CN115498702A - Path optimization method and device considering data transmission requirements of distribution network in operation or fault state - Google Patents

Path optimization method and device considering data transmission requirements of distribution network in operation or fault state Download PDF

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CN115498702A
CN115498702A CN202211180017.0A CN202211180017A CN115498702A CN 115498702 A CN115498702 A CN 115498702A CN 202211180017 A CN202211180017 A CN 202211180017A CN 115498702 A CN115498702 A CN 115498702A
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data
path
distribution network
state
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CN115498702B (en
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张博
窦春霞
岳东
张占强
张智俊
严婷
徐雷
李厚俊
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Nanjing University of Posts and Telecommunications
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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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

The invention discloses a path optimization method and a device for considering the data transmission requirements of a distribution network in an operation or fault state, wherein the method comprises the following steps: judging the physical system running state and the communication network reliable state of the power distribution network; evaluating the importance degree of data and constructing a target function based on the judged physical system running state of the power distribution network and the judged reliable state of the communication network; and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme. A data security transmission strategy based on matching of data importance degree and security degree in an operating state is designed; and the data emergency transmission strategy is matched with the transmission speed based on the data importance degree in the fault state. Through the route scheduling or path reconstruction mode, data with high importance degree are transmitted to a destination end through a path with superior performance preferentially, and the communication reliability of the active power distribution network under the condition of information physical fusion is improved.

Description

Path optimization method and device considering data transmission requirements of distribution network in operation or fault state
Technical Field
The invention belongs to the technical field of active power distribution network communication, and relates to a path optimization method and device considering the data transmission requirements of a distribution network in an operating or fault state.
Background
The active power distribution network is an advanced power distribution network with the capability of combined control of various distributed energy sources (distributed power sources, controllable loads, energy storage, demand side management and the like), generally combines an advanced regulation and control technology with an advanced communication technology, and can play a certain power supporting role on the basis of meeting supervision and access criteria. The construction target of the active power distribution network is to improve the power generation proportion of renewable distributed power sources such as photovoltaic power sources and fans in the power distribution network, improve the consumption capacity of the power distribution network to renewable energy sources, and on the basis of ensuring safe and high-quality power supply, the active power distribution network is also an important development direction of the future power distribution network.
In an active power distribution network, as the power generation proportion of distributed renewable energy sources is gradually increased and the degree of information physical fusion is deepened, the randomness problem of output, the fluctuation problem of output and the communication problem are easily superposed to cause the operation of the active power distribution network to be shifted from a normal state to an alert state, namely, the voltage, the frequency, the supply and demand balance state and the like of the system are threatened by the occurrence of disturbance. Besides the advanced communication technology, if the advanced regulation and control technology is not adopted, once the small disturbance is accumulated into the large disturbance, the system is switched from the alert state to the fault state and even crashes, and the system faces the risk of disconnection. Therefore, advanced communication and regulation technologies need to be cooperatively designed to ensure that the system safely operates in an alert state, reduce the voltage, frequency and out-of-limit output risks and avoid the problems.
New power systems are increasingly relying on communication, as they require advanced communication techniques to support advanced control techniques. But power system communication environments often have uncertainty for the following reasons: firstly, the transmission bandwidth of the channel is limited, inherent time lag, packet loss and noise exist, and the safety of the channel needs to be improved; secondly, the active power distribution network is susceptible to external emergencies, and particularly, injection attacks or external DoS attacks exist at distributed power access points, which further causes limited communication bandwidth, reduced data availability and even channel transmission interruption. The insecurity of data transmission will affect the normal execution of decision control, and even lead to system breakdown if the processing is not timely. Considering the problem of safe transmission in the operating state, the failure state and the recovery state, how to design a path optimization scheduling strategy to ensure that a transmission destination end has available data needs further research.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a path optimization method and a path optimization device considering the data transmission requirements of a distribution network in an operation or fault state, wherein after the requirements of a decision control target on the data transmission speed and precision in a power grid are considered, the path is scheduled or reconstructed based on a route so as to ensure that the data uploading and command issuing processes can be normally carried out and support the smooth execution of power services. Data transmission can be recovered through routing scheduling or path reconstruction, so that the requirement of the power grid on data transmission in different states can be supported. The data security transmission strategy design based on the matching of the data importance degree and the security degree in the operation state and the data emergency transmission and path reconstruction strategy design based on the matching of the data importance degree and the transmission speed in the fault state are provided, and the communication reliability of the intelligent power grid in the execution of the power service in different operation states is improved.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, a method for optimizing a path considering a requirement of a distribution network for data transmission in an operating or fault state is provided, including:
judging the physical system running state and the communication network reliable state of the power distribution network;
evaluating the importance degree of data and constructing a target function based on the judged physical system running state and communication network reliable state of the power distribution network;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
The method for constructing the target function comprises the following steps:
a data security transmission strategy based on matching of data importance degree and security degree in a running state;
and the data emergency transmission strategy is matched with the transmission speed based on the data importance degree in the fault state.
