Capacitor bank online monitoring method and device, electronic equipment and storage medium thereof

Abstract

The application discloses a capacitor bank online monitoring method and device, electronic equipment and a storage medium thereof. The method comprises the following steps: obtaining the bus voltage measurement value and the phase current measurement value of the capacitor bank; calculating the neutral point voltage according to the predicted three-phase equivalent impedance of the capacitor bank at the previous moment; obtaining the phase voltage according to the bus voltage measurement value and the neutral point voltage; and predicting the three-phase equivalent impedance of the capacitor bank according to the phase voltage and the current measurement value and by using the Kalman filtering algorithm. The application embodiment does not need to additionally increase sensors, can be implemented by using the existing equipment in the transformer substation, effectively reduces the implementation threshold and application cost, and improves the monitoring efficiency and accuracy of the frame capacitor bank by using the measurement data of the existing sensors.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method, device, electronic equipment and storage medium for online monitoring of capacitor banks. Background Technology

[0002] In recent years, high-voltage parallel capacitor banks have required frequent switching during operation, resulting in numerous faults. However, the current power grid maintenance and operation methods cannot effectively predict equipment faults in advance, making condition-based maintenance difficult to implement. This can easily lead to small capacitor unit faults within the capacitor gradually escalating into faults in the entire capacitor, posing significant equipment safety hazards.

[0003] Current online monitoring methods for capacitor banks include: one approach incorporates information fusion technology, using multi-source data to perform weighted calculations on capacitors, which avoids misjudgments caused by the failure of a single sensor and improves robustness; another approach calculates the relationship between the number of component breakdowns and current through prior simulation, and then predicts the number of component breakdowns within a capacitor unit based on current measurements in the field, thus achieving monitoring of the capacitor's operating status; and yet another approach uses wireless communication to create a network in the field to monitor the capacitor bank's status. The application of this sensor can accurately identify the capacitance value of each capacitor unit and has the ability to precisely locate faults.

[0004] However, existing online monitoring methods for capacitor banks are difficult to implement on-site, require significant upfront investment, are costly, and suffer from low monitoring accuracy due to insufficient sensor precision, bus voltage fluctuations, and environmental changes. Summary of the Invention

[0005] This invention provides a method, device, electronic device, and storage medium for online monitoring of capacitor banks, in order to solve the problem of low monitoring accuracy of existing capacitor banks.

[0006] According to one aspect of the present invention, an online monitoring method for capacitor banks is provided, comprising:

[0007] Obtain the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank;

[0008] Calculate the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment;

[0009] The phase voltage is obtained based on the measured bus voltage and the neutral point voltage.

[0010] Based on the measured phase voltage and current values, the three-phase equivalent impedance of the capacitor bank is predicted using a Kalman filter algorithm.

[0011] Optionally, the three-phase equivalent impedance of the capacitor bank is predicted using a Kalman filter algorithm, including:

[0012] The three-phase equivalent impedance of the capacitor bank is obtained using the following prediction equation:

[0013]

[0014]

[0015] P(t)=(1-K(t)U an (t))*P(t-1)

[0016] P(t+1)=P(t)+Q

[0017]

[0018] In the formula, t is time; K is Kalman gain; Ia and Uan are the measured phase current and phase voltage, respectively; P is the prediction error covariance; Ya is the equivalent admittance; R is the measurement error of the sensor measuring line voltage and phase current; and Q is the prediction error variance.

[0019] Optionally, the three-phase equivalent impedance of the capacitor bank is predicted using a Kalman filter algorithm, including:

[0020] The three-phase equivalent impedance of the capacitor bank is obtained using the following prediction equation:

[0021]

[0022]

[0023] P(t)=(1-K(t)U an (t))*P(t-1)

[0024]

[0025]

[0026] In the formula, t is time; K is Kalman gain; Ia and Uan are the measured phase current and phase voltage, respectively; P is the prediction error covariance; R is the measurement error of the sensor measuring line voltage and phase current; AQ is a constant coefficient; x is the integration window length; Q0 is the prediction error variance baseline; and D is the differential coefficient.

[0027] Optionally, after predicting the three-phase equivalent impedance of the capacitor bank based on the phase voltage and the measured current values ​​using a Kalman filter algorithm, the method further includes:

[0028] The covariance of the Nth prediction results of the obtained three-phase equivalent impedance sequences Za, Zb, and Zc is calculated together.

[0029] The factors causing changes in effective impedance are determined based on the calculated covariance.

