CN110929999A - Voltage sag severity calculation method considering tolerance characteristics of different devices - Google Patents

Voltage sag severity calculation method considering tolerance characteristics of different devices Download PDF

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CN110929999A
CN110929999A CN201911087353.9A CN201911087353A CN110929999A CN 110929999 A CN110929999 A CN 110929999A CN 201911087353 A CN201911087353 A CN 201911087353A CN 110929999 A CN110929999 A CN 110929999A
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distribution model
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voltage sag
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voltage
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周凯
王勇
许中
马智远
饶毅
栾乐
张群峰
曲烽瑞
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The application relates to a voltage sag severity calculation method considering tolerance characteristics of different devices. The method comprises the following steps: the server obtains an uncertain area of the electric power equipment to be quantized according to a voltage drop distribution model table, obtains a target probability distribution model corresponding to the uncertain area of the electric power equipment to be quantized, determines an expected tolerance curve of the electric power equipment to be quantized according to the target probability distribution model, and determines a voltage sag quantized value of the electric power equipment to be quantized according to the expected tolerance curve. In the application, when the server quantizes the voltage sag severity of different power equipment, the server is carried out pertinently, the accuracy of the quantization result of the voltage sag severity of each power equipment is improved, so that when the voltage sag severity of the power system is evaluated, excessive evaluation cannot occur, and the evaluation result is more accurate.

Description

Voltage sag severity calculation method considering tolerance characteristics of different devices
Technical Field
The application relates to the technical field of electric power, in particular to a voltage sag severity calculation method considering tolerance characteristics of different devices.
Background
With the rapid development of new power loads and the continuous improvement of the requirements on the quality of electric energy, the quality of electric energy has become a common concern for power supply departments and users. For example, voltage sag, although short in duration, seriously interferes with the normal operation of many modern devices, especially some sensitive devices applied to industrial and commercial users, and once voltage sag occurs, certain economic losses are caused.
The voltage sag represents that the effective value of the power supply voltage rapidly drops to 90% -10% of the rated value and lasts for 0.5-30 power frequency periods. In order to avoid economic loss caused by voltage sag, the severity of the voltage sag generally needs to be evaluated, and in the prior art, the evaluation of the severity of the voltage sag of each device is completed according to an F47 curve of Semiconductor Equipment and Material International (SEMI) as a basis for evaluating the severity of the voltage sag of each device in a power system.
However, in the prior art, the severity of the voltage sag of each device is over-evaluated, so that the evaluation result is rough and the real sag level of the node cannot be reflected.
Disclosure of Invention
In view of the above, it is necessary to provide a voltage sag severity calculation method considering different device tolerance characteristics in view of the above technical problems.
In a first aspect, the present application provides a voltage sag quantization method, including:
acquiring an uncertain area of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength caused by power equipment to be quantized is located;
obtaining a target probability distribution model of an uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table pre-stores the corresponding relation between the uncertain regions of various types of electric equipment and the probability distribution model;
determining an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model;
and determining a voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
In one embodiment, if a power node includes a plurality of sensitive power devices and a plurality of general power devices, the method further includes:
respectively acquiring voltage sag quantized values of each sensitive power device and each general power device and weights corresponding to each sensitive power device and each general power device;
and determining the voltage sag quantized value of each sensitive power device and each general power device as the voltage sag quantized value of the power node by the weighted sum of the voltage sag quantized values and the corresponding weights.
In one embodiment, the process of creating the pressure drop distribution model table includes:
acquiring uncertain areas of various types of electric equipment;
analyzing the distribution density rule of voltage reduction amplitude in each uncertain region;
determining a probability distribution model corresponding to each uncertain region according to a distribution density rule;
and storing each uncertain region and the corresponding probability distribution model into a pressure drop distribution model table.
In one embodiment, if an uncertainty area of an electrical device includes multiple sub-areas; one sub-region corresponds to one probability distribution model.
In one embodiment, the probability distribution model includes at least a normal distribution model, a uniform distribution model, and an exponential distribution model.
In one embodiment, the determining an expected tolerance curve of the power device to be quantified according to the target probability distribution model includes:
obtaining an expected value of the voltage drop amplitude of the uncertain region according to the voltage drop amplitude corresponding to the uncertain region boundary and a probability distribution model corresponding to the uncertain region;
obtaining an expected value of the duration of the uncertain region according to the duration corresponding to the uncertain region boundary and a probability distribution model corresponding to the uncertain region;
and determining an expected tolerance curve according to the expected value of the voltage drop amplitude and the expected value of the duration.
In one embodiment, the determining the quantized value of the voltage sag of the power device to be quantized according to the expected tolerance curve includes:
acquiring an actual voltage drop amplitude and an actual duration of the equipment to be quantized;
acquiring an expected voltage drop amplitude corresponding to the actual duration on an expected tolerance curve;
and determining a voltage sag quantized value according to the actual voltage sag amplitude and the expected voltage sag amplitude.
