Parameter intelligent analysis and processing system for energy storage battery and inverter

Abstract

The application discloses a kind of parameter intelligent analysis processing systems for energy storage battery and inverter, it is related to inverter power supply technical field, including state monitoring module, waveform diagnosis module, fault parameter diagnosis module and execution module;The voltage, current, power and frequency data are obtained in real time by the intelligent electric meter arranged in the energy storage battery and inverter circuit of state monitoring module, the temperature data of energy storage battery are obtained in real time by temperature sensor and data are visualized processing, data are extracted in the image after visualized processing by waveform diagnosis module and generate judging index.The data of state monitoring module and waveform diagnosis module are comprehensively analyzed by fault parameter diagnosis module, and the intelligent parameter analysis of energy storage battery and inverter is carried out to generate fault signal;The application provides an intelligent analysis processing system for the problem of higher debugging and maintenance cost in the hardware reliability to be improved in inverter power supply technology.

Description

Technical Field

[0001] This invention relates to the field of inverter power supply technology, specifically to an intelligent parameter analysis and processing system for energy storage batteries and inverters. Background Technology

[0002] Inverter power supply technology is a technology that converts direct current (DC) into alternating current (AC). It mainly uses electronic components (such as thyristor circuits) to convert the input DC power into high-frequency AC power through a high-frequency transformer, and then through rectification, filtering, voltage regulation and other stages to finally obtain a stable pure sine wave output AC power.

[0003] Inverter technology plays a crucial role in power systems, particularly in power conversion and supply. For example, static auxiliary inverters are increasingly used in subway vehicles, providing a stable and reliable power supply. Furthermore, inverters are key players in new energy fields, such as solar power systems, converting direct current (DC) generated by solar panels into alternating current (AC) to power homes and businesses. With the continuous development of power electronic devices and the improvement of control technology, inverter technology is evolving towards higher frequencies, higher efficiency, higher power density, higher reliability, and higher intelligence. Inverter efficiency will continue to increase, and its size will decrease. Simultaneously, with the continuous development of microelectronics and computer technology, inverters will become more integrated and intelligent, enabling remote monitoring and automatic control. These technological advancements not only improve the performance and quality of inverters but also provide new development opportunities for the inverter industry.

[0004] However, inverter power supply technology also has some shortcomings and challenges in practical applications, such as the need to improve hardware reliability and high debugging and maintenance costs. Summary of the Invention

[0005] To overcome the problems in the background art, embodiments of the present invention provide an intelligent parameter analysis and processing system for energy storage batteries and inverters.

[0006] The objective of this invention can be achieved through the following technical solution: an intelligent parameter analysis and processing system for energy storage batteries and inverters, comprising a status monitoring module, a waveform diagnosis module, a fault parameter diagnosis module, and an execution module.

[0007] The status monitoring module acquires voltage, current, power, and frequency data in real time through smart meters arranged in the energy storage battery and inverter circuit, and acquires temperature data of the energy storage battery in real time through temperature sensors.

[0008] The status monitoring module records the capacitor current (ic) at preset time intervals using a smart meter. The recorded data is organized into a two-dimensional vector (t, ic). A two-dimensional rectangular coordinate system is established with time (t) as the horizontal axis and capacitor current (ic) as the vertical axis. All points (t, ic) are filled into the coordinate system, and a smooth curve connects all points to generate a curve showing the capacitor current changing over time. A Fast Fourier Transform (FFT) is then used to transform the curve from the time domain to the frequency domain, obtaining the capacitor's current frequency spectrum.

[0009] The status monitoring module records the temperature T of the energy storage battery at preset time intervals using a temperature sensor. The recorded data is organized into a two-dimensional vector (t, T). A two-dimensional Cartesian coordinate system is established with time t as the horizontal axis and temperature T as the vertical axis. All points (t, T) are filled into the coordinate system, and a smooth curve is used to connect all points to generate a curve showing the temperature change of the energy storage battery over time. The smart meter records the current I, voltage U, and power P of the energy storage battery's output circuit at preset time intervals t; the recorded data is organized into two-dimensional vectors (t, I), (t, U), and (t, P). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and current I as the vertical axis. All points (t, I) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output current changing with time. A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and voltage U as the vertical axis. All points (t, U) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output voltage changing with time. A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and power P as the vertical axis. All points (t, P) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output power changing with time. The curves of the energy storage battery output current changing with time, the curves of the energy storage battery output voltage changing with time, and the curves of the energy storage battery output power changing with time are transformed from the time domain to the frequency domain using Fast Fourier Transform to obtain the energy storage battery current spectrum, energy storage battery voltage spectrum, and energy storage battery power spectrum.

