CN111162519A - Power grid topological structure node voltage sensing method and device - Google Patents
Power grid topological structure node voltage sensing method and device Download PDFInfo
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- CN111162519A CN111162519A CN201911363451.0A CN201911363451A CN111162519A CN 111162519 A CN111162519 A CN 111162519A CN 201911363451 A CN201911363451 A CN 201911363451A CN 111162519 A CN111162519 A CN 111162519A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
Abstract
The invention relates to a method and a device for sensing node voltage of a power grid topological structure, which comprises the following steps: acquiring power data of a power grid topological structure at the current moment; determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment; the power data of the power grid topological structure comprise active power and reactive power of each generator set and active power and reactive power of each load; the node voltage of the power grid topological structure is predicted based on the power data of the power grid topological structure, and the problem that the result of a traditional mechanism analysis model is inaccurate in node voltage prediction is solved.
Description
Technical Field
The invention relates to the technical field of power data prediction, in particular to a method and a device for sensing node voltage of a power grid topological structure.
Background
With the continuous improvement of the permeability of new energy in an electric power system, the complexity of the operation of a power grid is increased by the new energy such as wind power, photovoltaic and the like, the output fluctuation of the new energy such as the wind power, the photovoltaic and the like enables the reactive-voltage fluctuation of the power grid to be enhanced, and the traditional reactive-voltage mechanism analysis model is low in accuracy of obtained results when the nonlinear and time-varying problems are solved, and the expected effect is difficult to achieve. The power grid regulation and control system can efficiently acquire, transmit and store the real-time operation data of the power grid and store the real-time operation data, but the potential information contained in the real-time operation data of the power grid is not fully mined at present.
Therefore, the method for predicting the node voltage under the condition that a large amount of new energy is accessed into the power grid by using the traditional mechanism analysis model can be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a power grid topological structure node voltage sensing method and device, which are used for predicting the node voltage of a power grid topological structure through power data of the power grid topological structure and solving the problem that the result of a traditional mechanism analysis model is inaccurate when the node voltage is predicted.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a power grid topological structure node voltage sensing method, which is improved in that the method comprises the following steps:
acquiring power data of a power grid topological structure at the current moment;
determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment;
wherein the power data of the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
Preferably, the determining the voltage of each node of the power grid topology structure at the current moment according to the power data of the power grid topology structure at the current moment includes:
and taking the power data of the power grid topological structure at the current moment as the input layer data of the pre-acquired generalized regression neural network model, and acquiring the voltage of each node of the power grid topological structure at the current moment output by the pre-acquired generalized regression neural network model.
Further, the obtaining process of the pre-obtained generalized regression neural network model includes:
obtaining an active power sample value and a reactive power sample value of each generator set and an active power sample value and a reactive power sample value of each load;
performing load flow calculation based on the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load to obtain voltage sample values of each node of the power grid topological structure;
and taking the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load as input layer samples of the generalized regression neural network model, taking the voltage sample value of each node of the power grid topological structure as output layer samples of the generalized regression neural network model, training the generalized regression neural network model, and obtaining the pre-obtained generalized regression neural network model.
Further, the active power sample value and the reactive power sample value of each load are obtained according to the following method:
sampling a normally distributed probability model corresponding to active power of each load and a normally distributed probability model corresponding to reactive power of each load, which are obtained in advance, by utilizing a Monte Carlo sampling algorithm to obtain an active power sample value and a reactive power sample value of each load;
the method comprises the following steps of determining a distribution function of a normal distribution probability model corresponding to active power of each load, wherein the distribution function is obtained in advance according to the following formula:
wherein f (p) is the distribution probability corresponding to the load active power p, mupIs the mean value, sigma, of the historical active power of the loadpThe standard deviation of the historical active power of the load;
determining a distribution function of a normal distribution probability model corresponding to the reactive power of each load, which is obtained in advance, according to the following formula:
wherein f (q) is the distribution probability corresponding to the reactive power q of the load, muqIs the mean value, sigma, of the historical reactive power of the loadqIs the standard deviation of the load historical reactive power.