In a second aspect, the present application provides a voltage sag quantization apparatus, comprising:
the uncertain region acquisition module is used for acquiring an uncertain region of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength caused by power equipment to be quantized is located;
the target probability model acquisition module is used for acquiring a target probability distribution model of the uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table pre-stores the corresponding relation between the uncertain regions of various types of electric equipment and the probability distribution model;
the expected tolerance curve determining module is used for determining an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model;
and the voltage sag quantized value determining module is used for determining the voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
In a third aspect, the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the voltage sag quantization method provided in any one of the embodiments of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the voltage sag quantization method provided in any one of the embodiments of the first aspect.
According to the voltage sag severity calculation method considering the tolerance characteristics of different devices, the uncertain region of the power device to be quantized is obtained, the target probability distribution model corresponding to the uncertain region of the power device to be quantized is obtained according to the corresponding relation between the uncertain region and the probability distribution model of the power devices of various types stored in the voltage drop distribution model table in advance, the expected tolerance curve of the power device to be quantized is determined according to the target probability distribution model, and then the voltage sag quantized value of the power device to be quantized is determined according to the expected tolerance curve. In the application, due to the fact that the probability distribution models corresponding to the uncertain regions of different power equipment are established, the expected tolerance curves corresponding to the equipment can be established according to the probability distribution models corresponding to the equipment, and therefore when voltage sag severity of different power equipment is quantized, the voltage sag severity is pertinently conducted, the accuracy of the quantized result of the voltage sag severity of the power equipment is improved, and therefore when the voltage sag severity of the power equipment is evaluated in a power system, excessive evaluation cannot occur, and the evaluation result is more accurate.
Drawings
FIG. 1 is a diagram of an exemplary implementation of a voltage sag quantization method;
FIG. 2 is a flow diagram illustrating a voltage sag quantization method according to one embodiment;
FIG. 2a is a scatter plot of a power device multiple voltage droop duration and voltage droop amplitude in one embodiment;
FIG. 2b is a schematic diagram of the uncertainty region of the sensing device PLC in one embodiment;
FIG. 2c is a schematic illustration of the uncertainty region of a sensitive device ASD in one embodiment;
FIG. 2d is a schematic illustration of the uncertainty region of the sensing device PC in one embodiment;
fig. 2e is a schematic illustration of the uncertainty area of the sensitive device ACC in one embodiment;
FIG. 3 is a flow chart illustrating a voltage sag quantization method according to another embodiment;
FIG. 3a is a diagram illustrating an embodiment of an overall process flow of a voltage sag quantization method;
FIG. 3b is a diagram illustrating a comparison between voltage sag quantization and conventional simulation in one embodiment;
FIG. 3c is a graph illustrating expected endurance curves of each sensing device in the voltage sag quantification method in comparison with the F47 curve in one embodiment;
FIG. 4 is a flow chart illustrating a voltage sag quantization method according to another embodiment;
FIG. 4a is a schematic diagram of an uncertainty region probability distribution model analysis of a PLC, an ASD, and a PC in one embodiment;
FIG. 4b is a schematic diagram of an analysis of an uncertainty region probability distribution model of ACCs in one embodiment;
FIG. 5 is a flow chart illustrating a voltage sag quantization method according to another embodiment;
FIG. 5a is a schematic diagram of the expected tolerance curve of a sensing device PLC in one embodiment;
FIG. 5b is a graphical illustration of the expected tolerance curve of the ASD of the sensing device in one embodiment;
FIG. 5c is a graphical illustration of the expected tolerance curve of the sensing device PC in one embodiment;
FIG. 5d is a graphical illustration of the expected tolerance curve for the sensitive apparatus ACC in one embodiment;
FIG. 6 is a flow chart illustrating a voltage sag quantization method according to another embodiment;
FIG. 7 is a block diagram of an exemplary voltage sag quantization apparatus;
FIG. 8 is a block diagram of an alternative embodiment of a voltage sag quantization apparatus;
FIG. 9 is a block diagram of an alternative embodiment of a voltage sag quantization apparatus;
FIG. 10 is a block diagram of an alternative embodiment of a voltage sag quantization apparatus;
FIG. 11 is a block diagram of an alternative embodiment of a voltage sag quantization apparatus;
FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The voltage sag quantization method provided by the application can be applied to the application environment shown in fig. 1. The server 101 is respectively in communication with the sensitive devices 102 and the general devices 103 through a network, where the sensitive devices and the general devices may be collectively referred to as power devices, the sensitive devices represent power devices with a large influence due to abnormal operation in the power system, and the general devices are power devices with a small influence due to abnormal operation in the power system. The server 101 may be a console server in a power system, and is composed of an independent server or a plurality of servers, each sensitive device 102 may be, but is not limited to, a Programmable Logic Controller (PLC), an adjustable speed Drive device (ASD), a Personal Computer (PC), and an AC Contactor (ACC), and the general device 103 may be, but is not limited to, a signal device and other power supply devices.
The following describes in detail the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems by embodiments and with reference to the drawings. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. It should be noted that, in the voltage sag quantization method provided in the embodiments of fig. 2 to fig. 6 of the present application, an execution subject may be a server, such as a voltage sag quantization server, or may be a voltage sag quantization apparatus, and the voltage sag quantization apparatus may become a part or all of the voltage sag quantization server by software, hardware, or a combination of software and hardware. In the following method embodiments, the execution subject is a voltage sag quantization server as an example.