[0010] The status monitoring module acquires current, voltage, power, and frequency data from the inverter output circuit through smart meters deployed on the inverter output circuit. The smart meters record the current I, voltage U, power P, and frequency f of the inverter output circuit every preset time interval t. The recorded data are then organized into two-dimensional vectors (t, I), (t, U), (t, P), and (t, f). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and current I as the vertical axis. All points (t, I) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output current changing with time. Similarly, a two-dimensional rectangular coordinate system is established with time t as the horizontal axis and voltage U as the vertical axis. All points (t, U) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output voltage changing with time. A two-dimensional rectangular coordinate system is also established with time t as the horizontal axis and power P as the vertical axis. All points (t, P) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output power changing with time. Finally, a two-dimensional rectangular coordinate system is established with time t as the horizontal axis and frequency f as the vertical axis. A coordinate system is established, and all points (t, f) are filled into the coordinate system. Then, a smooth curve is used to connect all the points to generate a curve showing the inverter output frequency changing with time. The curves showing the inverter output current changing with time, the inverter output voltage changing with time, and the inverter output power changing with time are transformed from the time domain to the frequency domain using a fast Fourier transform, resulting in the inverter current spectrum, inverter voltage spectrum, and inverter power spectrum. The curves showing the energy storage battery temperature changing with time, the inverter output frequency changing with time, the energy storage battery current spectrum, the energy storage battery voltage spectrum, the energy storage battery power spectrum, the inverter current spectrum, the inverter voltage spectrum, and the inverter power spectrum are then sent to the waveform diagnostic module.

[0011] In a preferred embodiment of the present invention, the waveform diagnostic module extracts the area exceeding a preset temperature from the temperature-time curve of the energy storage battery, denoted as the high temperature index e1, and extracts the maximum slope of the curve from the temperature-time curve, denoted as the temperature change index e2, and then uses the formula... The abnormal temperature parameter E1 of the energy storage battery is obtained, where k1 and k2 are preset weighting factors. The maximum value fMAX and minimum value fMIN are extracted from the curve of the inverter output frequency versus time, and then calculated using the formula... The inverter frequency anomaly parameter E2 is obtained, where It is the preset standard frequency.

[0012] The waveform diagnostic module defines each peak in the energy storage battery current spectrum, energy storage battery voltage spectrum, energy storage battery power spectrum, inverter current spectrum, inverter voltage spectrum, and inverter power spectrum as a frequency peak. The highest point of each frequency peak is called the peak frequency, and the frequency corresponding to the peak frequency is called the peak frequency. Each frequency peak in the energy storage battery current spectrum is numbered using i1, i1 = 1, 2, 3...n1; where n1 is the total number of frequency peaks in the energy storage battery current spectrum. The module then extracts the frequency peak number i1 and the corresponding peak frequency for each frequency peak in the energy storage battery current spectrum. and peak frequency Through the formula Calculate the abnormal current parameter E3i of the energy storage battery, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the energy storage battery voltage spectrum is numbered using i2, i2=1,2,3...n2; where n2 is the total number of frequency peaks in the energy storage battery current spectrum. The frequency peak number i2 and the corresponding peak value are extracted from each frequency peak in the energy storage battery voltage spectrum. and peak frequency Through the formula Calculate the abnormal voltage parameter E3u of the energy storage battery, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the power spectrum of the energy storage battery is numbered using i3, i3=1,2,3...n3; where n3 is the total number of frequency peaks in the current spectrum of the energy storage battery. The frequency peak number i3 and the corresponding peak value are extracted from each frequency peak in the voltage spectrum of the energy storage battery. and peak frequency Through formula Calculate the power anomaly parameter E3p of the energy storage battery, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The abnormal parameter E3 of the energy storage battery was obtained, where , and These are preset weighting factors;

[0013] Each frequency peak in the inverter current spectrum is numbered using i4, where i4 = 1, 2, 3...n4; and n4 is the total number of frequency peaks in the inverter current spectrum. The peak number i4 and the corresponding peak value are then extracted from each frequency peak in the inverter current spectrum. and peak frequency Through the formula Calculate the inverter current anomaly parameter E4i, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the inverter voltage spectrum is numbered using i5, i5=1,2,3...n5; where n5 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i5 and the corresponding peak value are extracted from each frequency peak in the inverter voltage spectrum. and peak frequency Through the formula Calculate the inverter voltage anomaly parameter E4u, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the inverter power spectrum is numbered using i6, i6=1,2,3...n6; where n6 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i6 and the corresponding peak value are extracted from each frequency peak in the inverter voltage spectrum. and peak frequency Through formula Calculate the inverter power anomaly parameter E4p, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The inverter abnormal parameter E4 is obtained, where , and This is a preset weighting factor. When the abnormal temperature parameter E1 of the energy storage battery is greater than the preset threshold or the abnormal parameter E3 of the energy storage battery is greater than the preset threshold, a diagnostic signal for the energy storage battery is output; when the abnormal frequency parameter E2 of the inverter is greater than the preset threshold or the abnormal parameter E4 of the inverter is greater than the preset threshold, a diagnostic signal for the inverter is output.

