Calibration method based on PCS parameter characterization

Abstract

The invention discloses a calibration method based on PCS parameter representation. The calibration method comprises the following steps of calibration network construction, data preprocessing, model training, data generation and calibration processing. The calibration network is composed of a detection interface, a preprocessing unit, a model generator and a calibration unit; the method comprises the following steps: taking electrical quantity data acquired by an electric meter as a training sample set, taking electrical quantity data acquired by a PCS as a tested sample set, and respectively marking the training sample set and the tested sample set with uniquely determined labels; respectively training the training sample set and the tested sample set to obtain a calibration target value of the ammeter and an actual measurement value of the PCS; and the calibration unit calculates the calibration coefficient and the offset of the PCS based on the calibration target value of the electric meter corresponding to each label, and calibrates the actual measurement value of the corresponding PCS according to the calculated offset. Compared with a manual calibration method, the calibration method has the advantages that the labor input cost and the experiment cost are reduced to a great extent, and the calibration efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronic equipment, and in particular to a calibration method based on PCS parameter characterization. Background Art

[0002] The Power Storage Converter (PCS) is an essential component of energy storage systems and power conversion. The bidirectional conversion between energy storage systems and grid power is achieved by controlling the charging and discharging processes of the energy storage system, performing AC / DC conversion. This allows the system to invert the DC power of the energy storage system into AC power for transmission to the grid or AC loads, or rectify the AC power from the grid into DC power to charge the energy storage system. Furthermore, when connected to the grid, the energy storage system can implement constant power or constant current control based on microgrid monitoring instructions to charge or discharge the battery, while also smoothing the output of fluctuating power sources such as wind and solar power. In microgrids, the use of SVPWM or SPWM and closed-loop control enables precise and rapid regulation of output voltage, frequency, and active and reactive power. With the continuous advancement and development of installed system capacity and national energy policies, the updating and development of PCSs has also necessitated greater adjustments.

[0003] However, for PCS, since multiple PCSs may be used in series and parallel, in order to ensure that the PCS parameters can meet the usage requirements under any working conditions before leaving the factory, each PCS parameter needs to be accurately calibrated; the previous PCS calibration method requires manual measurement and comparison using experimental instruments before calibration, which not only requires a certain amount of labor costs, but also increases experimental costs, and the calibration efficiency is low. Summary of the Invention

[0004] In view of this, an object of the present invention is to provide a calibration method based on PCS parameter characterization to solve the technical problems mentioned in the prior art.

[0005] A calibration method based on PCS parameter characterization, the calibration method comprising the following steps:

[0006] S1. Constructing a calibration network: The calibration network consists of a detection interface, a preprocessing unit, a model generator, and a calibration unit. The data acquisition terminals of the electric meter and the PCS are respectively connected to the circuit under test to collect electrical quantity data in real time. The detection interface is connected to the communication interface terminals of the electric meter and the PCS via a communication bus to read the electrical quantity data collected by the electric meter and the PCS.

[0007] S2. Data preprocessing: The preprocessing unit selects the electrical quantity data collected by the electric meter in any detection cycle as a training sample set, and the electrical quantity data collected by the PCS as a measured sample set, and marks the training sample set and the measured sample set with unique labels respectively;

[0008] S3, model training and data generation: inputting the training sample set into the model generator for training to obtain the calibration target value of the electric meter; inputting the measured sample set into the model generator for training to obtain the actual measurement value of the PCS;

[0009] S4, calibration processing: the calibration unit calculates the calibration coefficient and bias of the PCS based on the calibration target value of the electric meter corresponding to each tag, and calibrates the actual measurement value of the corresponding PCS according to the calculated bias, wherein;

[0010]

[0011] Bias = meter calibration target value - (PCS actual measurement value × calibration coefficient).

[0012] Optionally, the pre-processing unit marks the training sample set or the tested sample set with a unique and determined label according to the detection path;

[0013] The label information of the detection path is set to any one or more of the voltage and / or current of the A-phase AC bus, the voltage and / or current of the B-phase AC bus, the voltage and / or current of the C-phase AC bus, and the voltage and / or current of the DC bus.

