider network for signal conditioning, and the current measurement front end uses a current sensor based on a magnetic modulation comparator; Based on the principle of electromagnetic induction, the theoretical error of the zero flux current transformer is equal to zero, and there is no ratio difference and angle difference. A compensation winding is used to provide the excitation current, so that the measuring winding will not be affected by the excitation current, and there is no ratio difference and angle difference, thereby achieving high-precision measurement; S6, FPGA combines GPS timing to strictly synchronize the acquisition of voltage and current signals, and converts analog signals into digital signals with time stamp information; high-precision GPS clock and data processing algorithms ensure the accuracy and precision of timestamps; timestamps record the specific time point when the state change occurs, allowing the system to accurately track and record the change history of the signal; digital signals with time stamp information are used to monitor the operating status of the power grid in real time, providing reliable data for fault location and analysis; S7, FPGA module as the core controller of data acquisition, is responsible for receiving high-speed data from the analog front end and writing it into the SDRAM synchronous dynamic random access memory; at the same time, the FPGA module also implements data preprocessing and real-time analysis functions, and stores the processing results in the SDRAM synchronous dynamic random access memory again, and then reads them out from the SDRAM synchronous dynamic random access memory when the CPU module needs them and sends them out through UART.

8. The acquisition method of the high-robustness data, high-precision and high-frequency acquisition system of virtual power plant aggregated resources according to claim 7 is characterized in that: The method for high-precision measurement in S5 is: The magnetic potential balance equation of the current sensor of the magnetic modulation comparator is: I1N1+I2N2=-I0N1 Among them: I1 is the primary current of the current transformer, I2 is the secondary current, I0 is the excitation current; N1 and N2 are the turns of the primary and secondary windings respectively; When the excitation ampere-turns I0N1 is zero, I1N1 = -I2N2, that is, the change in the ampere-turns of the secondary side can fully reflect the change in the ampere-turns of the primary side, and the error is zero; I0N1 is the absolute error, and I0N1 / I1N1 is the relative error; the error of the current transformer of the magnetic modulation comparator is a complex error, which is expressed by the ratio difference f and the angle difference δ; ε=-I0N1 / I1N1=f+jδ Where: f = (I2N2 / I1N1) / I1×100%, δ is the angle between I2 and I1 after rotating 180° counterclockwise; Due to the existence of I0N1, there is an angle difference δ and a ratio difference f between I2N2 and I1N1; if I0=0, the excitation magnetic potential is 0 and the error is 0; a dynamic balance electronic circuit is used to dynamically adjust it so that the core is always in a dynamic zero flux state; the magnetic potential balance equation of the zero flux current sensor is: I1N1+I2N2+I D N D =-I0N1 Where: N D To detect the winding, I D To detect winding N D Side current; The exciting magnetic flux generated by I1 is detected in the winding N D An induced potential is generated at both ends and added to the input end of the dynamic detection unit D. The secondary current I2 is generated through the active network G that generates the secondary current and is provided to the secondary winding. The magnetic flux generated by I2 demagnetizes the iron core, so that the iron core reaches magnetic potential balance. From the current transformer magnetic potential balance equation, it can be seen that the magnetic error consists of two parts: one is the error caused by the participating magnetic potential brought by I0, and the other is the error caused by the detection winding N D Side current I D The error caused by the additional magnetic potential is: Where: E D N D The induced potential, I is the length of the magnetic path, S is the cross-sectional area of ​​the core, μ0 is the initial magnetic permeability of the core, R i is the input impedance of the detection unit; it can be seen that to reduce the magnetic error, one is to select a core with a higher μ0 value and a suitable number of detection winding turns. The sensor selects an ultra-microcrystalline core with a μ0 of 6*104, N D The input impedance of the detection unit should be large, E D and I2 are controlled within the required range through an active dynamic balancing network; The current is converted into a small current after passing through the magnetic modulation comparator, and is differentially sampled through the resistor R to output a small voltage to the programmable amplifier. The signal is amplified and then transmitted to the FPGA module for digital processing through a 24-bit AD sampling chip and electrical isolation to measure the current.

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