[0014] In a preferred embodiment of the present invention, when the fault parameter diagnosis module receives the output energy storage battery diagnosis signal, it begins energy storage battery diagnosis; when the fault parameter diagnosis module receives the inverter diagnosis signal, it begins inverter diagnosis. The specific process of energy storage battery diagnosis is as follows: The battery diagnosis device is turned on to perform capacity detection on the energy storage battery. When the battery capacity is lower than a preset threshold, fault signal one is generated; the impedance detection device is turned on to obtain the impedance of the energy storage battery. When the impedance is greater than a preset threshold, fault signal two is generated; the value of the energy storage battery temperature anomaly parameter E1 is obtained from the waveform diagnosis module. When the energy storage battery temperature anomaly parameter is greater than a preset threshold, fault signal three is generated; the energy storage battery anomaly parameter E3 is obtained from the waveform diagnosis module. When the value of the energy storage battery anomaly parameter E3 is greater than a preset threshold, fault signal four is generated.

[0015] The fault parameter diagnosis module categorizes bridge circuit faults into four main categories and fifteen subcategories: 1. Single-transistor faults: S1, S2, S3, S4; 2. Two-transistor faults: S1+S2, S1+S3, S1+S4, S2+S3, S2+S4, S3+S4; 3. Three-transistor faults: S1+S2+S3, S1+S2+S4, S1+S3+S4, S2+S3+S4; 4. Four-transistor faults: S1+S2+S3+S4. Filter faults are categorized into three main categories and seven subcategories: 1. Unit faults: L1, L2, C; 2. Two-element faults: L1+L2, L2+C, L1+C; 3. Three-element faults: L1+L1+C. Each fault subcategory corresponds to a preset fault signal: S1 fault corresponds to fault signal five; S2 fault corresponds to fault signal six; S3 fault corresponds to fault signal seven; S4 fault corresponds to fault signal eight; S1+S2 fault corresponds to fault signal nine; S1+S3 fault corresponds to fault signal ten; S1+S4 fault corresponds to fault signal eleven; S2+S3 fault corresponds to fault signal twelve; S2+S4 fault corresponds to fault signal thirteen; S3+S4 fault corresponds to fault signal fourteen; S1+S2+S3 fault corresponds to fault signal fifteen; S1+S Fault 2+S4 corresponds to fault signal sixteen; fault S1+S3+S4 corresponds to fault signal seventeen; fault S2+S3+S4 corresponds to fault signal eighteen; fault S1+S2+S3+S4 corresponds to fault signal nineteen; fault L1 corresponds to fault signal twentieth; fault L2 corresponds to fault signal twenty-one; fault C corresponds to fault signal twenty-two; fault L1+L2 corresponds to fault signal twenty-three; fault L2+C corresponds to fault signal twenty-four; fault L1+C corresponds to fault signal twenty-five; fault L1+L1+C corresponds to fault signal twenty-six. Fault signals one through four are called energy storage battery fault signals; fault signals five through nineteen are called bridge circuit fault signals; fault signals twenty through twenty-six are called filter circuit fault signals.

[0016] The inverter's current frequency spectrum is obtained from the condition monitoring module. The area Si and peak frequency fi of each frequency peak are extracted from the inverter's current frequency spectrum and then calculated using the formula... Obtain eigenvalues Where i is the frequency peak number and n is the total number of frequency peaks. The inverter current anomaly parameter E4i is used as a characteristic value. The area of ​​the portion below the preset frequency f0 in the inverter's current frequency spectrum is extracted as a feature value. , eigenvalue , and Composition of eigenvalue vectors ( , , Each bridge circuit fault signal corresponds to a preset judgment feature value vector (). , , ),in , and These are preset fault judgment feature values. Calculate the feature value vector ( , , ) and judging the eigenvalue vector ( , , The distance between the two points is denoted as the first judgment distance. When the first judgment distance is less than the preset threshold, the corresponding bridge circuit fault signal is output.

[0017] Obtain the capacitor's current frequency spectrum from the condition monitoring module. Extract the frequency peak number j and the corresponding peak value for each frequency peak from the capacitor's current frequency spectrum. The area Qj and peak frequency vj of each frequency peak are given, where j is the frequency peak number; j = 1, 2, ..., m; and m is the total number of frequency peaks. This is achieved through the formula... Obtain eigenvalues Through the formula Obtain eigenvalues ,in It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies. The area of ​​the portion below the preset frequency v0 in the capacitor's current frequency spectrum is extracted as a characteristic value. , eigenvalue , and Composition of eigenvalue vectors ( , , Each filter circuit fault corresponds to a preset judgment feature value vector (). , , ),in , and These are preset fault judgment feature values. Calculate the feature value vector ( , , ) and judging the eigenvalue vector ( , , The distance between the two points is denoted as the second judgment distance. When the second judgment distance is less than the preset threshold, the corresponding filter circuit fault signal is output.

[0018] In a preferred embodiment of the present invention, the execution module displays the fault signal generated by the fault parameter diagnosis module on the display screen.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. This invention acquires voltage, current, power, and frequency data in real time through the energy storage battery in the circuit and the smart meter arranged in the inverter circuit, and acquires temperature data of the energy storage battery in real time through the temperature sensor, forming a three-dimensional data monitoring network, which ensures the relevance and reliability of subsequent data analysis.