[0030] Optionally, the factors causing changes in effective impedance can be determined based on the calculated covariance, including:

[0031] If the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) are positive, it indicates that normal operation or temperature influence has caused simultaneous changes in A, B, and C.

[0032] Optionally, the factors causing changes in effective impedance can be determined based on the calculated covariance, including:

[0033] If two of the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) are negative, then it is determined that one of the phases A, B, and C has failed.

[0034] Optionally, the covariance of the equivalent impedances of phase M and phase N is defined as:

[0035]

[0036] Where M and N are any two phases from A, B, and C; x is the integration window length; and t is time t. Z is the series average; Za(t) is the value of Za at each different time point; Z b (t) represents Z at each different time point. b The value of .

[0037] According to another aspect of the present invention, an online monitoring device for capacitor banks is provided, comprising:

[0038] The measurement value acquisition unit is used to acquire the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank.

[0039] The neutral point voltage calculation unit is used to calculate the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment.

[0040] A phase voltage calculation unit is used to obtain the phase voltage based on the measured bus voltage and the neutral point voltage.

[0041] An equivalent impedance prediction unit is used to predict the three-phase equivalent impedance of the capacitor bank based on the phase voltage and the measured current values, and using a Kalman filter algorithm.

[0042] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0043] At least one processor; and

[0044] A memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the online monitoring method for capacitor banks according to any embodiment of the present invention.

[0046] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the online monitoring method for capacitor banks according to any embodiment of the present invention.

[0047] The technical solution of this invention involves acquiring the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank; calculating the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment; obtaining the phase voltage based on the measured bus voltage and the neutral point voltage; and predicting the three-phase equivalent impedance of the capacitor bank using a Kalman filter algorithm based on the phase voltage and the measured current. This invention reduces the impact of sensor measurement errors by improving the Kalman filter algorithm; eliminates voltage fluctuation interference by introducing bus voltage monitoring; and finally, effectively identifies environmental change factors by calculating the covariance of the phase-to-phase predicted values. This invention does not require additional sensors and can be implemented using existing substation equipment, lowering the implementation threshold and application cost. Furthermore, by utilizing existing sensor measurement data, it improves the monitoring efficiency and accuracy of frame-type capacitor banks.

[0048] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of an online monitoring method for capacitor banks provided in Embodiment 1 of the present invention.

[0051] Figure 2 This is a schematic diagram of an online monitoring model for capacitor banks provided in Embodiment 2 of the present invention.

[0052] Figure 3This is a schematic diagram of the equivalent impedance circuit model of a capacitor bank provided in Embodiment 2 of the present invention.

[0053] Figure 4 This is a flowchart of an online monitoring method for capacitor banks provided in Embodiment 2 of the present invention.

[0054] Figure 5 This is a waveform diagram showing the measured and true values ​​of the equivalent impedance Za of a capacitor bank, provided in Embodiment 2 of the present invention.

[0055] Figure 6 This is a waveform diagram showing the predicted, measured, and true values ​​of the equivalent impedance Za of a capacitor bank, provided in Embodiment 2 of the present invention.

[0056] Figure 7 This is an error distribution diagram of the predicted and measured values ​​of the equivalent impedance Za of a capacitor bank provided in Embodiment 2 of the present invention.

[0057] Figure 8 This is a waveform diagram of the predicted, measured and true values ​​of the equivalent impedance Za of a capacitor bank provided in Embodiment 2 of the present invention.

[0058] Figure 9 This is an error distribution diagram of the predicted and measured values ​​of the equivalent impedance Za of an improved capacitor bank provided in Embodiment 2 of the present invention.

[0059] Figure 10 This is a schematic diagram of the Kalman filter algorithm provided in Embodiment 2 of the present invention.

[0060] Figure 11 The waveform diagram of the predicted output value of the Kalman filter algorithm provided in Embodiment 2 of the present invention is shown.

[0061] Figure 12 This is a waveform diagram of the covariance of the predicted three-phase equivalent impedance of capacitor bank A, B, and C provided in Embodiment 2 of the present invention.

[0062] Figure 13 The waveform diagram of the predicted output value of another Kalman filter algorithm provided in Embodiment 2 of the present invention is shown.

[0063] Figure 14 This is a waveform diagram of the covariance of the predicted three-phase equivalent impedance of capacitor bank A, B, and C provided in Embodiment 2 of the present invention.

[0064] Figure 15 This is a schematic diagram of the structure of an online monitoring device for capacitor banks provided in Embodiment 3 of the present invention.

