JP5358668B2 - Power system stable equilibrium point calculation device - Google Patents

Description

  The present invention relates to a stable equilibrium point calculation device for calculating a stable equilibrium point (SEP) of a system, and more particularly, from a conventional BCU classifier (Boundary of stability-region-based Controlling Unstable equilibrium point classifier) system. The present invention also relates to a BCU classifier system that can accurately and quickly classify contingencies.

  Since the stable equilibrium point of a nonlinear dynamic system is obtained by solving a multidimensional nonlinear equation, the well-known Newton method using a Jacobian matrix is often used.

  The Newton method sometimes does not converge because the convergence calculation is performed using a Jacobian matrix. In order to solve this non-convergence problem, various methods have been proposed [References 1 and 2]. Among them, a pseudo-transient simulation method has also been proposed [References 3 and 4].

Reference 1: S.-C. Fang, RC Melville, L. Trajkovic, and LT Watson, “Artificial parameter homotopy methods for the dc operating point problem,” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol. 12, no. 6, pp. 861-877, Jun. 1993.
Reference 2: TL Quarles, SPICE 3C.1 User's Guide. Berkeley: Univ. California, EECS Industrial Liaison Program, Apr. 1989.
Reference 3: TS Coffey, CT Kelley, and DE Keyes, “Pseudo transient continuation and differential algebraic equations,” SIAM J. Sci. Comput., Vol. 25, no. 2, pp. 553-569.
Reference 4: TS Coffey and DE Keyes, “Convergence analysis of pseudo-transient continuation,” SIAM J. Numer. Anal., Vol. 35, no. 2, pp. 508-523, 1998.
Determining the stable equilibrium point (SEP) of a nonlinear dynamic system is important to confirm system stability. To obtain the stable equilibrium point, the nonlinear simultaneous equations are solved. Nonlinear simultaneous equations are difficult to obtain analytically, and the Newton method is generally used. The Newton method finds a solution by repeatedly solving a linear simultaneous equation using a Jacobian matrix. Since the method is simple, it is used in a wide range of fields.

  Even in the transient stability screening program (BCU method), it is important to analyze the nature of the stable equilibrium point, so the stable equilibrium point is calculated by the Newton method. If a stable equilibrium point cannot be obtained, it must be determined that the system is extremely unstable, and a detailed time domain simulation with a large amount of calculation must be performed. For this reason, a large amount of labor is required to discriminate the assumed faults classified by the stable equilibrium point convergence problem of the BCU classification method.

  The Newton method is an easy-to-handle and highly practical method, but numerical divergence occurs when the initial value is not appropriate (there is no initial point in the converging area). In addition, no index is given to determine whether the value does not converge due to numerical divergence or whether there is really no stable equilibrium point. If there is no stable equilibrium point, the risk of system collapse is quite high, so appropriate measures are necessary, but if it is not clear whether there is really a stable equilibrium point, the study of countermeasures will not proceed smoothly Problems arise.

  The failure when the stable equilibrium point cannot be found by the Newton method can be a fatal defect especially when the transient stability evaluation needs to be performed online as in the BCU method.

  Nonlinear simultaneous equations are static calculations and do not have dynamic characteristics. However, by assuming virtual dynamic characteristics, dynamic simulation (implicit integration method with variable integration time increments) is performed. (Pseudo-Transient Method) is known to obtain the vicinity of the stable equilibrium point. Since the pseudo-transient simulation method is based on the simulation method, it is almost impossible to converge to the stable equilibrium point. Thus, the convergence point is obtained (FIG. 1). In the initial stage, when there is no stable equilibrium point, some features appear, such as the integration step time does not increase, so the distinction between numerical analysis problems and when there is no really stable equilibrium point becomes clear .

  In many cases, the pseudo-transient simulation method performs as expected, but if the system is not damped significantly, many integral calculations are required to calculate the stable equilibrium point, or if the oscillation characteristics are high. However, it is impossible to obtain the stable equilibrium point properly, because the pseudo-transient simulation method is applied to the stable equilibrium point calculation of the classifier II of the improved BCU classification method of the TEPCO-BCU method (reference 5). Turned out in.

  Reference 5: US Pat. No. 6,868,311

DISCLOSURE OF THE INVENTION An object of the present invention is to improve the performance of the improved BCU classification method by improving the pseudo-transient simulation method, improving the reliability of the stable equilibrium point calculation and the calculation speed.