Further, the active power sample value and the reactive power sample value of each generator set are obtained according to the following method:
s1, randomly generating a random number R of the running state of each generator set, and determining the running state of each generator set according to the following formula:
in the formula, G is the running state of the generator set, R belongs to [0,1], U is the probability of the stop running of the generator set, and PD is the probability of the derated running of the generator set;
and S2, determining an active power sample value and a reactive power sample value of each generator set according to the running state of each generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a wind turbine generator set, determining an active power reference value of the generator set according to the following formula:
in the formula, PfIs the active power reference value of the generator set, v is the working wind speed sample value of the generator set, v isciFor the cut-in wind speed, v, of the generator setcoCut-out wind speed, P, for a generator setrIs rated power of the generator set, vrThe rated working wind speed of the generator set;
determining a reactive power reference value of the generator set according to the following formula:
Qf＝Pftanθf
in the formula, QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Further, the method for obtaining the working wind speed sample value v of the generator set comprises the following steps:
sampling a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a working wind speed sample value v of the generator set;
determining a distribution function of a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set according to the following formula:
wherein f (v) is the distribution probability corresponding to the working wind speed sample value v of the generator set, k is the shape parameter,
μfis the mean value, sigma, of the historical wind speed of the generator setfIs the standard deviation of the historical wind speed of the generator set, and c isThe size of the scale parameter is such that,
exp (. cndot.) is an exponential function, and Γ (. cndot.) is a Gamma function.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a photovoltaic generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfThe active power reference value of the generator set is η, the photoelectric conversion efficiency of the generator set is β, the photovoltaic shading factor sample value of the generator set is β belongs to [0,1]]S is the total area of the photovoltaic panel of the generator set, R is irradiance and QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Further, the method for acquiring the photovoltaic shading factor sample value β of the generator set comprises the following steps:
sampling a Beta distribution probability model corresponding to a pre-acquired photovoltaic shielding factor of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a photovoltaic shielding factor sample value β of the generator set;
determining a distribution function of a Beta distribution probability model corresponding to the pre-acquired photovoltaic shielding factor of the generator set according to the following formula:
wherein f (β) is the distribution probability corresponding to the photovoltaic shading factor sample value β of the generator set, Γ () is a Gamma function, a is a first shape parameter, b is a second shape parameter,
μgis the mean value, sigma, of the historical photovoltaic shading factor of the generator setgStandard deviation of historical photovoltaic shading factor for Generator set, βmaxAnd the maximum value of the historical photovoltaic shading factor of the generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a thermal power generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfAs active power reference value, Q, of the generator setfIs a reactive power reference value of the generator set, thetafIs the power factor angle, P, of the generator setf' is rated power of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Based on the same inventive concept, the invention also provides a device for sensing the node voltage of the power grid topological structure, and the improvement is that the device comprises:
the acquisition unit is used for acquiring power data of the power grid topological structure at the current moment;
the sensing unit is used for determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment;
wherein the power data of the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a power grid topological structure node voltage sensing method and a device, which are used for acquiring power data of a power grid topological structure at the current moment; determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment; the power data of the power grid topological structure comprise active power and reactive power of each generator set and active power and reactive power of each load; according to the invention, the node voltage of the power grid topological structure is predicted based on the power data of the power grid topological structure, the information contained in the historical real-time operation data of the power grid is deeply mined, and the problem that the node voltage prediction result of a traditional mechanism analysis model is not accurate enough under the condition that a large amount of new energy is accessed into the power grid is solved.
Drawings
FIG. 1 is a flow chart of a power grid topology node voltage sensing method of the invention;
fig. 2 is a schematic diagram of a node voltage sensing device of a power grid topology structure.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a power grid topological structure node voltage sensing method, as shown in figure 1, the method comprises the following steps:
acquiring power data of a power grid topological structure at the current moment;
determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment;
wherein the power data of the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
Preferably, the determining the voltage of each node of the power grid topology structure at the current moment according to the power data of the power grid topology structure at the current moment includes:
and taking the power data of the power grid topological structure at the current moment as the input layer data of the pre-acquired generalized regression neural network model, and acquiring the voltage of each node of the power grid topological structure at the current moment output by the pre-acquired generalized regression neural network model.
