High-voltage parallel capacitor online monitoring method based on heuristic algorithm

Abstract

According to the high-voltage parallel capacitor online monitoring method based on the heuristic algorithm, in a high-voltage parallel capacitor circuit, inductive current iL and capacitor voltage vC are collected respectively to obtain system response R0 ((n + 1) * 2) of circuit parameters, diL0 / dt and dvC0 / dt are obtained through differential calculation and recorded as R0 (n * 2), calculation is conducted through the system response R0 ((n + 1) * 2), state predicted values diL / dt and dvC / dt are generated and recorded as R (n * 2), and the state predicted values are recorded as R (n * 2). Taking the sample as an input sample of the optimization model; a heuristic algorithm is adopted to optimize parameters r, RS, L, C and RP of a circuit model, each particle represents a group of possible parameter values, the target is to minimize the mean square error MSE = 1 / n * (sigma (R0-R) 2) between actual measurement data R0 (n * 2) and model prediction data R (n * 2), and when the error is minimum, the optimal parameters of the high-voltage parallel capacitor are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to an online monitoring method for high-voltage shunt capacitors based on a heuristic algorithm. Background Art

[0002] In the prior art, the capacitance of the capacitor is calculated by obtaining the electrical signal on the parallel capacitor device, and the online monitoring of the parallel capacitor is performed. In the prior art for online monitoring of parallel capacitors, in the past, only the power frequency signal was used as the calculation data of the capacitor parameters, and only the capacitance was used as the monitoring result. The specific implementation process is to install a sensor on the parallel capacitor, measure the voltage and current signals of the parallel capacitor, and then extract the power frequency components of the voltage and current signals, and directly calculate the capacitance value C of the capacitor to characterize the state of the capacitor. The direct calculation method will introduce sensor measurement errors, and the capacitance value C has a large error. In the prior art, only the power frequency electrical signal is used to calculate the characteristic value of the parallel capacitor, which has a large error, does not fully utilize the high-order harmonic components in the electrical signal, and only considers the capacitance C as the monitoring parameter, which is easy to cause misjudgment when judging the state of the capacitor. Therefore, it is necessary to make full use of the voltage and current signals that can characterize the state of the parallel capacitor and expand the monitoring dimension.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The present invention provides an online monitoring method for high-voltage parallel capacitors using a heuristic algorithm, which uses a heuristic algorithm to calculate the capacitance C and equivalent series resistance ESR of a device, thereby improving the accuracy of monitoring and realizing rapid acquisition of the state of the parallel capacitors.

[0005] A method for online monitoring of high-voltage shunt capacitors based on a heuristic algorithm includes:

[0006] S1: In the high-voltage parallel capacitor circuit, the inductor current i is collected separately L and capacitor voltage v C , the system response of the circuit parameters is obtained R0((n+1)×2), where n+1 represents the number of sampling points, 2 represents the two types of measurement values, current and voltage, and di is obtained by differential calculation. L0 / dt and dv C0 / dt is recorded as R0(n×2);

[0007] S2: The differential equation of the system dynamic model of the high-voltage parallel capacitor circuit is obtained by the equivalent circuit model of the high-voltage parallel capacitor group, which is:

[0008] ,

[0009] Among them, i L is the current flowing through the inductor L, v C are capacitors C and R S The voltage across the terminals, v i is the voltage of the busbar connected to the capacitor bank, r is the equivalent on-resistance of the circuit breaker and disconnector, L is the equivalent inductance of the reactor and line, R S is the equivalent series resistance of the capacitor, C is the capacitance of the capacitor, R P is the equivalent parallel resistance of the capacitor;

[0010] S3: Using the data obtained from the system response R0 ((n+1) × 2), the differential equation is used to calculate and generate the state prediction value di L / dt and dv C / dt, recorded as R(n×2), and used as the input sample of the optimization model;

[0011] S4: Use heuristic algorithm to adjust the parameters r and R of the circuit model S , L, C, R P Each particle represents a set of possible parameter values. The goal is to minimize the mean square error (MSE) between the actual measured data R0 (n×2) and the model predicted data R (n×2) = 1 / n*(Σ(R0-R) 2 ), when the error reaches the minimum value, the optimal parameters of the high-voltage shunt capacitor are obtained.

[0012] In the described online monitoring method for high-voltage shunt capacitors based on a heuristic algorithm, the heuristic algorithm is a particle swarm algorithm.