[0021] 2. This invention improves the efficiency of data analysis by using a waveform diagnostic module to analyze and process images after visualization and fast Fourier transform, thus providing data support for the subsequent fault parameter analysis of the fault parameter diagnostic module.

[0022] 3. This invention provides an intelligent analysis and processing system for inverter power supply technology, which further analyzes data through a fault parameter diagnosis module. This system addresses the issues of insufficient hardware reliability and high debugging and maintenance costs in inverter power supply technology. It improves the adaptability and diagnostic accuracy of the entire system and provides a new method for intelligent parameter analysis and fault diagnosis of energy storage batteries and inverters. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a system block diagram of the present invention;

[0025] Figure 2 This is a schematic diagram of the energy storage battery and inverter circuit of the present invention;

[0026] Figure 3 This is a schematic diagram of the current spectrum of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 As shown, an intelligent parameter analysis and processing system for energy storage batteries and inverters includes a status monitoring module, a waveform diagnosis module, a fault parameter diagnosis module, and an execution module.

[0029] The status monitoring module acquires voltage, current, power, and frequency data in real time through smart meters arranged in the energy storage battery and inverter circuit, and acquires temperature data of the energy storage battery in real time through temperature sensors.

[0030] Please see Figure 2The diagram shows the circuit of the energy storage battery and single-phase inverter. U represents the energy storage battery, L1 and L2 are inductors in the inverter, and C is a capacitor in the inverter. L1, L2, and C together form a filter element. S1, S2, S3, and S4 are insulated-gate bipolar transistors in the inverter, and together they form a bridge circuit element. U represents the output voltage of the energy storage battery, Ug is the grid voltage, Uc is the capacitor voltage, Ui is the inverter output voltage, i1 is the inverter output current, ic is the capacitor current, and i2 is the grid-side current. The output voltage of the energy storage battery, the grid voltage, the capacitor voltage, the inverter output voltage, the inverter output current, the capacitor current, and the grid-side current are acquired in real time by a smart meter and sent to the fault parameter diagnosis module.

[0031] The status monitoring module records the capacitor current (ic) at preset time intervals using a smart meter. The recorded data is organized into a two-dimensional vector (t, ic). A two-dimensional rectangular coordinate system is established with time (t) as the horizontal axis and capacitor current (ic) as the vertical axis. All points (t, ic) are filled into the coordinate system, and a smooth curve connects all points to generate a curve showing the capacitor current changing over time. A Fast Fourier Transform (FFT) is then used to transform the curve from the time domain to the frequency domain, obtaining the capacitor's current frequency spectrum.

[0032] The status monitoring module records the temperature T of the energy storage battery at preset time intervals using a temperature sensor. The recorded data is organized into a two-dimensional vector (t, T). A two-dimensional Cartesian coordinate system is established with time t as the horizontal axis and temperature T as the vertical axis. All points (t, T) are filled into the coordinate system, and a smooth curve is used to connect all points to generate a curve showing the temperature change of the energy storage battery over time. The smart meter records the current I, voltage U, and power P of the energy storage battery's output circuit at preset time intervals t; the recorded data is organized into two-dimensional vectors (t, I), (t, U), and (t, P). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and current I as the vertical axis. All points (t, I) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output current changing with time. A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and voltage U as the vertical axis. All points (t, U) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output voltage changing with time. A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and power P as the vertical axis. All points (t, P) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of the energy storage battery output power changing with time. The curves of the energy storage battery output current changing with time, the curves of the energy storage battery output voltage changing with time, and the curves of the energy storage battery output power changing with time are transformed from the time domain to the frequency domain using Fast Fourier Transform to obtain the energy storage battery current spectrum, energy storage battery voltage spectrum, and energy storage battery power spectrum.

[0033] The status monitoring module acquires current, voltage, power, and frequency data from the inverter output circuit through smart meters deployed on the inverter output circuit. The smart meters record the current I, voltage U, power P, and frequency f of the inverter output circuit every preset time interval t. The recorded data are then organized into two-dimensional vectors (t, I), (t, U), (t, P), and (t, f). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and current I as the vertical axis. All points (t, I) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output current changing with time. Similarly, a two-dimensional rectangular coordinate system is established with time t as the horizontal axis and voltage U as the vertical axis. All points (t, U) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output voltage changing with time. A two-dimensional rectangular coordinate system is also established with time t as the horizontal axis and power P as the vertical axis. All points (t, P) are filled into the coordinate system, and a smooth curve is used to connect all the points to generate the curve of inverter output power changing with time. Finally, a two-dimensional rectangular coordinate system is established with time t as the horizontal axis and frequency f as the vertical axis. A coordinate system is established, and all points (t, f) are filled into the coordinate system. Then, a smooth curve is used to connect all the points to generate a curve showing the inverter output frequency changing with time. The curves showing the inverter output current changing with time, the inverter output voltage changing with time, and the inverter output power changing with time are transformed from the time domain to the frequency domain using a fast Fourier transform, resulting in the inverter current spectrum, inverter voltage spectrum, and inverter power spectrum. The curves showing the energy storage battery temperature changing with time, the inverter output frequency changing with time, the energy storage battery current spectrum, the energy storage battery voltage spectrum, the energy storage battery power spectrum, the inverter current spectrum, the inverter voltage spectrum, and the inverter power spectrum are then sent to the waveform diagnostic module.

