JP2899406B2 - System stability monitoring device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Purpose of the Invention] (Industrial application field) The present invention relates to a system stability monitoring device for monitoring the stability of a power system online.

(Prior Art) Conventionally, as a monitoring device of this type, a measurement bus is determined in advance, a phase difference between several bus voltages is calculated from the operation state of the system, and a stability limit obtained in advance by offline calculation is calculated. The stability has been determined by comparing the phase difference angle.
That is, the system is stable if the phase difference angle of the current system calculated from the online data is equal to or less than the stability limit phase difference angle,
If it is more than this, it is determined to be unstable, and the system is operated so that the phase difference angle is always below the stability limit by system operation based on the experience of the operator.

In another method, instead of the phase difference angle, the power flow of the main interconnecting line in the system is monitored online, and operation is restricted so that the magnitude of this power flow is less than a predetermined limit. Some have ensured system stability.

(Problems to be Solved by the Invention) Any conventional monitoring device is a simple one in which the parameters of the system state for performing the stability determination are limited, and the accuracy of the stability determination method based on this is low. There were drawbacks. In addition, when it is judged that the system is unstable beyond the stability limit, general guidelines on how to operate the system and improve the system state to the stable side are not clear, and the operator is not clear. He had no choice but to make decisions based on operational experience.

The present invention has been made in view of the above-described drawbacks of the related art, and is intended for general arbitrary power systems, has a high determination accuracy of system stability, and provides a system operator with guidelines for system stabilization control. It is an object of the present invention to provide a system stability monitoring device that can be presented.

[Structure of the Invention] (Means for Solving the Problems) In order to achieve the above object, the present invention relates to a system stability monitoring device for monitoring the stability of an electric power system online, wherein a start time is determined in advance. A system data collection unit that collects system data when there is a request from the system operator, and simulates a change of the system in the apparatus only when there is a request from the system operator based on the result of the stability determination A system change simulation unit, a state estimation calculation unit that inputs system data from the system data collection and simulation data from the system change simulation unit and performs a tidal current calculation for determining an operation state of the target system, and the state estimation A synchronization torque calculation unit that receives power flow data of the power system from the calculation unit and calculates a synchronization torque coefficient of the power system to be monitored, and a synchronization torque coefficient from the synchronization torque calculation unit A hierarchical neural network unit that outputs a stability determination result as an input, and outputs data of the synchronization torque calculating unit and output data of the hierarchical neural network unit as inputs, and provides a grid operator with guidelines for system stabilization control. And a result display unit to be presented.

(Operation) First, the power flow data 11 of the target system is input to the synchronization torque coefficient calculator 1, and 12 synchronization torque coefficients K _ij are calculated based on the data. As will be described later, the synchronization torque coefficient K _ij indicates the number of generator models constituting the power system by n.
, N × n are defined. That is, it is a real number represented by an n × n-dimensional matrix element. The calculated synchronization torque coefficient K _ij is used as input data of the hierarchical neural network unit 2, and the output data is the stability determination result 13. The twelve synchronization torque coefficients K _ij are also input to the result display unit 3 and serve as data for presenting a guideline for grid stabilization control to the grid operator, as described later.

 Hereinafter, this will be described in more detail.

FIG. 2 is a power system model for explaining the function of the present invention. In FIG. 2, G _{1 to} G ₅ indicate generators,
It is a system composed of the generator of the machine. L ₁ ~L ₄ represents a system load. The equation of motion of the generator is given by:

M _i P ² δ _i = P _mi -P _ei (1) Here, assuming that the mechanical input P _mi is constant, the following equation can be obtained by rewriting the equation (1) with respect to a change around a certain operating point. Get.

M _i P ² Δδ _i = ΔP _ei = O (2) E _i ∠δ _i is the internal induced voltage of generator i, δ _ij = δ _i −δ _j is the phase difference of the rotor of generator i, and Y _ij ∠δ _ij is the system reduced between the internal induced voltages of generator. This is the ij element of the system admittance matrix when reduced.

When the equation (2) is expressed in the form of a matrix, it is multiplied as follows.

([M] p ² + [K]) | Δδ | = [O] (4) where [M] is a diagonal matrix having the inertia constant of each generator as a diagonal element, and [K] is (3a ) Is a synchronized torque coefficient matrix having the expression as an ij element and the expression (3b) as a diagonal element ii, and each has the same number of dimensions as the number of generators.

The physical meaning of the synchronized torque coefficient matrix element K _ij represents the rate of change of the active power output _Pei of the generator i with respect to the internal induced voltage phase δj of the generator _j , and if synchronous operation between the generators is to be maintained. The strength inherent in the system when the influence of the control system is not considered. This value depends on the configuration state of the system and the operation state of the generator. In the case of the system model shown in FIG.
The matrix of the synchronization torque coefficient shown in FIG. 5 generators
Since it is a machine, it is a 5 × 5 square matrix. From the experience of the system stability analysis so far, the magnitude of the synchronizing torque coefficient and the system stability are closely related, and the system stability can be evaluated using this as basic data. However, AVR of generator
Since the characteristics of the control system such as the control system and the governor system are not taken into account, the evaluation error is included and it is not practical. However, since the qualitative tendency can be grasped and the rough evaluation can be made by using this value, the matrix element pattern can be used as a guideline for the system operator for the system stabilization control.