In one embodiment, fig. 2 provides a voltage sag quantization method, which relates to a specific process in which a server determines a corresponding probability distribution model through an uncertain region of each power device, determines a tolerance curve of each power device according to the probability distribution model, and then calculates a voltage sag quantization value of each power device according to the tolerance curve, as shown in fig. 2, the method includes the following steps:
s201, acquiring an uncertain area of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength on the power equipment to be quantized is located.
The uncertain region represents a region in which the reduced voltage amplitude value can cause damage to the power equipment when the power equipment generates voltage sag, but the damage degree is uncertain, that is, corresponding data in the uncertain region are voltage reduction amplitude values. For example, as shown in fig. 2a, when a voltage sag occurs in an electrical power device, a specific voltage drop amplitude and a voltage drop duration (hereinafter referred to as voltage dip duration) corresponding to the voltage drop amplitude are generated, and if a coordinate system is established with the voltage drop amplitude as an ordinate and the voltage drop duration as an abscissa, a scatter diagram is formed by a plurality of data points in the coordinate system, and the scatter diagram is processed to form an uncertain region; the uncertain region may be a regular region, such as a quadrilateral region, or an irregular region.
In this embodiment, the scattergram is processed, for example, by taking an upper limit value and a lower limit value of the duration of all data points in the scattergram, taking an upper limit value and a lower limit value of the voltage amplitude of all data points, and drawing a line segment perpendicular to the vertical axis and the horizontal axis with respect to each of the above points, thereby determining the uncertainty area of the electric power equipment.
Taking the sensitive devices PLC, ASD, PC and ACC as examples, as shown in fig. 2b, 2c, 2d and 2e, the shaded portion in the figure is the uncertainty area of each power device, fig. 2b is the uncertainty area of PLC, fig. 2c is the uncertainty area of ASD, fig. 2d is the uncertainty area of PC, and fig. 2e is the uncertainty area of ACC. As can be seen from fig. 2b, 2c, 2d and 2e, the uncertainty area corresponding to the PLC, the ASD and the PC can be regarded as an irregular uncertainty area formed by two regular areas, and the uncertainty area corresponding to the ACC can be regarded as an irregular uncertainty area formed by a plurality of regular areas, which is not limited in this embodiment.
S202, acquiring a target probability distribution model of the uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table stores the corresponding relation between the uncertain regions of various types of electric power equipment and the probability distribution model in advance.
The voltage drop distribution model table refers to a table in which correspondence between uncertain regions of various types of power equipment and probability distribution models are stored in advance, and is used for determining the probability distribution model corresponding to the uncertain regions according to the uncertain regions of the power equipment to be quantized, wherein the determined probability distribution model is the target probability distribution model. Specifically, the uncertain region includes a plurality of data points of the power equipment to be quantized, and a corresponding relationship between the data point distribution density and the probability distribution model is set according to the distribution density of the data points, so as to generate a pressure drop distribution model table. In practical application, the server may directly obtain the probability distribution model corresponding to the uncertain region of the current device to be quantized from the pressure drop distribution model table.
In this embodiment, the uncertain region includes data points composed of a plurality of voltage drop amplitudes and durations of the voltage drop amplitudes, and the server analyzes a distribution density rule of the data points in the plurality of types of uncertain regions to determine a corresponding probability distribution model.
Optionally, the probability distribution model at least includes a normal distribution model, a uniform distribution model, and an exponential distribution model. The data point distribution density rule may include a normal distribution, a uniform distribution, an exponential distribution, and the like. For example, the server determines that the probability distribution model corresponding to the uncertain region of the device to be quantized is a normal distribution model according to the corresponding relationship between the multiple types of uncertain regions and the probability distribution model in the pressure drop distribution model table when analyzing that the data point distribution density of the uncertain region of the device to be quantized is in a normal distribution, or determines that the probability distribution model corresponding to the uncertain region of the device to be quantized is a uniform distribution model according to the pressure drop distribution model table when analyzing that the data point distribution density of the uncertain region of the device to be quantized is in a uniform distribution, which is not limited in this embodiment.
S203, determining an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model.
The expected tolerance curve refers to an expected value set of a voltage drop amplitude value and a voltage drop duration time in an uncertain region of the device to be quantized, and is used for indicating the rated degree of voltage sag of the device to be quantized, and if the voltage sag amplitude value corresponding to the actual voltage drop duration time of the device to be quantized is larger than the voltage sag amplitude value corresponding to the voltage drop duration time in the expected tolerance curve, the current voltage sag degree of the device to be quantized can be regarded as the severity degree.
In this embodiment, the server may first obtain an upper limit value and a lower limit value of the voltage drop duration and an upper limit value and a lower limit value of the voltage drop amplitude in the uncertain region of the device to be quantized, and calculate and determine an expected value set of the tolerance curve corresponding to the device to be quantized according to different target probability distribution models of the device to be quantized and the upper limit value and the lower limit value of the duration and the upper limit value and the lower limit value of the voltage amplitude, so as to form an expected tolerance curve. For example, as can be seen from fig. 4a, if the target probability distribution model of the uncertainty region of the PLC is a normal distribution model, the expected value set of the expected tolerance curve may be values of the middle points of the upper limit value and the lower limit value of the duration and the upper limit value and the lower limit value of the voltage amplitude in the uncertainty region, as shown in fig. 5 a; if the target probability distribution model is a uniform distribution model, the expected value set of the expected tolerance curve may be an average value of an upper limit and a lower limit of the duration and an upper limit and a lower limit of the voltage amplitude in the uncertainty region, which is not limited in this embodiment.