[0034] The waveform diagnostic module extracts the area exceeding a preset temperature from the temperature-time curve of the energy storage battery, denoted as the high temperature index e1, and extracts the maximum slope of the curve, denoted as the temperature change index e2. This is then analyzed using the formula... The abnormal temperature parameter E1 of the energy storage battery is obtained, where k1 and k2 are preset weighting factors. The maximum value fMAX and minimum value fMIN are extracted from the curve of the inverter output frequency versus time, and then calculated using the formula... The inverter frequency anomaly parameter E2 is obtained, where It is the preset standard frequency.

[0035] Please see Figure 3As shown, the waveform diagnostic module defines each peak in the energy storage battery current spectrum, energy storage battery voltage spectrum, energy storage battery power spectrum, inverter current spectrum, inverter voltage spectrum, and inverter power spectrum as a frequency peak. The highest point of the frequency peak is called the peak frequency, and the frequency corresponding to the peak frequency is called the peak frequency. Each frequency peak in the energy storage battery current spectrum is numbered using i1, i1 = 1, 2, 3...n1; where n1 is the total number of frequency peaks in the energy storage battery current spectrum. The module extracts the frequency peak number i1 and the corresponding peak frequency for each frequency peak in the energy storage battery current spectrum. and peak frequency Through the formula Calculate the abnormal current parameter E3i of the energy storage battery, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the energy storage battery voltage spectrum is numbered using i2, i2=1,2,3...n2; where n2 is the total number of frequency peaks in the energy storage battery current spectrum. The frequency peak number i2 and the corresponding peak value are extracted from each frequency peak in the energy storage battery voltage spectrum. and peak frequency Through the formula Calculate the abnormal voltage parameter E3u of the energy storage battery, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the power spectrum of the energy storage battery is numbered using i3, i3=1,2,3...n3; where n3 is the total number of frequency peaks in the current spectrum of the energy storage battery. The frequency peak number i3 and the corresponding peak value are extracted from each frequency peak in the voltage spectrum of the energy storage battery. and peak frequency Through formula Calculate the abnormal power parameter E3p of the energy storage battery, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The abnormal parameter E3 of the energy storage battery was obtained, where , and These are preset weighting factors;

[0036] Each frequency peak in the inverter current spectrum is numbered using i4, where i4 = 1, 2, 3...n4; and n4 is the total number of frequency peaks in the inverter current spectrum. The peak number i4 and the corresponding peak value are then extracted from each frequency peak in the inverter current spectrum. and peak frequency Through the formula Calculate the inverter current anomaly parameter E4i, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the inverter voltage spectrum is numbered using i5, i5=1,2,3...n5; where n5 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i5 and the corresponding peak value are extracted from each frequency peak in the inverter voltage spectrum. and peak frequency Through the formula Calculate the inverter voltage anomaly parameter E4u, where It is a set of preset standard frequency peaks. This is a set of preset standard peak frequencies; each frequency peak in the inverter power spectrum is numbered using i6, i6=1,2,3...n6; where n6 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i6 and the corresponding peak value are extracted from each frequency peak in the inverter voltage spectrum. and peak frequency Through formula Calculate the inverter power anomaly parameter E4p, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The inverter abnormal parameter E4 is obtained, where , and This is a preset weighting factor. When the abnormal temperature parameter E1 of the energy storage battery is greater than the preset threshold or the abnormal parameter E3 of the energy storage battery is greater than the preset threshold, a diagnostic signal for the energy storage battery is output; when the abnormal frequency parameter E2 of the inverter is greater than the preset threshold or the abnormal parameter E4 of the inverter is greater than the preset threshold, a diagnostic signal for the inverter is output.

[0037] When the fault parameter diagnosis module receives the output energy storage battery diagnosis signal, it begins energy storage battery diagnosis; when it receives the inverter diagnosis signal, it begins inverter diagnosis. The specific process of energy storage battery diagnosis is as follows: The battery diagnosis device is activated to perform capacity detection on the energy storage battery. When the battery capacity is lower than a preset threshold, fault signal one is generated. The impedance detection device is activated to obtain the impedance of the energy storage battery. When the impedance is greater than a preset threshold, fault signal two is generated. The value of the energy storage battery temperature anomaly parameter E1 is obtained from the waveform diagnosis module. When the energy storage battery temperature anomaly parameter is greater than a preset threshold, fault signal three is generated. The energy storage battery anomaly parameter E3 is obtained from the waveform diagnosis module. When the value of the energy storage battery anomaly parameter E3 is greater than a preset threshold, fault signal four is generated.

