CN111969662B - Data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method - Google Patents
Data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method Download PDFInfo
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Abstract
A data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method comprises the following steps: inputting basic parameter information of the system according to the selected active power distribution network; respectively obtaining the voltage of each node of the power distribution network and the measured variable quantity of active and reactive power, and determining the effective regulation area of each intelligent soft switch; defining a region close to a source node as an upstream region, and defining a region close to the tail end of a branch as a downstream region; establishing an intelligent soft switch self-adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of the upstream region as targets, and solving; establishing an intelligent soft switch self-adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of the downstream region as targets, and solving; judging whether the time-shifting step number of the control domain reaches a set value or not; and if the current time t reaches the total control duration, ending the self-adaptive voltage control process. The invention is beneficial to improving the voltage optimization control effect of the power distribution network, thereby improving the safety and reliability of the power distribution network.
Description
Technical Field
The invention relates to a voltage control method. In particular to a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method.
Background
The power distribution network is a junction for receiving a power generation system, a power transmission system and a user side, and plays an important role in safe, reliable and economic power supply, and the voltage level of the power distribution network directly influences the safety and reliability of equipment on the user side. The optimal control of the voltage of the power distribution network is beneficial to improving the satisfaction degree of users, and the importance of the optimal control is self-evident. The intelligent soft switching technology is a new power electronic technology, and can realize the optimal operation of a system by improving reactive power distribution and active power flow in a network, thereby becoming a current research hotspot. The current power distribution system is increasingly large in scale, the operation task is increasingly complex, and the active and reactive power output of intelligent soft switches in all areas in the network can be dynamically adjusted through reasonable power distribution network partitions, so that the problem of power distribution network voltage optimization can be effectively solved.
Most of traditional power distribution network voltage optimization control methods adopt mathematical models to describe the state of a power distribution network. However, in actual operation, accurate power distribution network parameters are difficult to obtain due to the influences of power distribution network operation conditions, line environments and the like; in addition, after a large amount of renewable energy is accessed at high permeability, the operating characteristics of the renewable energy are greatly influenced by the environment, and the output has obvious randomness and fluctuation. Therefore, it is difficult to describe the state of the distribution network with an accurate mathematical model. This also makes voltage optimization methods that rely on mathematical models of power distribution networks problematic.
In recent years, power distribution system measurement and communication systems have been rapidly developed. Including wide area measurement systems, synchronous phasor measurement systems, advanced measurement systems, etc., have become mature and widely used; communication systems already enable real-time transmission of data. The operation data of the power distribution network contains a large amount of information, and important information contained in the operation data can be fully mined by a data driving method. The power distribution network model is constructed by using a data driving method, and the method has the advantages of avoiding complicated mathematical models, simplifying the solving process and the like. According to the data-driven multi-intelligent-soft-switch partition cooperative operation optimization method, a detailed mathematical model of the power distribution network is not needed to be known, a data model is established in a partition mode according to real-time operation data of the power distribution network, information interaction is carried out in an interval mode, the goal of partition-area cooperative power distribution network voltage control can be achieved, the problem that the overall information amount is increased sharply due to centralized control is solved, and the calculation efficiency is guaranteed.
Therefore, a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is researched and mastered, a new thought is provided for the problem of power distribution network voltage partition cooperative operation optimization, the effect of power distribution network voltage optimization control is improved, and the safety and reliability of a power distribution network are improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method capable of solving the problem of multi-intelligent soft switch partition cooperative operation optimization.
The technical scheme adopted by the invention is as follows: a data-driven multi-intelligent soft switch partition cooperative self-adaptive voltage control method comprises the following steps:
1) inputting basic parameter information of a system according to the selected active power distribution network, wherein the basic parameter information comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t 0 Optimizing the total control time length to be T, and setting the current time T to be T 0 The predicted domain time interval delta T is s delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimized time period [ t, t + s delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjusting area of each intelligent soft switch;
3) according to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step;
4) in a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information;
5) establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region;
6) updating the control time t to be t + delta t, the time shifting step number k to be k +1, judging whether the time shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control duration T, if not, making k equal to 1, returning to the step 2), and if so, ending the adaptive voltage control process.