FIG. 4 shows a configuration diagram of the hierarchical neural network unit 2. As shown in FIG. The number of neurons in the input layer is defined by the number of synchronization torque coefficient matrix elements K _ij shown in FIG. If the number of generators is n, there are n × n. The output of each neuron in the input layer is input to a neuron in the intermediate layer, and there is no limit on the number of stages in the intermediate layer. An appropriate number of stages may be defined as needed. The output of the hidden layer is input to two neurons of the output layer. Of the two neurons, one is the stable signal O
_s and other neurons output an unstable signal O _u .
The value of the stable signal O _s is the power system of interest is greater than the value of the instability signal O _u determines stable, unstable otherwise. The values of these two signals correspond to the stability determination result 13 in FIG. In this stage type neural network part, the synchronization torque coefficient matrix element K _ij when the characteristics of the control system such as the AVR of the generator and the governor system are not considered is used as basic data, and various control systems in the actual system are used. A stability determination in consideration of characteristics is performed. In order to realize this function, the values of the synapse weighting coefficient W _i and the threshold θ _i defined for each neuron of the neutral net must be determined, but this applies a learning method generally called back propagation. Can be realized.

That is, for a large number of learning cases in which the operation conditions of the system are changed for the target system, a system stability analysis simulation taking into account a detailed control system is performed, and stability determination is performed. In FIG. 1, the power flow data of each learning case is used as an input to the synchronization torque coefficient calculator 1 and a stability determination result 13 which is an output of the hierarchical neural network 2 is obtained.
Is learned so as to match the stability determination result obtained by the system stability analysis simulation described above. In the hierarchical neural network section in which the value of the coefficient is appropriately determined by repeating the learning as described above, information on the characteristics of the generator control system and the stability characteristics of the target system are incorporated in the coefficient setting.

A result display unit 3 in a stable signal O _s and the system stability determination result by displaying the value of the instability signal O _u, Show those patterns expressed in a matrix form of synchronization torque coefficient matrix elements K _ij to the system operator.

(Example) Hereinafter, an example is described with reference to drawings.

FIG. 5 is a flowchart showing the processing contents of one embodiment of the present invention.

In FIG. 5, the same reference numerals are given to the same function distributions as in FIG.

101 is a target system data collection unit, 102 is a state estimation calculation unit for uniquely determining the operation state of the target system, 103 is a system operator who operates the system stability monitoring device of the present invention, This is a system change simulation unit when performing the operation. Reference numeral 111 denotes system data which is input data of the state estimation calculation unit 102.

 Next, the operation will be described.

As shown in the figure, the arithmetic processing flow of the present embodiment has two processing modes. The two types, the regular start mode and the system simulation mode, are distinguished as follows.

Periodic startup mode This mode is a process for performing stability determination online using system data at that time. The start-up of the device is performed in advance when the start-up time is determined, or when the system operator requests. After startup, the system data is automatically collected by the system data collection unit 101 and sent to the state estimation calculation unit 102.

System simulation mode This mode is performed only when requested by the system operator, and when the system operator simulates the change of the system state in the device based on the result of the stability determination, for example, For this reason, there may be a case where the system operator verifies whether the result of the system change by the system operator is appropriate. The system data 111 reflecting the result of the system change is sent to the state estimation calculation unit 102.

On the other hand, the state estimation calculation unit 102 performs a power flow calculation for uniquely determining the operation state of the target system. The result is the power system flow data 11, and the subsequent arithmetic processing is the same as that described in FIG.

As described above, according to the present embodiment, there are two modes, the regular startup mode and the system simulation mode, so that in addition to the stability monitoring function of the target system, a system change by the system operator is assumed. In addition, the effect on stabilization can be evaluated, and the useful range of the present invention is widened, and the usefulness is enhanced.

[Effects of the Invention] As described above, according to the present invention, in a system stability monitoring device that monitors the stability of a power system online, a first unit that calculates a synchronization torque coefficient of the power system, A second means for inputting output data of the first means and forming a hierarchical neural network, wherein the output data of the first means and the output data of the second means are displayed to a system operator; As a result, a stability determination result with high determination accuracy can be obtained by the output data of the second means.
An effect that a guideline for system stabilization control can be presented to the system operator by the output data of the first means can be expected.

[Brief description of the drawings]

FIG. 1 is a configuration diagram of a system stability monitoring device according to the present invention, FIG. 2 is a power system model for explaining functions of the present invention, FIG. 3 is a matrix of synchronization torque coefficients, and FIG. FIG. 5 is a configuration diagram of the neutral net unit, and FIG. 5 is a flowchart showing the contents of the arithmetic processing according to one embodiment of the present invention. DESCRIPTION OF SYMBOLS 1 ... Synchronization torque coefficient calculation part 2 ... Hierarchical neural network part 3 ... Result display part 11 ... Power flow data of power system 12 ... Synchronization torque coefficient _Kij 13 ... Stability judgment result 101 ... System data collection part 102 ... State estimation Calculation unit 103 ... System change simulation unit 111 ... System data

Claims (1)

(57) [Claims] A system stability monitoring device for monitoring the stability of an electric power system online, wherein a start time is determined in advance or system data is collected when a request is made from a system operator. A collection unit, a system change simulation unit that simulates a change of the system in the apparatus only when requested by the system operator based on the result of the stability determination, and system data and system change simulation unit from the system data collection. And a state estimation calculation unit for inputting simulated data and performing a power flow calculation for determining the operation state of the target system, and a power system to be monitored by receiving the power flow data of the power system from the state estimation calculation unit. A synchronization torque calculation unit for calculating a synchronization torque coefficient, a hierarchical neural network unit that receives a synchronization torque coefficient from the synchronization torque calculation unit and outputs a stability determination result, And a result display unit that receives output data of the reset torque calculation unit and output data of the hierarchical neural network unit and presents a guideline for system stabilization control to a system operator. Degree monitoring device.