A method and device for estimating state of a distribution network

Abstract

The present invention provides a state estimation method and device for a distribution network, which initialize control parameters and initialize control variables based on current vectors of loads and reactive power compensation devices; based on the initialized control variables, update the control variables in a Lagrangian inner loop; determine whether the Lagrangian outer loop converges, and if so, end the process; if not, update the control parameters, and continue to update the control variables in the Lagrangian inner loop until the Lagrangian outer loop converges, thereby improving the accuracy and solution capability of state estimation results, and better handling the virtual measurement problem of zero injection nodes; and the inversion method of the measurement error covariance matrix in the Lagrangian function is simple, the result is reliable, and it is beneficial to improve the state estimation solution capability.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a state estimation method and device for a power distribution network. Background Art

[0002] With the development of power systems, people's requirements for lean control are constantly increasing. The development of measurement technology and the advancement of communication technology provide a data foundation for lean control of power systems. As a technology that uses data redundancy to improve data quality, state estimation plays an important supporting role in lean control of power systems. Compared with general artificial intelligence methods and data mining methods, state estimation has strong pertinence, high accuracy and high credibility, and has important research value.

[0003] Currently, the state estimation of distribution networks is generally achieved through the weighted least squares method with superior comprehensive performance. The weighted least squares state estimation is an optimization problem. If the power flow constraint is treated as a virtual measurement, the constraint equation is empty, but there is a problem that the accuracy of the virtual measurement is difficult to determine, resulting in low accuracy of the state estimation result and poor solving ability. Summary of the invention

[0004] In order to overcome the shortcomings of low accuracy and poor solution capability of state estimation results in the above-mentioned prior art, the present invention provides a state estimation method and device for a distribution network, which initialize control parameters and initialize control variables based on current vectors of loads and reactive power compensation devices; based on the initialized control variables, update the control variables in a Lagrangian inner loop; determine whether the Lagrangian outer loop converges, and if so, end the process; if not, update the control parameters, and continue to update the control variables in the Lagrangian inner loop until the Lagrangian outer loop converges, thereby improving the accuracy and solution capability of the state estimation results.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme:

[0006] In one aspect, the present invention provides a method for estimating a state of a distribution network, comprising:

[0007] Initialize control parameters and initialize control variables based on current vectors of loads and reactive power compensation devices;

[0008] Based on the initialized control variables, the control variables are updated in the Lagrangian inner loop;

[0009] Determine whether the Lagrangian outer loop converges. If so, end the process. If not, update the control parameters and continue to update the control variables in the Lagrangian inner loop until the Lagrangian outer loop converges.

[0010] The control parameters include an outer loop Lagrangian multiplier vector, an outer loop penalty factor vector, a growth coefficient of the penalty factor vector, an improvement degree coefficient, a Lagrangian outer loop control coefficient, an upper limit of the number of Lagrangian outer loop iterations, an Lagrangian outer loop iteration number, a Lagrangian inner loop control coefficient, an upper limit of the number of Lagrangian inner loop iterations, and an Lagrangian inner loop iteration number.

[0011] The current vector initialization control variable based on the load and the reactive power compensation device includes:

[0012] Initialize the control variables as follows:

[0013]

[0014] Where V N is the node voltage vector, I N is the injected current vector of the current source, V S is the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I S is the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix and E is the unit matrix.

[0015] The updating of the control variables in the Lagrangian inner loop based on the initialized control variables includes:

[0016] Set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable;

[0017] determining a control variable correction amount, and updating the control variable based on the control variable correction amount;

[0018] Determine whether the Lagrangian inner loop converges. If the Lagrangian inner loop converges, set the control variable of the k+1th Lagrangian outer loop iteration to the control variable of the tth Lagrangian inner loop iteration, and exit the Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, add 1 to the number of Lagrangian inner loop iterations, and enter the next Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, set the control variable of the k+1th Lagrangian outer loop iteration to the initialized control variable, and exit the Lagrangian inner loop.