According to the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method, the unknown parameters of the power distribution network line, the distributed power supply position and the uncertainty of the output condition are comprehensively considered, the multi-intelligent-soft-switch-partition cooperative operation optimization strategy of data driving is adopted, the multi-intelligent-soft-switch-partition cooperative operation optimization problem is solved, the voltage optimization control effect of the power distribution network is improved, and the safety and the reliability of the power distribution network are improved.
Drawings
FIG. 1 is a flow chart of a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method of the present invention;
FIG. 2 is a diagram of a selected power distribution network topology and 10:00 zone division;
FIG. 3 is a 10:00 active distribution network voltage variation curve;
FIG. 4 is a graph of voltage changes at 18 and 33 nodes of a 10:00 active power distribution network;
FIG. 5 is a 10:00 active output variation curve of the intelligent soft switch;
FIG. 6 is a 10:00 intelligent soft switch reactive power output variation curve;
fig. 7 is a 24-hour active output curve of the intelligent soft switch;
FIG. 8 is a 24-hour reactive power output curve for the intelligent soft switch;
FIG. 9 is a graph of 24 hour power loss versus active power distribution network;
fig. 10 is a graph comparing the maximum voltage of the active distribution network for 24 hours.
Detailed Description
The data-driven multi-intelligent soft-switch partition cooperative adaptive voltage control method is described in detail below with reference to embodiments and drawings.
As shown in fig. 1, the data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method of the present invention includes the following steps:
1) inputting basic parameter information of the system according to the selected active power distribution network, wherein the basic parameter information comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t 0 Optimizing the total control time length to be T, and setting the current time T to be T 0 The predicted domain time interval delta T is s delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimized time period [ t, t + s delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjusting area of each intelligent soft switch; wherein the content of the first and second substances,
(1) the pseudo-electric distance between the nodes of the active power distribution network is calculated by adopting the following formula:
in the formula s ij The pseudo-electric distance between the node i and the node j of the active power distribution network, alpha and beta are weight coefficients,
representing a measure of the magnitude of the voltage at node i,
representing the active power variation measurement of node j,
represents the measurement of the reactive power variation of the node j,
to represent
For is to
The partial derivative of (a) of (b),
to represent
To pair
The partial derivative of (a) of (b),
the method is an active power distribution network node set.
(2) The node clustering by adopting a clustering density peak method is represented as follows:
in the formula, delta i Represents the node density of the node i,
indicating a cut operation, when (·) < 0
When (-) is not less than 0
Sigma is a set pseudo-electrical distance threshold,
set of nodes representing access of intelligent soft switches in an active distribution network, d i Represents the distance of node i to the rest of the nodes in the active distribution network, c i Denotes the cluster density index, s ij Is the pseudo-electrical distance between node i and node j of the active distribution network.
3) According to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step; the voltage control error of each region and the boundary iteration convergence error of the adjacent subareas are expressed as follows:
in the formula, R 1 Indicates the voltage control error of each region, X [ t ]]And X [ t- Δ t]Intelligent soft switch output for representing t moment and t-delta t momentForce, Y ref Representing the respective zone voltage and net load reference vectors,
representing the voltage and payload estimates for each region at time t + Δ t, R 2 Representing the boundary iterative convergence error, r, of adjacent partitions p Represents the iterative convergence error of the boundary power, r v The iterative convergence error of the boundary voltage is represented,
and
respectively represents the active power transmitted by the boundary line l of the area a and the area b at the time t,
and
respectively representing the reactive power transmitted by the lines l of the area a and the area b at the time t,
the voltages at the boundary nodes m and n in the regions a and b at time t are shown, respectively.
