CN110994595A - Power grid key equipment heavy load and out-of-limit distribution monitoring method - Google Patents
Power grid key equipment heavy load and out-of-limit distribution monitoring method Download PDFInfo
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- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
The embodiment of the invention discloses a method for monitoring heavy load and out-of-limit distribution of key equipment of a power grid, which comprises the following optimization models: s1, establishing an upper-layer optimization model of a line channel distribution factor and a load reduction factor; s2, constructing a global load balancing model, utilizing actual line load distribution to approach predicted load pre-distribution, and utilizing iterative updating of an algorithm to find out an actual topological structure under the condition of minimum load balancing degree to realize global load balancing; s3, determining load distribution of the global power grid line; s4, constructing a data display platform with different system interaction, accessing map data distributed by a power grid line by using a load threshold distribution map, customizing an uploading interval of a remote measurement value of the overload or voltage threshold of the key equipment, dynamically updating the remote measurement value in real time, displaying the overload and threshold-crossing distribution condition of the integrated equipment in real time, and assisting a controller to quickly perform mode adjustment or voltage adjustment decision.
Description
Technical Field
The embodiment of the invention relates to the technical field of power grid substations, in particular to a method for monitoring heavy load and out-of-limit distribution of key equipment of a power grid.
Background
At present, the number of Dongguan power grid substations is nearly 200, but the highest load of the Dongguan power grid substations is frequently innovative all the year round, the power supply density of partial areas is too high, the load distribution is uneven, the capacity-load ratio difference of each town area is large, the long-term overload of key equipment sections or circuits of partial power supply chip areas is caused, meanwhile, the voltage jump is serious during the load peak-valley conversion period, the large-area out-of-limit phenomenon of voltage is easily caused, but due to the fact that the equipment overload and the out-of-limit distribution are dispersed and are large in number, regulators can only remind and discover through out-of-limit signals and voice, the overload and out-of-limit distribution conditions of the power grid are difficult to control and effectively intervene in.
Aiming at the prior art, the potential safety and stability hazards of the existing Dongguan power grid have the following points:
(1) the regulation and control personnel can only find the overloading and out-of-limit distribution condition of the equipment through out-of-limit signals and voice reminding, and are difficult to comprehensively control and effectively intervene at the first time;
(2) and the out-of-limit signal is uploaded to a monitoring alarm window when the out-of-limit critical value is triggered, the uploading is disordered, the primary and secondary are not distinguished, and the regulating and controlling personnel cannot judge the sequence of the heavy load and the out-of-limit of the manual intervention equipment.
Disclosure of Invention
Therefore, the embodiment of the invention provides a method for monitoring the overloading and out-of-limit distribution of key equipment of a power grid, which is characterized in that the method for monitoring the overloading and out-of-limit distribution of the key equipment of the power grid is adopted, the uploading interval of the overloaded or out-of-limit remote measurement value of the key equipment is customized, the remote measurement value is dynamically updated in real time, the overloading and out-of-limit distribution condition of the equipment is integrated in real time, and a regulator is assisted to quickly carry out mode adjustment or voltage regulation decision, so that the problems that in the prior art, out-of-limit signals and voice reminding cannot be monitored in real time, and the sequence of manual intervention equipment overloading and out-of-limit cannot be judged due to the fact that an alarm.
In order to achieve the above object, an embodiment of the present invention provides the following:
a method for monitoring heavy load and out-of-limit distribution of key equipment of a power grid comprises the following optimization steps:
s1, establishing an upper-layer optimization model of the line channel distribution factor and the load reduction factor, and randomly generating the channel distribution factor and the load reduction factor of the specific line in the internal load unit group of the power grid line by using a target function;
s2, a global load balancing model is built according to the channel distribution factor and the load reduction factor, actual line load distribution is close to predicted load pre-distribution, an actual topological structure under the condition of minimum load balancing degree is found out through iterative updating of an algorithm, and global load balancing is achieved;
s3, determining load distribution of the global power grid line through a balancing model, and establishing a flexible alternating current transmission acquisition system based on a particle swarm algorithm by using the static voltage stability and the line participation factor obtained through the load balancing;
s4, constructing different system interactive data display platforms, accessing map data distributed by a power grid line on the flexible alternating current transmission acquisition system by using the load threshold distribution diagram, and rendering the map data and the line optimization data to perform layered display on different system platforms.