In some embodiments, determining the physical system operating state of the power distribution network includes:
(A) When supply is short of demand, the following needs to be considered:
case 1: supply and demand balance can be realized only by means of photovoltaic power, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand:
Figure RE-GDA0003927510350000031
wherein P is PVv (t) and P WTy (t) respectively represents the power generation amount of the v photovoltaic fan and the y fan; n is a radical of PV ，N WT And N Load Respectively the number of photovoltaic, fan and load; p is Loadz (t) represents the z-th load demand; in this event, the photovoltaic will operate in maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will run in either charge or shutdown mode;
event 1-2: the power generation capacity of the photovoltaic and wind power generation set is not enough to meet the load demand, but when some stored energy runs in a discharge mode, the problem can be solved, and the related expression is as follows:
Figure RE-GDA0003927510350000032
wherein, P ESUw (t) represents the w-th stored energy generation amount; n is a radical of hydrogen ESU Representing the amount of stored energy;
case 2: in addition to the photovoltaic, wind and energy storage involved in the energy supply, the diesel DG will also be involved in the electric energy supply:
Figure RE-GDA0003927510350000041
wherein, P DGn (t) represents the power generation amount of the nth diesel engine DG; n is a radical of DG Representing the number of diesel engines DG;
case 3: when overload is serious, the redundant load is cut off from the system and the system is operated in a fault state only by adjusting the source end to meet the load end requirement;
(B) When supply is greater than demand, the following needs to be considered:
event 1: the power generation of some distributed power sources is reduced, and
Figure RE-GDA0003927510350000042
Figure RE-GDA0003927510350000043
wherein,
Figure RE-GDA0003927510350000044
representing the output adjustment of the mth interruptible distributed power source; n is a radical of IDER Represents the number of interruptible distributed power sources;
event 2: part of the power supply will be removed from the distribution network system, i.e.
Figure RE-GDA0003927510350000045
The system is in a fault state at this time.
In some embodiments, determining the reliable status of the communication network of the power distribution network comprises:
when a route scheduling or path reconstruction is selected to form defense after the physical operation state is determined, the availability of a router needs to be judged first, namely whether a path can transmit data from the router to other routers is available; setting n routers in the system, wherein the availability judgment conditions are as follows;
except for the initial and destination ends, the input and output channels of each router must be connected simultaneously, for the xth router, there are
Figure RE-GDA0003927510350000046
Wherein x 'and x' respectively represent the head end router label of the input channel and the tail end router label of the output channel of the x-th router;&&is the "logical AND" operator, a xx' 、a x”x Respectively representing the connection relationship from the xth router to the xth' router, and the definitions of similar symbols are similar; k represents the set of neighboring routers of the xth router.
In some embodiments, assessing the importance of the data comprises:
the importance degree of the data is calculated by the partial derivative of the decision control target to the data; wherein the decision control target expression is modeled as f (x) 1 ,…,u 1 8230in which x 1 ，x 2 \8230representingstate variables; u. of 1 ，u 2 8230indicating control amount;
when the system is in an operating state, a sensitivity calculation expression of data change on a decision control effect is as follows:
for x 1 ：
Figure RE-GDA0003927510350000051
For x 2 ：
Figure RE-GDA0003927510350000052
For u 1 ：
Figure RE-GDA0003927510350000053
For u 2 ：
Figure RE-GDA0003927510350000054
Wherein
Figure RE-GDA0003927510350000055
And
Figure RE-GDA0003927510350000056
respectively represent x in the operating state 1 ，x 2 ，u 1 And u 2 The sensitivity of (2); the greater the sensitivity, the greater the data importance;
when the system is in a fault state, the sensitivity calculation expression of data change to time is as follows:
for x 1 ：
Figure RE-GDA0003927510350000057
For x 2 ：
Figure RE-GDA0003927510350000058
For u 1 ：
Figure RE-GDA0003927510350000059
For u 2 ：
Figure RE-GDA00039275103500000510
Wherein
Figure RE-GDA00039275103500000511
And
Figure RE-GDA00039275103500000512
respectively represent x in a fault state 1 ，x 2 ，u 1 And u 2 The sensitivity of (c); the greater the sensitivity, the greater the data importance;
according to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths corresponding to different attributes of different data can be arranged conveniently, and the influence of unreliable communication on the execution effect of the decision control strategy of the power distribution network is reduced.
In some embodiments, in the running state, the constructing the objective function Z based on the data security transmission policy with the data importance degree matched with the security degree includes:
in the operating state:
because the running time scale of the system is longer in the running state and the requirement on the transmission speed is not strict, the data transmission precision is preferentially ensured; optimizing the transmission path of each data according to the sequence of the importance degree of the data from large to small, wherein the corresponding optimization targets are divided into the following categories:
if the attributes of all channels are to be added together, there are:
Figure RE-GDA0003927510350000061
wherein N represents the number of regions; min represents a minimum value calculation algorithm; sign is a sign function; a is a ij Representing the connection relationship from the ith node to the jth node, if there is a connection, a ij =1; otherwise, a ij ＝0； Att ij.k Representing the kth attribute value in a channel from the ith node to the jth node, wherein the attributes comprise time delay, packet loss rate and signal-to-noise ratio;
if only the channel with the most outstanding attributes in the path is required to satisfy the constraint condition, then there are:
Z＝min{max[Att ij.k ·sign(a ij )]},j∈N i (8)
dynamically adjusting the path:
when a path from a starting position to a destination position is determined, whether the path meets related constraint conditions or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the selected constraints are as follows:
6) There is only one output channel at the start position:
Figure RE-GDA0003927510350000062
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is jstart Representing the connection relationship from the jth node to the starting position; n is a radical of start The number of adjacent relay routes representing the starting position;
7) There is only one input channel at the destination location:
Figure RE-GDA0003927510350000071
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is jend Representing the connection relationship from the jth node to the destination location; n is a radical of end The number of adjacent relay routes representing the destination position;
8) The relay route has both input and output channels:
Figure RE-GDA0003927510350000072
wherein
Figure RE-GDA0003927510350000073
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000074
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000075
representing the number of adjacent relay routes of the l relay route;
9) The number of input channels and the number of output channels of the relay route do not exceed the allowed upper limit:
Figure RE-GDA0003927510350000076
Figure RE-GDA0003927510350000077
wherein
Figure RE-GDA0003927510350000078
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000079
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA00039275103500000710
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-GDA00039275103500000711
representing the upper limit of the number of output channels allowed by the l relay route;
10 Properties of the planned path meet set requirements:
Figure RE-GDA00039275103500000712
Figure RE-GDA00039275103500000713
wherein
Figure RE-GDA0003927510350000081
Representing the defining condition of the k-th attribute value.
