System dominant instability form recognition method and terminal based on fuzzy evaluation function

Abstract

The present invention discloses a method and terminal for identifying the dominant instability form of a system based on a fuzzy evaluation function, calculates instability identification indicators, wherein the instability identification indicators include a generator instability indicator, a power angle stability margin indicator, a voltage collapse proximity indicator, a motor instability indicator, and a reactive power margin indicator; performs normalization processing on the power angle instability indicator and the voltage instability indicator based on a fuzzy membership function according to the instability identification indicators; calculates a fuzzy comprehensive evaluation value of power angle stability and a fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result; inputs the fuzzy comprehensive evaluation value of power angle stability and the fuzzy comprehensive evaluation value of voltage stability into an objective function to obtain the value of the objective function, and identifies the dominant instability form of the system according to the value of the objective function. Therefore, based on the voltage instability and power angle instability mechanisms, combined with five stability analysis indicators and fuzzy identification functions, the dominant instability form of the system can be identified, so that the selection of control strategies after instability is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and a terminal for identifying a system-dominant instability form based on a fuzzy evaluation function. Background Art

[0002] For the sake of economy, the operating point of the power system is getting closer and closer to the stability boundary. Therefore, the power angle instability and voltage instability in the power system do not always occur separately, so identifying the dominant instability form has a certain guiding significance for stability control. The stability of the power system is essentially a single problem, and its unified mathematical model is a set of nonlinear differential-difference-algebraic equations (DDAE). However, due to the multidimensionality and complexity of its stability problem, when analyzing a specific type of problem, it is usually necessary to simplify the problem through appropriate assumptions. Simplified mathematical models are usually only applicable to power angle stability research or voltage stability research. For example, in the study of transient power angle stability, the dynamic characteristics of the load are ignored in the model, making the study of voltage stability difficult. The identification of system instability mainly depends on the instability criterion. At present, many engineering practical and theoretical standards have been formulated for power angle instability and voltage instability, but when these standards are used to identify instability, only a single instability form can be identified. However, the dynamic processes of the two instability forms are coupled with each other, so the two instability phenomena may occur simultaneously. It is difficult to determine which instability form is dominant with a single stability analysis and instability criterion, so identifying the dominant instability form has a certain guiding significance for stability control. Generally speaking, if voltage instability is the main instability factor, reactive power compensation or load shedding is mainly adopted for stability control; if power angle instability is the main instability factor, active power adjustment or generator shedding is mainly adopted for stability control.

[0003] At present, the physical characteristics identification methods based on voltage instability and power angle instability include methods based on oscillation center, methods based on Thevenin equivalent, methods based on tie line power difference, etc. When the above methods are directly extended from a two-machine system to a multi-machine system, the analysis results may not be reliable when the system does not have obvious characteristics of the two types of instability. In addition, there is an identification method based on dynamic theory. This method is difficult to calculate and requires more simplification of the system model, which is often not adopted in engineering. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and a terminal for identifying the dominant instability form of a system based on a fuzzy evaluation function, which can improve the accuracy of identifying the dominant instability form of a power system.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The system-dominant instability morphology recognition method based on fuzzy evaluation function includes the following steps:

[0007] Calculating instability identification indicators, wherein the instability identification indicators include a generator instability indicator, a power angle stability margin indicator, a voltage collapse imminent indicator, a motor instability indicator, and a reactive power margin indicator;

[0008] According to the instability identification index, normalization processing of the power angle instability index and the voltage instability index is performed based on a fuzzy membership function;

[0009] Calculating a fuzzy comprehensive evaluation value of power angle stability and a fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result;

[0010] The fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are input into the objective function to obtain the value of the objective function, and the dominant instability form of the system is identified according to the value of the objective function.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A system-dominant instability morphology recognition terminal based on a fuzzy evaluation function comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the system-dominant instability morphology recognition method based on a fuzzy evaluation function of the above-mentioned scheme is implemented.