As a preferable aspect of the present invention, in step S1,
the line channel distribution factor is a functional unit group GωBy power supply road TjωDivided to power supply point SjLoad ratio of (tau)jωIn which τ isjωThe constraint condition satisfied is sigma taujω＝1
Wherein tau isjωGroup of presentation and function units GωAll channels of interest are assigned a factor.
The load reduction factor is a functional unit group GωPercentage of active load cut off in the group as a whole, i.e. load reduction ratio hωThen h isωSatisfies the following conditions:
P`ωj＝Pωj(1-hω)
wherein, PωjRepresents a group of functional units GωTotal active load, P' before load sheddingωjReduced functional unit group GωThe total load.
As a preferred embodiment of the present invention, the objective function of the upper layer optimization model by the line channel allocation factor and the load reduction factor in step S1 is:
F＝Min(K+μ1H)
wherein K is the load balance degree of the transformer substation, H represents the total load reduction proportion of the whole system, and mu1A penalty factor added to the load reduction degree H.
As a preferred embodiment of the present invention, the equalization degree in step S2 is mainly determined by the proximity factor λwpAnd a reduction vector X, λwpRepresenting the closeness of the actual allocation proportion of the load in the unit group to the channel allocation factor:
X＝[x1，…，xn]
wherein λ iswpShowing that in the p-th feasible topological stateGroup of functional units GωCloseness of (g)pqwRepresents a group of functional units GωLoad sharing rate, τ, per power supply channel determined in the p-th stateqwRepresenting the channel allocation factor given by the upper optimization model; x is the number ofjRepresents the load proportion that the functional unit j can reduce, wherein 0 is less than or equal to xj1, n represents the total number of functional units in the group.
As a preferred embodiment of the present invention, the iterative process of the algorithm in step S2 is:
s21, initializing parameters including factor population number NpDimension d of problem variable, cross probability CR, maximum number of iterations Gm, search field S ═ X | XL≤X≤XU}；
S22, initializing the population,
wherein rand represents a random number between 0 and 1;
s23, solving the average value X of all individuals in the populationmeanForming a new individual Xnew；
S24, randomly extracting an individual X from the populationjLearning according to the difference between the user and the user, and updating the operation after the learning stage is finished;
s25, performing interactive operation in the individual learning stage, wherein the expression is as follows:
wherein rand represents a random number between 0 and 1, CR represents a crossover probability, the size is predetermined, randn (i) takes a value between [1, d ], and indexes are dimension variables selected randomly to ensure that at least one dimension variable is provided by an individual after the learning stage. After the cross operation is finished, updating the operation;
s25, if the iteration end condition is met, ending the optimization; otherwise, the process proceeds to step S23 to continue the iteration.
As a preferred embodiment of the present invention, in the step S3, the determining of the critical line load mainly includes determining a stability degree of the state voltage according to a characteristic value of a power flow equation matrix, and solving according to the equation matrix to obtain:
wherein, Δ P is the node active power micro-increment change, Δ Q is the node reactive power micro-increment change, Δ θ is the node voltage angle micro-increment change, Δ U is the node voltage magnitude change, JPθ、JPU、JQθ、JQUAnd forming a sub-array of the Jacbi matrix for the trend equation partial derivatives.
As a preferred scheme of the present invention, the node reactive power micro-increment Δ Q is mainly a voltage difference between a head end and a tail end of a line when a reactive power point of a transmission line is maintained at a midpoint of the line, wherein a reactive power compensation capacity is solved as follows:
wherein Q iscompRequired reactive compensation capacity, Δ QTIs the reactive power that is lost by the line transformer,
is one half of the i-th line of the line transformer bus, n is the number of the cable lines on the substation bus, and QloadIs the reactive load on the bus of the substation.