And S204, determining a voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
The voltage sag quantized value represents a quantized value of the severity of the voltage sag, which occurs in the power equipment to be quantized and affects the power equipment.
In this embodiment, the server obtains an expected tolerance curve of the power device to be quantified, as a basis for evaluating the severity of the next voltage sag of the power device. For example, the server may obtain a voltage drop duration and a voltage drop amplitude of the current power device in practice, according to the determined expected tolerance curve corresponding to the power device to be quantized, obtain a voltage drop amplitude corresponding to the current voltage drop duration in the expected tolerance curve as an expected value of the device to be quantized, and perform processing calculation on the expected value and the actual voltage drop amplitude, so as to determine a difference value between the actual voltage drop amplitude and the expected value, where the difference value is a quantized value, and the severity may be evaluated according to the quantized value, if the quantized value is less than 1, the voltage sag degree of the device to be quantized is relatively severe, if the quantized value is greater than or equal to 1, the voltage sag degree of the device to be quantized is slight, which is not limited in this embodiment.
In the voltage sag quantization method, the server acquires a target probability distribution model corresponding to the uncertain region of the power equipment to be quantized by acquiring the uncertain region of the power equipment to be quantized according to the correspondence relationship between the uncertain region and the probability distribution model, in which various types of power equipment are pre-stored in the voltage drop distribution model table, determines an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model, and then determines the voltage sag quantization value of the power equipment to be quantized according to the expected tolerance curve. In the method, because the probability distribution models corresponding to the uncertain regions of different power equipment are established, the expected tolerance curve corresponding to each equipment can be established according to the probability distribution models corresponding to each equipment, so that the voltage sag severity of different power equipment is quantified in a targeted manner, the accuracy of the quantification result of the voltage sag severity of each power equipment is improved, and thus, when the voltage sag severity of the power system is evaluated, over-evaluation cannot occur, and the evaluation result is more accurate.
In some scenarios, when evaluating an electrical device for a certain time node in an electrical power system, there are not only a sensitive device but also a general device, an embodiment is provided below, where if there are multiple sensitive devices and multiple general devices in a node, as shown in fig. 3, the method further includes:
and S301, respectively acquiring voltage sag quantized values of each sensitive electric power device and each general electric power device and corresponding weights of each sensitive electric power device and each general electric power device.
The weight value represents the proportion of each power device in one node, each power device can be set to be equal, and different weight values can be set for different devices according to actual conditions.
In this embodiment, when a voltage sag occurs, the server may obtain the quantized voltage sag values of the sensitive devices through the expected tolerance curves corresponding to the sensitive devices in the above method, or may obtain the quantized voltage sag values of the general electric power devices through the conventional SEMI F47 curves, and the server obtains the preset quantized voltage sag values of the electric power devices and sets corresponding weights, so as to calculate the node-integrated quantized voltage sag values.
Specifically, taking the examples that the sensitive power devices are PLC, ASD, PC and ACC, the corresponding quantized value of voltage sag may be represented as SPLC、SASD、SPCAnd SACCThe voltage sag quantization value of other general power equipment is SothersThen, the weight corresponding to each sensitive power device can be represented as ωPLC、ωASD、ωPCAnd ωACCThe weight of a general power device may be represented as ωothersWherein, ω isPLC、ωASD、ωPC、ωACCAnd ωothersThe values may be set to 20% for all of them, which is not limited in this embodiment.
And S302, determining the voltage sag quantized value of each sensitive power device and each general power device as the voltage sag quantized value of the power node by the weighted sum of the voltage sag quantized values and corresponding weights.
In this embodiment, the server performs weighted calculation on the acquired voltage sag quantized values of each sensitive power device and each general power device and corresponding weights to obtain a comprehensive voltage sag quantized value of the power node.
Specifically, according to the expression of each parameter, the node synthesizes the voltage sag quantized value SnCan be expressed as:
Sn＝ωPLC·SPLC+ωASD·SASD+ωPC·SPC+ωACC·SACC+ωothers·Sothers(1)
in this embodiment, the server obtains the voltage sag quantized values of all the power devices included in one node and the weights corresponding to the voltage sag quantized values, performs weighted calculation on the voltage sag quantized values of the power devices to obtain a comprehensive voltage sag quantized value of the node, and in the calculation process, each device is regarded as a whole, rather than one node, so that the node comprehensive voltage sag quantized value can more accurately reflect the node voltage sag degree.
Further, the flow chart of the method can be referred to fig. 3 a. The server establishes an expected tolerance curve corresponding to each sensitive device according to the method, and calculates the severity index of voltage sag of each sensitive device through the expected tolerance curve to serve as a voltage sag quantized value; calculating the voltage sag severity index of each general device through a SEMI47 curve in a traditional method, and taking the voltage sag severity index as a voltage sag quantized value; and carrying out weighted summation on the voltage sag quantized values of the power equipment to obtain a node comprehensive voltage sag severity index, namely the node comprehensive voltage sag quantized value.