4) In a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information; wherein the content of the first and second substances,
(1) the target of the minimum voltage deviation and the minimum network loss in the upstream area is expressed as follows:
in the formula, X a [t]And X a [t-Δt]Respectively representing intelligent soft switch output vector X 'in the area a at the time t and the time t-delta t' a [t]The auxiliary variable is represented by a number of variables,
representing the voltage reference value of the boundary node n in the area a,
and
respectively representing the boundary active and reactive power transferred by the downstream region b at the time t-deltat,
represents the voltage estimate at the boundary node n of region a at time t + at, represents a weighting factor,
representing the voltage and payload reference vectors in region a,
representing the voltage and incoming power estimate vectors in region a at time t + deltat,
and
respectively representing the node voltage and the inflow power estimate in region a at time t + deltat,
and
respectively, the node voltage and the payload reference value in the region a.
(2) The intelligent soft switch adaptive voltage control model driven by the upstream region data is expressed as follows:
in the formula, Y a [t]And
a measured value and an estimated value vector, X, respectively representing the voltage and the inflow power of the region a at time t a [t]And X a [t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and the t-delta t moment area a a [t]And phi a [t-Δt]Respectively representing a pseudo Jacobian matrix of the region a at the time t and the time t-delta t, and mu represents a weight coefficient.
(3) The intelligent soft switch adaptive voltage control strategy obtained by solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method is expressed as follows:
in the formula, X a [t]And X a [t-Δt]Respectively represents the intelligent soft switch output vector P in the t moment and the t-delta t moment area a r A projection operator representing the intelligent soft switch output constraint domain,
representing the voltage and payload reference vectors in region a,
representing the vector of voltage and inflow power estimates, phi, in region a at time t + deltat a [t]And phi a [t-Δt]Respectively representing the t time and t-delta t time zonesField a pseudo-jacobian matrix, Δ Y a [t]Represents Y a [t]-Y a [t-Δt]Wherein Y is a [t]And Y a [t-Δt]Vector of measured values, Δ X, representing the voltage and the power flowing in the region a at time t and at-time t- Δ t, respectively a [t-Δt]Represents X a [t-Δt]-X a [t-2Δt]，X a [t-Δt]And X a [t-2Δt]Respectively representing intelligent soft switch output vectors in a region a at the time t and the time t-delta t, wherein rho, lambda, mu and eta represent weight coefficients.
5) Establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region; wherein the content of the first and second substances,
(1) the target of the minimum voltage deviation and network loss of the downstream area is expressed as follows:
in the formula, X b [t]And X b [t-Δt]Respectively representing intelligent soft switch output vector X 'in a t moment area b and a t-delta t moment area b' b [t]The extension variable is represented by a number of extension variables,
representing the voltage reference value of node m in region b,
representing the voltage estimate at node m in region b at time t + at, lambda represents a weighting factor,
and
respectively representing the boundary active and reactive power transferred by the upstream zone a at times t-deltat,
representing the voltage and payload reference vectors in region b,
representing the voltage and incoming power estimate vectors in region b at time t + deltat,
and
respectively representing the node voltage and the inflow power estimate in region b at time t + deltat,
and
respectively, the node voltage and the payload reference value in region b.
(2) The downstream region data-driven intelligent soft switch adaptive voltage control model is expressed as follows:
in the formula (I), the compound is shown in the specification,
vector of estimated values, Y, representing voltage and inflow power in region b at time t + Δ t b [t]And
a measured value and an estimated value vector X respectively representing the voltage and the inflow power in the t-time region b b [t]And X b [t-Δt]Respectively representing intelligence in the region b at time t and at-time t-delta tSoft switch output vector, phi b [t]And phi b [t-Δt]And respectively representing a pseudo Jacobian matrix of the region b at the time t and the time t-delta t, and mu represents a weight coefficient.