As a preferred solution of the present invention, the data transfer platform in step S4 includes the following modules:
the authority distribution module: the system is used for managing and controlling the user information and the user authority;
a data query module: for query location of data;
a data analysis module: the data processing system is used for displaying the change trend of the monitoring data compared with the threshold data, performing corresponding analysis according to the change trend, and performing descending order arrangement on the data information of which the change trend or the deviation degree exceeds the threshold;
the data display module: the method mainly displays data information of the load exceeding a threshold value or exceeding an out-of-limit value, dynamically updates in real time, and simultaneously transmits a related section overloading or voltage out-of-limit signal to a system alarm interface.
As a preferred scheme of the present invention, the display process of the data display platform is as follows:
s41, starting a data query module and a data display module according to the load line load information sequence;
s42, calling WFS service loading data through Openlayers to load the line information positioned by the data query module;
and S43, respectively processing according to different requests sent by the data server.
As a preferred scheme of the present invention, the data display module uses a visual mapping component to map data to visual elements, and constructs a dynamic graph after the transmission line is adjusted or migrated.
The embodiment of the invention has the following advantages:
the invention realizes automatic screening of remote measurement values of acquisition equipment by integrating current out-of-limit, voltage out-of-limit signals and section heavy load signals of a circuit through an upper layer optimization model and a balance model built in a display platform, when the load rate of a relevant section reaches 90% or above or the voltage is out of limit, the section load or voltage out-of-limit condition can be automatically uploaded to the display platform and dynamically updated in real time, relevant section heavy load or voltage out-of-limit signals are simultaneously uploaded to a PCS9000 system alarm interface, when a plurality of sections are heavy or voltage out of limit, relevant remote measurement values can be simultaneously uploaded to the display platform and arranged in a descending order according to the heavy load or out-of-limit severity degree, a regulation and control person is assisted to carry out mode regulation or voltage regulation decision, manual intervention is rapidly carried out, and the safe and stable operation of a power grid.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
Fig. 1 is a flowchart of a method for monitoring heavy load and out-of-limit distribution of key equipment of a power grid in an embodiment of the invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
As shown in figure 1, the invention provides a method for monitoring the distribution of power grid key equipment overloading and out-of-limit, which mainly aims to integrate the line current out-of-limit, voltage out-of-limit signal and section overloading signal through an upper layer optimization model and a balance model built in a data display platform, realize automatic screening and acquisition of equipment telemetering values, when the relevant section loading rate reaches 90% or above or the voltage is out-of-limit, the section loading or voltage out-of-limit condition can be automatically uploaded to the data display platform of different systems and dynamically updated in real time, and simultaneously the relevant section overloading or voltage out-of-limit signal is uploaded to a PCS9000 system alarm interface, when a plurality of sections overloading or voltage are out-of-limit, the relevant telemetering values can be simultaneously uploaded to the data display platform and arranged in descending order according to the overloading or out-of-limit severity degree, an auxiliary regulator can make mode adjustment or voltage adjustment decision, and can quickly make manual intervention, the safe and stable operation of the power grid is guaranteed.
One of the characteristics of the power grid key equipment overloading and out-of-limit distribution monitoring method is that the uploading interval of the telemetering values of the key equipment overloading or voltage out-of-limit can be customized, the telemetering values are dynamically updated in real time, the telemetering values are sorted according to the out-of-limit degree of the telemetering values, and the telemetering values are sequentially displayed on the display platform.
The method specifically comprises the following optimization steps:
s1, establishing an upper-layer optimization model of a line channel distribution factor and a load reduction factor, and randomly generating a channel distribution and load reduction factor of a specific line in a load unit group in the power grid line by using a target function;
s2, a global load balancing model is built according to the distribution factor and the load reduction factor, actual line load distribution is close to predicted load pre-distribution, an actual topological structure under the condition of minimum load balancing degree is found out through iterative updating of an algorithm, and global load balancing is achieved;
s3, determining load distribution of the global power grid line through a balancing model, and establishing a flexible alternating current transmission acquisition system based on a particle swarm algorithm by using the static voltage stability and the line participation factor obtained through the load balancing;
s4, constructing different system interactive data display platforms, accessing map data distributed by a power grid line on the flexible alternating current transmission acquisition system by using the load threshold distribution diagram, and rendering the map data and the optimized line data to perform layered display on different system platforms.