[0019] The updating of the control variable based on the control variable correction amount includes:

[0020] The control variable of the tth Lagrangian inner loop iteration is summed with the control variable correction amount to obtain the control variable of the t+1th Lagrangian inner loop iteration.

[0021] The control variable correction amount is determined by the following formula:

[0022]

[0023] In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), and H is the Hessian matrix of F(x).

[0024] The F(x) is determined as follows:

[0025]

[0026] Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r k is the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

[0027] The step of judging whether the Lagrangian inner loop converges includes:

[0028] If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

[0029] The determining whether the Lagrangian outer loop converges includes:

[0030] If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

[0031] Update the Lagrange multiplier as follows:

[0032]

[0033] In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r k is the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

[0034] Update the penalty factor as follows:

[0035]

[0036] In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

[0037] On the other hand, the present invention also provides a state estimation device for a power distribution network, comprising:

[0038] An initialization module, used for initializing control parameters and initializing control variables based on current vectors of loads and reactive power compensation devices;

[0039] An updating module, used for updating the control variables in the Lagrangian inner loop based on the initialized control variables;

[0040] The judgment module is used to judge whether the Lagrangian outer loop converges. If it converges, the process ends; if it does not converge, the control parameters are updated and the control variables are continued to be updated in the Lagrangian inner loop until the Lagrangian outer loop converges.

[0041] The control parameters include an outer loop Lagrangian multiplier vector, an outer loop penalty factor vector, a growth coefficient of the penalty factor vector, an improvement degree coefficient, a Lagrangian outer loop control coefficient, an upper limit of the number of Lagrangian outer loop iterations, an Lagrangian outer loop iteration number, a Lagrangian inner loop control coefficient, an upper limit of the number of Lagrangian inner loop iterations, and an Lagrangian inner loop iteration number.

[0042] The initialization module is specifically used for:

[0043] Initialize the control variables as follows:

[0044]

[0045] Where V N is the node voltage vector, I N is the injected current vector of the current source, V S is the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I Sis the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix and E is the unit matrix.

[0046] The update module includes:

[0047] Determine the unit, set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable;

[0048] A first updating unit, used for determining a control variable correction amount and updating the control variable based on the control variable correction amount;

[0049] The first judgment unit is used to judge whether the Lagrangian inner loop converges. If the Lagrangian inner loop converges, the control variable of the k+1th Lagrangian outer loop iteration is set as the control variable of the tth Lagrangian inner loop iteration, and the Lagrangian inner loop is exited; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, the number of Lagrangian inner loop iterations is increased by 1, and the next Lagrangian inner loop is entered; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, the control variable of the k+1th Lagrangian outer loop iteration is set as the initialized control variable, and the Lagrangian inner loop is exited.

[0050] The first updating unit is specifically used for:

[0051] The control variable of the tth Lagrangian inner loop iteration is summed with the control variable correction amount to obtain the control variable of the t+1th Lagrangian inner loop iteration.

[0052] The first updating unit determines the control variable correction amount according to the following formula:

[0053]

[0054] In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), H is the Hessian matrix of F(x); F(x) is determined by the following formula:

[0055]

[0056] Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r kis the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

[0057] The first judgment unit is specifically used for:

[0058] If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

[0059] The judging module includes a second judging unit, and the second judging unit is specifically configured to:

[0060] If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

[0061] The determining module further includes a second updating unit, wherein the second updating unit is specifically configured to:

[0062] Update the Lagrange multiplier as follows:

[0063]

[0064] In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r k is the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

[0065] The determination module further includes a third updating unit, and the third updating unit is specifically configured to:

[0066] Update the penalty factor as follows:

[0067]

[0068] In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

[0069] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0070] In the state estimation method of the distribution network provided by the present invention, the control parameters are initialized, and the control variables are initialized based on the current vectors of the load and the reactive power compensation device; based on the initialized control variables, the control variables are updated in the Lagrangian inner loop; it is determined whether the Lagrangian outer loop converges, and if so, the method ends; if not, the control parameters are updated, and the control variables are continuously updated in the Lagrangian inner loop until the Lagrangian outer loop converges, thereby improving the accuracy and solution capability of the state estimation result;