[0014] Optionally, the pre-processing unit removes the electrical quantity data collected by the electric meter that exceeds a preset detection threshold range, and uses the electrical quantity data within the preset detection threshold range as the training sample set.

[0015] Optionally, the method for setting the preset detection threshold range includes:

[0016] Based on the detection path of the electric meter corresponding to each tag, the average value of all electrical quantity data collected during the detection period is calculated, and the set error allowance is added to the average value to generate the preset detection threshold range of the corresponding tag.

[0017] Optionally, the model generator is provided with an input layer, a hidden layer, a summation layer and an output layer that are connected to each other in sequence along the direction of the data flow;

[0018] The input layer is used to identify label information of sample data;

[0019] The hidden layer is configured with a first neuron corresponding to each of the labels, and the first neuron is used to capture and store the sample data marked with the corresponding label;

[0020] The summation layer is configured with a second neuron for any number of the first neurons, and the second neuron is provided with a summation node, and the summation node is used to synchronously sum all the sample data stored in the plurality of mutually related first neurons to obtain an average value;

[0021] The output layer outputs the calculated average value as an output result.

[0022] Optionally, before calculating the calibration coefficient and offset of the PCS, the calibration unit further includes:

[0023] The actual measurement value of the PCS is classified into the calibration target value category of the electric meter that is closest to it, and then the calibration coefficient and bias of the PCS are calculated based on the calibration target value of the electric meter that is closest to it.

[0024] The beneficial effects that the present invention can produce include:

[0025] The calibration method based on PCS parameter characterization provided by the present invention constructs a calibration network to automatically collect electrical quantity data from the electricity meter and PCS in real time, and preprocesses the electrical quantity data collected by the electricity meter to eliminate data that exceeds the preset detection threshold range, thereby ensuring the quality of the training sample set and improving the accuracy of subsequent model training and calibration; and synchronously processes different label data through a model generator. Compared with the manual calibration method, this method greatly reduces the labor input cost and experimental cost, and significantly improves the calibration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the process of the calibration method based on PCS parameter characterization of the present invention;

[0027] Figure 2 Schematic diagram of the architecture of the model generator in the calibration method of the present invention;

[0028] Figure 3 Schematic diagram of the circuit connection of the circuit to be tested in the calibration method of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 The present invention provides a calibration method based on PCS parameter characterization, the calibration method comprising the following steps:

[0031] Step 1: Construct a calibration network: The calibration network consists of a detection interface, a preprocessing unit, a model generator, and a calibration unit; Figure 3 As shown, the data acquisition terminals of the electric meter and PCS are respectively connected to the designated key node positions of the circuit to be tested, so as to realize the real-time and accurate collection of various dynamic electrical quantity data in the circuit to be tested. Among them, the electric meter can use an AD conversion chip to collect electrical quantity data, and the PCS can use an analog-to-digital converter (ADC) to collect electrical quantity data; and the communication interface terminals of the electric meter and PCS (such as RS485, RS232, Ethernet, etc.) are respectively connected to the detection interface through the communication bus, so as to realize the reading of the electrical quantity data collected by the electric meter and PCS, providing effective data support for subsequent data processing and analysis.

[0032] Step 2, data preprocessing: The preprocessing unit selects the electrical quantity data collected by the meter in any detection cycle as the training sample set, and the electrical quantity data collected by the PCS as the measured sample set, and marks the training sample set and the measured sample set with uniquely determined labels respectively. The labels are respectively marked according to the detection path, and the label information of the detection path is specifically set to the voltage and / or current of the A-phase AC bus, the voltage and / or current of the B-phase AC bus, the voltage and / or current of the C-phase AC bus, and the voltage and / or current of the DC bus. In addition, the preprocessing unit will eliminate the electrical quantity data collected by the meter that exceeds the preset detection threshold range, and only include the electrical quantity data within the preset detection threshold range into the training sample set. The detection threshold range is set as follows: for the detection path of the meter corresponding to each label, the average value of all electrical quantity data collected during the detection cycle is calculated, and then the set error tolerance value is added to the average value to generate the detection threshold range of the corresponding label.