In this embodiment, in the process of establishing the upper layer optimization model, the optimization model of the two-layer structure selected by the topology state guided by the channel allocation factor is mainly used, the channel allocation and load reduction factors of the corresponding columns are randomly generated in the search domain by using an intelligent algorithm, and the two generated parameters form a load pre-distribution scheme of the network and find a scheme with the optimal balance, but the scheme does not represent actual load distribution and only fits the actual load distribution as much as possible.
In this embodiment, a global load balancing model is constructed, feasible topological states of each functional unit group of 10KV, 110KV and 220KV are enumerated, so as to calculate actual network load distribution, the actual load distribution is used to approach the load pre-distribution determined at the upper layer, a topological state with the minimum closeness, that is, an actual topological state closest to the pre-distribution scheme, is found, and finally, a load flow check is performed, and the actual topological state closest to the load is found by using iterative update of an algorithm under the condition that the load balancing degree is minimum.
In the embodiment, a research method taking the unit group as an object is adopted to reconstruct the unit group to realize balanced operation of the global load, so that the problem of large combination amount possibly occurring in a reconstruction switch in the whole system is avoided, a large amount of non-topological feasible solutions generated in the optimization searching process of an intelligent algorithm are reduced, the problem of the reconstruction switch is converted into the problem of feasible topological state selection, and the method has the advantages of low control variable dimension and high calculation speed.
In the step S1, in the step S,
the line channel distribution factor is a functional unit group GωBy power supply road TjjωDivided to power supply point SjLoad ratio of (tau)jωIn which τ isjωThe constraint condition satisfied is sigma taujω＝1
Wherein tau isjωGroup of presentation and function units GωAll channels of interest are assigned a factor.
In this embodiment, the unit group G1For example, there is τ11+τ21+τ311, here τjwThe actual load distribution proportion of each power supply channel is not, but only represents a distribution scheme, that is, actually, a pre-distribution mode of unit group loads at different power supply points, and after the channel distribution factor is determined, the load ratio of the 220KV substation is as follows:
K(Sj)＝∑P`j*ετj/Pjmax
wherein, K (S)j) Representing a 220KV substation SjLoad factor of τjRepresents all and power supply points SjAssociated channel allocation factor, P ″j*εPresentation and power point SjRelated group of functional units GωThe sum of the coordinated total active powers, PjmaxIndicating a power supply point SjThe capacity of (c).
In order to meet load rate balance, the load balance degree of the 220KV transformer substation is described in a 2-norm form:
K＝||K(Sj)-K0||2
wherein the content of the first and second substances,
representing the mean load rate.
The load reduction factor is a functional unit group GωPercentage of active load cut off in the group as a whole, i.e. load reduction ratio hωThen h isωSatisfies the following conditions:
P`ωj＝Pωj(1-hω)
wherein, PωjRepresents a group of functional units GωTotal active load, P' before load sheddingωjReduced functional unit group GωThe total load.
In step S1, the objective function of the upper layer optimization model based on the line channel allocation factor and the load reduction factor is:
F＝Min(K+μ1H)
wherein K is the load balance degree of the transformer substation, H represents the total load reduction proportion of the whole system, and mu1A penalty factor added to the load reduction degree H.
In this embodiment, the total load reduction ratio of the entire system is represented by H:
H＝∑hv
in the optimization process, it should be ensured that H is as small as possible, and the larger H, the more inconsistent the relationship between the capacity and the load, the more the load is cut off, and the lower the power supply reliability, and if H exceeds an acceptable level, the equipment investment should be increased in consideration of a planning level to improve the support capability of the power grid, so as to meet the power supply requirement.
The equalization degree in step S2 is mainly determined by the proximity factor λwpAnd a reduction vector X, λwpRepresenting the closeness of the actual allocation proportion of the load in the unit group to the channel allocation factor:
X＝[x1，…，xn]
wherein λ iswpShowing that in the p-th feasible topological state, the functional unit group GωCloseness of (g)pqwRepresents a group of functional units GωLoad sharing rate, τ, per power supply channel determined in the p-th stateqwRepresenting the channel allocation factor given by the upper optimization model; x is the number ofjRepresents the load proportion that the functional unit j can reduce, wherein 0 is less than or equal to xj1, n represents the total number of functional units in the group.