Based on the method, the reliability of the method for evaluating the degree of the node voltage sag can be verified by performing simulation evaluation on a plurality of node voltage sags.
For example, the voltage drop amplitude and the voltage drop duration of each power device can be obtained by a method of randomly generating data such as measured data or monte carlo, and the weight ω of each power device is setPLC、ωASD、ωPC、ωACC、ωothersAll 20 percent.
Fig. 3b is a schematic diagram illustrating a comparison between the quantized voltage sag values of the sensitive devices and the general devices calculated by combining the above method and the conventional method, and the quantized voltage sag values of the sensitive devices and the general devices calculated by the conventional method;
fig. 3c shows a comparison graph of the expected tolerance curve of each sensitive device calculated by the above method, and the SEMI F47 curve in the conventional method.
As can be seen from fig. 3b and 3c, the evaluation result trend of the above method is consistent with that of the conventional method, which shows that different node sag levels can be effectively represented; the area of the voltage sag region surrounded by the conventional SEMI F47 curve is the largest and almost all higher than the expected tolerance curve of each sensitive device calculated based on the above method in the same duration, which indicates that the conventional method may overestimate the sag severity of each sensitive device.
In this embodiment, the server calculates and obtains the voltage sag quantized values of each sensitive device and each general device by combining the method with the conventional method, sets corresponding weights for the voltage sag quantized values of each sensitive device and each general device, and performs weighted summation calculation to obtain the comprehensive voltage sag quantized value of the node.
In an embodiment, before obtaining the target probability distribution model of the uncertain region according to the pressure drop distribution model table, as shown in fig. 4, the process of establishing the pressure drop distribution model table includes:
s401, acquiring uncertain areas of various types of electric equipment.
In this embodiment, the multiple types of uncertain regions refer to multiple types of data point distribution densities in the uncertain regions, the server obtains the data point distribution densities in the uncertain regions of each electrical device, and the data point distribution densities in the uncertain regions of each electrical device may be partially similar, and may not be similar, so as to form multiple types of data point distribution densities.
S402, analyzing the distribution density rule of the voltage reduction amplitude in each uncertain area.
And the distribution density rule of the voltage reduction amplitude is the distribution density rule of the data points in the uncertain area.
In this embodiment, the server may analyze the distribution density rule by acquiring 90% to 100% of the data points in the uncertainty area, where 90% is taken to omit individual limit values, so as to reduce the influence on the analysis process. If the uncertain region of the power equipment is an irregular region, the irregular region is divided into several regular sub-regions, and the data point distribution density rule of each sub-region is analyzed, which is not limited in this embodiment.
And S403, determining a probability distribution model corresponding to each uncertain region according to the distribution density rule.
In this embodiment, the server determines the probability distribution model corresponding to each uncertain region according to the data point distribution density rule obtained through the analysis.
Optionally, if the uncertainty area of one power device includes a plurality of sub-areas; one sub-region corresponds to one probability distribution model.
The uncertain region of the device to be quantified can comprise at least one sub-region, and if the uncertain region comprises a plurality of sub-regions, the probability distribution model corresponding to the sub-region is determined according to the data point distribution density rule of the sub-region. The probability distribution models of the sub-regions use the boundary values of the sub-regions as constraint conditions, and the probability distribution models of the plurality of sub-regions form a target probability distribution model of the uncertain region of the power equipment.
In this embodiment, if the uncertain region of the device to be quantized includes a plurality of sub-regions, the server finally forms a target probability distribution model of the whole uncertain region of the device to be quantized by determining the probability distribution models for the plurality of sub-regions, so that the target probability distribution model represents the data distribution density more accurately.
Exemplarily, according to fig. 2b, 2c, and 2d, for rectangular uncertain regions of three types of sensitive devices, i.e., PLC, ASD, and PC, when the data point distribution density rule is analyzed to be normally distributed, the normal distribution model is determined to be the target probability distribution model of the three types of sensitive devices, i.e., PLC, ASD, and PC, as shown in fig. 4a, the horizontal coordinate is the duration of voltage sag, T is TminIs a lower limit value of duration, TmaxIs the upper limit value of the duration; the vertical coordinate is the amplitude of the voltage sag, VminIs the lower limit of amplitude, VmaxIs the upper limit value of the amplitude. According to fig. 2e, irregular uncertainty regions, which are more complex for ACC; as shown in fig. 4b, the uncertain region is divided into a sub-region 1, a sub-region 2, a sub-region 3, and a sub-region 4, which are analyzed respectively, to determine that a multi-region segmented probability distribution model combining a uniform distribution model and an exponential distribution model is a target probability distribution model of the uncertain region of the device, which is not limited in this embodiment.
S404, storing each uncertain region and the corresponding probability distribution model into a pressure drop distribution model table.
In this embodiment, the server stores the correspondence between each different type of uncertain region and the probability distribution model in the pressure drop distribution model table by analyzing the probability distribution model corresponding to each uncertain region of the electrical equipment, so as to facilitate the selection of the probability model next time.