[0013] In the above-mentioned method for online monitoring of high-voltage shunt capacitors based on heuristic algorithm, the particle swarm algorithm is used to calculate the parameters r and R of the circuit model. S , L, C, R P Optimization includes:

[0014] Initialization, randomly generate a set of particles, each particle contains five circuit parameters r, R to be optimized S , L, C, R P ;

[0015] Velocity update: updates the velocity of particles based on the historical best positions of individuals and groups;

[0016] Position update, adjust the particle position according to the updated velocity, i.e., circuit parameters;

[0017] Evaluation: Calculate the mean square error (MSE) between the model prediction value and the actual measured value under the circuit parameter configuration represented by each particle.2 ), when the mean square error is the smallest, the optimal parameters of the circuit parameters are obtained, that is, the monitoring results of the high-voltage shunt capacitor.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] The present invention can improve the online monitoring accuracy of high-voltage shunt capacitors; more parameters are monitored online; the heuristic algorithm is used for calculation, and the parameters can be accurately calculated with a small number of data samples; the heuristic algorithm is used for calculation, and the monitoring inaccuracy caused by sensor measurement errors can be reduced, and there is no need to use a filtering algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work. Moreover, the same reference numerals are used to represent the same components throughout the drawings.

[0021] In the attached picture:

[0022] Figure 1 It is a schematic diagram of a high-voltage shunt capacitor circuit connection of a high-voltage shunt capacitor online monitoring method based on a heuristic algorithm in one embodiment of the present invention;

[0023] Figure 2 It is a structural schematic diagram of a high-voltage shunt capacitor circuit equivalent model of an online monitoring method for high-voltage shunt capacitors based on a heuristic algorithm in one embodiment of the present invention;

[0024] Figure 3 It is a flow chart of an online monitoring method of a high-voltage shunt capacitor based on a heuristic algorithm in one embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of a particle swarm algorithm convergence curve of an online monitoring method for high-voltage shunt capacitors based on a heuristic algorithm in one embodiment of the present invention.

[0026] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0027] The specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0028] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0029] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0030] like Figures 1 to 4 As shown, the online monitoring method of high-voltage shunt capacitors based on heuristic algorithm includes the following steps:

[0031] S1: In the high-voltage parallel capacitor circuit, the current i of the current transformer coil TA is collected respectively L and the voltage v of the capacitor bank discharge coil voltage transformer TV C , the system response R0((n+1)×2) of the circuit parameters is obtained, where n+1 represents the number of sampling points, 2 represents the two types of measurement values, current and voltage, and di is obtained by differential calculation. L0 / dt and dv C0 / dt is recorded as R0(n×2); Figure 1 As shown, QS is a circuit breaker, QF is an isolating switch, TA is a current transformer coil, L is a series reactor, C is a high-voltage shunt capacitor, and TV is a capacitor bank discharge coil voltage sensor.

[0032] S2: The differential equation of the system dynamic model of the high-voltage parallel capacitor circuit is obtained by the equivalent circuit model of the high-voltage parallel capacitor group:

[0033] ,

[0034] Among them, i Lis the current flowing through the inductor L, v C are capacitors C and R S The voltage across the terminals, v i is the voltage of the busbar connected to the capacitor bank, r is the equivalent on-resistance of the circuit breaker and disconnector, L is the equivalent inductance of the reactor and line, R S is the equivalent series resistance of the capacitor, C is the capacitance of the capacitor, R P is the equivalent parallel resistance of the capacitor; Figure 2 In the equivalent circuit model of the high-voltage parallel capacitor bank shown in the figure, r is the equivalent on-resistance of the circuit breaker and the disconnector, L is the equivalent inductance of the reactor and the line, R S is the equivalent series resistance of the capacitor, C is the capacitance of the capacitor, R P is the equivalent parallel resistance of the capacitor;

[0035] S3: Using the data obtained from the system response R0 ((n+1) × 2), the differential equation is used to calculate and generate the state prediction value di L / dt and dv C / dt, recorded as R(n×2), and used as the input sample of the optimization model;

[0036] S4: Use heuristic algorithm to adjust the parameters r and R of the circuit model S , L, C, R P The optimization is performed, and each particle represents a set of possible parameter values. The goal is to minimize the mean square error between the actual measured data R0(n×2) and the model predicted data R(n×2). When the error reaches the minimum value, the optimal parameters of the high-voltage shunt capacitor are obtained.

[0037] In a preferred implementation of the heuristic algorithm-based online monitoring method for high-voltage shunt capacitors, the heuristic algorithm is a particle swarm algorithm.

[0038] In a preferred embodiment of the online monitoring method of high-voltage shunt capacitors based on a heuristic algorithm, the particle swarm algorithm is used to calculate the parameters r, R S , L, C, R P Optimization includes:

[0039] Initialization, randomly generate a set of particles, each particle contains five circuit parameters r, R to be optimized S , L, C, R P ;

[0040] Speed ​​update, update the speed of particles according to the historical best positions of individuals and groups,

[0041] Position update, adjust the particle position according to the updated velocity, i.e. the circuit parameters,

[0042] Evaluation: Calculate the mean square error (MSE) between the model prediction value and the actual measured value under the circuit parameter configuration represented by each particle. 2 ), when the mean square error is the smallest, the optimal parameters of the circuit parameters are obtained, that is, the monitoring results of the high-voltage shunt capacitor.