The algorithm iteration process in step S2 includes:
s21, initializing parameters including factor population number NpDimension d of problem variable, cross probability CR, maximum number of iterations Gm, search field S ═ X | XL≤X≤XU}；
S22, initializing the population,
wherein rand represents a random number between 0 and 1;
s23, solving the average value X of all individuals in the populationmeanForming a new individual Xnew；
S24, randomly extracting an individual X from the populationjLearning according to the difference between the user and the user, and updating the operation after the learning stage is finished;
s25, performing interactive operation in the individual learning stage, wherein the expression is as follows:
wherein rand represents a random number between 0 and 1, CR represents a crossover probability, the size is predetermined, randn (i) takes a value between [1, d ], and indexes are dimension variables selected randomly to ensure that at least one dimension variable is provided by an individual after the learning stage. After the cross operation is finished, updating the operation;
s25, if the iteration end condition is met, ending the optimization; otherwise, the process proceeds to step S23 to continue the iteration.
The second characteristic of the power grid key equipment heavy load and out-of-limit distribution monitoring method is that a layered optimization model for load transfer is adopted, namely, a solving strategy of the out-of-limit value is divided into an upper layer optimization model and a balance model, a series of corresponding channel distribution factors and load reduction factors are randomly generated in a search domain by an intelligent algorithm in the upper layer optimization model, and then a target function value of the upper layer optimization model is calculated according to the generated channel distribution factors and load reduction factors; in the equalization model, enumerating feasible topological states of all functional unit groups, calculating a proximity factor between the actual load distribution rate of a channel and the channel distribution rate generated by an upper layer, finding out the topological state with the minimum proximity factor, calculating a target function value of a lower layer optimization model, then carrying out power flow verification, and finding out the actual topological structure which is closest to the optimization model under the condition of minimum balance according to continuous iterative updating of an intelligent optimization algorithm.
In the embodiment, the hierarchical optimization model for guiding the selection of the feasible topological state in the unit group of the equalization model by using the channel distribution factor generated by the upper-layer optimization model is compared with the hierarchical optimization model for directly controlling the opening and closing states of all switches of the system and carrying out modeling based on the state variable of the switch 0-1, so that the dimensionality of the control variable is effectively reduced, whether the topological state is feasible or not does not need to be judged on line, and the larger the scale of the network is, the more obvious the dimensionality reduction effect is.
In the step S3, the determination of the critical line load mainly includes determining the stability of the state voltage by using the eigenvalue of the power flow equation matrix, and solving according to the equation matrix to obtain:
wherein, Δ P is the node active power micro-increment change, Δ Q is the node reactive power micro-increment change, Δ θ is the node voltage angle micro-increment change, Δ U is the node voltage magnitude change, JPθ、JPU、JQθ、JQUAnd forming a sub-array of the Jacbi matrix for the trend equation partial derivatives.
In this embodiment, when Δ P is 0, the following can be obtained:
in the formula, JRCalled a system reduced-order Jacbi matrix, the voltage stability characteristic of the power grid utilizes JRThe characteristic value and the characteristic vector of the image are judged.
In this embodiment, the value of the line participation factor represented by the subarray of the Jacbi matrix reflects the degree of influence of the line on the voltage stability, the larger the participation factor is, the more the line can influence the stability of the grid voltage, the larger the degree of influence on the system voltage stability is, and in addition, the larger the line participation factor is, the larger the load connected to the line is, or the line with a weak connection line is.
The node reactive power micro-increment delta Q is mainly a voltage difference between the head end and the tail end of a line when the reactive power of a power transmission line is maintained at the midpoint of the line, wherein the reactive power compensation capacity is solved as follows:
wherein Q iscompRequired reactive compensation capacity, Δ QTIs the reactive power that is lost by the line transformer,
is one half of the i-th line of the line transformer bus, n is the number of the cable lines on the substation bus, and QloadIs the reactive load on the bus of the substation.