Further, the obtaining process of the pre-obtained generalized regression neural network model includes:
obtaining an active power sample value and a reactive power sample value of each generator set and an active power sample value and a reactive power sample value of each load;
performing load flow calculation based on the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load to obtain voltage sample values of each node of the power grid topological structure;
and taking the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load as input layer samples of the generalized regression neural network model, taking the voltage sample value of each node of the power grid topological structure as output layer samples of the generalized regression neural network model, training the generalized regression neural network model, and obtaining the pre-obtained generalized regression neural network model.
Further, the active power sample value and the reactive power sample value of each load are obtained according to the following method:
sampling a normally distributed probability model corresponding to active power of each load and a normally distributed probability model corresponding to reactive power of each load, which are obtained in advance, by utilizing a Monte Carlo sampling algorithm to obtain an active power sample value and a reactive power sample value of each load;
the method comprises the following steps of determining a distribution function of a normal distribution probability model corresponding to active power of each load, wherein the distribution function is obtained in advance according to the following formula:
wherein f (p) is the distribution probability corresponding to the load active power p, mupIs the mean value, sigma, of the historical active power of the loadpThe standard deviation of the historical active power of the load;
determining a distribution function of a normal distribution probability model corresponding to the reactive power of each load, which is obtained in advance, according to the following formula:
wherein f (q) is the distribution probability corresponding to the reactive power q of the load, muqIs the mean value, sigma, of the historical reactive power of the loadqIs the standard deviation of the load historical reactive power.
Further, the active power sample value and the reactive power sample value of each generator set are obtained according to the following method:
s1, randomly generating a random number R of the running state of each generator set, and determining the running state of each generator set according to the following formula:
in the formula, G is the running state of the generator set, R belongs to [0,1], U is the probability of the stop running of the generator set, and PD is the probability of the derated running of the generator set;
and S2, determining an active power sample value and a reactive power sample value of each generator set according to the running state of each generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a wind turbine generator set, determining an active power reference value of the generator set according to the following formula:
in the formula, PfIs the active power reference value of the generator set, v is the working wind speed sample value of the generator set, v isciFor the cut-in wind speed, v, of the generator setcoCut-out wind speed, P, for a generator setrIs rated power of the generator set, vrThe rated working wind speed of the generator set;
determining a reactive power reference value of the generator set according to the following formula:
Qf＝Pftanθf
in the formula, QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Further, the method for obtaining the working wind speed sample value v of the generator set comprises the following steps:
sampling a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a working wind speed sample value v of the generator set;
determining a distribution function of a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set according to the following formula:
wherein f (v) is the distribution probability corresponding to the working wind speed sample value v of the generator set, k is the shape parameter,
μfto send outMean value of historical wind speed of the generator set, sigmafIs the standard deviation of the historical wind speed of the generator set, c is a scale parameter,
exp (. cndot.) is an exponential function, and Γ (. cndot.) is a Gamma function.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a photovoltaic generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfThe active power reference value of the generator set is η, the photoelectric conversion efficiency of the generator set is β, the photovoltaic shading factor sample value of the generator set is β belongs to [0,1]]S is the total area of the photovoltaic panel of the generator set, R is irradiance and QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfFor generatorsThe ratio of the quota.