[0013] The beneficial effects of the present invention are: calculating the instability identification index, wherein the instability identification index includes the generator instability index, the power angle stability margin index, the voltage collapse approaching index, the motor instability index and the reactive margin index, which can comprehensively consider the acceleration kinetic energy and power angle of the generator, distinguish the relatively decelerated units and the generators that have a major impact on the transient power angle instability of the system, and improve the accuracy of voltage instability identification through the transient voltage stability evaluation index. According to the instability identification index, the power angle instability index and the voltage instability index are normalized based on the fuzzy membership function; the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are calculated according to the normalization processing result; the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are input into the objective function to obtain the value of the objective function, and the dominant instability form of the system is identified according to the value of the objective function. Therefore, based on the voltage instability and power angle instability mechanism, combined with five stability analysis indicators, and a method for identifying the dominant instability form based on the fuzzy identification function is proposed, which can identify the dominant instability form of the system, making the selection of the control strategy after instability more efficient, which is difficult to achieve with a single stability analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a method for identifying system-dominated instability morphology based on fuzzy evaluation function according to an embodiment of the present invention;

[0015] Figure 2 It is a schematic diagram of a system-dominant instability morphology recognition terminal based on a fuzzy evaluation function according to an embodiment of the present invention;

[0016] Figure 3 is the system-dominant instability morphology recognition method based on fuzzy evaluation function according to an embodiment of the present invention. e -T m Schematic diagram of changes over time;

[0017] Figure 4 It is an ascending semi-trapezoidal distribution diagram of the system-dominant instability morphology identification method based on fuzzy evaluation function according to an embodiment of the present invention;

[0018] Figure 5 It is a descending semi-trapezoidal distribution diagram of the system-dominant instability morphology identification method based on fuzzy evaluation function according to an embodiment of the present invention;

[0019] Figure 6 is the plane partition of (y1, y2) and the value of the Z(y1, y2) function in the brackets of the system-dominated instability morphology recognition method based on the fuzzy evaluation function in an embodiment of the present invention;

[0020] Figure 7 A schematic diagram of a fuzzy comprehensive evaluation network for dominant instability identification of a method for identifying a dominant instability form of a system based on a fuzzy evaluation function according to an embodiment of the present invention;

[0021] Figure 8 It is an overall calculation flow chart of the dominant instability pattern recognition of the system dominant instability morphology recognition method based on fuzzy evaluation function according to an embodiment of the present invention;

[0022] Fig. 9 This is a curve diagram of power angle and bus voltage amplitude when no control measures are taken in Example 1 of Case 1;

[0023] Fig.10 This is a curve diagram of power angle and bus voltage amplitude when reactive power compensation is performed at bus 27 in Example 1 of Case 1;

[0024] Fig.11 This is a curve diagram of power angle and bus voltage amplitude when the 35th generator is cut off in Example 3 of Case 1;

[0025] Fig.12 This is the power angle and bus voltage amplitude curve when no control measures are taken in case 2;

[0026] Fig.13 This is the power angle and bus voltage amplitude curve when the 33rd generator is removed in Case 2;

[0027] Fig.14 This is the power angle and bus voltage amplitude curve when the load on bus 15 is partially removed in Case 2. DETAILED DESCRIPTION

[0028] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0029] Please refer to Figure 1 , Figures 3 to 8 The embodiment of the present invention provides a method for identifying the dominant instability form of a system based on a fuzzy evaluation function, comprising the steps of:

[0030] Calculating instability identification indicators, wherein the instability identification indicators include a generator instability indicator, a power angle stability margin indicator, a voltage collapse imminent indicator, a motor instability indicator, and a reactive power margin indicator;

[0031] According to the instability identification index, normalization processing of the power angle instability index and the voltage instability index is performed based on a fuzzy membership function;

[0032] Calculating a fuzzy comprehensive evaluation value of power angle stability and a fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result;

[0033] The fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are input into the objective function to obtain the value of the objective function, and the dominant instability form of the system is identified according to the value of the objective function.