  In order to solve this object, the following two techniques were invented.

1) Increasing the damping of the equation of motion makes the convergence to the equilibrium point stable and fast. 2) Even if the damping of the equation of motion of the generator is increased artificially , the SEP does not change.

  Using these two technologies, we were able to solve the objectives at a high level by developing logic to calculate the stable equilibrium point.

ここで、
Ｆ（ｘ，ｙ）：電力システムの機械系方程式
Ｇ（ｘ、ｙ）：電力システムの潮流方程式
ｘ，ｙ：電力システムの非線形微分代数方程式の変数であり、
前記発電機の機械系微分方程式は、下記式で表わされ、

ここで、
ｉ：電力システムのｉ番目の発電機、
Ｈ：発電機単位慣性定数(per unit inertia constant)
δ：発電機内部位相角(internal angle)
ω ₀ ：システム角周波数
Ｄ：ダンピング係数(Rated angular velocity)
Ｔm：機械入力トルク
Ｔe：電気トルク
であり、
前記ダンピング係数は、前記電力システムの角周波数の１倍乃至１０倍の範囲内に設定 され、
前記所定の条件は、前記求められたノルムが想定故障発生前の健全状態のもとでの前記 電力システムの非線形微分代数方程式の機械系方程式の２０％以下であり、かつ疑似過渡 シミュレーション方法の１回当りの繰り返し計算で、ノルムの変化量が２以下の場合であ る、電力システムの安定平衡点計算装置である。 A first aspect of the present invention is the generator machine of the power system in which the mechanical damping coefficient of the generator in the power system is set larger than the actual mechanical damping coefficient of the generator by a predetermined magnitude. Mechanical system of power system nonlinear differential algebraic equation including mechanical system differential equation of generator set with mechanical damping coefficient using pseudo transient simulation method for nonlinear differential algebraic equation of power system including system differential equation In the means for obtaining the norm of the equation and the nonlinear differential algebraic equation of the power system, an assumed failure reflecting the influence of the disturbance of the virtual power system is reflected as a variable parameter,
Means for determining whether the determined norm satisfies a predetermined condition;
When it is determined that a predetermined condition is satisfied, a variable value of a nonlinear differential algebraic equation including a mechanical differential equation of the generator when a value determined to satisfy the predetermined condition is obtained, Means for setting the initial value of a variable in the system's nonlinear differential algebraic equation;
Means for determining a stable equilibrium point using Newton's method for the nonlinear differential algebraic equation of the power system in which the initial value is set ;
The nonlinear differential algebraic equation of the power system is represented by the following equation:

here,
F (x, y): Mechanical system equation of electric power system
G (x, y): Power system flow equation
x, y are variables of the nonlinear differential algebraic equation of the power system,
The mechanical differential equation of the generator is represented by the following equation:

here,
i: i-th generator of the power system,
H: Per unit inertia constant
δ: Generator internal phase angle (internal angle)
ω ₀ : System angular frequency
D: Damping coefficient (Rated angular velocity)
Tm: Machine input torque
Te: Electric torque
And
The damping coefficient is set within a range of 1 to 10 times the angular frequency of the power system ,
The predetermined condition is that the obtained norm is 20% or less of the mechanical system equation of the nonlinear differential algebraic equation of the power system under a healthy state before the occurrence of the assumed failure , and 1 of the pseudo transient simulation method in iteration per dose, the amount of change in norm Ru der case of 2 or less, a stable equilibrium point calculation device for a power system.

FIG. 1 is a diagram showing the concept of the pseudo transient simulation method.
FIG. 2 is a diagram showing a hardware configuration of the BCU classifier system according to the embodiment of the present invention.
FIG. 3 is a functional block diagram of the BCU classifier system.
FIG. 4 is a flowchart for explaining a method of determining a stable equilibrium point in BCU classifier II according to the embodiment of the present invention.
FIG. 5 is a diagram illustrating an example in which the pseudo transient simulation is simply applied to the nonlinear differential algebraic equation of the power system.
FIG. 6 is a diagram illustrating an example in which the pseudo transient simulation is applied to the nonlinear differential algebraic equation of the power system when the damping coefficient D is set large.

  Hereinafter, a BCU classifier system according to an embodiment of the present invention will be described with reference to the drawings.