[0065] Figure 16 A schematic diagram of the electronic device for an online monitoring method for capacitor banks provided in Embodiment 4 of the present invention. Detailed Implementation

[0066] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0067] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0068] Example 1

[0069] Figure 1 This is a flowchart of an online monitoring method for capacitor banks provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where high-voltage parallel capacitor banks need to be frequently switched on and off during operation. The method can be executed by an online monitoring device for the capacitor banks, which can be implemented in hardware and / or software. This online monitoring device can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method includes the following steps:

[0070] S110. Obtain the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank;

[0071] Specifically, the voltage of the capacitor bank connected to the bus and the phase current of the capacitor bank are measured by voltage transformers and current transformers. The purpose of obtaining the bus voltage measurement is to provide basic data for calculating the phase voltage.

[0072] S120. Calculate the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment.

[0073] The equivalent impedances of phases A, B, and C can be denoted as Za, Zb, and Zc, respectively; therefore, the impedance Za of phase A can be obtained using the following formula:

[0074]

[0075] In the formula, Ra is the resistance value of phase A; La is the inductance value of phase A; Ca is the capacitance value of phase A; j is a complex operator; and ω is the angular frequency.

[0076] The neutral point voltage can be calculated using the following formula:

[0077]

[0078] In the formula, Z a Z b Z c These represent the equivalent impedances of phases A, B, and C, respectively; Un is the neutral point voltage; Ua, Ub, and Uc are the measured values ​​of the bus voltage connected to the capacitor bank; and dots represent vectors.

[0079] S130. Obtain the phase voltage based on the measured bus voltage and neutral point voltage;

[0080] The phase voltage is the voltage of each phase capacitor, which can be calculated from the difference between the measured bus voltage and the neutral point voltage.

[0081] S140. Based on the measured phase voltage and current values, and using the Kalman filter algorithm, predict the three-phase equivalent impedance of the capacitor bank.

[0082] Specifically, the Kalman filter algorithm is used to predict the three-phase equivalent impedance of a capacitor bank. Its purpose is to accurately identify the operating state of the capacitors by utilizing changes in their three-phase equivalent impedance values. The phase current measurements Ia, Ib, and Ic of the capacitor bank can be calculated using the following formulas:

[0083]

[0084] The technical solution of this invention involves acquiring the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank; calculating the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment; obtaining the phase voltage based on the bus voltage measurement and the neutral point voltage; and predicting the three-phase equivalent impedance of the capacitor bank using a Kalman filter algorithm based on the phase voltage and the measured current. This invention reduces the impact of sensor measurement errors by improving the Kalman filter algorithm; eliminates voltage fluctuation interference by introducing bus voltage monitoring; and finally, effectively identifies environmental change factors by calculating the covariance of the phase-to-phase predicted values. This invention does not require additional sensors and can be implemented using existing substation equipment, lowering the implementation threshold and application cost. Furthermore, by utilizing existing sensor measurement data, it improves the monitoring efficiency and accuracy of frame-type capacitor banks.

[0085] Example 2

[0086] Figure 2 This is a schematic diagram of an online monitoring model structure for a capacitor bank provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the 10kV power supply in a substation is typically connected in a delta configuration, and the compensation capacitor bank usually adopts a star-connected, ungrounded neutral point structure. (Refer to...) Figure 2 The system acquires the line voltage and phase current measurements of the bus connected to the capacitor bank. The line voltage measurements Ua, Ub, and Uc of the bus connected to the capacitor bank can be obtained from the voltage transformer PT, and the phase current measurements Ia, Ib, and Ic of the capacitor bank can be obtained from the current transformer CT. The equivalent impedance of the parallel surge arrester and discharge coil of the capacitor bank is extremely high during normal operation. Even during degradation, as long as breakdown does not occur, its equivalent impedance remains extremely high, therefore its influence can be ignored. Therefore, the schematic diagram of the capacitor bank online monitoring model can be converted into a schematic diagram of the equivalent impedance circuit model, as shown below. Figure 3 As shown: Figure 3 This is a schematic diagram of the equivalent impedance circuit model of a capacitor bank provided in Embodiment 2 of the present invention.