[0038] The fault parameter diagnosis module categorizes bridge circuit faults into four main categories and fifteen subcategories: 1. Single-transistor faults: S1, S2, S3, S4; 2. Two-transistor faults: S1+S2, S1+S3, S1+S4, S2+S3, S2+S4, S3+S4; 3. Three-transistor faults: S1+S2+S3, S1+S2+S4, S1+S3+S4, S2+S3+S4; 4. Four-transistor faults: S1+S2+S3+S4. Filter faults are categorized into three main categories and seven subcategories: 1. Unit faults: L1, L2, C; 2. Two-element faults: L1+L2, L2+C, L1+C; 3. Three-element faults: L1+L1+C. Each fault subcategory corresponds to a preset fault signal: S1 fault corresponds to fault signal five; S2 fault corresponds to fault signal six; S3 fault corresponds to fault signal seven; S4 fault corresponds to fault signal eight; S1+S2 fault corresponds to fault signal nine; S1+S3 fault corresponds to fault signal ten; S1+S4 fault corresponds to fault signal eleven; S2+S3 fault corresponds to fault signal twelve; S2+S4 fault corresponds to fault signal thirteen; S3+S4 fault corresponds to fault signal fourteen; S1+S2+S3 fault corresponds to fault signal fifteen; S1+S Fault 2+S4 corresponds to fault signal sixteen; fault S1+S3+S4 corresponds to fault signal seventeen; fault S2+S3+S4 corresponds to fault signal eighteen; fault S1+S2+S3+S4 corresponds to fault signal nineteen; fault L1 corresponds to fault signal twentieth; fault L2 corresponds to fault signal twenty-one; fault C corresponds to fault signal twenty-two; fault L1+L2 corresponds to fault signal twenty-three; fault L2+C corresponds to fault signal twenty-four; fault L1+C corresponds to fault signal twenty-five; fault L1+L1+C corresponds to fault signal twenty-six. Fault signals one through four are called energy storage battery fault signals; fault signals five through nineteen are called bridge circuit fault signals; fault signals twenty through twenty-six are called filter circuit fault signals.

[0039] Circuit faults typically exhibit certain characteristics in the current spectrum; for example, arcing faults produce high-frequency components, while short-circuit faults produce low-frequency components. By performing a Fourier transform, components of different frequencies can be separated and their features extracted, leading to better fault analysis. The inverter's current frequency spectrum is obtained from the condition monitoring module. The area Si and peak frequency fi of each frequency peak are extracted from the inverter's current frequency spectrum and then analyzed using the formula... Obtain eigenvalues Where i is the frequency peak number and n is the total number of frequency peaks. The inverter current anomaly parameter E4i is used as a characteristic value. The area of ​​the portion below the preset frequency f0 in the inverter's current frequency spectrum is extracted as a feature value. , eigenvalue , and Composition of eigenvalue vectors ( , , Each bridge circuit fault signal corresponds to a preset judgment feature value vector (). , , ),in , and These are preset fault judgment feature values. Calculate the feature value vector ( , , ) and judging the eigenvalue vector ( , , The distance between the two points is denoted as the first judgment distance. When the first judgment distance is less than the preset threshold, the corresponding bridge circuit fault signal is output.

[0040] Obtain the capacitor's current frequency spectrum from the condition monitoring module. Extract the frequency peak number j and the corresponding peak value for each frequency peak from the capacitor's current frequency spectrum. The area Qj and peak frequency vj of each frequency peak are given, where j is the frequency peak number; j = 1, 2, ..., m; and m is the total number of frequency peaks. This is achieved through the formula... Obtain eigenvalues Through the formula Obtain eigenvalues ,in It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies. The area of ​​the portion below the preset frequency v0 in the capacitor's current frequency spectrum is extracted as a characteristic value. , eigenvalue , and Composition of eigenvalue vectors ( , , Each filter circuit fault corresponds to a preset judgment feature value vector (). , , ),in , and These are preset fault judgment feature values. Calculate the feature value vector ( , , ) and judging the eigenvalue vector ( , , The distance between the two points is denoted as the second judgment distance. When the second judgment distance is less than the preset threshold, the corresponding filter circuit fault signal is output.

[0041] The execution module displays the fault signals generated by the fault parameter diagnosis module on the display screen.