In some embodiments, the constructing the objective function Z based on the data emergency transmission policy that the data importance degree matches with the transmission speed in the fault state includes:
in a fault state:
considering that data should be recovered and transmitted at the fastest speed, and ensuring that the data with high importance degree and high time sensitivity is recovered and transmitted preferentially; the data importance degree and the sensitivity of the data to time need to be calculated in a superposition manner, and the method specifically comprises the following steps:
1. de-unitization:
Figure RE-GDA0003927510350000082
and
Figure RE-GDA0003927510350000083
wherein
Figure RE-GDA0003927510350000084
The sensitivity of the decision control target to the ith data after the unit removal;
Figure RE-GDA0003927510350000085
is the sensitivity of the ith data to time after the demosaicing;
3. and (3) weighting: when the two types of sensitivities are comprehensively considered, the comprehensive sensitivity is obtained
Figure RE-GDA0003927510350000086
Wherein λ i1 And λ i2 Is a weight coefficient, and λ i1 +λ i2 ＝1；
When optimizing, should follow
Figure RE-GDA0003927510350000087
Optimizing the transmission path of each data in a descending order, wherein the corresponding optimization targets are as follows:
Figure RE-GDA0003927510350000088
wherein, delay ij Representing the skew in the channel from the ith node to the jth node; the constraint conditions other than (9) to (15),
6) There is only one output channel at the start position:
Figure RE-GDA0003927510350000089
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is jstart Representing the connection relationship from the jth node to the starting position; n is a radical of start The number of adjacent relay routes representing the starting position;
7) There is only one input channel at the destination location:
Figure RE-GDA0003927510350000091
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is a jend Representing the connection relationship from the jth node to the destination location; n is a radical of hydrogen end The number of adjacent relay routes representing the destination position;
8) The relay route has both input and output channels:
Figure RE-GDA0003927510350000092
wherein
Figure RE-GDA0003927510350000093
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000094
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000095
representing the number of adjacent relay routes of the l relay route;
9) The number of input channels and the number of output channels of the relay route do not exceed the allowed upper limit:
Figure RE-GDA0003927510350000096
Figure RE-GDA0003927510350000097
wherein
Figure RE-GDA0003927510350000098
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000099
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA00039275103500000910
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-GDA00039275103500000911
representing the upper limit of the number of output channels allowed by the l relay route;
10 Properties of the planned path meet the set requirements:
Figure RE-GDA00039275103500000912
Figure RE-GDA00039275103500000913
wherein
Figure RE-GDA00039275103500000914
A qualifier representing a kth attribute value;
it should also be ensured that the data transmission accuracy meets the requirements, i.e. the optimized path needs to additionally meet the constraint conditions
Figure RE-GDA0003927510350000101
Wherein risk ij Representing the probability of risk in the channel from the ith node to the jth node; condition risk Is a set risk constraint index.
In some embodiments, solving the objective function using a particle swarm optimization algorithm combined with random variation includes:
step 4-1: selecting an initial population:
selecting an initial population quantity: sizepop, variable dimension: spaedenim; maximum number of iterations: ger; a position limit; speed limitation; inertial weight: c _1; individual learning factors: c _2; group learning factor: c _3;
step 4-2: judging whether the individual meets the constraint condition and selecting the optimal individual:
by substituting the individual into equations (9) to (15), whether or not the expression satisfies
Figure RE-GDA0003927510350000102
Further, an optimal selection is made among all the individuals satisfying the constraint condition, i.e., argmax (Z);
step 4-3: updating the population by adopting a random variation mode, and carrying out position variation on corresponding individuals in the current iteration according to pop _ x (: j) = pop _ x (random (1, dim), j) + xi to expand the number attribute of the population and facilitate jumping out of a local optimal solution, wherein pop _ x (: j) represents the jth position in the current iteration individuals; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; xi ∈ [ -0.2,0.2];
step 4-4: judging whether the updated population individual meets the constraint condition or not and selecting the optimal individual, and substituting the individual into the formulas (9) to (15) to judge whether the updated population individual meets the constraint condition or not
Figure RE-GDA0003927510350000111
Furthermore, the optimal choice among all the individuals who satisfy the constraint condition, namely argmax (Z);
and 4-5: and repeating the step 4-3 and the step 4-4 until a set iteration number is achieved.
In a second aspect, the present invention provides a path optimization apparatus considering the requirement of a distribution network for data transmission in an operating or fault state, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a third aspect, the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
Has the advantages that: the method and the device for optimizing the path of the distribution network in consideration of the data transmission requirement in the running or fault state have the following advantages:
1. in the operating state, the data communication path is matched with the importance degree of the data, the data with high importance degree is transmitted to a destination terminal through a path with superior performance preferentially, the rationality of optimization is improved, and the requirement of power service on data transmission is met;
2. under a fault state, the sensitivity of data change to time is considered, the data communication path is matched with the importance degree of data, the data with high importance degree and high sensitivity of data change to time is transmitted to a destination end through a path with superior performance preferentially, the basic electric power service is supported and completed quickly, and the system is restored to an operating state in time;
3. in the optimization process, by designing the particle swarm optimization solution algorithm with random variation, the solution speed and the generalization capability of the algorithm are improved, and a reasonable path reconstruction scheme is obtained.
Drawings
FIG. 1 is a schematic diagram of policy enforcement according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of scenario 1 in an operating state of the embodiment of the present invention.
Fig. 3 is a schematic diagram of scenario 2 in an operating state of the embodiment of the present invention.