(3) The intelligent soft-switch adaptive voltage control strategy obtained by solving the intelligent soft-switch adaptive voltage control model driven by the downstream region data by adopting the gradient descent method is expressed as follows:
in the formula, X b [t]And X b [t-Δt]Respectively representing intelligent soft switch output vectors P in t moment and t-delta t moment regions b r A projection operator representing the intelligent soft switch output constraint domain,
representing the voltage and payload reference vectors in region b,
representing the vector of voltage and inflow power estimates, phi, in region b at time t + deltat b [t]And phi b [t-Δt]Respectively representing the t time and t-delta t time regions b pseudo Jacobian matrix, delta Y b [t]Represents Y b [t]-Y b [t-Δt]Wherein Y is b [t]And Y b [t-Δt]Vector of measurements, Δ X, representing the voltage and the power flowing in the region b at time t and at-time t- Δ t, respectively b [t-Δt]Represents X b [t-Δt]-X b [t-2Δt]，X b [t-Δt]And X b [t-2Δt]Respectively representing intelligent soft switch output vectors in a t moment region b and a t-delta t moment region b, wherein rho, lambda, mu and eta represent weight coefficients.
6) Updating the control time t to be t + delta t, the time-shifting step number k to be k +1, judging whether the time-shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control time length T, if not, making k equal to 1, returning to the step 2), and if so, ending the adaptive voltage control process.
Specific examples are given below:
for the present embodiment, the topology connection condition of the distribution network with IEEE33 nodes is as shown in fig. 2, and 3 sets of capacity 1MVA are respectively connected to nodes 25-29, 12-22, 8-21, and the upper limits of active and reactive output power are 800kW and 500kvar, respectively; 7. the nodes 13 and 27 are connected into the photovoltaic system; 10. the 16, 17, 30 and 33 nodes are connected with the fan; optimizing and controlling the total time length T to be 24 h; the prediction domain time interval Δ T is 1 h; control field time interval Δ t is 20 s; the sensitivity zone threshold is set to 0.5; setting the voltage reference value of the power distribution network to be 1.0 p.u; voltage control error requirement is 10 -2 . The weighting coefficients lambda, rho, eta and mu take the values of 1, 1 and 5. The data-driven multi-intelligent soft switch partition cooperative self-adaptive voltage control method is adopted for optimization, and intelligent soft switch output strategies at all times can be obtained. In order to verify the effectiveness of the method, the following two control schemes are adopted for comparison aiming at the power distribution network.
The first scheme is as follows: the intelligent soft switch is not controlled, and the initial running state of the power distribution network is obtained;
scheme two is as follows: a data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is adopted.
The computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-16030, the main frequency is 2.8GHz, and the memory is 16 GB; the software environment is a Windows 10 operating system.
By adopting the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method, the result of partitioning each intelligent soft-switch control area of the power distribution network in the embodiment is shown in fig. 2. Taking 10:00 as an example, the comparison result of the voltage values of the nodes after the voltage control of the scheme one and the scheme two is shown in fig. 3, the voltage change curves of the 18 node and the 33 node are shown in fig. 4, and the active transmission power and the reactive power change of the 10:00 intelligent soft switch are shown in fig. 5 and fig. 6; active transmission power and reactive output of the 24-hour intelligent soft switch are shown in fig. 7 and 8, network loss conditions after voltage control of the scheme I and the scheme II are shown in fig. 9, the comparison result of the maximum node voltage is shown in fig. 10, and the optimization result is shown in table 1. It can be seen from fig. 3 to fig. 10 and table 1 that the data-driven multi-intelligent-soft-switch-partition cooperative adaptive voltage control method of the present invention can effectively solve the problem of partition cooperative operation optimization control of the power distribution network, and has an important meaning for the adaptive operation optimization of the active power distribution network.