  The BCU classifier system has a function of selecting unstable contingencies from contingencies that reflect the influence of disturbances in a virtual power system (power system).

  FIG. 2 is a diagram showing a hardware configuration of the BCU classifier system according to the embodiment of the present invention.

  As shown in the figure, in the BCU classifier system 1, a CPU 12, a communication unit 13, a display unit 14, a memory 15, an input unit 16, and a storage device 17 are connected to a bus 11.

  The CPU 12 cooperates with the BCU classifier system program 22 stored in the storage device 17 to perform contingency failure selection processing according to the embodiment of the present invention and to control the entire BCU classifier system 1. It is.

  The communication unit 13 receives data reflecting disturbance from the power system. This received data serves as the basis for contingency data as needed.

  The display unit 14 displays a result related to the assumed failure selection process by the BCU classifier system program 22, and is a display device such as a liquid crystal display or a plasma display.

  The memory 15 is used as a work area required when the BCU classifier system program 22 is executed.

  The input unit 16 is an interface for the user to input data such as an assumed failure, and is, for example, a keyboard or a touch panel.

  The storage device 17 is for storing programs and data required for the assumed failure selection process, and is, for example, a hard disk drive (HDD). The storage device 17 stores an OS (Operating System) 21, a BCU classifier system program 22, and a contingency list 23.

  The BCU classifier system program 22 is an application program that runs on the OS 21 and implements the assumed failure sorting process according to the embodiment of the present invention in cooperation with the CPU 12.

  The assumed failure list 23 stores various assumed failure data to be selected in the assumed failure selection process according to the embodiment of the present invention. The assumed failure data is reflected as a parameter in a variable of a nonlinear differential algebraic equation indicating the power system.

  When performing a simulation to determine a stable equilibrium point, it is impractical to actually generate a failure and determine the stability of the power system, so the assumed failure data set in advance by the user is used. .

  That is, the assumed failure data is virtual data indicating the state of the power system that occurs when a certain failure occurs in the power system. Note that the assumed failure data itself to be selected in the BCU classifier is known and will not be described in detail here.

  FIG. 3 is a functional block diagram of the BCU classifier system according to the embodiment of the present invention. In the figure, functions other than the BCU classifier II have the functions disclosed in Reference 5, and are known techniques.

  Hereinafter, each component will be briefly described.

  The BCU classifier I (for network separation problems) selects highly unstable contingency faults that cause network segregation problems as a result of contingent faults that are sequentially input from the contingency faults stored in the contingency list. Designed to be.

  The BCU classifier II (for the stable equilibrium point convergence problem) applies a numerical method to the remaining contingencies that have not been selected by the BCU classifier I and starts from the pre-failure stable equilibrium point. Is designed to detect potentially unstable contingencies that cause the following stable equilibrium convergence problem.

(I) (Numerical divergence problem), there is a divergence problem when calculating the stable equilibrium point after failure starting from the pre-failure stable equilibrium point, or
(Ii) (Inaccurate convergence problem) Convergence to an EP (equilibrium point) after an erroneous failure.

  A method of determining a stable equilibrium point according to the embodiment of the present invention of the BCU classifier II will be described later.

  The BCU classifier III-A (classifier for the high stability region) has a large post-failure stable equilibrium point (with sufficient size) for the remaining contingencies that are not screened by the BCU classifier II. ) Designed to screen for highly stable contingencies that result in stability areas.

This classifier uses some dynamic information during the exit point search process of the BCU method. The following two indicators are designed for this classifier:
_Texit : time required to reach the exit point of the trajectory at the time of failure.

Δ _smax : Maximum angular difference between the pre-failure stable equilibrium point and the calculated post-failure EP.

The BCU classifier III-B (classifier for the exit point problem) is potentially unstable, resulting in a so-called exit point problem for the remaining contingencies that were not screened by the BCU classifier III-A. It is designed to sort out contingencies. This uses dynamic information that exists while searching for exit points. Two indicators are designed for this classifier. The following indicators are:
_Texit : time required to reach the exit point of the trajectory at the time of failure.

• Potential energy difference between the stable equilibrium point before failure and the exit point.

Considering the survey contingency, if an exit point problem occurs, i.e. if the exit point can be found between time intervals [0, _Texit ], and if the potential energy difference is negative, Contingency failures are classified as potentially unstable.