Fig. 4 is a schematic diagram of scenario 3 in an operating state of the embodiment of the present invention.
Fig. 5 is a schematic view of scene 4 in an operating state according to the embodiment of the present invention.
Fig. 6 is a schematic diagram of scenario 1 in a failure state according to the embodiment of the present invention.
Fig. 7 is a schematic diagram of scenario 2 in a failure state according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and larger, smaller, larger, etc. are understood as excluding the present numbers, and larger, smaller, inner, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
A path optimization method considering the data transmission requirements of a distribution network in an operation or fault state comprises the following steps:
judging the physical system running state and the communication network reliable state of the power distribution network;
evaluating the importance degree of data and constructing a target function based on the judged physical system running state and communication network reliable state of the power distribution network;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
In some embodiments, based on the determined physical system operating state of the power distribution network and the determined reliable state of the communication network, the importance degree of the data is evaluated, and an objective function is constructed, including:
a data security transmission strategy based on matching of data importance degree and security degree in a running state;
and the data emergency transmission strategy is matched with the transmission speed based on the data importance degree in the fault state.
In some embodiments, determining the physical system operating state of the power distribution network includes:
(A) When supply is short of demand, the following needs to be considered:
case 1: supply and demand balance can be realized only by means of photovoltaic power, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand:
Figure RE-GDA0003927510350000141
wherein P is PVv (t) and P WTy (t) respectively represents the power generation amount of the v photovoltaic fan and the y fan; n is a radical of hydrogen PV ，N WT And N Load Respectively the number of photovoltaic, fan and load; p Loadz (t) represents a demanded quantity of a z-th load; in this event, the photovoltaic will operate in maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will run in a charging or shutdown mode;
event 1-2: the power generation capacity of the photovoltaic and wind power generation set is not enough to meet the load demand, but when some stored energy runs in a discharge mode, the problem can be solved, and the related expression is as follows:
Figure RE-GDA0003927510350000142
wherein, P ESUw (t) representsw generated energy of stored energy; n is a radical of ESU Representing the amount of stored energy;
case 2: in addition to the photovoltaic, wind and energy storage involved in the energy supply, the diesel DG will also be involved in the electric energy supply:
Figure RE-GDA0003927510350000143
wherein, P DGn (t) represents the power generation amount of the nth diesel engine DG; n is a radical of DG Represents the number of diesel engines DG;
case 3: when overload is serious, the redundant load is cut off from the system and the system is in a fault state only by adjusting that the source end can not meet the requirement of the load end;
note: the method does not consider main network power supply, and only carries out strategy research on the active power distribution network.
(B) When supply is greater than demand, the following needs to be considered:
event 1: the power generation of some distributed power sources is reduced, and
Figure RE-GDA0003927510350000151
Figure RE-GDA0003927510350000152
wherein,
Figure RE-GDA0003927510350000153
representing the output adjustment of the mth interruptible distributed power source; n is a radical of IDER Representing the number of interruptible distributed power sources;
event 2: part of the power supply will be removed from the distribution network system, i.e.
Figure RE-GDA0003927510350000154
The system is in a fault state at this time.
In some embodiments, determining the reliable status of the communication network of the power distribution network comprises:
when a route scheduling or path reconstruction is selected to form defense after the physical operation state is determined, the availability of a router needs to be judged first, namely whether a path can transmit data from the router to other routers or not is judged; setting n routers in the system, wherein the availability judgment conditions are as follows;
apart from the initial and destination ends, the input and output channels of each router must be connected simultaneously, for the xth router, there are
Figure RE-GDA0003927510350000155
Wherein x 'and x' respectively represent the head end router label of the input channel and the tail end router label of the output channel of the x-th router;&&is a logical AND operator, a xx' 、a x”x Respectively representing the connection relation from the xth router to the xth' router, and the definitions of similar symbols are similar; k represents the set of neighboring routers of the xth router.
Further, the data security transmission policy design based on the matching of the data importance degree and the security degree in the operating state is considered in the following cases:
scene 1:the communication network transmission is not attacked in the operation state, and a plurality of selectable paths are provided for data transmission
Aiming at the problem of safe transmission in an operating state, a multi-path planning strategy is researched, and the method specifically comprises the following steps: 1) Evaluating the importance degree of terminal data based on the sensitivity analysis of the speed and precision of acquiring state data on the influence of the multivariate decision control service; 2) Constructing a single-target optimization model based on the principle that the importance degree of data is matched with the safety measure of the path of the data and the transmission rapidity is used as a constraint condition; 3) Based on the data importance degree sequencing, sequentially solving the optimization model to obtain a path optimization strategy, and transmitting data by selecting a path with optimal safety performance to ensure that the influenced data are sequentially transmitted to a destination end according to the importance degree and support the execution of the power service;
scene 2:the communication network transmission in the running state is subjected to malicious attacks, so that the problems of light congestion of channel transmission and the like are caused, but the influence of data transmission caused by congestion does not exceed the tolerance of a decision control taskLimiting;
1) Similarly to scenario 1, the importance of the data is studied first based on sensitivity analysis; 2) And researching a path optimization strategy based on the principle that the importance degree of data is matched with the path safety measure of the data so as to ensure the effectiveness of the service. Firstly, judging whether a standby path exists in the existing communication network, if so, carrying out routing scheduling according to the principle that the importance degree of data is matched with the safety degree of the path; otherwise, determining the head and tail nodes, and performing path reconstruction according to the head and tail nodes by taking the optimal safety, rapidity or transmission length as a target to obtain an optimal path scheme.
Scene 3:under the operation state, the communication network transmission is attacked maliciously, so that the channel transmission congestion is serious and even interrupted, but the important degree of the influenced data is low, and the safe operation of a novel power system is not influenced. Examples are: cost information in energy management, such as time-of-use electricity price and the like, can still be safely operated even if interrupted.