In this embodiment, the server establishes a corresponding relationship between different types of uncertain regions and corresponding probability distribution models by analyzing data point distribution density rules of the uncertain regions of different devices, so that when a new uncertain region of the electric power device is obtained, the determination of a target probability model of the uncertain region of the electric power device can be completed.
In an embodiment, the determining an expected tolerance curve of the power device to be quantified according to the target probability distribution model includes, as shown in fig. 5, the following specific steps:
s501, obtaining an expected value of the voltage drop amplitude of the uncertain region according to the voltage drop amplitude corresponding to the uncertain region boundary and the probability distribution model corresponding to the uncertain region.
Wherein, the voltage drop amplitude corresponding to the uncertain region boundary refers to the upper limit value V of the voltage drop amplitudemaxAnd a lower limit value Vmin。
In this embodiment, the server obtains an upper limit value and a lower limit value of a voltage drop amplitude in an uncertain region of the electrical device, and a probability distribution model corresponding to the uncertain region, and calculates an expected value of the voltage drop amplitude, where a specific calculation manner may be represented as:
Figure BDA0002265826010000121
wherein f (V) is the amplitude in the boundary range [ V ]min,Vmax]The probability density function in (b) is not limited in this embodiment.
S502, obtaining an expected value of the duration of the uncertain region according to the duration corresponding to the uncertain region boundary and the probability distribution model corresponding to the uncertain region.
Wherein, the duration corresponding to the uncertain region boundary refers to the upper limit value T of the voltage drop duration in the uncertain regionmaxAnd a lower limit value Tmin。
In this embodiment, the server obtains an upper limit value and a lower limit value of a voltage drop duration in an uncertain region of the electrical device, and a probability distribution model corresponding to the uncertain region, and calculates an expected value of a duration, where a specific calculation manner may be represented as:
Figure BDA0002265826010000131
wherein f (T) is the duration within the boundary range [ Tmin,Tmax]The probability density function in (b) is not limited in this embodiment.
S503, determining an expected tolerance curve according to the expected value of the voltage drop amplitude and the expected value of the duration.
In this embodiment, the voltage drop amplitude and the voltage drop duration are analyzed as two features, and for a rectangular uncertain region of three types of sensitive devices, namely PLC, ASD, and PC, since the two features of the voltage drop duration T and the voltage drop amplitude V of the tolerance curve are both normal distribution models, according to fig. 4a, the corresponding expected value is the uncertain region boundary range [ V [ ]min,Vmax]And [ Tmin,Tmax]The midpoint value of (a).
For the non-rectangular uncertain region of ACC, as can be seen from fig. 4b, the sub-region 2 follows uniform distribution on the vertical axis of the voltage drop amplitude characteristic, and follows exponential distribution increasing from left to right on the horizontal axis of the voltage drop duration characteristic; the two characteristics of the sub-region 4 are subjected to exponential distribution, wherein the exponential distribution which is increased from bottom to top is subjected to the vertical axis of the voltage drop amplitude characteristic, and the exponential distribution which is increased from left to right is subjected to the horizontal axis of the voltage drop duration characteristic; the sub-regions 1 and 3 only consider the distribution of voltage reduction amplitude and respectively obey uniform distribution and exponential distribution increasing from top to bottom. Therefore, according to fig. 2d and fig. 4b, the probability density function is established for each sub-region as follows:
region 1: the voltage drop amplitude has a boundary range of L1-V＝[0.35,0.39]Subject to uniform distribution of U [0.35,0.39 ]]Then, the expected value E (V)1)＝0.37；
Region 2: the voltage drop amplitude has a boundary range of L2-V＝[0.16,0.39]Obey uniform distribution of U [0.16,0.39 ]]Then, the expected value E (V)2) 0.28; the voltage drop duration has a boundary range of L2-T＝[5,30]Obeying an exponential distribution E [ lambda ]2-T]Assuming that the cumulative probability of the interval is 99%, λ can be obtained2-TWhen 0.18, the expected value E (T) is obtained2)＝24；
Region 3: the voltage drop amplitude has a boundary range of L3-V＝[0.39,0.45]Obeying an exponential distribution E [ lambda ]3-T]，λ3-TWhen 76.75, the desired value E (V) is obtained3)＝0.41；
Region 4: the voltage drop amplitude has a boundary range of L4-V＝[0,0.16]Obeying an exponential distribution E [ lambda ]4-T]，λ4-TAt 28.78, the desired value E (V)4) 0.12; the voltage drop duration has a boundary range of L4-T＝[5,80]Obeying an exponential distribution E [ lambda ]4-T]，λ4-T0.06, the expected value E (T)4)＝63。
Thus, the expected tolerance curves of the above-mentioned sensitive devices are determined as shown in fig. 5a, 5b, 5c and 5d, wherein fig. 5a is an expected tolerance curve of PLC, fig. 5b is an expected tolerance curve of ASD, fig. 5c is an expected tolerance curve of PC, and fig. 5d is an expected tolerance curve of ACC.
In this embodiment, the server establishes corresponding expected tolerance curves for different devices, so that when the voltage sag degree of each device is evaluated, overestimation does not occur, and the evaluation result is more accurate to the greatest extent.