  The BCU classifier IV (the classifier for trajectory adjustment problems) is based on some dynamic information during the search for the minimum slope point for the remaining contingencies that have not been screened by the BCU classifier III-B. Instability assumptions are screened out.

If the trajectory adjustment fails during the search for the minimum slope point, it means that the heuristic where the maximum point along the trajectory is on the stability boundary of the reduced system in the BCU method does not apply, and the survey failure is potentially unstable Indicates that We propose the following indicators for this classifier:
N (trajectory adjustment): the total number of failures in the trajectory adjustment process.

  Considering the investigation assumed failure, if the numerical value N (trajectory adjustment) is greater than the threshold, the assumed failure is considered to have a locus adjustment problem and is classified as unstable.

Classifier V (Classifier for energy function problem)
The energy function is derived on the assumption that the transfer conductance of the power system is small enough so that this function satisfies the three conditions necessary to be an energy function. If the transfer conductance is not small enough, it is impossible to directly evaluate the transient stability using a (numerical) energy function.

  In this classifier we design the index with the property that the energy function decreases along the system trajectory. The BCU classifier V is one that, for the remaining contingencies that are not screened by the BCU classifier IV, if the potential energy at the minimum slope point is greater than that at the exit point, the corresponding contingency causes an energy function problem. And is classified as potentially unstable.

  The BCU classifier VI (for unstable equilibrium point convergence problem) applies a numerical method to the remaining unstable faults that have not been selected by the BCU classifier V, starting from the minimum gradient point, and the dominant unstable equilibrium point. Is designed to detect the following unstable equilibrium convergence problem.

  (I) (Numerical convergence problem) There is a divergence problem when calculating the dominant unstable equilibrium point (CUEP) starting from the minimum gradient point.

  (Ii) (Inaccurate convergence problem) converges to the wrong dominant unstable equilibrium point (ie, the minimum gradient point is not in the convergence region of the dominant unstable equilibrium point but in the convergence region of another unstable equilibrium point) Exist).

  In this classifier, the following two indicators are designed to identify contingencies that cause an unstable equilibrium convergence problem. Survey contingency faults with unstable equilibrium point contingency problems are classified as potentially unstable.

I _smax : Maximum number of iterations in the calculation of the dominant unstable equilibrium point.

Δ _{smax is} the maximum angle difference between the minimum slope point and the calculated unstable equilibrium point.

  The BCU classifier VII (classifier for the dominant unstable equilibrium point) uses the energy value at the dominant unstable equilibrium point as the critical energy for the remaining contingencies that are not screened by the BCU classifier VI, Each remaining contingency is classified as (clearly) stable or (possibly) unstable. According to the theory of the dominant unstable equilibrium point method, if the energy value at the time of failure resolution is less than the critical energy value, the corresponding contingency is (clearly) stable, otherwise it is (possibly There is unstable).

  The contingency faults selected by the BCU classifier II to BCU classifier VI are analyzed by the BCU guided time domain method. This BCU guided time domain method is a known technique (see Reference 5), and input / output is as follows.

Input: Power system having related data for dynamic reliability evaluation and assumed fault output: Stability evaluation and energy margin value of assumed fault for power system Next, stable equilibrium in the BCU classifier II according to the embodiment of the present invention The point determination method will be described with reference to the flowchart of FIG.

To find the stable equilibrium point, use the nonlinear differential equation

That is, a nonlinear simultaneous equation of 0 = F (x) is solved. In the case of a power system, if the form of a nonlinear differential equation is used, the matrix becomes a dense matrix, which is not preferable from the viewpoint of reducing the calculation load, and is therefore expressed by a nonlinear differential algebraic equation (DAE format).

First, when a contingency is input, a nonlinear differential algebraic equation (DAE format) of the power system is created (S1). This non-linear differential algebraic equation is expressed by equation (1).

here,
F (x, y): Mechanical system equation of power system G (x, y): Power flow equation of power system x, y: Variables of nonlinear differential algebraic equation of power system.

  Note that the assumed failure reflects the influence of the disturbance of the virtual power system, and is reflected as a parameter in the variable of the nonlinear differential algebraic equation of the power system.

  Next, using the Newton method, the stable equilibrium point is calculated from the nonlinear differential algebraic equation (Equation (1)) of the power system generated in S1 (S2), and whether or not the stable equilibrium point can be calculated. A determination is made (S3).