Where the enforcement policy is similar to scenario 2.
Scene 4:communication equipment such as a route or a channel and the like in the running state breaks down, transmission is interrupted, the importance degree of influenced data is low, and the safe running of a novel power system is not influenced.
Where the enforcement policy is similar to scenario 2.
Further, the design of the data emergency transmission strategy based on the matching of the data importance degree and the transmission speed in the fault state is considered in the following cases:
scene 1:the communication network transmission is attacked maliciously, which causes serious congestion and even interruption of channel transmission, and the important degree of the influenced data is high, which influences the safe operation of the novel power system.
Aiming at the problems of data incompleteness or unavailability in a fault state, aiming at emergency recovery of important data, a path optimization strategy is designed, and the steps are as follows: 1) Analyzing the information-physical sensitivity of the influence of data change on the safe operation of the power system and the information-time sensitivity of the data change on time based on the speed and the precision of the acquired state data, and comprehensively evaluating the importance degree of the data; 2) Based on the rapidity evaluation of the transmission path, the principle of matching the data importance degree with the path transmission speed is used, the important data is ensured to be rapidly transmitted as a main target, the transmission precision is used as a constraint condition, an optimization model is constructed, an optimal transmission path strategy is obtained, and meanwhile, the data transmission safety is improved.
Scene 2:communication equipment such as a route or a channel breaks down in the running state, transmission is interrupted, the important degree of influenced data is high, and safe running of a novel power system is influenced. In this case, the execution policy is similar to scenario 1.
In some embodiments, assessing the importance of the data comprises:
the importance degree of the data is calculated by the partial derivative of the decision control target to the data; wherein the decision control target expression is modeled as f (x) 1 ,…,u 1 8230in which x 1 ，x 2 \8230denotesa state variable; u. of 1 ，u 2 8230indicating control amount;
when the system is in an operating state, a sensitivity calculation expression of data change on a decision control effect is as follows:
for x 1 ：
Figure RE-GDA0003927510350000171
For x 2 ：
Figure RE-GDA0003927510350000172
For u 1 ：
Figure RE-GDA0003927510350000173
For u 2 ：
Figure RE-GDA0003927510350000174
Wherein
Figure RE-GDA0003927510350000175
And
Figure RE-GDA0003927510350000176
respectively represent x in the operating state 1 ，x 2 ，u 1 And u 2 The sensitivity of (c); the greater the sensitivity, the greater the data importance;
when the system is in a fault state, the sensitivity calculation expression of data change to time is as follows:
for x 1 ：
Figure RE-GDA0003927510350000181
For x 2 ：
Figure RE-GDA0003927510350000182
For u 1 ：
Figure RE-GDA0003927510350000183
For u 2 ：
Figure RE-GDA0003927510350000184
Wherein
Figure RE-GDA0003927510350000185
And
Figure RE-GDA0003927510350000186
respectively represent x in a fault state 1 ，x 2 ，u 1 And u 2 The sensitivity of (c); the greater the sensitivity, the greater the data importance;
according to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths corresponding to different attributes of different data can be arranged conveniently, and the influence of unreliable communication problems on the execution effect of the decision control strategy of the power distribution network is reduced.
In some embodiments, constructing the objective function Z comprises:
in the operating state:
because the running time scale of the system is longer in the running state and the requirement on the transmission speed is not strict, the data transmission precision is preferentially ensured; optimizing the transmission path of each data according to the sequence of the importance degree of the data from large to small, wherein the corresponding optimization targets are divided into the following categories:
if the attributes of all channels are to be added together, there are:
Figure RE-GDA0003927510350000187
wherein N represents the number of regions; min represents a minimum value calculation algorithm; sign is a sign function; a is ij Representing the connection relationship from the ith node to the jth node, if there is a connection, a ij =1; otherwise, a ij ＝0； Att ij.k Representing the kth attribute value in a channel from the ith node to the jth node, wherein the attribute comprises time delay, packet loss rate and signal-to-noise ratio;
if only the channel with the most outstanding attributes in the path is required to satisfy the constraint condition, then there are:
Z＝min{max[Att ij.k ·sign(a ij )]},j∈N i (8)
dynamically adjusting the path:
when a path from a starting position to a destination position is determined, whether the path meets a relevant constraint condition or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the constraints selected are as follows:
11 Only one output channel at the start position:
Figure RE-GDA0003927510350000191
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is jstart Representing the connection relationship from the jth node to the starting position; n is a radical of start The number of adjacent relay routes representing the starting position;
12 Only one input channel at the destination location:
Figure RE-GDA0003927510350000192
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is jend Representing the connection relationship from the jth node to the destination location; n is a radical of hydrogen end The number of adjacent relay routes representing the destination position;
13 Relay routes exist for both input and output channels:
Figure RE-GDA0003927510350000193
wherein
Figure RE-GDA0003927510350000194
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000195
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000196
representing the number of adjacent relay routes of the l relay route;
14 The number of input channels and the number of output channels of the relay route do not exceed the allowable upper limit:
Figure RE-GDA0003927510350000197
Figure RE-GDA0003927510350000201
wherein
Figure RE-GDA0003927510350000202
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000203
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000204
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-GDA0003927510350000205
representing the upper limit of the number of output channels allowed by the ith relay route;
15 Properties of the planned path meet set requirements:
Figure RE-GDA0003927510350000206
Figure RE-GDA0003927510350000207
wherein
Figure RE-GDA0003927510350000208
Representing the defining condition of the k-th attribute value.