Further, the method for acquiring the photovoltaic shading factor sample value β of the generator set comprises the following steps:
sampling a Beta distribution probability model corresponding to a pre-acquired photovoltaic shielding factor of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a photovoltaic shielding factor sample value β of the generator set;
determining a distribution function of a Beta distribution probability model corresponding to the pre-acquired photovoltaic shielding factor of the generator set according to the following formula:
wherein f (β) is the distribution probability corresponding to the photovoltaic shading factor sample value β of the generator set, Γ () is a Gamma function, a is a first shape parameter, b is a second shape parameter,
μgis the mean value, sigma, of the historical photovoltaic shading factor of the generator setgStandard deviation of historical photovoltaic shading factor for Generator set, βmaxAnd the maximum value of the historical photovoltaic shading factor of the generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a thermal power generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfAs active power reference value, Q, of the generator setfIs a reactive power reference value of the generator set, thetafIs the power factor angle, P, of the generator setf' is rated power of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Based on the same inventive concept, the invention also provides a device for sensing the node voltage of the power grid topological structure, as shown in fig. 2, the device comprises:
the acquisition unit is used for acquiring power data of the power grid topological structure at the current moment;
the sensing unit is used for determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment;
wherein the power data of the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
Preferably, the sensing unit is specifically configured to:
and taking the power data of the power grid topological structure at the current moment as the input layer data of the pre-acquired generalized regression neural network model, and acquiring the voltage of each node of the power grid topological structure at the current moment output by the pre-acquired generalized regression neural network model.
Further, the obtaining process of the pre-obtained generalized regression neural network model includes:
obtaining an active power sample value and a reactive power sample value of each generator set and an active power sample value and a reactive power sample value of each load;
performing load flow calculation based on the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load to obtain voltage sample values of each node of the power grid topological structure;
and taking the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load as input layer samples of the generalized regression neural network model, taking the voltage sample value of each node of the power grid topological structure as output layer samples of the generalized regression neural network model, training the generalized regression neural network model, and obtaining the pre-obtained generalized regression neural network model.
Further, the active power sample value and the reactive power sample value of each load are obtained according to the following method:
sampling a normally distributed probability model corresponding to active power of each load and a normally distributed probability model corresponding to reactive power of each load, which are obtained in advance, by utilizing a Monte Carlo sampling algorithm to obtain an active power sample value and a reactive power sample value of each load;
the method comprises the following steps of determining a distribution function of a normal distribution probability model corresponding to active power of each load, wherein the distribution function is obtained in advance according to the following formula:
wherein f (p) is the distribution probability corresponding to the load active power p, mupIs the mean value, sigma, of the historical active power of the loadpThe standard deviation of the historical active power of the load;
determining a distribution function of a normal distribution probability model corresponding to the reactive power of each load, which is obtained in advance, according to the following formula:
wherein f (q) is a distribution profile corresponding to the reactive power q of the loadRate, muqIs the mean value, sigma, of the historical reactive power of the loadqIs the standard deviation of the load historical reactive power.
Further, the active power sample value and the reactive power sample value of each generator set are obtained according to the following method:
s1, randomly generating a random number R of the running state of each generator set, and determining the running state of each generator set according to the following formula:
in the formula, G is the running state of the generator set, R belongs to [0,1], U is the probability of the stop running of the generator set, and PD is the probability of the derated running of the generator set;
and S2, determining an active power sample value and a reactive power sample value of each generator set according to the running state of each generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a wind turbine generator set, determining an active power reference value of the generator set according to the following formula:
in the formula, PfIs the active power reference value of the generator set, v is the working wind speed sample value of the generator set, v isciFor the cut-in wind speed, v, of the generator setcoCut-out wind speed, P, for a generator setrIs rated power of the generator set, vrThe rated working wind speed of the generator set;
determining a reactive power reference value of the generator set according to the following formula:
Qf＝Pftanθf
in the formula, QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Further, the method for obtaining the working wind speed sample value v of the generator set comprises the following steps:
sampling a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a working wind speed sample value v of the generator set;
determining a distribution function of a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set according to the following formula:
wherein f (v) is the distribution probability corresponding to the working wind speed sample value v of the generator set, k is the shape parameter,
μfis the mean value, sigma, of the historical wind speed of the generator setfIs the standard deviation of the historical wind speed of the generator set, c is a scale parameter,
exp (. cndot.) is an exponential function, and Γ (. cndot.) is a Gamma function.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a photovoltaic generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfThe active power reference value of the generator set is η, the photoelectric conversion efficiency of the generator set is β, the photovoltaic shading factor sample value of the generator set is β belongs to [0,1]]S is the total area of the photovoltaic panel of the generator set, R is irradiance and QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
Further, the method for acquiring the photovoltaic shading factor sample value β of the generator set comprises the following steps:
sampling a Beta distribution probability model corresponding to a pre-acquired photovoltaic shielding factor of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a photovoltaic shielding factor sample value β of the generator set;
determining a distribution function of a Beta distribution probability model corresponding to the pre-acquired photovoltaic shielding factor of the generator set according to the following formula:
wherein f (β) is the distribution probability corresponding to the photovoltaic shading factor sample value β of the generator set, Γ () is a Gamma function, a is a first shape parameter, b is a second shape parameter,
μgis the mean value, sigma, of the historical photovoltaic shading factor of the generator setgStandard deviation of historical photovoltaic shading factor for Generator set, βmaxAnd the maximum value of the historical photovoltaic shading factor of the generator set.