  It is well known that nonlinear simultaneous equations are calculated by convergence using the Newton method using the Jacobian matrix, and are also used in the BCU method. However, the Newton method may diverge when the distance between the initial value and the solution is large. . Divergence occurs when no solution is found or no solution exists, but if there is no solution, the system is extremely unstable and requires further evaluation The amount of calculation becomes enormous. In many cases, it is rare that there is no real solution, and since it is normal that a solution cannot be found, it is effective to improve the convergence performance.

  If it is determined in S3 that a stable equilibrium point can be calculated, the contingency fault input to the classifier II is subjected to a screening out process by the classifier III-A.

  Next, the case where the stable equilibrium point is not calculated by the Newton method will be described.

If the pseudo- transient simulation is simply applied to the nonlinear differential algebraic equation of the power system, the vibration mode of the power fluctuation existing in the power system has an adverse effect, and an appropriate initial value for the Newton method cannot be obtained. FIG. 5 is a diagram illustrating an example in which the pseudo transient simulation is simply applied to the nonlinear differential algebraic equation of the power system. As shown in the figure, even if simulation is performed for 10 seconds, it can be understood that the mismatch (= norm) is not small in terms of the distance between the current position and the solution.

Therefore, the damping coefficient of all the generators existing in the system is increased. The mechanical differential equation of the i-th generator is as follows.

here,
H: per unit inertia constant
δ: Generator internal phase angle
ω ₀ : Rated angular velocity
D: Damping factor
Tm: Machine input torque
Te: Electric torque
By virtually increasing the value of D to 1 to 10 times the system angular frequency for all generators, the effect of the power oscillation mode is reduced. If this effect is eliminated, the power fluctuation cannot be reproduced, but a good initial value used for the Newton method, which is the original purpose of the pseudo- transient simulation method, can be obtained at high speed.

FIG. 6 is a diagram illustrating an example in which the pseudo transient simulation is applied to the nonlinear differential algebraic equation of the power system when the damping coefficient D is set large. In FIG. 6, the virtually introduced value of D is 500 and ω 0 is 120π.

  Reference Documents 6 and 7 disclose that increasing the damping coefficient does not affect the location of the SEP.

Reference 6: HD Chiang, FF Wu, PP Varaiya, “Foundation of direct methods for power system transient stability analysis”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, VOL. 34, No. 2, February 1987
Reference 7: HD Chiang and FF Wu, “Stability of Nonlinear Systems Described by a Second-Order Vector Differential Equation”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, VOL. 35, NO. 6, JUNE 1988
That is, when it is determined in S3 that the stable equilibrium point cannot be calculated, the damping coefficients of the mechanical differential equations of all the generators existing in the power system of the nonlinear differential algebraic equation of the power system are actually set. It is set larger than the value of the power system (S4).

The mechanical differential equation of the i-th generator is as follows.

here,
H: per unit inertia constant
δ: Generator internal phase angle
ω ₀ : Rated angular velocity
D: Damping factor
Tm: Machine input torque
Te: Electric torque The mechanical differential equation of this generator acts on the mechanical differential equation of the nonlinear differential algebraic equation (Equation (1)) of the power system.

  In the present embodiment, the value of D is virtually set to be about 1 to 10 times the system angular frequency for all the generators.

Next, the pseudo-transient simulation method is applied to the nonlinear differential algebra equation of the power system including the mechanical differential equation of all the generators for which the damping coefficient is set in S4, so that the mechanical system equation of the power system of the nonlinear differential algebraic equation is obtained. The norm || F (x, y) || of F (x, y) is calculated (S5).

If the calculation of norm || F (x, y) || is not successful, the value may increase and the calculation may diverge. In this case, the calculation is forcibly terminated. The threshold value for forced termination is, for example, ten times the norm || F (x0, y0) ||. Here, norm || F (x0, y0) || is an initial value (norm calculated by substituting a stable equilibrium point before failure occurrence) before starting the pseudo transient simulation.

Next, it is determined whether or not the norm || F (x, y) || calculated in S5 is equal to or smaller than a predetermined value (S6). Here, the predetermined value is, for example, 20% of the norm of the mechanical system equation of the nonlinear differential algebraic equation of the power system under the sound state before the occurrence of the assumed failure. Needless to say, this value can be appropriately changed. If it is determined in S6 that the value is not less than the predetermined value, the process returns to S5, and the norm is calculated again by the pseudo transient simulation method.