In some embodiments, constructing the objective function Z comprises:
in a fault state:
considering that data should be recovered and transmitted at the fastest speed, and ensuring that the data with high importance degree and high time sensitivity is recovered and transmitted preferentially; the data importance degree and the sensitivity of the data to time need to be calculated in a superposition manner, and the method specifically comprises the following steps:
1. de-unitization:
Figure RE-GDA0003927510350000209
and
Figure RE-GDA00039275103500002010
wherein
Figure RE-GDA00039275103500002011
The sensitivity of the decision control target to the ith data after the unit removal;
Figure RE-GDA00039275103500002012
is the sensitivity of the ith data to time after the demonitation;
4. and (3) weighting: when the two types of sensitivities are comprehensively considered, the comprehensive sensitivity is obtained
Figure RE-GDA00039275103500002013
Wherein λ i1 And λ i2 Is a weight coefficient, and λ i1 +λ i2 ＝1；
When optimizing, should follow
Figure RE-GDA00039275103500002014
Optimizing the transmission path of each data in a descending order, wherein the corresponding optimization targets are as follows:
Figure RE-GDA0003927510350000211
wherein, delay ij Representing the time lag in the channel from the ith node to the jth node; the constraint conditions other than (9) to (15),
11 Only one output channel at the start position:
Figure RE-GDA0003927510350000212
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is jstart Representing the connection relationship from the jth node to the starting position; n is a radical of start The number of adjacent relay routes representing the starting position;
12 Only one input channel at the destination location:
Figure RE-GDA0003927510350000213
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is jend Representing the connection relationship from the jth node to the destination location; n is a radical of end The number of adjacent relay routes representing the destination position;
13 Relay routes exist for both input and output channels:
Figure RE-GDA0003927510350000214
wherein
Figure RE-GDA0003927510350000215
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000216
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000217
representing the number of adjacent relay routes of the l relay route;
14 The number of input channels and the number of output channels of the relay route do not exceed the allowable upper limit:
Figure RE-GDA0003927510350000218
Figure RE-GDA0003927510350000219
wherein
Figure RE-GDA0003927510350000221
Representing the connection relation from the ith relay route to the jth node;
Figure RE-GDA0003927510350000222
representing the connection relationship from the jth node to the ith relay route;
Figure RE-GDA0003927510350000223
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-GDA0003927510350000224
representing the upper limit of the number of output channels allowed by the l relay route;
15 Properties of the planned path meet set requirements:
Figure RE-GDA0003927510350000225
Figure RE-GDA0003927510350000226
wherein
Figure RE-GDA0003927510350000227
A qualifier representing a kth attribute value;
it should also be ensured that the data transmission accuracy meets the requirements, i.e. the optimized path needs to additionally meet the constraint conditions
Figure RE-GDA0003927510350000228
Wherein risk ij Representing the probability of risk in the channel from the ith node to the jth node; condition risk Is a set risk constraint index.
In some embodiments, solving the objective function using a particle swarm optimization algorithm combined with random variation includes:
step 4-1: selecting an initial population:
selecting an initial population quantity: sizepop, variable dimension: spaedenim; maximum number of iterations: ger; a position limit; speed limitation; inertial weight: c _1; individual learning factors: c _2; group learning factor: c _3;
step 4-2: judging whether the individual meets the constraint condition and selecting the optimal individual:
by substituting the individual into equations (9) to (15), whether or not the expression satisfies
Figure RE-GDA0003927510350000231
Further, an optimal selection is made among all the individuals satisfying the constraint condition, i.e., argmax (Z);
step 4-3: updating the population by adopting a random variation mode, and carrying out position variation on corresponding individuals in the current iteration according to pop _ x (: j) = pop _ x (random (1, dim), j) + xi to expand the number attribute of the population and facilitate jumping out of a local optimal solution, wherein the pop _ x (: j) represents the jth position in the current iteration individuals; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; xi ∈ [ -0.2,0.2];
step 4-4: judging whether the updated population individuals meet the constraint conditions or not and selecting the optimal individuals, and substituting the individuals into the formulas (9) to (15) to judge whether the updated population individuals meet the constraint conditions or not
Figure RE-GDA0003927510350000241
Furthermore, the optimal choice among all the individuals who satisfy the constraint condition, namely argmax (Z);
and 4-5: and repeating the step 4-3 and the step 4-4 until a set iteration number is achieved.
Example 2
In a second aspect, the present embodiment provides a path optimization apparatus considering the requirement of a distribution network for data transmission in an operating or failure state, including a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to embodiment 1.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of embodiment 1.
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.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (9)

1. A path optimization method considering the data transmission requirements of a distribution network in an operation or fault state is characterized by comprising the following steps:
judging the physical system running state and the communication network reliable state of the power distribution network;
evaluating the importance degree of data and constructing a target function based on the judged physical system running state and communication network reliable state of the power distribution network;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
2. The method for optimizing the path of the distribution network for the data transmission requirement under the consideration of the operation or fault state as claimed in claim 1, wherein the step of judging the operation state of the physical system of the distribution network comprises the following steps:
(A) When supply is short, the following needs to be considered:
case 1: supply and demand balance can be realized only by means of photovoltaic power, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand:
Figure RE-FDA0003927510340000011
wherein P is PVv (t) and P WTy (t) respectively represents the power generation amount of the v photovoltaic fan and the y fan; n is a radical of PV ，N WT And N Load Respectively the number of photovoltaic, fan and load;P Loadz (t) represents the z-th load demand; in this event, the photovoltaic will operate in maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will run in a charging or shutdown mode;
event 1-2: the power generation capacity of the photovoltaic and wind power generation set is not enough to meet the load demand, but when some stored energy runs in a discharge mode, the problem can be solved, and the related expression is as follows:
Figure RE-FDA0003927510340000012
Figure RE-FDA0003927510340000021
wherein, P ESUw (t) represents the w-th stored energy generation amount; n is a radical of ESU Representing the amount of stored energy;
case 2: in addition to the photovoltaic, wind and energy storage involved in the energy supply, the diesel DG will also be involved in the electric energy supply:
Figure RE-FDA0003927510340000022
wherein, P DGn (t) represents the power generation amount of the nth diesel engine DG; n is a radical of DG Representing the number of diesel engines DG;
case 3: when overload is serious, the redundant load is cut off from the system and the system is in a fault state only by adjusting that the source end can not meet the requirement of the load end;
(B) When supply is greater than demand, the following needs to be considered:
event 1: the power generation of some distributed power sources is reduced, and
Figure RE-FDA0003927510340000023
Figure RE-FDA0003927510340000024
wherein,
Figure RE-FDA0003927510340000025
representing the output adjustment of the mth interruptible distributed power source; n is a radical of IDER Represents the number of interruptible distributed power sources;
event 2: part of the power supply will be removed from the distribution network system, i.e.