[0087] Figure 3 In the capacitor bank, L, R, and C represent the equivalent parameters of the series reactor and the capacitor, respectively. Since the capacitive reactance is much greater than the inductive reactance and resistance, the entire capacitor bank exhibits capacitive behavior. Let the three-phase equivalent impedances be Za, Zb, and Zc, then:

[0088]

[0089] According to KCL, we have:

[0090]

[0091] Based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment, the neutral point voltage is calculated. Therefore, the neutral point voltage Un can be calculated by the following formula:

[0092]

[0093] The phase voltage is obtained from the line voltage measurement and the neutral point voltage, where the phase voltage is the difference between the measured line voltage and the neutral point voltage.

[0094] If the three phases are symmetrical, the neutral point voltage Un is 0. Therefore, the phase current measurements Ia, Ib, and Ic of the capacitor bank can be calculated using the following formula.

[0095]

[0096] Based on the measured phase voltage and current values, the three-phase equivalent impedance of the capacitor bank is predicted using a Kalman filter algorithm. The Kalman filter (KF) is an optimal estimation-based filtering algorithm that determines the optimal estimate of the system by calculating the measured and predicted values. The measured line voltage values ​​Ua, Ub, and Uc of the bus connected to the capacitor bank, as well as the measured phase current values ​​Ia, Ib, and Ic of the capacitor bank, can all be obtained from... Figure 2 The measured values ​​obtained by the PT and CT are shown. Theoretically, the capacitor bank impedances Za, Zb, and Zc can be solved by solving the system of equations (2-3). That is, the accuracy of identifying the equivalent impedance of the capacitor bank directly depends on the measurement accuracy of the PT and CT. However, in practice, the measurement accuracy of the PT and CT is usually no higher than 0.2, meaning that the identification of the equivalent impedance of the capacitor bank will introduce at least a 0.4% error. When there are many parallel capacitors in a frame-type capacitor bank, if the capacitance value of a single capacitor changes by more than 5%, the change reflected in the phase current will be drowned out by measurement noise, leading to the inability to detect individual capacitor values ​​exceeding the limit in a timely manner. On the other hand, the significance of online equipment monitoring lies in the early detection of potential equipment hazards. According to regulations, the change in the value of a single parallel capacitor should not exceed 5%. If it exceeds 5%, the probability of complete capacitor failure will increase significantly, and the probability of triggering the unbalanced current operation of the relay protection will also increase. Therefore, it is necessary to improve the identification accuracy of individual capacitors in order to detect potential equipment hazards in advance.

[0097] Figure 4 This is a flowchart of an online monitoring method for capacitor banks provided in Embodiment 2 of the present invention. This embodiment further refines the aforementioned embodiments based on the previous embodiments, such as... Figure 4 As shown, the method includes the following steps:

[0098] S210. Obtain the measured values ​​of the bus voltage connected to the capacitor bank and the phase current of the capacitor bank;

[0099] S220. Calculate the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment.

[0100] S230. Obtain the phase voltage based on the measured bus voltage and neutral point voltage;

[0101] S240. Based on the measured phase voltage and current values, and using the Kalman filter algorithm, predict the three-phase equivalent impedance of the capacitor bank;

[0102] S250. Calculate the covariance of the N prediction results of the obtained three-phase equivalent impedance sequences Za, Zb, and Zc.

[0103] S260. Determine the factors causing changes in effective impedance based on the calculated covariance.

[0104] Specifically, this invention improves the identification accuracy of capacitor banks based on an improved Kalman filter method. For example, taking phase A as an example, firstly, the equivalent admittance of phase A is set to Ya (Ya = 1 / Za). According to the KF algorithm, the Kalman filter algorithm is used to predict the three-phase equivalent impedance of the capacitor bank, including:

[0105] The three-phase equivalent impedance of the capacitor bank is obtained using the following prediction equation:

[0106]

[0107]

[0108] P(t)=(1-K(t)U an (t))*P(t-1) (6)

[0109] In the formula, t is time; K is Kalman gain; Ia and Uan are the measured phase current and phase voltage, respectively; P is the prediction error covariance; Ya is the equivalent admittance; and R is the measurement error of the sensor that measures the line voltage and phase current. Let be the predicted equivalent admittance at time t, where the inverted triangle sign indicates the predicted value; Equation (4-6) is the prediction equation of the Kalman filter (KF) algorithm. In reality, there is always an error in the estimation of the system state, therefore:

[0110] P(t+1)=P(t)+Q (7)

[0111] Q represents the prediction error variance; since the equipment operates under normal conditions most of the time, we generally estimate that the admittance of each phase of the capacitor bank will always remain consistent with the value at the previous moment, therefore:

[0112]

[0113] Thus, the core calculation formula of the Kalman filter (KF) algorithm is Equation (4-8). Once the initial values ​​are set, the equivalent admittance Ya of phase A can be estimated using Equation (4-8).