[0034] From the above description, it can be seen that the beneficial effects of the present invention are: calculating the instability identification index, wherein the instability identification index includes the generator instability index, the power angle stability margin index, the voltage collapse approaching index, the motor instability index and the reactive margin index, which can comprehensively consider the acceleration kinetic energy and power angle of the generator, distinguish the relatively decelerated units and the generators that have a major impact on the transient power angle instability of the system, and improve the accuracy of voltage instability identification through the transient voltage stability evaluation index. Based on the fuzzy membership function, the power angle instability index and the voltage instability index are normalized; according to the normalization result, the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are calculated; the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are input into the objective function to obtain the value of the objective function, and the dominant instability form of the system is identified according to the value of the objective function. Therefore, based on the voltage instability and power angle instability mechanism, combined with five stability analysis indicators, and a method for identifying the dominant instability form based on the fuzzy identification function is proposed, which can identify the dominant instability form of the system, making the selection of the control strategy after instability more efficient, which is difficult to achieve with a single stability analysis method.

[0035] Furthermore, normalizing the power angle instability index and the voltage instability index based on the fuzzy membership function according to the instability identification index includes:

[0036] The generator instability index and voltage critical collapse index are normalized using the ascending semi-trapezoidal fuzzy membership function. The ascending semi-trapezoidal distribution expression is:

[0037] ;

[0038] Where g1(x) represents a rising semi-trapezoidal distribution function, x represents the input index value, which includes the generator instability index and the voltage critical collapse index, and a and b represent the lower limit and upper limit of the distribution function determined by each index value, respectively.

[0039] From the above description, it can be seen that the generator instability index and the voltage critical collapse index are normalized using the ascending semi-trapezoidal fuzzy membership function, which facilitates the subsequent calculation of the fuzzy comprehensive evaluation value according to the membership of the generator instability index and the voltage critical collapse index.

[0040] Furthermore, normalizing the power angle instability index and the voltage instability index based on the fuzzy membership function according to the instability identification index also includes:

[0041] The power angle stability margin index, motor instability index and reactive power margin evaluation index are normalized using the descending semi-trapezoidal fuzzy membership function. The expression of descending semi-trapezoidal distribution is:

[0042] ;

[0043] Where g2(x) represents a descending semi-trapezoidal distribution function, x represents the input index value, and c and d represent the lower limit and upper limit of the distribution function determined by each index value, respectively.

[0044] From the above description, it can be seen that the power angle stability margin index, the motor instability index and the reactive margin evaluation index are normalized using a descending semi-trapezoidal fuzzy membership function, which facilitates the subsequent calculation of the fuzzy comprehensive evaluation value according to the membership of the power angle stability margin index, the motor instability index and the reactive margin evaluation index.

[0045] Further, calculating the fuzzy comprehensive evaluation value of power angle stability and the fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result includes:

[0046] Calculate the fuzzy comprehensive evaluation value y1 of power angle stability and the fuzzy comprehensive evaluation value y2 of voltage stability:

[0047] ;

[0048] ;

[0049] Where, s1 represents the membership of the generator instability index, s2 represents the membership of the power angle stability margin index, s3 represents the membership of the voltage collapse approaching index, s4 represents the membership of the motor instability index, s5 represents the membership of the reactive power margin index, w1 and w2 represent the weights of the generator instability index and the power angle stability margin index, respectively, w3, w4 and w5 represent the weights of the voltage critical collapse index, the motor instability index and the reactive power margin index, respectively;

[0050] The corresponding weights are determined according to the relative values ​​of the five instability indicators:

[0051] For the fuzzy comprehensive evaluation value y1 of power angle stability, if s i =max{ s 1, s 2}( i =1,2), then w i =0.6, otherwise w i =0.4;

[0052] For the voltage stability fuzzy comprehensive evaluation value y2, if s j =max{ s 3, s 4, s 5}( j =3,4,5), then w j =0.4, otherwise w j =0.3.

[0053] From the above description, it can be seen that according to the indicators corresponding to the power angle stability and the voltage stability, the corresponding fuzzy comprehensive evaluation values ​​are calculated respectively, so as to facilitate the subsequent evaluation of the dominant instability form of the system according to the fuzzy comprehensive evaluation values ​​of the power angle stability and the voltage stability.