On the other hand, the norm || F (x, y) in S6 if || is determined to be equal to or less than the predetermined value, a repeated calculation of per transient simulation method pseudo-, variation norm 2 It is determined whether or not the following is true (S7).

  Although the change amount is 2 or less here, the set value of the change amount can be set in the range of 1 to 2.

  If it is determined in S7 that the norm change amount is not 2 or less, the process proceeds to a known time domain stability analysis process (S12).

  If it is determined in S7 that the value is 2 or less, the variable values x and y determined to be less than or equal to the predetermined value are set as initial values of the variables of the nonlinear differential algebraic equation of the power system ( S8).

  Next, the Newton method is applied to the nonlinear differential algebraic equation of the power system for which the initial value is set to calculate the stable equilibrium point (S9), and it is determined whether the stable equilibrium point can be calculated. (S10).

If it is determined in S10 that the stable equilibrium point can be calculated, the assumed fault is considered to be stable and sent to processing by the classifier III-A (S11). On the other hand, if it is determined that the stable equilibrium point cannot be calculated, the process returns to S5, and the norm is calculated by the pseudo transient simulation method.

  According to the invention according to the present embodiment, among 59 cases in which a Newton method could not find a stable equilibrium point in a large-scale system of 13,000 buses, 42 cases of stable equilibrium points could be found. . In the case where a stable equilibrium point was not found, there was almost no possibility that it could not be calculated numerically, and the reliability of the system was greatly improved.

  In the present embodiment, the case where the method for determining the stable equilibrium point of the present embodiment is incorporated into the BCU classifier II has been described. However, the present invention is not limited to this, and the method for determining the stable equilibrium point of the power system Can be widely applied.

  Furthermore, the determination method of the stable equilibrium point of the present embodiment is not limited to the method of determining the stable equilibrium point of the power system, and can determine the stable equilibrium point of various systems.

INDUSTRIAL APPLICABILITY The present invention can be used for a stable equilibrium point calculation device that calculates a stable equilibrium point of a system.

Claims (2)

Non-linearity of the power system including a mechanical differential equation of the generator of the power system in which the mechanical damping coefficient of the generator in the power system is set larger than the actual mechanical damping coefficient of the generator by a predetermined magnitude Means for determining a norm of a mechanical system equation of a nonlinear differential algebraic equation of a power system including a mechanical system differential equation of a generator in which the mechanical system damping coefficient is set using a pseudo-transient simulation method for the differential algebra equation; In the nonlinear differential algebraic equation of the power system, the assumed failure reflecting the influence of the disturbance of the virtual power system is reflected as the parameter of the variable.
Means for determining whether the determined norm satisfies a predetermined condition;
When it is determined that a predetermined condition is satisfied, a variable value of a nonlinear differential algebraic equation including a mechanical differential equation of the generator when a value determined to satisfy the predetermined condition is obtained, Means for setting the initial value of a variable in the system's nonlinear differential algebraic equation;
Means for determining a stable equilibrium point using Newton's method for the nonlinear differential algebraic equation of the power system in which the initial value is set ;
The nonlinear differential algebraic equation of the power system is represented by the following equation:

here,
F (x, y): Mechanical system equation of electric power system
G (x, y): Power system flow equation
x, y are variables of the nonlinear differential algebraic equation of the power system,
The mechanical differential equation of the generator is represented by the following equation:

here,
i: i-th generator of the power system,
H: Per unit inertia constant
δ: Generator internal phase angle (internal angle)
ω ₀ : System angular frequency
D: Damping coefficient (Rated angular velocity)
Tm: Machine input torque
Te: Electric torque
And
The damping coefficient is set within a range of 1 to 10 times the angular frequency of the power system ,
The predetermined condition is that the obtained norm is 20% or less of the mechanical system equation of the nonlinear differential algebraic equation of the power system under a healthy state before the occurrence of the assumed failure , and 1 of the pseudo transient simulation method in iteration per dose, the amount of change in norm Ru der case of 2 or less, stable equilibrium point calculation device for a power system.   For the nonlinear differential algebraic equation of the power system, the Newton method is used to Means for determining whether or not a stable equilibrium point of the force system can be calculated;
  The means for obtaining the norm is executed when it is determined that the calculation is not possible. A stable equilibrium point calculation device for the listed power system.

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