[0114] To verify the accuracy of the KF algorithm in predicting the equivalent impedance of capacitor banks, simulation parameters were set as shown in Table 1 below.

[0115] Table 1

[0116]

[0117] It can be seen that the equivalent impedance Za calculated by measuring voltage and current is similar to the true value. Figure 5 As shown, Figure 5 This is a waveform diagram showing the measured and true values ​​of the equivalent impedance Za of a capacitor bank, provided in Embodiment 2 of the present invention. Figure 5 It can be seen that the result calculated based on the equivalent impedance Za measurement has an error of 1% compared with the true value. For capacitor banks with multiple capacitor units connected in parallel, it is difficult to detect the small number of capacitor units in the capacitor in advance.

[0118] According to the Kalman filter (KF) algorithm formula (4-8) provided in this embodiment of the invention, when Q is set to 1 / 10^-9, the predicted equivalent impedance Za is as follows: Figure 6 As shown, Figure 6 This is a waveform diagram showing the predicted, measured, and true values ​​of the equivalent impedance Za of a capacitor bank, provided in Embodiment 2 of the present invention. Figure 6 It can be seen that the error distribution between the predicted and measured values ​​is as follows: Figure 7 As shown, Figure 7 This is an error distribution diagram of the predicted and measured values ​​of the equivalent impedance Za of a capacitor bank provided in Embodiment 2 of the present invention.

[0119] like Figure 6 , Figure 7 As shown, the equivalent impedance calculated using voltage and current measurements has a significant error, up to 1%. After the data stabilizes, the error in the prediction results using the Kalman filter (KF) algorithm will be less than 0.1%. While the simulation results demonstrate that the Kalman filter (KF) algorithm can improve overall monitoring accuracy, they also reveal that when the equivalent impedance value changes, the predicted value of the Kalman filter (KF) algorithm cannot quickly follow the true value. Therefore, for a period after the change, the error in the output result of the Kalman filter (KF) algorithm remains relatively large, only slowly decreasing with the increase of the number of data sampling points. To improve the algorithm's speed of following the true value when the equivalent impedance changes, and further improve the algorithm's monitoring accuracy, this embodiment of the invention adds a differential term to the system prediction equation to enhance the sensitivity of the Kalman filter (KF) algorithm. When the predicted value changes in the same direction, it indicates a change in the system state; in this case, the differential term can accelerate the approach. When the predicted value fluctuates in different directions, it indicates that measurement noise is playing a major role; in this case, the differential term will suppress it. Meanwhile, although the introduction of the differential term enables rapid tracking when the system state changes, the system error between the predicted value and the true value still cannot be effectively reduced. Therefore, it is necessary to dynamically adjust the Q value (prediction error variance) based on the integral of the difference between the predicted and measured values ​​to further improve the prediction accuracy of the Kalman filter (KF) algorithm. In summary, equations (7) and (8) are changed to:

[0120]

[0121]

[0122] Accordingly, the Kalman filter algorithm is used to predict the three-phase equivalent impedance of the capacitor bank, including:

[0123] The three-phase equivalent impedance of the capacitor bank is obtained using the following prediction equation:

[0124]

[0125]

[0126] P(t)=(1-K(t)U an (t))*P(t-1)

[0127]

[0128]

[0129] In the formula, t is time; K is Kalman gain; Ia and Uan are the measured phase current and phase voltage, respectively; P is the prediction error covariance; R is the measurement error of the sensor measuring line voltage and phase current; AQ is a constant coefficient; x is the integration window length; Q0 is the prediction error variance baseline; and D is the differential coefficient.

[0130] Using the improved formula (9-10), and recalculating according to the parameters shown in Table 1, the simulation results are as follows. Figure 8 As shown, the error distribution is as follows Figure 9 As shown, Figure 8 This is a waveform diagram of the predicted, measured and true values ​​of the equivalent impedance Za of a capacitor bank provided in Embodiment 2 of the present invention. Figure 9 This is an error distribution diagram between the predicted and measured values ​​of the improved equivalent impedance Za of a capacitor bank provided in Embodiment 2 of the present invention. Figure 8 , Figure 9 Simulation results show that the improved Kalman filter (KF) algorithm can predict the true value more quickly. Figure 7 The error shown is still close to 0.2% at t=600, while Figure 9 The results show that the error at time t=600 has converged to below 0.05%. A prediction accuracy of 0.05% is sufficient to meet the monitoring accuracy requirements of ten or fewer parallel capacitors in a frame-type capacitor bank, meaning the capacitance of a single capacitor does not exceed 5%. Furthermore, changes in other components of the capacitor bank, such as discharge coils and series reactors, are more noticeably reflected in the changes in the equivalent impedance of the capacitor bank. Therefore, a monitoring accuracy of 0.05% is generally sufficient to meet the monitoring requirements of the capacitor bank.