[0054] Furthermore, inputting the fuzzy comprehensive evaluation value of power angle stability and the fuzzy comprehensive evaluation value of voltage stability into the objective function to obtain the value of the objective function includes:

[0055] Set the objective function to a discrete function, divide the (y1, y2) plane into multiple regions, and set the corresponding Z value for each region:

[0056] .

[0057] From the above description, it can be seen that the objective function Z is a discrete function, and the (y1, y2) plane is divided into 7 regions, which can accurately determine the dominant instability form. The set of Z values ​​represents the degree, and dividing it into multiple regions can explain the dominant degree of the dominant instability mode in more detail.

[0058] Furthermore, the dominant instability form of the system is identified according to the value of the objective function, including:

[0059] If the value of the objective function is less than 0, the system tends to experience voltage instability;

[0060] If the value of the objective function is greater than 0, the system tends to be unstable at the power angle;

[0061] If the value of the objective function is equal to 0, it means that the power angle instability and voltage instability are strongly coupled;

[0062] If the value of the objective function is equal to NaN, it means that the system is in a critical state of instability, and either power angle control or voltage control can restore the system to stability.

[0063] From the above description, it can be seen that the dominant instability form of the system is identified according to the value of the objective function. When the value of the objective function is greater than 0, the system is more inclined to power angle instability; when the value of the objective function is less than 0, the system is more inclined to voltage instability; when the value of the objective function is equal to NaN, the degree of system voltage instability and power angle instability are both low, so the system is in a critical state of instability, and power angle control or voltage control can restore the system to stability. Among them, the closer the Z value is to 0, the stronger the coupling between power angle instability and voltage instability, and because the dominant degree can affect the judgment of control measures, when the dominance is small, it can be considered that the two instability forms are strongly coupled and difficult to decouple from the control.

[0064] Furthermore, the calculation of instability identification indicators includes:

[0065] Calculation of generator instability index U i :

[0066] ;

[0067] Where sign(x) represents the sign function, t0 represents the generator instability time, and t bi represents the first minimum time of the kinetic energy of the i-th generator, represents the rotor angle of the i-th generator at time t, V KEi (t bi ) indicates that the i-th generator is bi The kinetic energy relative to the center of inertia is , T j represents the inertia time constant of the i-th generator, represents the speed of the rotor at time t;

[0068] Calculate the power angle stability margin index η:

[0069] ;

[0070] In the formula, A dec Indicates the area of ​​the generator deceleration zone, A acc Represents the area of ​​the generator acceleration zone.

[0071] From the above description, it can be seen that the calculation of the generator instability index U i The power angle stability margin index η takes into account the acceleration kinetic energy and power angle of the generator, and can distinguish between relatively decelerated units and generators that have a major impact on the transient power angle instability of the system, which facilitates the calculation of the fuzzy comprehensive evaluation value of power angle stability.

[0072] Furthermore, the calculation of the instability identification index also includes:

[0073] Calculate the voltage collapse imminent indicator VCPI:

[0074] VCPI=max{|VCPI Pi |,|VCPI Qi |};

[0075] ;

[0076] ;

[0077] Where ΣΔQ gi Represents the total reactive power increment in the system, ΔP Li represents the active power increment of the i-th load node, ΔQ Li represents the reactive power increment of the i-th load node, x represents the system algebraic variable, x includes the voltage amplitude and phase angle of the generator node, and the voltage amplitude and voltage phase angle of the load node;

[0078] Calculation of motor instability index S L :

[0079] ;

[0080] Where T e Represents electromagnetic torque, T m represents the mechanical torque, and s represents the motor slip rate;

[0081] Calculate the reactive power margin evaluation index VQ:

[0082] The VQ curve of the load bus is calculated by the continuous power flow equation. The bottom Q of the VQ curve of the critical load bus is , define the distance from the current operating point to the bottom of the VQ curve as .