[0071] The present invention initializes the control variables based on the current vector of the voltage source, the current vector of the transformer, the current vector of the switch, and the current vector of the load and the reactive compensation device, and better handles the virtual measurement problem of the zero injection node;

[0072] The inversion method of the measurement error covariance matrix in the Lagrangian function of the present invention is simple, the result is reliable, and it is conducive to the improvement of the state estimation solution capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of a method for estimating a state of a distribution network in an embodiment of the present invention;

[0074] Figure 2 Schematic diagram of ideal transformer modeling in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0076] Example 1

[0077] Embodiment 1 of the present invention provides a method for estimating the state of a distribution network, and the specific flow chart is as follows: Figure 1 As shown, the specific process is as follows:

[0078] S101: Initializing control parameters and initializing control variables based on current vectors of loads and reactive power compensation devices;

[0079] S102: Based on the initialized control variables, update the control variables in the Lagrangian inner loop;

[0080] S103: Determine whether the Lagrangian outer loop converges. If so, terminate the process. If not, update the control parameters (specifically update the Lagrangian multipliers and penalty factors in the control parameters), and continue to update the control variables in the Lagrangian inner loop until the Lagrangian outer loop converges.

[0081] The control parameters that need to be initialized include the outer loop Lagrange multiplier vector, the outer loop penalty factor vector, the growth coefficient of the penalty factor vector, the improvement coefficient, the Lagrange outer loop control coefficient, the upper limit of the Lagrange outer loop iterations, the Lagrange outer loop iterations, the Lagrange inner loop control coefficient, the upper limit of the Lagrange inner loop iterations, and the Lagrange inner loop iterations. Specifically, the Lagrange multiplier vector is initialized to 0, the penalty factor vector is initialized to 1, the growth coefficient of the penalty factor vector is initialized to 10, the improvement coefficient is initialized to 0.25, and the Lagrange outer loop control coefficient is initialized to 1x10 -5 , initialize the number of Lagrangian outer loop iterations and the number of Lagrangian inner loop iterations to 0, and initialize the Lagrangian inner loop control coefficient to 1x10 -5 .

[0082] In the above S101, the control variables are initialized based on the current vector of the load and the reactive power compensation device, including:

[0083] Initialize the control variables as follows:

[0084]

[0085] Where V N is the node voltage vector, I N is the injected current vector of the current source, V S is the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I S is the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix and E is the unit matrix.

[0086] The node admittance matrix Y n When constructing, you need to remove V c , D c , S c The equipment described;

[0087] The voltage source connection matrix V c For an ideal voltage source, m is the value for the high voltage node. v , low voltage node is n v The pth v A power supply; in is the supply voltage, and

[0088] The above transformer connection matrix D c Applicable to ideal transformer or voltage regulator, for the p o If the connection nodes of a transformer winding are Figure 2 As shown, and the transformation ratio of the secondary side to the primary side is ζ, then Figure 2 In the example, m1, m2, n1 and n2 are nodes, V m1 is the voltage of node m1, V n1 is the voltage of node n1, V m2 is the voltage of node m2, V n2 is the voltage of node n2.

[0089] Switch connection matrix S c Applicable to small impedance switches, ideal switches or small impedance lines; for the p w If there are m devices of this type, and their connected nodes are w 、n w , the impedance is Z s ; then S d (p,p)=Z s ,and

[0090] The values ​​of matrix and vector elements not mentioned above are all 0.

[0091] In the above S102, based on the initialized control variables, the control variables are updated in the Lagrangian inner loop, including:

[0092] Set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable;

[0093] determining a control variable correction amount, and updating the control variable based on the control variable correction amount;

[0094] Determine whether the Lagrangian inner loop converges (i.e., the Lagrangian inner loop converges and regardless of whether the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations). If the Lagrangian inner loop converges, set the control variable of the k+1th Lagrangian outer loop iteration to the control variable of the tth Lagrangian inner loop iteration, and exit the Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, add 1 to the number of Lagrangian inner loop iterations, and enter the next Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, set the control variable of the k+1th Lagrangian outer loop iteration to the initialized control variable, and exit the Lagrangian inner loop.