TABLE 1 comparison of control effects of scheme one and scheme two
| Voltage minimum/p.u. | Voltage maximum/p.u. | Voltage deviation/p.u. | Energy loss/kWh | |
| Scheme one | 0.9330 | 1.0313 | 0.0172 | 1179.7000 |
| Scheme two | 0.9710 | 1.0065 | 0.0035 | 865.2918 |
Claims (1)
1. A data-driven multi-intelligent soft switch partition cooperative adaptive voltage control method is characterized by comprising the following steps:
1) inputting basic parameter information of the system according to the selected active power distribution network, wherein the basic parameter information comprises the following steps: the access position, capacity and active/reactive output power upper limit of the intelligent soft switch, the node voltage reference value, the control error precision, the initial value of the pseudo Jacobian matrix of the controller and the initial time t 0 Optimizing the total control time length to be T, and setting the current time T to be T 0 The predicted domain time interval delta T is s delta T, the domain time interval delta T is controlled, and the control time-shifting step number k is 1;
2) according to the active power distribution network given in the step 1), in an optimized time period [ t, t + s delta t ], adjusting active transmission power and reactive power output levels of two ports of an intelligent soft switch, respectively obtaining the voltage of each node of the power distribution network and the variable quantity of active and reactive power measurement, calculating the pseudo-electric distance between nodes of the active power distribution network, clustering the nodes by using each intelligent soft switch access node as a clustering center and adopting a clustering density peak method, and determining an effective adjusting area of each intelligent soft switch;
3) according to the effective adjusting area of each intelligent soft switch determined in the step 2), defining an area close to a source node as an upstream area, and an area close to the tail end of a branch as a downstream area, judging whether the maximum values of the voltage control error of each area and the boundary iteration convergence error of the adjacent subarea meet the precision requirement, if so, turning to the step 6), and if not, turning to the next step;
wherein the voltage control error of each region and the boundary iteration convergence error of the adjacent subareas are expressed as:
in the formula, R 1 Indicates the voltage control error of each region, X [ t ]]And X [ t- Δ t ]]Expressing the intelligent soft switch output at t moment and t-delta t moment, Y ref Representing the respective zone voltage and net load reference vectors,
representing the voltage and net load estimate vectors, R, for each region at time t + Δ t 2 Representing the boundary iterative convergence error, r, of adjacent partitions p Represents the iterative convergence error of the boundary power, r v The iterative convergence error of the boundary voltage is represented,
and
respectively represents the active power transmitted by the boundary line l of the area a and the area b at the time t,
and
respectively representing the reactive power transmitted by the lines l of the area a and the area b at the time t,
respectively showing the voltages of boundary nodes m and n of the area a and the area b at the time t;
4) in a control time period [ t, t + delta t ], establishing an intelligent soft switch adaptive voltage control model driven by upstream region data by taking the minimum voltage deviation and network loss of an upstream region as targets, solving the intelligent soft switch adaptive voltage control model by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, sending the intelligent soft switch adaptive voltage control strategy to the upstream region intelligent soft switch to obtain the voltage measurement of each region node, and transmitting the voltage measurement of the boundary node and the active and reactive power measurement of a boundary branch circuit to a downstream region as boundary information; wherein the content of the first and second substances,
the target J' (X) is the minimum of voltage deviation and network loss in the upstream area a [t],X′ a [t]) Expressed as:
in the formula, X a [t]And X a [t-Δt]Respectively representing intelligent soft switch output vector X 'in a region a at the time t and the time t-delta t' a [t]The auxiliary variable is represented by a number of variables,
representing the voltage reference value of the boundary node n in the area a,
and
respectively representing the boundary active and reactive power transferred by the downstream region b at time t-deltat,
represents the voltage estimate at the boundary node n of region a at time t + at, represents a weighting factor,
representing the voltage and payload reference vectors in region a,
representing the voltage and incoming power estimate vectors in region a at time t + deltat,
and
respectively representing the node voltage and the inflow power estimate in region a at time t + deltat,
and
respectively representing node voltage and a net load reference value in the area a;
the intelligent soft switch adaptive voltage control model driven by the upstream region data is expressed as follows:
in the formula, Y a [t]And
a measured value and an estimated value vector, X, respectively representing the voltage and the inflow power of the region a at time t a [t]And X a [t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and the t-delta t moment area a a [t]And phi a [t-Δt]Respectively representing a pseudo Jacobian matrix of an area a at the time t and the time t-delta t, and mu represents a weight coefficient;
5) establishing an intelligent soft switch adaptive voltage control model driven by downstream region data by taking the minimum voltage deviation and network loss of a downstream region as targets, solving the intelligent soft switch adaptive voltage control model driven by the downstream region data by adopting a gradient descent method to obtain an intelligent soft switch adaptive voltage control strategy, issuing the intelligent soft switch adaptive voltage control strategy to the downstream region, and transmitting boundary node voltage measurement and boundary branch active and reactive power requirements as boundary information to an upstream region; wherein the content of the first and second substances,
the target of the minimum voltage deviation and network loss of the downstream area is expressed as follows:
in the formula, X b [t]And X b [t-Δt]Respectively representing the t time and t-delta t time regionsb is the intelligent soft switch output vector X' b [t]The extension variable is represented by a number of extension variables,
representing the voltage reference value of node m in region b,
representing the voltage estimate at node m in region b at time t + at, lambda represents a weighting factor,
and
respectively representing the boundary active and reactive power transferred by the upstream zone a at times t-deltat,
representing the voltage and payload reference vectors in region b,
a vector of estimated values representing voltage and inflow power in region b at time t + deltat,
and
respectively representing the node voltage and the inflow power estimate in region b at time t + deltat,
and
respectively, the node voltage and the payload reference value in region b.