[0083] From the above description, it can be seen that the calculation of voltage collapse approach index VCPI and motor instability index S L And reactive margin evaluation index VQ, which is convenient for calculating the fuzzy comprehensive evaluation value of voltage stability.

[0084] Please refer to Figure 2 An embodiment of the present invention provides a system-dominant instability form recognition terminal based on a fuzzy evaluation function, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the system-dominant instability form recognition method based on a fuzzy evaluation function of the above-mentioned scheme is implemented.

[0085] The system dominant instability form identification method and terminal based on fuzzy evaluation function of the present invention are suitable for identifying the dominant instability form of power in more complex instability scenarios, so that the post-instability control strategy selection is more efficient, which is described below through specific implementation methods:

[0086] Embodiment 1

[0087] Please refer to Figure 1 , Figures 3 to 8 , a system-dominant instability morphology recognition method based on fuzzy evaluation function, comprising the steps of:

[0088] S1. Calculating instability identification indicators, wherein the instability identification indicators include a generator instability indicator, a power angle stability margin indicator, a voltage collapse proximity indicator, a motor instability indicator, and a reactive power margin indicator.

[0089] S11. Calculate the generator instability index U i .

[0090] Enter the rotor speed at time t , the rotor angle of the i-th generator at time t , the inertia time constant T of the i-th generator j , calculate the generator instability index U i :

[0091] ;

[0092] Wherein, sign(x) represents the sign function. When x>0, the function value is 1; when x<0, the function value is -1; when x=0, the function value is 0; t0 represents the generator instability time; t bi represents the first minimum time of the kinetic energy of the i-th generator; V KEi (t bi ) indicates that the i-th generator isbi The kinetic energy relative to the center of inertia.

[0093] Where V KEi (t) is calculated as:

[0094] .

[0095] S12. Calculate the power angle stability margin index η.

[0096] Enter the generator deceleration area A dec 、Generator acceleration area A acc , define the power angle stability margin evaluation index η, calculate η,

[0097] ;

[0098] In the formula, A dec Represents the area of ​​the deceleration zone, A acc Represents the area of ​​the acceleration zone.

[0099] When the system power angle is stable, the generator power angle δ in the single-machine infinite equivalent model reaches the maximum offset position and then swings back, and the deceleration area is equal to the acceleration area, η = 0. At this time, it is necessary to estimate the potential deceleration area to calculate the system power angle stability margin, and enter the potential deceleration area area A pot , calculate η:

[0100] ;

[0101] Where A pot A represents the potential deceleration area of ​​the generator. dec Indicates the area of ​​the generator deceleration zone, A acc Represents the area of ​​the generator acceleration zone.

[0102] S13. Calculate the Voltage Collapse Proximity Indicator (VCPI):

[0103] Input the algebraic variable x in the system, the increment of all reactive powers ΣΔQ gi , the active power increment ΔP of the i-th load node Li , reactive power increment ΔQ of the i-th load node Li , the Jacobian matrix J of the algebraic equation system acobi .

[0104] Among them, J acobi The calculation method is: .

[0105] Defining VCPI Qi and VCPI Pi, calculate VCPI respectively Qi and VCPI Pi :

[0106] ;

[0107] ;

[0108] Where ΣΔQ gi Represents the total reactive power increment in the system, corresponding to the change in reactive output of the generator and reactive power compensation equipment (such as SVC); ΔP Li represents the active power increment of the i-th load node; ΔQ Li represents the reactive power increment of the i-th load node; x represents the system algebraic variables, including the voltage amplitude and phase angle of the generator node, and the voltage amplitude and voltage phase angle of the load node.

[0109] When light load VCPI Qi Close to 1, and tends to infinity when voltage collapse occurs in the power system. VCPI Pi The bus with the largest value is the most effective place for load shedding. Qi The bus with the largest value is the place where reactive power compensation is most effective. When analyzing the dominant instability form, define VCPI=max{|VCPI Pi |,|VCPI Qi |}.