[0095] The above-mentioned control variable is updated based on the control variable correction amount. The specific process is as follows:

[0096] The control variable of the tth Lagrangian inner loop iteration is summed with the control variable correction amount to obtain the control variable of the t+1th Lagrangian inner loop iteration.

[0097] The above control variable correction amount is determined by the following formula:

[0098]

[0099] In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), and H is the Hessian matrix of F(x). If the number of Lagrangian outer loop iterations is ignored, F(x) is determined by the following formula:

[0100]

[0101] Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r k is the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

[0102] The above judgment of whether the Lagrangian inner cycle converges is as follows:

[0103] If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

[0104] In the above S103, it is determined whether the Lagrangian outer loop converges. The specific process is as follows:

[0105] If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

[0106] In the above S103, the Lagrange multiplier is updated as follows:

[0107]

[0108] In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r kis the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

[0109] In the above S103, the penalty factor is updated according to the following formula:

[0110]

[0111] In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

[0112] Example 2

[0113] Based on the same inventive concept, Embodiment 2 of the present invention further provides a state estimation device for a distribution network, including an initialization module, an update module and a judgment module. The functions of the above modules are described in detail below:

[0114] An initialization module, used for initializing control parameters and initializing control variables based on current vectors of loads and reactive power compensation devices;

[0115] An updating module, used for updating the control variables in the Lagrangian inner loop based on the initialized control variables;

[0116] The judgment module is used to judge whether the Lagrangian outer loop converges. If it converges, the process ends; if it does not converge, the control parameters are updated and the control variables are continued to be updated in the Lagrangian inner loop until the Lagrangian outer loop converges.

[0117] The above control parameters include an outer loop Lagrangian multiplier vector, an outer loop penalty factor vector, a growth coefficient of the penalty factor vector, an improvement degree coefficient, a Lagrangian outer loop control coefficient, an upper limit of the number of Lagrangian outer loop iterations, an upper limit of the number of Lagrangian outer loop iterations, a Lagrangian inner loop control coefficient, an upper limit of the number of Lagrangian inner loop iterations, and an upper limit of the number of Lagrangian inner loop iterations.

[0118] The above initialization module initializes the control variables as follows:

[0119]

[0120] Where V N is the node voltage vector, I N is the injected current vector of the current source, V Sis the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I S is the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix and E is the unit matrix.

[0121] The above update modules include:

[0122] Determine the unit, set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable;

[0123] A first updating unit, used for determining a control variable correction amount and updating the control variable based on the control variable correction amount;

[0124] The first judgment unit is used to judge whether the Lagrangian inner loop converges. If the Lagrangian inner loop converges, the control variable of the k+1th Lagrangian outer loop iteration is set as the control variable of the tth Lagrangian inner loop iteration, and the Lagrangian inner loop is exited; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, the number of Lagrangian inner loop iterations is increased by 1, and the next Lagrangian inner loop is entered; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, the control variable of the k+1th Lagrangian outer loop iteration is set as the initialized control variable, and the Lagrangian inner loop is exited.

[0125] The first updating unit sums the control variable of the t-th Lagrangian inner loop iteration and the control variable correction amount to obtain the control variable of the t+1-th Lagrangian inner loop iteration.

[0126] The first updating unit above determines the control variable correction amount according to the following formula:

[0127]

[0128] In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), H is the Hessian matrix of F(x); F(x) is determined by the following formula:

[0129]

[0130] Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r k is the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

[0131] The first judgment unit judges whether the Lagrangian inner loop converges. The specific process is as follows:

[0132] If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

[0133] The above-mentioned judgment module includes a second judgment unit, and the second judgment unit judges whether the Lagrangian outer loop converges. The specific process is as follows:

[0134] If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

[0135] The above-mentioned judgment module further includes a second updating unit, which updates the Lagrange multiplier according to the following formula:

[0136]

[0137] In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r k is the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

[0138] The judgment module further includes a third updating unit, which updates the penalty factor according to the following formula:

[0139]

[0140] In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

[0141] For the convenience of description, the various parts of the above-mentioned device are divided into various modules or units according to their functions and described separately. Of course, when implementing the present application, the functions of each module or unit can be implemented in the same or multiple software or hardware.