Further, the determining the active power sample value and the reactive power sample value of each generator set according to the operating state of each generator set includes:
when the generator set is a thermal power generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfAs active power reference value, Q, of the generator setfIs a reactive power reference value of the generator set, thetafIs the power factor angle, P, of the generator setf' is rated power of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
In summary, the present invention provides a method and an apparatus for sensing node voltage of a power grid topology structure, which are used for obtaining power data of the power grid topology structure at the current moment; determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment; the power data of the power grid topological structure comprise active power and reactive power of each generator set and active power and reactive power of each load; according to the invention, based on the power data of the power grid topological structure, the node voltage of the power grid topological structure is predicted by utilizing the generalized regression neural network, the information contained in the historical real-time operation data of the power grid is deeply mined, and the problem that the node voltage prediction result of a traditional mechanism analysis model is not accurate enough under the condition that a large amount of new energy is accessed into the power grid is solved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. A grid topology node voltage sensing method is characterized by comprising the following steps:
acquiring power data of a power grid topological structure at the current moment;
and determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment.
2. The method of claim 1, wherein the power data for the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
3. The method of claim 2, wherein determining the voltage of each node of the power grid topology at the current time based on the power data of the power grid topology at the current time comprises:
and taking the power data of the power grid topological structure at the current moment as the input layer data of the pre-acquired generalized regression neural network model, and acquiring the voltage of each node of the power grid topological structure at the current moment output by the pre-acquired generalized regression neural network model.
4. The method of claim 3, wherein the obtaining of the pre-obtained generalized recurrent neural network model comprises:
obtaining an active power sample value and a reactive power sample value of each generator set and an active power sample value and a reactive power sample value of each load;
performing load flow calculation based on the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load to obtain voltage sample values of each node of the power grid topological structure;
and taking the active power sample value and the reactive power sample value of each generator set and the active power sample value and the reactive power sample value of each load as input layer samples of the generalized regression neural network model, taking the voltage sample value of each node of the power grid topological structure as output layer samples of the generalized regression neural network model, training the generalized regression neural network model, and obtaining the pre-obtained generalized regression neural network model.
5. The method of claim 4, wherein the real and reactive power sample values for each load are obtained as follows:
sampling a normally distributed probability model corresponding to active power of each load and a normally distributed probability model corresponding to reactive power of each load, which are obtained in advance, by utilizing a Monte Carlo sampling algorithm to obtain an active power sample value and a reactive power sample value of each load;
the method comprises the following steps of determining a distribution function of a normal distribution probability model corresponding to active power of each load, wherein the distribution function is obtained in advance according to the following formula:
wherein f (p) is the distribution probability corresponding to the load active power p, mupIs the mean value, sigma, of the historical active power of the loadpThe standard deviation of the historical active power of the load;
determining a distribution function of a normal distribution probability model corresponding to the reactive power of each load, which is obtained in advance, according to the following formula:
wherein f (q) is the distribution probability corresponding to the reactive power q of the load, muqIs the mean value, sigma, of the historical reactive power of the loadqIs the standard deviation of the load historical reactive power.