[0110] S14. Calculate the motor instability index S L :

[0111] Input motor electromagnetic torque T e , Mechanical torque T m , motor slip rate s, and calculate the quantitative analysis index S of the motor transient stability according to the following formula L :

[0112] ;

[0113] Where, T e Represents electromagnetic torque; T m Represents mechanical torque; t max Indicates (T e -T m ) maximum value; t min Indicates (T e -T m ), please refer to the physical meaning of t1 and t2 Figure 3 .

[0114] S L The denominator of S normalizes the index to a range of [-1, 1].L The numerator of the expression corresponds to the single-machine potential energy function of the motor, and also reflects the kinetic energy of the generator. A brief description is as follows:

[0115] Motor rotor motion equation:

[0116] ;

[0117] Multiply both sides by (1-s) and then integrate both sides of the equation:

[0118] ;

[0119] Where, T j is the inertia time constant; s is the slip rate; T e Represents electromagnetic torque; T m Represents mechanical torque; V k Expressed as the motor kinetic energy; V PE Represents the potential energy of the motor, also known as S L The numerator of the indicator.

[0120] When S L When S is greater than 0, the motor is stable, and the larger the value, the more stable the motor is. L When it is less than 0, the motor is transiently unstable, and the smaller the value is, the more unstable the motor will be.

[0121] S15. Calculate the reactive power margin evaluation index VQ:

[0122] Based on the power flow equation, the reactive power of the load bus is increased from the current operating point, and the VQ curve of the load bus is calculated by continuous power flow. The bottom of the VQ curve of the critical load bus is Q= ,satisfy , define the distance from the current operating point to the bottom of the VQ curve as .

[0123] S2. According to the instability identification index, normalization processing is performed on the power angle instability index and the voltage instability index based on a fuzzy membership function.

[0124] Specifically, the five indicators calculated in step S1 are input: the generator instability index U i , power angle stability margin index η, voltage collapse approach index VCPI, motor instability index S L , reactive margin evaluation index VQ. The power angle instability index and voltage instability index are normalized based on the membership function.

[0125] S21, using the ascending semi-trapezoidal fuzzy membership function to calculate the generator instability index U i It is normalized with the voltage critical collapse index VCPI.

[0126] The curve of the ascending semi-trapezoidal distribution expression is as follows Figure 4 As shown, the expression is as follows:

[0127] ;

[0128] Where g1(x) is a semi-trapezoidal distribution function, x is the input index value, including the generator instability index U i and voltage critical collapse index VCPI, a and b are the lower limit and upper limit of the distribution function determined by each index value.

[0129] S22, using the descending semi-trapezoidal fuzzy membership function to calculate the power angle stability margin index η and the motor instability index S L And the reactive margin evaluation index VQ is normalized.

[0130] The curve of the descending semi-trapezoidal distribution expression is as follows Figure 5 As shown, the expression is as follows:

[0131] ;

[0132] Where g2(x) is a descending semi-trapezoidal distribution function, x is the input index value, and c and d are the lower and upper limits of the distribution function determined by each index value.

[0133] Output normalized index value s1=g1(U i ), s2=g2(η), s3=g1(VCPI), s4=g2(S L ), s5=g2(VQ).

[0134] S3. Calculate a fuzzy comprehensive evaluation value of power angle stability and a fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result.

[0135] Specifically, power angle instability is closely related to voltage instability, and it is difficult to distinguish them due to the similarity of external phenomena. The above steps propose several indicators to reasonably describe and organize the indicators to accurately reflect the instability form of the system. This method proposes a fuzzy comprehensive recognition method to identify the two instability forms with unclear boundaries.

[0136] Input index x i The weight w i , the normalized index value s i , calculate the fuzzy comprehensive evaluation values ​​y1 and y2 according to the following formula:

[0137] ;

[0138] ;

[0139] In the formula, w i Indicates the index x i The weight of i Indicates the index x i The membership degree, s1=g1(U i ), s2=g2(η), s3=g1(VCPI), s4=g2(S L ), s5=g2(VQ), y1 is the fuzzy comprehensive evaluation value of power angle stability, y2 is the fuzzy comprehensive evaluation value of voltage stability; w1 and w2 represent the weights of generator instability index and power angle stability margin index respectively; w3, w4 and w5 represent the weights of voltage critical collapse index, motor instability index and reactive power margin index respectively.