[0142] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Ordinary technicians in the relevant field can still modify or make equivalent substitutions to the specific implementation methods of the present invention with reference to the above embodiments. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the scope of protection of the claims of the present invention to be approved.

Claims

1. A method for estimating the state of a distribution network, characterized in that: include: Initialize control parameters and initialize control variables based on current vectors of loads and reactive power compensation devices; Based on the initialized control variables, the control variables are updated in the Lagrangian inner loop; Determine whether the Lagrangian outer loop converges. If so, end the process. If not, update the control variables and continue to update the control variables in the Lagrangian inner loop until the Lagrangian outer loop converges. The updating of the control variables in the Lagrangian inner loop based on the initialized control variables includes: Set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable; determining a control variable correction amount, and updating the control variable based on the control variable correction amount; Determine whether the Lagrangian inner loop converges. If the Lagrangian inner loop converges, set the control variable of the k+1th Lagrangian outer loop iteration to the control variable of the tth Lagrangian inner loop iteration, and exit the Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, add 1 to the number of Lagrangian inner loop iterations, and enter the next Lagrangian inner loop; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, set the control variable of the k+1th Lagrangian outer loop iteration to the initialized control variable, and exit the Lagrangian inner loop; The updating of the control variable based on the control variable correction amount includes: The control variable of the t-th Lagrangian inner loop iteration and the control variable correction amount are summed to obtain the control variable of the t+1-th Lagrangian inner loop iteration; The control variable correction amount is determined by the following formula: In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), and H is the Hessian matrix of F(x); The F(x) is determined as follows: Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r k is the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

2. The method for estimating the state of a distribution network according to claim 1, characterized in that: The control parameters include an outer loop Lagrangian multiplier vector, an outer loop penalty factor vector, a growth coefficient of the penalty factor vector, an improvement degree coefficient, a Lagrangian outer loop control coefficient, an upper limit of the number of Lagrangian outer loop iterations, an Lagrangian outer loop iteration number, a Lagrangian inner loop control coefficient, an upper limit of the number of Lagrangian inner loop iterations, and an Lagrangian inner loop iteration number.

3. The state estimation method of the distribution network according to claim 1, characterized in that: The current vector initialization control variable based on the load and the reactive power compensation device includes: Initialize the control variables as follows: Where V N is the node voltage vector, I N is the injected current vector of the current source, V S is the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I S is the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix, and E is the unit matrix.

4. The method for estimating the state of a distribution network according to claim 1, characterized in that: The step of judging whether the Lagrangian inner loop converges includes: If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

5. The method for estimating the state of a distribution network according to claim 1, characterized in that: The determining whether the Lagrangian outer loop converges includes: If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

6. The method for state estimation of a distribution network according to claim 2, characterized in that: Update the Lagrange multiplier as follows: In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r k is the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

7. The method for estimating the state of a distribution network according to claim 2, characterized in that: Update the penalty factor as follows: In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