[0043] In one embodiment, the method includes,

[0044] S1: In the high-voltage parallel capacitor circuit, the inductor current i is collected separately L and capacitor voltage v C , the system response of the circuit parameters is obtained R0((n+1)×2), where n+1 represents the number of sampling points, 2 represents the two types of measurement values, current and voltage, and di is obtained by differential calculation. L0 / dt and dv C0 / dt is recorded as R0(n×2);

[0045] S2: Write the differential equations of the system dynamic model through the circuit topology of high-voltage shunt capacitors;

[0046]

[0047] S3: Using the data obtained from the system response R0(n×2), the state equation is discretized to generate the state prediction value di L / dt and dv C / dt, recorded as R(n×2), and used as the input sample of the optimization model;

[0048] S4: Use heuristic algorithm to adjust the parameters r and R of the circuit model S , L, C, R P Each particle represents a set of possible parameter values. The goal is to minimize the mean square error (MSE) between the actual measured data R0 (n×2) and the model predicted data R (n×2) = 1 / n*(Σ(R0-R) 2 ), when MSE reaches the minimum value, the optimal parameters of the high-voltage parallel capacitor are obtained;

[0049] S5: Use particle swarm algorithm to perform online monitoring of high-voltage shunt capacitors. The method is as follows:

[0050] Initialization: A set of particles is randomly generated, each particle contains five circuit parameters to be optimized.

[0051] Velocity Update: Update the velocity of particles based on their individual and group best historical positions.

[0052] Position update: Adjust the particle’s position, i.e., circuit parameter, according to the updated velocity.

[0053] Evaluation: Calculate the mean square error (MSE) between the model prediction value and the actual measured value under the circuit parameter configuration represented by each particle. 2 ).

[0054] When the mean square error is the smallest, the optimal parameters of the circuit parameters are obtained, that is, the monitoring results of the high-voltage shunt capacitor.

[0055] Calculate the convergence curve as Figure 4 As shown, the objective function converges to the minimum after 9 rounds of iterations. The true value is the set value, the monitoring value is the value calculated by this algorithm, and the error is |monitoring value-true value| / true value.

[0056]

[0057] The online monitoring method of high-voltage shunt capacitors based on heuristic algorithm can achieve a monitoring error of no more than 0.02% for the capacitance value and a monitoring error of no more than 2% for the equivalent series resistance value, which can effectively realize the monitoring of high-voltage shunt capacitors.

[0058] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A method for online monitoring of high-voltage shunt capacitors based on a heuristic algorithm, characterized in that: The steps include: S1: In the high-voltage parallel capacitor circuit, the inductor current i is collected separately L and capacitor voltage v C , the system response of the circuit parameters is obtained R0((n+1)×2), where n+1 represents the number of sampling points, 2 represents the two types of measurement values, current and voltage, and di is obtained by differential calculation. L0 / dt and dv C0 / dt is recorded as R0(n×2); S2: The differential equation of the system dynamic model of the high-voltage parallel capacitor circuit is obtained by the equivalent circuit model of the high-voltage parallel capacitor group, which is: ， Among them, i L is the current flowing through the inductor L, v C are capacitors C and R S The voltage across the terminals, v i is the voltage of the busbar connected to the capacitor bank, r is the equivalent on-resistance of the circuit breaker and disconnector, L is the equivalent inductance of the reactor and line, R S is the equivalent series resistance of the capacitor, C is the capacitance of the capacitor, R P is the equivalent parallel resistance of the capacitor; S3: Using the data obtained from the system response R0 ((n+1) × 2), the differential equation is used to calculate and generate the state prediction value di L / dt and dv C / dt, recorded as R(n×2), and used as the input sample of the optimization model; S4: Use heuristic algorithm to adjust the parameters r and R of the circuit model S , L, C, R P Each particle represents a set of possible parameter values. The goal is to minimize the mean square error (MSE) between the actual measured data R0 (n×2) and the model predicted data R (n×2) = 1 / n*(Σ(R0-R) 2 ), when the error reaches the minimum value, the optimal parameters of the high-voltage shunt capacitor are obtained.

2. The method for online monitoring of high-voltage shunt capacitors based on a heuristic algorithm according to claim 1, characterized in that: Preferably, the heuristic algorithm is a particle swarm algorithm.

3. The method for online monitoring of high-voltage shunt capacitors based on a heuristic algorithm according to claim 1, characterized in that: Particle swarm algorithm for parameters r, R of circuit model S , L, C, R P Optimization includes: Initialization, randomly generate a set of particles, each particle contains five circuit parameters r, R to be optimized S , L, C, R P ; Velocity update: updates the velocity of particles based on the historical best positions of individuals and groups; Position update, adjust the particle position according to the updated velocity, i.e., circuit parameters; Evaluation, calculate the mean square error MSE = 1 / n*(Σ(R0-R)) between the model prediction value and the actual measurement value under the circuit parameter configuration represented by each particle 2 ), when the mean square error is the smallest, the optimal parameters of the circuit parameters are obtained, that is, the monitoring results of the high-voltage shunt capacitor.

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