The downstream region data-driven intelligent soft switch adaptive voltage control model is expressed as follows:
in the formula (I), the compound is shown in the specification,
vector of estimated values, Y, representing voltage and inflow power in region b at time t + Δ t b [t]And
a measured value and an estimated value vector X respectively representing the voltage and the inflow power in the t-time region b b [t]And X b [t-Δt]Respectively represents the output vector phi of the intelligent soft switch in the t moment and t-delta t moment region b b [t]And phi b [t-Δt]Respectively representing a t moment area b and a t-delta t moment area b pseudo Jacobian matrix, and mu represents a weight coefficient;
6) updating the control time t to be t + delta t, the time-shifting step number k to be k + t, judging whether the time-shifting step number k in the control domain reaches a set value s, and if not, returning to the step 4); if yes, entering step 7);
7) and judging whether the current time T reaches the total control duration T, if not, making k equal to T, returning to the step 2), and if so, ending the adaptive voltage control process.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105896537A (en) * | 2016-06-21 | 2016-08-24 | 中国南方电网有限责任公司电网技术研究中心 | Power supply restoration method for power distribution network based on intelligent soft switch |
| CN108923459A (en) * | 2018-07-10 | 2018-11-30 | 华北电力大学（保定） | A kind of alternating current-direct current power distribution network optimal control method based on intelligent Sofe Switch |
| CN109149586A (en) * | 2018-09-13 | 2019-01-04 | 国网天津市电力公司电力科学研究院 | Active power distribution network subregion distributing voltage control method towards intelligent Sofe Switch |
| CN109494721A (en) * | 2018-11-20 | 2019-03-19 | 浙江大学 | A kind of power distribution network distributed self-adaption control method suitable for being switched containing flexible multimode |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105896537A (en) * | 2016-06-21 | 2016-08-24 | 中国南方电网有限责任公司电网技术研究中心 | Power supply restoration method for power distribution network based on intelligent soft switch |
| CN108923459A (en) * | 2018-07-10 | 2018-11-30 | 华北电力大学（保定） | A kind of alternating current-direct current power distribution network optimal control method based on intelligent Sofe Switch |
| CN109149586A (en) * | 2018-09-13 | 2019-01-04 | 国网天津市电力公司电力科学研究院 | Active power distribution network subregion distributing voltage control method towards intelligent Sofe Switch |
| CN109494721A (en) * | 2018-11-20 | 2019-03-19 | 浙江大学 | A kind of power distribution network distributed self-adaption control method suitable for being switched containing flexible multimode |
Non-Patent Citations (1)
| Title |
|---|
| 基于负载自适应的辅助LC网络宽范围零电压开通的输入串联输出并联移相全桥DC-DC变换器;沙德尚等;《中国电机工程学报》;20160705;第36卷(第13期);3558-3564 * |
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