Figure RE-FDA0003927510340000026
The system is in a fault state at this time.
3. The method for optimizing the path of the distribution network for the data transmission requirement under the condition of considering the operation or the fault according to claim 1, wherein the step of judging the reliable state of the communication network of the distribution network comprises the following steps:
when a route scheduling or path reconstruction is selected to form defense after the physical operation state is determined, the availability of a router needs to be judged first, namely whether a path can transmit data from the router to other routers is available; setting n routers in the system, wherein the availability judgment conditions are as follows;
the input and output channels of each router must be connected simultaneously, except for the initial and destination, and for the xth router, there is
Figure RE-FDA0003927510340000031
Wherein x 'and x' respectively represent the head end router label of the input channel and the tail end router label of the output channel of the x-th router;&&is a logical AND operator, a xx' 、a x″x Respectively representing the connection relationship from the xth router to the xth' router, and the definitions of similar symbols are similar; k represents the neighbor set of the xth router.
4. The method for optimizing the path of the distribution network considering the data transmission requirement in the operation or fault state as claimed in claim 1, wherein the evaluating the importance degree of the data comprises:
the importance degree of the data is calculated by the partial derivative of the decision control target to the data; wherein the decision control target expression is modeled as f (x) 1 ,…,u 1 8230in which x 1 ，x 2 \8230denotesa state variable; u. of 1 ，u 2 8230indicating control amount;
when the system is in an operating state, a sensitivity calculation expression of data change on a decision control effect is as follows:
for x 1 ：
Figure RE-FDA0003927510340000032
For x 2 ：
Figure RE-FDA0003927510340000033
For u 1 ：
Figure RE-FDA0003927510340000034
For u 2 ：
Figure RE-FDA0003927510340000035
Wherein
Figure RE-FDA0003927510340000036
And
Figure RE-FDA0003927510340000037
respectively represent x in the running state 1 ，x 2 ，u 1 And u 2 The sensitivity of (c); the greater the sensitivity, the greater the data importance;
when the system is in a fault state, the sensitivity calculation expression of data change to time is as follows:
for x 1 ：
Figure RE-FDA0003927510340000038
For x 2 ：
Figure RE-FDA0003927510340000039
For u 1 ：
Figure RE-FDA00039275103400000310
For u 2 ：
Figure RE-FDA00039275103400000311
Wherein
Figure RE-FDA0003927510340000041
And
Figure RE-FDA0003927510340000042
respectively represent x in a fault state 1 ，x 2 ，u 1 And u 2 The sensitivity of (c); the greater the sensitivity, the greater the data importance;
according to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths corresponding to different attributes of different data can be arranged conveniently, and the influence of unreliable communication on the execution effect of the decision control strategy of the power distribution network is reduced.
5. The method for optimizing the path of the distribution network for the data transmission requirement under the condition of considering the operation or the fault according to claim 1, wherein the constructing of the objective function Z comprises the following steps:
in the operating state:
because the running time scale of the system is longer in the running state and the requirement on the transmission speed is not strict, the data transmission precision is preferentially ensured; optimizing the transmission path of each data according to the sequence of the importance degree of the data from large to small, wherein the corresponding optimization targets are divided into the following categories:
if the attributes of all channels are to be added together, there are:
Figure RE-FDA0003927510340000043
wherein N represents the number of regions; min represents a minimum value calculation algorithm; sign is a sign function; a is ij Representing the connection relationship from the ith node to the jth node, if there is a connection, a ij =1; otherwise, a ij ＝0；Att ij.k Representing the kth attribute value in a channel from the ith node to the jth node, wherein the attribute comprises time delay, packet loss rate and signal-to-noise ratio;
if only the channel with the most outstanding attributes in the path is required to satisfy the constraint condition, then there are:
Z＝min{max[Att ij.k ·sign(a ij )]},j∈N i (8)
dynamically adjusting the path:
when a path from a starting position to a destination position is determined, whether the path meets related constraint conditions or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the constraints selected are as follows:
1) There is only one output channel at the start position:
Figure RE-FDA0003927510340000051
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is jstart Representing the connection relationship from the jth node to the starting position; n is a radical of hydrogen start The number of adjacent relay routes representing the starting position;
2) There is only one input channel at the destination location:
Figure RE-FDA0003927510340000052
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is a jend Representing the connection relationship from the jth node to the destination location; n is a radical of end The number of adjacent relay routes representing the destination position;
3) The relay route has both input and output channels:
Figure RE-FDA0003927510340000053
wherein
Figure RE-FDA0003927510340000054
Representing the connection relation from the ith relay route to the jth node;
Figure RE-FDA0003927510340000055
representing the connection relationship from the jth node to the ith relay route;
Figure RE-FDA0003927510340000056
representing the number of adjacent relay routes of the l relay route;
4) The number of input channels and the number of output channels of the relay route do not exceed the allowed upper limit:
Figure RE-FDA0003927510340000057
Figure RE-FDA0003927510340000058
wherein
Figure RE-FDA0003927510340000059
Representing the connection relation from the ith relay route to the jth node;
Figure RE-FDA00039275103400000510
representing from the jth node to the thThe connection relation of the l relay routes;
Figure RE-FDA00039275103400000511
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-FDA00039275103400000512
representing the upper limit of the number of output channels allowed by the l relay route;
5) The properties of the planned path meet the set requirements:
Figure RE-FDA0003927510340000061
Figure RE-FDA0003927510340000062
wherein
Figure RE-FDA0003927510340000063
Representing the defining condition of the k-th attribute value.