6. The method of claim 4, wherein the active power sample value and the reactive power sample value of each genset are obtained as follows:
s1, randomly generating a random number R of the running state of each generator set, and determining the running state of each generator set according to the following formula:
in the formula, G is the running state of the generator set, R belongs to [0,1], U is the probability of the stop running of the generator set, and PD is the probability of the derated running of the generator set;
and S2, determining an active power sample value and a reactive power sample value of each generator set according to the running state of each generator set.
7. The method of claim 6, wherein determining the active power sample value and the reactive power sample value for each genset based on the operating state of each genset comprises:
when the generator set is a wind turbine generator set, determining an active power reference value of the generator set according to the following formula:
in the formula, PfIs the active power reference value of the generator set, v is the working wind speed sample value of the generator set, v isciFor the cut-in wind speed, v, of the generator setcoCut-out wind speed, P, for a generator setrIs rated power of the generator set, vrThe rated working wind speed of the generator set;
determining a reactive power reference value of the generator set according to the following formula:
Qf＝Pftanθf
in the formula, QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfFor reactive power of generator setsSample values;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
8. The method of claim 5, wherein the method for obtaining the operating wind speed sample value v of the generator set comprises:
sampling a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a working wind speed sample value v of the generator set;
determining a distribution function of a Weibull distribution probability model corresponding to the pre-acquired working wind speed of the generator set according to the following formula:
wherein f (v) is the distribution probability corresponding to the working wind speed sample value v of the generator set, k is the shape parameter,
μfis the mean value, sigma, of the historical wind speed of the generator setfIs the standard deviation of the historical wind speed of the generator set, c is a scale parameter,
exp (. cndot.) is an exponential function, and Γ (. cndot.) is a Gamma function.
9. The method of claim 6, wherein determining the active power sample value and the reactive power sample value for each genset based on the operating state of each genset comprises:
when the generator set is a photovoltaic generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfThe active power reference value of the generator set is η, the photoelectric conversion efficiency of the generator set is β, the photovoltaic shading factor sample value of the generator set is β belongs to [0,1]]S is the total area of the photovoltaic panel of the generator set, R is irradiance and QfIs a reactive power reference value of the generator set, thetafIs the power factor angle of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
10. The method of claim 9, wherein the method of obtaining the photovoltaic shading factor sample value β of the generator set comprises:
sampling a Beta distribution probability model corresponding to a pre-acquired photovoltaic shielding factor of the generator set by utilizing a Monte Carlo sampling algorithm to obtain a photovoltaic shielding factor sample value β of the generator set;
determining a distribution function of a Beta distribution probability model corresponding to the pre-acquired photovoltaic shielding factor of the generator set according to the following formula:
wherein f (β) is the distribution probability corresponding to the photovoltaic shading factor sample value β of the generator set, Γ () is a Gamma function, a is a first shape parameter, b is a second shape parameter,
μgis the mean value, sigma, of the historical photovoltaic shading factor of the generator setgStandard deviation of historical photovoltaic shading factor for Generator set, βmaxAnd the maximum value of the historical photovoltaic shading factor of the generator set.
11. The method of claim 6, wherein determining the active power sample value and the reactive power sample value for each genset based on the operating state of each genset comprises:
when the generator set is a thermal power generator set, determining an active power reference value and a reactive power reference value of the generator set according to the following formula:
in the formula, PfAs active power reference value, Q, of the generator setfIs a reactive power reference value of the generator set, thetafIs the power factor angle, P, of the generator setf' is rated power of the generator set;
if the running state of the generator set is normal running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, pfFor the active power sample value of the generator set, qfThe value of the reactive power sample of the generator set is obtained;
if the running state of the generator set is derating running, determining an active power sample value and a reactive power sample value of the generator set according to the following formula:
in the formula, BfIs the derating ratio of the generator set.
12. A grid topology node voltage sensing device, the device comprising:
the acquisition unit is used for acquiring power data of the power grid topological structure at the current moment;
the sensing unit is used for determining the voltage of each node of the power grid topological structure at the current moment according to the power data of the power grid topological structure at the current moment;
wherein the power data of the grid topology comprises: the active power and the reactive power of each generator set and the active power and the reactive power of each load.
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