In an embodiment, the determining the quantized value of the voltage sag of the power device to be quantized according to the expected tolerance curve, as shown in fig. 6, includes:
s601, obtaining an actual voltage drop amplitude and an actual duration of the device to be quantized.
The actual voltage drop amplitude refers to the actual voltage drop amplitude when the voltage sag occurs at present in the equipment to be quantized; the actual duration is the actual voltage drop duration of the device to be quantified when a voltage sag occurs.
In this embodiment, after determining the expected tolerance curve corresponding to each electrical device, the server obtains an actual voltage drop amplitude and a voltage drop duration of the current voltage sag of the device to be quantized, for example, the actual voltage drop amplitude of the server obtaining the PLC may be 06p.u., and the duration may be 220ms, which is not limited in this embodiment.
And S602, acquiring a corresponding expected voltage drop amplitude of the actual duration on the expected endurance curve.
In this embodiment, the server obtains an expected value of the voltage amplitude corresponding to the duration on the expected tolerance curve according to the obtained actual duration, and calculates the expected value, for example, according to the duration of 30ms, and according to fig. 5a, the server can calculate that the voltage amplitude corresponding to the duration in the PLC expected tolerance curve is 0.65p.u., which is not limited in this embodiment.
And S603, determining a voltage sag quantized value according to the actual voltage sag amplitude and the expected voltage sag amplitude.
In this embodiment, the server calculates a voltage sag quantization value of the power device according to the obtained actual voltage amplitude and the voltage amplitude of the expected tolerance curve, and the specific calculation manner may be represented as:
Figure BDA0002265826010000141
where V is the amplitude, T is the duration, Vcurve(T) is the amplitude corresponding to the reference curve over the duration T. The reference curve may be the expected tolerance curve determined in the above method, and may also be a SEMI F47 curve in a conventional method.
In the same example, when the actual amplitude is 0.6p.u. and the expected value is 0.65p.u., the quantized voltage sag value S is 2.85, and it is known from the above embodiment that when the quantized voltage sag value is greater than 1, the current voltage sag degree is a slight degree, which is not limited in the present embodiment.
In this embodiment, the server obtains the actual voltage drop duration and the voltage drop amplitude of the power device, and the voltage drop amplitude in the expected tolerance curve of the power device, and through the calculated quantized value of the voltage sag with the power device, through the magnitude of the quantized value, the severity of the voltage sag of the power device can be intuitively known.
It should be understood that although the various steps in the flow charts of fig. 2-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a voltage sag quantization apparatus including: an uncertain region obtaining module 701, a target probability model obtaining module 702, an expected tolerance curve determining module 703 and a voltage sag quantized value determining module 704, wherein:
an uncertain region obtaining module 701, configured to obtain an uncertain region of the power device to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength caused by power equipment to be quantized is located;
a target probability model obtaining module 702, configured to obtain a target probability distribution model of the uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table pre-stores the corresponding relation between the uncertain regions of various types of electric equipment and the probability distribution model;
an expected tolerance curve determining module 703, configured to determine an expected tolerance curve of the to-be-quantized power device according to the target probability distribution model;
and a voltage sag quantized value determining module 704, configured to determine a voltage sag quantized value of the power device to be quantized according to the expected tolerance curve.
In one embodiment, as shown in fig. 8, the apparatus further includes an obtaining module 705 and a quantizing module 706, wherein:
an obtaining module 705, configured to obtain voltage sag quantization values of each sensitive power device and each general power device, and weights corresponding to each sensitive power device and each general power device, respectively;
and a quantization module 706, configured to determine a weighted sum of the voltage sag quantized values of the sensitive power devices and the general power devices and the corresponding weights as the voltage sag quantized value of the power node.
In one embodiment, as shown in fig. 9, the target probability model obtaining module 702 includes an obtaining unit 7021, an analyzing unit 7022, a determining unit 7023, and a storing unit 7024, where:
an obtaining unit 7021, configured to obtain an uncertain region of multiple types of electric power devices;
an analyzing unit 7022, configured to analyze a distribution density rule of voltage drop amplitudes in each uncertain region;
a determining unit 7023, configured to determine, according to the distribution density rule, a probability distribution model corresponding to each uncertain region;
the storage unit 7024 is configured to store each uncertainty area and the corresponding probability distribution model in the pressure drop distribution model table.
In one embodiment, if the uncertainty region of one power device includes a plurality of sub-regions; one sub-region corresponds to one probability distribution model.
In one embodiment, the probability distribution model includes at least a normal distribution model, a uniform distribution model, an exponential distribution model.
In one embodiment, as shown in fig. 10, the expected tolerance curve determining module 703 includes a first obtaining unit 7031, a second obtaining unit 7032, and a calculating unit 7033, wherein:
a first obtaining unit 7031, configured to obtain an expected value of the voltage drop amplitude of the uncertain region according to the voltage drop amplitude corresponding to the boundary of the uncertain region and the probability distribution model corresponding to the uncertain region;
a second obtaining unit 7032, configured to obtain an expected value of the duration of the uncertain region according to the duration corresponding to the boundary of the uncertain region and the probability distribution model corresponding to the uncertain region;
a calculating unit 7033 is configured to determine an expected endurance curve according to the expected value of the voltage drop amplitude and the expected value of the duration.