[0131] This invention eliminates the impact of bus voltage fluctuations on capacitor bank current by introducing bus voltage. However, environmental factors, such as temperature, can affect the equivalent impedance of the capacitor bank. The temperature coefficient of a frame-type polypropylene film dielectric capacitor is approximately 0.03%, meaning that a one-degree temperature change alters the capacitance by 0.03%. A temperature difference of 50°C can result in a 1.5% change in capacitance. Therefore, monitoring the equivalent impedance of a single-phase capacitor bank is easily affected by temperature changes, leading to misjudgments. However, it is not difficult to observe that the effect of temperature on three-phase capacitor banks is considered synchronous. Affected by temperature, the equivalent impedance of the capacitors will increase or decrease simultaneously; therefore, by comparing data laterally, the influence of temperature can be eliminated. To eliminate the impact of temperature changes on capacitor bank monitoring, this solution uses the method of calculating the covariance of the equivalent impedance of the three-phase capacitor bank. When affected by temperature, the three-phase equivalent impedance will show the same increase or decrease, resulting in a higher covariance. If the equivalent impedance of the capacitor bank itself changes, the covariance will decrease. By judging the magnitude of the covariance, it can be determined whether the change in the capacitor bank is caused by temperature changes.

[0132] Optionally, after predicting the three-phase equivalent impedance of the capacitor bank based on phase voltage and current measurements and using a Kalman filter algorithm, the method further includes:

[0133] The covariance of the Nth prediction results of the obtained three-phase equivalent impedance sequences Za, Zb, and Zc is calculated together.

[0134] The factors causing changes in effective impedance are determined based on the calculated covariance.

[0135] Among them, the factors that cause changes in effective impedance are determined based on the calculated covariance, including:

[0136] If the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) are positive, it indicates that normal operation or temperature influence has caused simultaneous changes in A, B, and C.

[0137] Optionally, the factors causing changes in effective impedance can be determined based on the calculated covariance, including:

[0138] If two of the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) are negative, then it is determined that one of the phases A, B, and C has failed.

[0139] Optionally, the covariance of the equivalent impedances of phase M and phase N is defined as:

[0140]

[0141] For example, the covariance of the equivalent impedances of phase A and phase B is defined as:

[0142]

[0143] Where M and N are any two phases from A, B, and C; x is the integration window length; t is time t; -Z is the sequence average; Z m (t) represents Z at each different time point. m The value of Z; n (t) represents Z at each different time point. n The value of .

[0144] For example, when the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) are greater than zero, it indicates that the two sets of sequences are positively correlated, and the larger the value, the stronger the correlation; when the calculated value is zero, it indicates that the two sets of sequences are uncorrelated; when the calculated value is negative, it indicates that the two sets of sequences are negatively correlated. The equivalent impedance change of a capacitor bank due to changes in its own state cannot occur simultaneously across all three phases. Therefore, during normal operation or when the three phases change simultaneously due to temperature influences, the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) will be positive. When a fault occurs in one phase, two of the calculated values ​​of Cov(A,B), Cov(A,C), and Cov(B,C) will become negative. Therefore, this rule can be used to distinguish whether the change in the equivalent impedance of the capacitor bank is caused by changes in its own state.

[0145] In summary, the Kalman filtering algorithm flow provided in this embodiment of the invention is as follows: Figure 10 As shown, Figure 10 This is a schematic diagram of the Kalman filter algorithm provided in Embodiment 2 of the present invention. Specifically, the main idea of ​​the Kalman filter algorithm provided in this embodiment is as follows: First, an equivalent model of the capacitor bank is established to obtain the relationship between the bus voltage, phase current, and equivalent impedance of the capacitor bank. The system state equation and iterative calculation formula are derived based on the Kalman filter algorithm. The algorithm updates the system prediction equation according to the trend of predicted value changes. The variance of the estimation error is dynamically adjusted based on the integral of the difference between the measured and predicted values, thereby improving the algorithm's speed and accuracy in identifying changes in the operating state of the capacitor bank. Finally, by introducing the covariance of the predicted values ​​of the three-phase equivalent impedance, the influence of environmental changes on capacitor bank monitoring is eliminated, and the results are output.