[0042] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A parameter intelligent analysis and processing system for energy storage batteries and inverters, comprising a waveform diagnosis module and a fault parameter diagnosis module, characterized in that: The waveform diagnostic module extracts the area exceeding a preset temperature from the temperature-time curve of the energy storage battery and records it as a high-temperature index; it also extracts the maximum slope of the temperature-time curve and records it as a temperature variation index. The module calculates the abnormal temperature parameters of the energy storage battery using both the high-temperature index and the temperature variation index. Furthermore, it extracts the maximum and minimum values ​​from the inverter output frequency-time curve and calculates the abnormal frequency parameters using formulas. The module also extracts data from the energy storage battery current spectrum, voltage spectrum, power spectrum, inverter current spectrum, voltage spectrum, and power spectrum, and calculates the abnormal parameters for the energy storage battery and the inverter. Finally, it determines whether to output a diagnostic signal for the energy storage battery and the inverter based on the values ​​of the abnormal parameters. When the fault parameter diagnosis module receives the output energy storage battery diagnosis signal, it starts energy storage battery diagnosis; when the fault parameter diagnosis module receives the inverter diagnosis signal, it starts inverter diagnosis. The specific process of inverter diagnosis is as follows: inverter faults are divided into bridge circuit faults and filter circuit faults, and the bridge circuit faults and filter circuit faults are classified according to the number of components. Each category corresponds to a preset fault signal. The feature value vector is obtained through feature value calculation, and the fault signal is matched by the operation on the feature value vector. The waveform diagnostic module refers to each peak image in the energy storage battery current spectrum, energy storage battery voltage spectrum, energy storage battery power spectrum, inverter current spectrum, inverter voltage spectrum, and inverter power spectrum as a frequency peak. The highest point of the frequency peak is called the frequency peak value, and the frequency corresponding to the frequency peak value is called the peak frequency. Each frequency peak in the energy storage battery current spectrum is numbered with i1, i1=1,2,3...n1; Where n1 is the total number of frequency peaks in the current spectrum of the energy storage battery, the frequency peak number i1 and the corresponding peak value of each frequency peak in the current spectrum of the energy storage battery are extracted. and peak frequency Through formula Calculate the abnormal current parameter E3i of the energy storage battery, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; each frequency peak in the energy storage battery voltage spectrum is numbered using i2, i2=1,2,3...n2; where n2 is the total number of frequency peaks in the energy storage battery current spectrum. The frequency peak number i2 and the corresponding peak value of each frequency peak in the energy storage battery voltage spectrum are extracted. and peak frequency Through formula Calculate the abnormal voltage parameter E3u of the energy storage battery, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; each frequency peak in the power spectrum of the energy storage battery is numbered using i3, i3=1,2,3...n3; Where n3 is the total number of frequency peaks in the current spectrum of the energy storage battery, and the frequency peak number i3 and the corresponding peak value of each frequency peak in the voltage spectrum of the energy storage battery are extracted. and peak frequency Through formula Calculate the power anomaly parameter E3p of the energy storage battery, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The abnormal parameter E3 of the energy storage battery was obtained, where , and These are preset weighting factors; Use i4 to number each frequency peak in the inverter current spectrum, i4=1,2,3...n4; Where n4 is the total number of frequency peaks in the inverter current spectrum, the frequency peak number i4 and the corresponding peak value of each frequency peak in the inverter current spectrum are extracted. and peak frequency Through formula Calculate the inverter current anomaly parameter E4i, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; each frequency peak in the inverter voltage spectrum is numbered using i5, i5=1,2,3...n5; where n5 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i5 and the corresponding peak value of each frequency peak in the inverter voltage spectrum are extracted. and peak frequency Through formula Calculate the inverter voltage anomaly parameter E4u, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; each frequency peak in the inverter power spectrum is numbered using i6, i6=1,2,3...n6; where n6 is the total number of frequency peaks in the inverter current spectrum. The frequency peak number i6 and the corresponding peak value of each frequency peak in the inverter voltage spectrum are extracted. and peak frequency Through formula Calculate the inverter power anomaly parameter E4p, where It is a set of preset standard frequency peaks. It is a set of preset standard peak frequencies; through the formula The inverter abnormal parameter E4 is obtained, where , and It is a preset weighting factor. When the abnormal temperature parameter E1 of the energy storage battery is greater than the preset threshold or the abnormal parameter E3 of the energy storage battery is greater than the preset threshold, the energy storage battery diagnostic signal is output; when the abnormal frequency parameter E2 of the inverter is greater than the preset threshold or the abnormal parameter E4 of the inverter is greater than the preset threshold, the inverter diagnostic signal is output.