In one embodiment, as shown in fig. 11, the voltage sag quantization value determining module 704 includes a first obtaining unit 7041, a second obtaining unit 7042, and a calculating unit 7043, where:
a first obtaining unit 7041, configured to obtain an actual voltage drop amplitude and an actual duration of the device to be quantized;
a first obtaining unit 7042, configured to obtain a desired voltage drop amplitude corresponding to the actual duration on the desired tolerance curve;
and a calculating unit 7043, configured to determine a voltage sag quantization value according to the actual voltage sag amplitude and the expected voltage sag amplitude.
The implementation principle and technical effect of all the embodiments of the voltage sag quantization apparatus are similar to those of the embodiments corresponding to the voltage sag quantization method, and are not described herein again.
For the specific limitation of the voltage sag quantization apparatus, reference may be made to the above limitation of the voltage sag quantization method, and details are not described herein again. The modules in the voltage sag quantization apparatus can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 12. The computer device comprises a processor, a memory, a network interface, a database, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The database of the computer device is used for storing voltage sag quantification data. The computer program is executed by a processor to implement a voltage sag quantification method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring an uncertain area of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength caused by power equipment to be quantized is located;
obtaining a target probability distribution model of an uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table pre-stores the corresponding relation between the uncertain regions of various types of electric equipment and the probability distribution model;
determining an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model;
and determining a voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an uncertain area of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength caused by power equipment to be quantized is located;
obtaining a target probability distribution model of an uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table pre-stores the corresponding relation between the uncertain regions of various types of electric equipment and the probability distribution model;
determining an expected tolerance curve of the power equipment to be quantized according to the target probability distribution model;
and determining a voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for voltage sag quantization, the method comprising:
acquiring an uncertain area of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength on the to-be-quantized power equipment is located;
obtaining a target probability distribution model of the uncertain region according to a pressure drop distribution model table; the pressure drop distribution model table is pre-stored with the corresponding relation between the uncertain regions of various types of electric power equipment and the probability distribution model;
determining an expected tolerance curve of the power equipment to be quantified according to the target probability distribution model;
and determining a voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
2. The method of claim 1, wherein if a power node includes a plurality of sensitive power devices and a plurality of general power devices, the method further comprises:
respectively acquiring voltage sag quantized values of each sensitive power device and each general power device and weights corresponding to each sensitive power device and each general power device;
and determining the voltage sag quantized value of each sensitive power device and each general power device as the voltage sag quantized value of the power node by the weighted sum of the voltage sag quantized values and the corresponding weights.
3. The method according to claim 1 or 2, wherein the pressure drop distribution model table establishing process comprises:
acquiring uncertain areas of various types of electric equipment;
analyzing the distribution density rule of the voltage reduction amplitude in each uncertain region;
determining a probability distribution model corresponding to each uncertain region according to the distribution density rule;
and storing each uncertain region and the corresponding probability distribution model into the pressure drop distribution model table.
4. The method of claim 3, wherein if the uncertainty region of one power device comprises a plurality of sub-regions; one sub-region corresponds to one probability distribution model.
5. The method of claim 4, wherein the probability distribution model comprises at least a normal distribution model, a uniform distribution model, and an exponential distribution model.
6. The method according to claim 1 or 2, wherein the determining a desired tolerance curve of the power device to be quantified according to the target probability distribution model comprises:
obtaining an expected value of the voltage drop amplitude of the uncertain region according to the voltage drop amplitude corresponding to the uncertain region boundary and a probability distribution model corresponding to the uncertain region;
obtaining an expected value of the duration of the uncertain region according to the duration corresponding to the uncertain region boundary and a probability distribution model corresponding to the uncertain region;
and determining the expected tolerance curve according to the expected value of the voltage drop amplitude and the expected value of the duration.
7. The method according to claim 1 or 2, wherein the determining a voltage sag quantization value of the power device to be quantized according to the desired tolerance curve comprises:
acquiring the actual voltage drop amplitude and the actual duration of the equipment to be quantized;
acquiring an expected voltage drop amplitude corresponding to the actual duration on the expected tolerance curve;
and determining the voltage sag quantized value according to the actual voltage drop amplitude and the expected voltage drop amplitude.
8. An apparatus for voltage sag quantization, the apparatus comprising:
the uncertain region acquisition module is used for acquiring an uncertain region of the power equipment to be quantized; the uncertain region represents a region where a voltage drop amplitude with uncertain influence strength on the to-be-quantized power equipment is located;
the target probability model obtaining module is used for obtaining a target probability distribution model of the uncertain region according to the pressure drop distribution model table; the pressure drop distribution model table is pre-stored with the corresponding relation between the uncertain regions of various types of electric power equipment and the probability distribution model;
an expected tolerance curve determining module, configured to determine an expected tolerance curve of the power device to be quantized according to the target probability distribution model;
and the voltage sag quantized value determining module is used for determining the voltage sag quantized value of the power equipment to be quantized according to the expected tolerance curve.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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