6. The method for optimizing the path of the distribution network for the data transmission requirement under the condition of considering the operation or the fault according to claim 1, wherein the constructing of the objective function Z comprises the following steps:
in a fault state:
the data is considered to be recovered and transmitted at the fastest speed, and the data with high importance degree and high time sensitivity is ensured to be recovered and transmitted preferentially; the data importance degree and the sensitivity of the data to time need to be calculated in a superposition manner, and the method specifically comprises the following steps:
1. de-unitization:
Figure RE-FDA0003927510340000064
and
Figure RE-FDA0003927510340000065
wherein
Figure RE-FDA0003927510340000066
The sensitivity of the decision control target to the ith data after the unit removal;
Figure RE-FDA0003927510340000067
is the sensitivity of the ith data to time after the demonitation;
2. and (3) weighting: when the two types of sensitivities are comprehensively considered, the comprehensive sensitivity is obtained
Figure RE-FDA0003927510340000068
Wherein λ i1 And λ i2 Is a weight coefficient, and λ i1 +λ i2 ＝1；
When optimizing, should follow
Figure RE-FDA0003927510340000069
Optimizing the transmission path of each data in a descending order, wherein the corresponding optimization targets are as follows:
Figure RE-FDA00039275103400000610
wherein, delay ij Representing the skew in the channel from the ith node to the jth node; the constraint conditions other than (9) to (15),
1) There is only one output channel at the start position:
Figure RE-FDA0003927510340000071
wherein a is startj Representing the connection relationship from the starting position to the jth node; a is a jstart Representing the connection relationship from the jth node to the starting position; n is a radical of start The number of adjacent relay routes representing the starting position;
2) There is only one input channel at the destination location:
Figure RE-FDA0003927510340000072
wherein a is endj Representing the connection relationship from the destination position to the jth node; a is a jend Representing the connection relationship from the jth node to the destination location; n is a radical of end The number of adjacent relay routes representing the destination position;
3) The relay route has both input and output channels:
Figure RE-FDA0003927510340000073
wherein
Figure RE-FDA0003927510340000074
Representing the connection relation from the ith relay route to the jth node;
Figure RE-FDA0003927510340000075
representing the connection relationship from the jth node to the ith relay route;
Figure RE-FDA0003927510340000076
representing the number of adjacent relay routes of the l relay route;
4) The number of input channels and the number of output channels of the relay route do not exceed the allowed upper limit:
Figure RE-FDA0003927510340000077
Figure RE-FDA0003927510340000078
wherein
Figure RE-FDA0003927510340000079
Representing the connection relation from the ith relay route to the jth node;
Figure RE-FDA00039275103400000710
representing the connection relationship from the jth node to the ith relay route;
Figure RE-FDA00039275103400000711
represents the upper limit of the number of input channels allowing the ith relay route;
Figure RE-FDA00039275103400000712
representing the upper limit of the number of output channels allowed by the l relay route;
5) The properties of the planned path meet the set requirements:
Figure RE-FDA0003927510340000081
Figure RE-FDA0003927510340000082
wherein
Figure RE-FDA0003927510340000083
A qualifier representing a k-th attribute value;
it should also be ensured that the data transmission accuracy meets the requirements, i.e. the optimized path needs to additionally meet the constraint conditions
Figure RE-FDA0003927510340000084
Wherein risk ij Representing the probability of risk in the channel from the ith node to the jth node; condition risk Is a set risk constraint index.
7. The method for optimizing the path of the distribution network for the data transmission requirement under the consideration of the operation or fault state as claimed in claim 5 or 6, wherein the solving of the objective function by adopting the particle swarm optimization algorithm combined with the random variation comprises the following steps:
step 4-1: selecting an initial population:
selecting an initial population quantity: sizepop, variable dimension: spaedenim; maximum number of iterations: ger; a position limit; speed limitation; inertial weight: c _1; individual learning factors: c _2; group learning factor: c _3;
step 4-2: judging whether the individual meets the constraint condition and selecting the optimal individual:
by substituting the individual into equations (9) to (15), whether or not the expression satisfies
Figure RE-FDA0003927510340000091
Furthermore, the optimal choice among all the individuals who satisfy the constraint condition, namely argmax (Z);
step 4-3: updating the population by adopting a random variation mode, and carrying out position variation on corresponding individuals in the current iteration according to pop _ x (: j) = pop _ x (random (1, dim), j) + xi to expand the number attribute of the population and facilitate jumping out of a local optimal solution, wherein the pop _ x (: j) represents the jth position in the current iteration individuals; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; xi ∈ [ -0.2,0.2];
step 4-4: judging whether the updated population individual meets the constraint condition or not and selecting the optimal individual, and substituting the individual into the formulas (9) to (15) to judge whether the updated population individual meets the constraint condition or not
Figure RE-FDA0003927510340000101
Furthermore, the optimal choice among all the individuals who satisfy the constraint condition, namely argmax (Z);
and 4-5: and repeating the step 4-3 and the step 4-4 until a set iteration number is achieved.
8. A path optimization device considering the requirement of a distribution network on data transmission in an operation or fault state is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 7.
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