In this embodiment, the optimal target of reactive compensation is: under the condition of transmitting natural power, the reactive power generated by the capacitor in the line is exactly equal to the reactive power absorbed by the inductor.
The method for monitoring the overloading and out-of-limit distribution of the key equipment of the power grid is characterized in that the static voltage stability and the line participation factor are utilized to queue the positions of the power grid lines, so that the search range is favorably narrowed, the calculation speed is improved, the calculation time and the storage space are saved, and convenience is provided for the later searching and positioning of out-of-limit signals.
The data transfer platform in step S4 includes the following modules:
the authority distribution module: the system is used for managing and controlling the user information and the user authority;
a data query module: for query location of data;
a data analysis module: the data processing system is used for displaying the change trend of the monitoring data compared with the threshold data, performing corresponding analysis according to the change trend, and performing descending order arrangement on the data information of which the change trend or the deviation degree exceeds the threshold;
the data display module: the method mainly displays data information with larger load or out-of-limit, dynamically updates in real time, and transmits related section overloading or voltage out-of-limit signals to a system alarm interface.
In this embodiment, the permission distribution module, the data query module, the data analysis module and the data display module are all implemented by calling database information codes through a C language.
In the embodiment, the data is displayed by using the visual graphic image, so that the data can be displayed more accurately, efficiently, simply and comprehensively.
The display process of the data display platform comprises the following steps:
s41, starting a data query module and a data display module according to the load line load information sequence;
s42, calling WFS service loading data through Openlayers to load the line information positioned by the data query module;
s43, and then according to different requests sent by the data server, respectively making corresponding processing.
And the data display module adopts a visual mapping component to map data to visual elements and construct a dynamic graph after the power transmission line is adjusted or transferred.
The method for monitoring the overloading and out-of-limit distribution of the key equipment of the power grid is characterized in that the automatic analysis module is used for automatically screening and sequencing the telemetering data, the uploading interval of the overloaded or out-of-limit telemetering value of the key equipment can be customized, the telemetering value can be dynamically updated in real time, and according to the overloading and out-of-limit display condition of the integrated equipment, a regulator is assisted by the warning system to rapidly perform mode adjustment or voltage regulation decision making, so that the display of the data by the whole data display platform is more visual and visual, and the data can be transmitted more directly and accurately.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A method for monitoring heavy load and out-of-limit distribution of key equipment of a power grid is characterized by comprising the following optimization steps:
s1, establishing an upper-layer optimization model of a line channel distribution factor and a load reduction factor; randomly generating a channel distribution factor and a load reduction factor of a specific line by using a target function in a load unit group in the power grid line;
s2, constructing a global load balancing model according to the channel distribution factor and the load reduction factor; actual line load distribution is close to predicted load pre-distribution, and an actual topological structure under the condition of minimum load balance degree is found out by iterative updating of an algorithm, so that global load balancing is realized;
s3, determining load distribution of the global power grid line through a balancing model; establishing a flexible alternating current transmission acquisition system based on a particle swarm algorithm by using the static voltage stability and the line participation factor obtained by the load balance degree;
s4, constructing data display platforms of different system interactions; the method comprises the steps of accessing map data distributed by power grid lines on a flexible alternating current transmission acquisition system by utilizing a load threshold distribution diagram, and performing layered display on different system platforms by rendering the map data and line optimization data.
2. The method for monitoring the heavy load and out-of-limit distribution of the key devices in the power grid as claimed in claim 1, wherein in step S1,
the line channel distribution factor is a functional unit group GωBy power supply road TjωDivided to power supply point SjLoad ratio of (tau)jωIn which τ isjωThe constraint condition is satisfied as
∑τjω＝1
Wherein tau isjωGroup of presentation and function units GωAll channel allocation factors that are relevant;
the load reduction factor is a functional unit group GωPercentage of active load cut off in the group as a whole, i.e. load reduction ratio hωThen h isωSatisfies the following conditions:
P`ωj＝Pωj(1-hω)
wherein, PωjRepresents a group of functional units GωTotal active load, P' before load sheddingωjReduced functional unit group GωThe total load.