[0146] To verify the effectiveness of the Kalman filter algorithm provided in this embodiment of the invention, a comprehensive simulation of the online monitoring of the capacitor bank was performed. First, the simulation parameters were set according to Table 2.

[0147] Table 2

[0148]

[0149]

[0150] The temperature coefficient represents the effect of temperature changes on the equivalent impedance of the capacitor bank. The simulation consists of five stages, with five varying temperature coefficients to simulate the change in the equivalent impedance of the capacitor bank due to temperature. At time t = 2000, the equivalent impedance of phase A is set to change, while the other two phases remain constant. The simulation results are as follows: Figure 11 , Figure 12 As shown, Figure 11 This is a waveform diagram of the predicted output value of the Kalman filter algorithm provided in Embodiment 2 of the present invention. Figure 12 This is a waveform diagram of the covariance of the predicted three-phase equivalent impedance of capacitor bank A, B, and C provided in Embodiment 2 of the present invention. Figure 11 , Figure 12 It is evident that the equivalent impedance changes with temperature, but the effect of temperature on all three phases is consistent. At t=2000, the equivalent impedance of phase A changes, and the predicted value changes as well. The equivalent impedances of phases B and C themselves do not change; they are only affected by temperature, so their covariance remains positive. (Since the calculation sequence length N=1000, the covariance was not calculated before t=1000, and it is shown as 0 in the image. The negative value of Cov(B,C) near t=1000 is due to interference, so the threshold can be appropriately lowered to improve system robustness.) When t=2000, due to the change in the equivalent impedance of phase A, Cov(A,B) and Cov(A,C) gradually decrease to negative values ​​at t=2000, while Cov(B,C) remains positive, indicating that the equivalent impedance of phase A has changed. Therefore, an alarm signal can be triggered based on this characteristic to indicate that the operating status of capacitor bank A has changed, and the magnitude of the change can be calculated from the ratio before and after.

[0151] For comparison, the equivalent impedances of phases A, B, and C were set to be affected only by temperature during the time interval t = 0-5000, with their intrinsic states remaining unchanged. The simulation results are as follows: Figure 13 , Figure 14 As shown, Figure 13 This is a waveform diagram of the predicted output value of another Kalman filter algorithm provided in Embodiment 2 of the present invention. Figure 14 This is a waveform diagram showing the covariance of the predicted three-phase equivalent impedance of capacitor bank A, B, and C, provided in Embodiment 2 of the present invention. Figure 13 , Figure 14 It can be seen that, affected by temperature changes, the equivalent impedances of phases A, B, and C changed in the same trend. Figure 14 The covariance values ​​are all positive, indicating that the changes in all three phases are caused by temperature changes. Figure 12 The simulation results are compared. In summary, the Kalman filter algorithm provided in this embodiment of the invention can accurately identify the operating state of the capacitor bank and effectively distinguish whether the change in the equivalent impedance of the capacitor bank is due to internal or external factors.

[0152] The technical solution provided by this invention reduces the impact of sensor measurement errors by improving the Kalman filtering algorithm; it reduces interference factors and improves the accuracy of capacitor bank monitoring by reducing hardware and algorithm design; it eliminates voltage fluctuation interference factors by introducing bus voltage monitoring; and finally, it achieves effective identification of environmental change factors by calculating the covariance of phase-to-phase predicted values. This invention does not require additional sensors and can be implemented using existing substation equipment, lowering the implementation threshold and application cost. By utilizing existing sensor measurement data, it improves the monitoring efficiency and accuracy of frame-type capacitor banks.

[0153] Example 3

[0154] Figure 15 This is a schematic diagram of the structure of an online monitoring device for capacitor banks provided in Embodiment 3 of the present invention. Figure 15 As shown, the device includes a measurement value acquisition unit 1501, a neutral point voltage calculation unit 1502, a phase voltage calculation unit 1503, and an equivalent impedance prediction unit 1504, wherein...

[0155] The measurement value acquisition unit 1501 is used to acquire the measured value of the bus voltage connected to the capacitor bank and the measured value of the phase current of the capacitor bank.

[0156] The neutral point voltage calculation unit 1502 is used to calculate the neutral point voltage based on the predicted three-phase equivalent impedance of the capacitor bank at the previous moment.