[0033] Step 3, model training and data generation: the training sample set is input into the model generator for training, and finally the calibration target value of the electric meter is obtained; the measured sample set is input into the model generator for training, and the actual measurement value of the PCS is obtained. Specifically, the model generator is provided with an input layer, a hidden layer, a summation layer and an output layer that are interconnected in sequence according to the direction of the data flow. Among them, the input layer is responsible for identifying the label information of the sample data; the hidden layer is configured with a first neuron for each label, and the first neuron is used to capture and store the sample data marked with the corresponding label; the summation layer is configured with a second neuron for any number of first neurons, and the second neuron is provided with a summation node, which can synchronously sum all the sample data stored in the several first neurons that are related to each other to obtain an average value; the output layer outputs the calculated average value as the output result, and the output result is specifically the calibration target value of the electric meter or the actual measurement value of the PCS.

[0034] Step 4: Calibration: Before calculating the calibration coefficient and offset of a PCS, the calibration unit must first classify the actual measurement value of the PCS into the calibration target value category of the closest meter. Based on the calibration target value of the closest meter, the calibration coefficient and offset of the PCS are calculated, with the calibration coefficient rounded to three decimal places. The actual measurement value of the corresponding PCS is then calibrated based on the calculated offset.

[0035]

[0036] Bias = meter calibration target value - (PCS actual measurement value × calibration coefficient).

[0037] For example, 670-685V is used as the training label 1 of the meter; 705V-730V is used as the training label 2 of the meter; if the actual measured voltage value collected by the PCS is 700V, which is closer to the training label 2, it will be classified into the training label 2. Furthermore, if the real-time calibration target voltage collected by the meter corresponding to the training label 2 is 705V, then,

[0038]

[0039] Bias = 705V - (700V × 1.007) = 0.001V;

[0040] Therefore, the calibration unit increases the actual measurement value of PCS by 0.001V, and the final electrical quantity data of PCS is 700.001V.

[0041] In the above, when the meter and PCS are connected to the circuit to be tested, the line connection test is first performed on the meter and PCS respectively, where:

[0042] 1) Perform a functional test on the meter: Use a standard signal source to input known standard voltage and current signals into the meter to simulate the electrical parameters in an actual circuit. Observe the meter's display to confirm that it accurately displays the input voltage and current values and that the error is within the meter's accuracy range. Also, test the meter's various measurement functions, such as active power, reactive power, and power factor, to verify the accuracy of the results. For example, by inputting a test signal for a specific power factor and comparing the displayed power factor value with the standard value, the reliability of the meter for that function can be determined.

[0043] 2) Functional testing of PCS: Targeted testing is performed on the core functions of PCS, such as power conversion and data acquisition. In terms of power conversion function testing, the PCS is connected to a simulated power supply to observe whether it can stably convert the input power into the required output form according to the set conversion mode, such as converting DC power into AC power, and whether the output power quality meets the relevant standards, including indicators such as voltage fluctuation and frequency deviation within the allowable range. For the data acquisition function of the PCS, the self-test program set inside it is used to trigger the self-test program of the PCS when the PCS is turned on for the first time or connected to the circuit to be tested to collect simulated electrical quantity data, and check the accuracy and completeness of the data collection to ensure that it can accurately collect various electrical parameters and there is no data loss or erroneous collection.

[0044] Exemplarily, part of the self-test procedure of the PCS is as follows:

[0045]

[0046]

[0047]

[0048] 3) Verify compatibility between the meter and the PCS: Given the complexities of multiple electrical devices operating in conjunction within the circuit under test, the compatibility of the PCS and meter with other devices must be verified. Connect the PCS and meter to a simulated circuit environment containing some common devices found in the circuit under test, and observe whether they interfere with each other when operating alongside other devices. For example, check whether the meter's measurement accuracy is affected by electromagnetic radiation from the PCS, or whether the PCS's operating status fluctuates abnormally due to the meter's connection. Ensure that they can operate harmoniously and stably with other devices in the actual application environment.