2. The intelligent parameter analysis and processing system for energy storage batteries and inverters according to claim 1, characterized in that, It also includes a status monitoring module and an execution module; The status monitoring module records the capacitor current at preset time intervals using a smart meter. The recorded data is arranged into a two-dimensional vector, and a two-dimensional rectangular coordinate system is established with time as the horizontal axis and capacitor current as the vertical axis. All data points are filled into the coordinate system, and a smooth curve connects all points to generate a curve showing the capacitor current changing over time. A fast Fourier transform is used to convert the curve from the time domain to the frequency domain, obtaining the capacitor's current frequency spectrum. Similarly, a temperature sensor records the temperature of the energy storage battery at preset time intervals. The recorded data is also arranged into a two-dimensional vector, and a two-dimensional rectangular coordinate system is established with time as the horizontal axis and temperature as the vertical axis. All data points are filled into the coordinate system, and a smooth curve connects all points. Generate a curve showing the temperature change of the energy storage battery over time; record the current, voltage, and power of the energy storage battery output circuit at preset time intervals using a smart meter; assemble the recorded data into a two-dimensional vector, establish a two-dimensional rectangular coordinate system with time (t) as the horizontal axis and current as the vertical axis, fill all current data points into the coordinate system, and then connect all points with a smooth curve to generate a curve showing the output current of the energy storage battery over time; establish a two-dimensional rectangular coordinate system with time as the horizontal axis and voltage as the vertical axis, fill all voltage data points into the coordinate system, and then connect all points with a smooth curve to generate a curve showing the output voltage of the energy storage battery over time; establish a two-dimensional rectangular coordinate system with time as the horizontal axis and power as the vertical axis, and fill all power data points into the coordinate system. Points are filled into a coordinate system, and then a smooth curve is used to connect all the points to generate a curve showing the output power of the energy storage battery as a function of time. The curves showing the output current, output voltage, and output power of the energy storage battery as a function of time are transformed from the time domain to the frequency domain using a Fast Fourier Transform (FFT) to obtain the current spectrum, voltage spectrum, and power spectrum of the energy storage battery. The current, voltage, power, and frequency of the inverter output circuit are recorded at preset time intervals using a smart meter. The recorded data are arranged into a two-dimensional vector. A two-dimensional rectangular coordinate system is established with time as the horizontal axis and current as the vertical axis. All current data points are filled into the coordinate system, and then a smooth curve is used to connect all the points. A curve is generated by connecting all points with a line to show the inverter output current as a function of time. A two-dimensional Cartesian coordinate system is established with time as the horizontal axis and voltage as the vertical axis. All voltage data points are filled into the coordinate system, and a smooth curve is then used to connect all points to generate a curve showing the inverter output voltage as a function of time. Similarly, a two-dimensional Cartesian coordinate system is established with time as the horizontal axis and power as the vertical axis. All power data points are filled into the coordinate system, and a smooth curve is then used to connect all points to generate a curve showing the inverter output power as a function of time. Finally, a two-dimensional Cartesian coordinate system is established with time as the horizontal axis and frequency as the vertical axis. All frequency data points are filled into the coordinate system, and a smooth curve is then used to connect all points to generate a curve showing the inverter output frequency as a function of time.The curves of inverter output current, inverter output voltage, and inverter output power over time are transformed from the time domain to the frequency domain using Fast Fourier Transform (FFT) to obtain inverter current spectrum, inverter voltage spectrum, and inverter power spectrum. The curves of energy storage battery temperature over time, inverter output frequency over time, energy storage battery current spectrum, energy storage battery voltage spectrum, energy storage battery power spectrum, inverter current spectrum, inverter voltage spectrum, and inverter power spectrum are then sent to the waveform diagnostic module. The execution module displays the fault signals generated by the fault parameter diagnosis module on the display screen.

3. The intelligent parameter analysis and processing system for energy storage batteries and inverters according to claim 1, characterized in that, The specific process for diagnosing energy storage batteries is as follows: The battery diagnostic device is activated to perform capacity testing on the energy storage battery. When the battery capacity is lower than a preset threshold, fault signal one is generated. The impedance detection device is activated to obtain the impedance of the energy storage battery. When the impedance is greater than a preset threshold, fault signal two is generated. The energy storage battery temperature anomaly index E1 is obtained from the waveform diagnostic module. When the energy storage battery temperature anomaly index is greater than a preset threshold, fault signal three is generated. Abnormal parameters of the energy storage battery are obtained from the waveform diagnostic module. When the value of the abnormal parameter of the energy storage battery is greater than a preset threshold, fault signal four is generated.

4. The intelligent parameter analysis and processing system for energy storage batteries and inverters according to claim 1, characterized in that, The process of obtaining an eigenvalue vector through eigenvalue operations, and then matching the fault signal through operations on the eigenvalue vector, is as follows: Obtain the inverter's current frequency spectrum, extract the area and peak frequency of each frequency peak from the inverter's current frequency spectrum, and obtain characteristic value one through calculation; use the inverter's abnormal current parameters as characteristic value two; extract the area of ​​the part less than the preset frequency from the inverter's current frequency spectrum as characteristic value three, and combine characteristic value one, characteristic value two, and characteristic value three to form a characteristic value vector; each bridge circuit fault signal corresponds to a preset judgment characteristic value vector; calculate the distance between the characteristic value vector and the judgment characteristic value vector and record it as judgment distance one; when judgment distance one is less than a preset threshold, output the corresponding bridge circuit fault signal; Obtain the current frequency spectrum of the capacitor; extract the frequency peak number, corresponding peak value, area, and peak frequency of each frequency peak from the current frequency spectrum of the capacitor; obtain characteristic value four and characteristic value five by calculating the frequency peak number, corresponding peak value, area, and peak frequency of each frequency peak; extract the area of ​​the part less than the preset frequency from the current frequency spectrum of the capacitor as characteristic value six, and form characteristic value vector by characteristic values ​​four, five, and six; each filter circuit fault corresponds to a preset judgment characteristic value vector; calculate the distance between the characteristic value vector and the judgment characteristic value vector and record it as judgment distance two; when judgment distance two is less than the preset threshold, output the corresponding filter circuit fault signal.

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