8. A state estimation device for a distribution network, characterized in that: include: An initialization module, used for initializing control parameters and initializing control variables based on current vectors of loads and reactive power compensation devices; An updating module, used for updating the control variables in the Lagrangian inner loop based on the initialized control variables; A judgment module is used to judge whether the Lagrangian outer loop converges. If it converges, the process ends; if it does not converge, the control parameters are updated, and the control variables are continued to be updated in the Lagrangian inner loop until the Lagrangian outer loop converges; The update module includes: Determine the unit, set the control variable of the kth Lagrangian outer loop iteration to the initialized control variable; A first updating unit, used for determining a control variable correction amount and updating the control variable based on the control variable correction amount; The first judgment unit is used to judge whether the Lagrangian inner loop converges. If the Lagrangian inner loop converges, the control variable of the k+1th Lagrangian outer loop iteration is set as the control variable of the tth Lagrangian inner loop iteration, and the Lagrangian inner loop is exited; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations does not exceed the maximum number of Lagrangian inner loop iterations, the number of Lagrangian inner loop iterations is increased by 1, and the next Lagrangian inner loop is entered; if the Lagrangian inner loop does not converge and the number of Lagrangian inner loop iterations exceeds the maximum number of Lagrangian inner loop iterations, the control variable of the k+1th Lagrangian outer loop iteration is set as the initialized control variable, and the Lagrangian inner loop is exited; The first updating unit is specifically used for: The control variable of the t-th Lagrangian inner loop iteration and the control variable correction amount are summed to obtain the control variable of the t+1-th Lagrangian inner loop iteration; The first updating unit determines the control variable correction amount according to the following formula: In the formula, dx is the control variable correction, F(x) is the Lagrangian function, is the first-order derivative of F(x), H is the Hessian matrix of F(x); F(x) is determined by the following formula: Where z is the measured value, h(x) is the state function of the measurement, R is the measurement error covariance matrix, g(x) is the equality constraint, and r k is the penalty factor for the kth Lagrangian outer loop iteration, λ k is the Lagrange multiplier of the kth Lagrange outer loop iteration.

9. The state estimation device of the power distribution network according to claim 8, characterized in that: The control parameters include an outer loop Lagrangian multiplier vector, an outer loop penalty factor vector, a growth coefficient of the penalty factor vector, an improvement degree coefficient, a Lagrangian outer loop control coefficient, an upper limit of the number of Lagrangian outer loop iterations, an Lagrangian outer loop iteration number, a Lagrangian inner loop control coefficient, an upper limit of the number of Lagrangian inner loop iterations, and an Lagrangian inner loop iteration number.

10. The state estimation device of the power distribution network according to claim 8, characterized in that: The initialization module is specifically used for: Initialize the control variables as follows: Where V N is the node voltage vector, I N is the injected current vector of the current source, V S is the voltage vector of the voltage source, I V is the current vector of the voltage source, I D is the current vector of the transformer, I S is the current vector of the switch, I L is the current vector of the load and reactive power compensation device; Y n is the node admittance matrix, V c is the voltage source connection matrix, D c is the transformer connection matrix, S c is the switch connection matrix, S d is the switch impedance matrix and E is the unit matrix.

11. The state estimation device of the power distribution network according to claim 8, characterized in that: The first judging unit is specifically configured to: If the norm of the control variable correction amount is less than or equal to the Lagrangian inner loop control coefficient, the Lagrangian inner loop is determined to converge, otherwise the Lagrangian inner loop does not converge.

12. The state estimation device of the power distribution network according to claim 8, characterized in that: The judging module includes a second judging unit, and the second judging unit is specifically configured to: If the constraint error vector norm is less than or equal to the Lagrangian outer loop control coefficient, or the number of Lagrangian outer loop iterations is greater than or equal to the upper limit of the number of Lagrangian outer loop iterations, it is determined that the Lagrangian outer loop converges, otherwise the Lagrangian outer loop does not converge.

13. The state estimation device of the power distribution network according to claim 12, characterized in that: The determining module further includes a second updating unit, wherein the second updating unit is specifically configured to: Update the Lagrange multiplier as follows: In the formula, λ k+1 is the Lagrange multiplier of the k+1th Lagrange outer loop iteration, λ k is the Lagrange multiplier of the k-times Lagrange outer loop iteration, r k is the penalty factor for the kth Lagrangian outer loop iteration, is the gradient of the Lagrangian function.

14. The state estimation device of the power distribution network according to claim 12, characterized in that: The determination module further includes a third updating unit, and the third updating unit is specifically configured to: Update the penalty factor as follows: In the formula, r k+1 is the penalty factor for the k+1th Lagrangian outer loop iteration, r k is the penalty factor of the kth Lagrangian outer loop iteration; β is the growth coefficient of the penalty factor vector, γ is the improvement coefficient, g(x k ) is the equality constraint of the kth Lagrangian outer loop iteration, g(x k+1 ) is the equality constraint of the k+1th Lagrangian outer loop iteration.

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