[0157] Phase voltage calculation unit 1503 is used to obtain phase voltage based on bus voltage measurement and neutral point voltage;

[0158] The equivalent impedance prediction unit 1504 is used to predict the three-phase equivalent impedance of the capacitor bank based on the phase voltage and current measurements and using a Kalman filter algorithm.

[0159] The capacitor bank online monitoring device provided in this embodiment of the invention can execute the capacitor bank online monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0160] Example 4

[0161] Figure 16 This is a schematic diagram of the electronic device 10 for an online monitoring method for capacitor banks provided in Embodiment 4 of the present invention, as shown below. Figure 16 As shown, the electronic device includes:

[0162] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0163] The memory stores a computer program executable by at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the online capacitor bank monitoring method according to any embodiment of the present invention. The present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute and implement the online capacitor bank monitoring method according to any embodiment of the present invention.

[0164] See Figure 16 Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0165] like Figure 16 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0166] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0167] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as an online monitoring method for capacitor banks.

[0168] In some embodiments, the capacitor bank online monitoring method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the capacitor bank online monitoring method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the capacitor bank online monitoring method by any other suitable means (e.g., by means of firmware).

[0169] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0170] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0171] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0172] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0173] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0174] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0175] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0176] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for on-line monitoring of a capacitor bank, characterized by The method comprises: obtaining bus voltage measurement values and phase current measurement values of the capacitor bank; calculating the neutral point voltage according to the predicted three-phase equivalent impedance of the capacitor bank at the previous time; obtaining phase voltage according to the bus voltage measurement values and the neutral point voltage; predicting the three-phase equivalent impedance of the capacitor bank according to the phase voltage and the phase current measurement values by using the Kalman filtering algorithm.

2. The method of claim 1, wherein, The method for predicting the three-phase equivalent impedance of the capacitor bank by using the Kalman filtering algorithm comprises: obtaining the three-phase equivalent impedance of the capacitor bank by using the following prediction equation: Where t is the current time; K is the Kalman gain; With I and V are the measured phase current and phase voltage respectively; P is the prediction error covariance; Y is the equivalent admittance, R is the measurement error of the sensor measuring the bus voltage and phase current; Q is the prediction error variance.

3. The method of claim 1, wherein, The method for predicting the three-phase equivalent impedance of the capacitor bank by using the Kalman filtering algorithm comprises: obtaining the three-phase equivalent impedance of the capacitor bank by using the following prediction equation: where t is the current time; K is the Kalman gain; With are the phase current measurement and phase voltage, respectively; P is the prediction error covariance; R is the measurement error of the sensor measuring the bus voltage and phase current; is a constant coefficient; x is the integral window length; is the prediction error variance base value; D is the differential coefficient.

4. The method according to any one of claims 1-3, characterized in that, After predicting the three-phase equivalent impedance of the capacitor bank according to the phase voltage and the current measurement values by using the Kalman filtering algorithm, the method further comprises: calculating the covariances of the obtained N predicted results of the three-phase equivalent impedance sequence Za, Zb, Zc with each other; determining the factors causing the change of the equivalent impedance according to the calculated covariances.

5. The method of claim 4, wherein, The method for determining the factors causing the change of the equivalent impedance according to the calculated covariances comprises: if the calculated values of Cov(A, B), Cov(A, C) and Cov(B, C) are positive values, it is determined that the normal operation or the temperature influence causes the simultaneous change of the three phases A, B and C.

6. The method of claim 5, wherein, The method for determining the factors causing the change of the equivalent impedance according to the calculated covariances comprises: if two of the calculated values of Cov(A, B), Cov(A, C) and Cov(B, C) are negative values, it is determined that a fault occurs in one of the three phases A, B and C.

7. A capacitor bank on-line monitoring device, characterized by The method comprises: a measurement obtaining unit configured to obtain bus voltage measurement values and phase current measurement values of the capacitor bank; a neutral point voltage calculating unit configured to calculate the neutral point voltage according to the predicted three-phase equivalent impedance of the capacitor bank at the previous time; a phase voltage calculating unit configured to obtain phase voltage according to the bus voltage measurement values and the neutral point voltage; an equivalent impedance predicting unit configured to predict the three-phase equivalent impedance of the capacitor bank according to the phase voltage and the phase current measurement values by using the Kalman filtering algorithm.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the capacitor bank online monitoring method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the capacitor bank online monitoring method in any one of claims 1-6 when executed.

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