3. The method according to claim 1, wherein the objective function of the upper-layer optimization model based on the line channel distribution factor and the load shedding factor in step S1 is as follows:
F＝Min(K+μ1H)
wherein K is the load balance degree of the transformer substation, H represents the total load reduction proportion of the whole system, and mu1A penalty factor added to the load reduction degree H.
4. The method as claimed in claim 1, wherein the equalization degree in step S2 is mainly determined by a proximity factor λwpAnd a reduction vector X, λwpRepresenting the closeness of the actual allocation proportion of the load in the unit group to the channel allocation factor:
X＝[x1，…，xn]
wherein λ iswpShowing that in the p-th feasible topological state, the functional unit group GωCloseness of (g)pqwRepresents a group of functional units GωLoad sharing rate, τ, per power supply channel determined in the p-th stateqwRepresenting the channel allocation factor given by the upper optimization model; x is the number ofjRepresents the load proportion that the functional unit j can reduce, wherein 0 is less than or equal to xj1, n represents the total number of functional units in the group.
5. The method for monitoring the heavy load and out-of-limit distribution of the key devices of the power grid as claimed in claim 1, wherein the iterative process of the algorithm in step S2 is as follows:
s21, initializing parameters including factor population number NpDimension d of problem variable, cross probability CR, maximum number of iterations Gm, search field S ═ X | XL≤X≤XU}；
S22, initializing the population,
wherein rand represents a random number between 0 and 1;
s23, solving the average value X of all individuals in the populationmeanForming a new individual Xnew；
S24, randomly extracting an individual X from the populationjLearning according to the difference between the user and the user, and updating the operation after the learning stage is finished;
s25, performing interactive operation in the individual learning stage, wherein the expression is as follows:
wherein rand represents a random number between 0 and 1, CR represents a crossover probability, the size is predetermined, randn (i) takes a value between [1, d ], and indexes are dimension variables selected randomly to ensure that at least one dimension variable is provided by an individual after the learning stage. After the cross operation is finished, updating the operation;
s25, if the iteration end condition is met, ending the optimization; otherwise, the process proceeds to step S23 to continue the iteration.
6. The method for monitoring the heavy load and out-of-limit distribution of the key equipment of the power grid as claimed in claim 1, wherein the determination of the key line load in step S3 is mainly obtained by determining the stability degree of the state voltage according to the eigenvalue of the power flow equation matrix and solving according to the equation matrix:
wherein, Δ P is the node active power micro-increment change, Δ Q is the node reactive power micro-increment change, Δ θ is the node voltage angle micro-increment change, Δ U is the node voltage magnitude change, JPθ、JPU、JQθ、JQUAnd forming a sub-array of the Jacbi matrix for the trend equation partial derivatives.
7. The method of claim 6, wherein the node reactive power micro-increment Δ Q is mainly a voltage difference between a head end and a tail end of the line when the reactive power point of the transmission line is maintained at a midpoint of the line, and wherein the reactive power compensation capacity is solved as:
wherein Q iscompRequired reactive compensation capacity, Δ QTIs the reactive power that is lost by the line transformer,
is one half of the i-th line of the line transformer bus, n is the number of the cable lines on the substation bus, and QloadIs the reactive load on the bus of the substation.
8. The method according to claim 1, wherein the data transmission platform in step S4 includes the following modules:
the authority distribution module: the system is used for managing and controlling the user information and the user authority;
a data query module: for query location of data;
a data analysis module: the data processing system is used for displaying the change trend of the monitoring data compared with the threshold data, performing corresponding analysis according to the change trend, and performing descending order arrangement on the data information of which the change trend or the deviation degree exceeds the threshold;
the data display module: the method mainly displays data information with larger load or out-of-limit, dynamically updates in real time, and transmits related section overloading or voltage out-of-limit signals to a system alarm interface.
9. The method according to claim 8, wherein the data display platform comprises the following display processes:
s41, starting a data query module and a data display module according to the load line load information sequence;
s42, calling WFS service to load the line information positioned by the data query module;
and S43, respectively processing according to different requests sent by the data server.
10. The method according to claim 8, wherein the data display module adopts a visual mapping component to map data to visual elements to construct a dynamic graph after the transmission line is adjusted or migrated.
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