[0049] In the present invention, the operation and maintenance of traditional power systems rely on a large number of manual inspections and experience-based judgments, which are costly and have limited accuracy. This calibration method achieves real-time monitoring of the operating status of power equipment and fault warnings through continuous and accurate electrical quantity monitoring and calibration. For example, through long-term calibration and analysis of the PCS data of the substation, potential fault hazards such as overheating and insulation aging of the transformer can be discovered in advance, and the operation and maintenance personnel can arrange maintenance in advance to avoid high maintenance costs and large-scale power outages caused by sudden equipment failures. According to actual case statistics, line failures occur frequently during the operation of smart grids. This calibration method, relying on its real-time acquisition of electrical quantity data, can quickly capture abnormal fluctuations in electrical quantities such as current and voltage at the moment of the fault. For example, when a line short circuit fault occurs due to a lightning strike in a certain area of the power grid, the data collected in real time by the meter and PCS is quickly analyzed by the calibration network to accurately locate the A-phase AC bus or other specific line location where the fault is located. Based on the precise calibrated data, operation and maintenance personnel can quickly determine the type and severity of the fault, significantly shortening the troubleshooting time from several hours to less than half an hour. This greatly improves the efficiency of power grid repair, reduces the economic losses and social impact caused by power outages, and reduces the overall operation and maintenance costs of the power system by 20%-30%, significantly improving the economic benefits of power companies.

Claims

1. A calibration method based on PCS parameter characterization, characterized in that: The calibration method comprises the following steps: S1. Constructing a calibration network: The calibration network consists of a detection interface, a preprocessing unit, a model generator, and a calibration unit. The data acquisition terminals of the electric meter and the PCS are respectively connected to the circuit under test to collect electrical quantity data in real time. The detection interface is connected to the communication interface terminals of the electric meter and the PCS via a communication bus to read the electrical quantity data collected by the electric meter and the PCS. S2. Data preprocessing: The preprocessing unit selects the electrical quantity data collected by the electric meter in any detection cycle as a training sample set, and the electrical quantity data collected by the PCS as a measured sample set, and marks the training sample set and the measured sample set with unique labels respectively; S3, model training and data generation: inputting the training sample set into the model generator for training to obtain the calibration target value of the electric meter; Inputting the measured sample set into the model generator for training to obtain the actual measurement value of the PCS; S4, calibration processing: the calibration unit calculates the calibration coefficient and bias of the PCS based on the calibration target value of the electric meter corresponding to each tag, and calibrates the actual measurement value of the corresponding PCS according to the calculated bias, wherein; Bias = meter calibration target value - (PCS actual measurement value × calibration coefficient).

2. The calibration method based on PCS parameter characterization according to claim 1, characterized in that: The pre-processing unit marks the training sample set or the tested sample set with a unique and determined label according to the detection path; The label information of the detection path is set to any one or more of the voltage and / or current of the A-phase AC bus, the voltage and / or current of the B-phase AC bus, the voltage and / or current of the C-phase AC bus, and the voltage and / or current of the DC bus.

3. The calibration method based on PCS parameter characterization according to claim 2, characterized in that: The pre-processing unit removes the electrical quantity data collected by the electric meter that exceeds a preset detection threshold range, and uses the electrical quantity data within the preset detection threshold range as the training sample set.

4. The calibration method based on PCS parameter characterization according to claim 3, characterized in that: The method for setting the preset detection threshold range includes: Based on the detection path of the electric meter corresponding to each tag, the average value of all electrical quantity data collected during the detection period is calculated, and the set error allowance is added to the average value to generate the preset detection threshold range of the corresponding tag.

5. The calibration method based on PCS parameter characterization according to claim 1, characterized in that: The model generator is provided with an input layer, a hidden layer, a summation layer and an output layer connected to each other in sequence along the direction of the data flow; The input layer is used to identify label information of sample data; The hidden layer is configured with a first neuron corresponding to each of the labels, and the first neuron is used to capture and store the sample data marked with the corresponding label; The summation layer is configured with a second neuron for any number of the first neurons, and the second neuron is provided with a summation node, and the summation node is used to synchronously sum all the sample data stored in the plurality of mutually related first neurons to obtain an average value; The output layer outputs the calculated average value as an output result.

6. The calibration method based on PCS parameter characterization according to claim 1, characterized in that: Before calculating the calibration coefficient and offset of the PCS, the calibration unit further includes: The actual measurement value of the PCS is classified into the calibration target value category of the electric meter that is closest to it, and then the calibration coefficient and bias of the PCS are calculated based on the calibration target value of the electric meter that is closest to it.

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