[0140] Determine the corresponding weights according to the relative sizes of the five instability index values: For the fuzzy comprehensive evaluation value y1 of power angle stability, if s i =max{ s 1, s 2}( i =1,2), then w i =0.6, otherwise w i =0.4. For the voltage stability fuzzy comprehensive evaluation value y2, if s j =max{ s 3, s 4, s 5}( j =3,4,5), then w j =0.4, otherwise w j =0.3.

[0141] S4. Input the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability into an objective function to obtain a value of the objective function, and identify the dominant instability form of the system according to the value of the objective function.

[0142] S41. Input fuzzy comprehensive evaluation values ​​y1 and y2, and identify the dominant instability form of the system according to the value of the defined objective function Z(y1, y2).

[0143] The objective function Z can be calculated as follows:

[0144] .

[0145] For details, please refer to Figure 6, Region 1, 3: the value of the objective function is less than 0; Region 2, 4: the value of the objective function is greater than 0, tending to power angle instability; Region 5: the value of the objective function is equal to NaN; Region 6, 7: the value of the objective function is equal to 0, and power angle instability and voltage instability are strongly coupled.

[0146] The Z value set is {-1, 0.5, 0, 0.5, 1, NaN}, and the 1 and 0.5 in the Z value set represent the degree of dominance, which is divided into multiple regions to illustrate the dominance of the dominant instability form. Since the dominance can affect the judgment of control measures, when the dominance is small, it can be considered that the two instability forms are strongly coupled and difficult to decouple from the control.

[0147] And, NaN represents the default value, which means that the degree of voltage instability and power angle instability is very low, and the system is in a critical state of instability. At this time, there is no need to identify the dominant instability form, because power angle control or voltage control can usually restore the system to stability.

[0148] S42. Identification of the dominant instability pattern.

[0149] According to the objective function Z value, the dominant instability form of the system is identified. When the Z value is greater than 0, the system is more inclined to power angle instability; when the Z value is less than 0, the system is more inclined to voltage instability. The closer the Z value is to 0, the stronger the coupling between power angle instability and voltage instability.

[0150] Therefore, based on the voltage instability and power angle instability mechanisms, the embodiment combines five stability analysis methods, proposes a method for identifying the dominant instability form of the power system based on fuzzy functions, proposes a power angle instability index and a power angle instability index, and uses a method for identifying the dominant instability form of the power system based on fuzzy functions. Finally, this method is verified in the actual IEEE39 node system and can accurately identify the dominant instability form of the system. In addition, in this embodiment, the power angle and voltage can be evaluated separately, and then the dominant instability form is evaluated, and the sensitivity of the identification result to the weight is reduced. Since the existing single instability form identification method only identifies a single instability form and lacks the function of dominant identification, it is difficult to accurately identify and analyze the dominant instability form of the system. Therefore, the system dominant instability form identification method based on fuzzy evaluation function in this embodiment is more suitable for more complex instability scenarios than other methods.

[0151] Embodiment 2

[0152] The difference between this embodiment and the first embodiment is that a specific implementation case is provided:

[0153] In Case 1, the IEEE39 bus system is used as an example on the PSCAD / EMTDC platform to verify the correctness of the system dominant instability form identification method based on the fuzzy evaluation function. In order to simulate the voltage instability caused by motor instability, the motor load on buses 15 and 18 is increased.

[0154] The three-phase short circuit fault on bus 27 was eliminated after 0.17s / 0.175s / 0.18s, and recorded as Example 1 / Example 2 / Example 3. The system simultaneously experienced power angle instability and voltage instability. Through a single power angle stability evaluation index or voltage stability evaluation index (generator instability index, power angle stability margin index, motor instability index, voltage collapse approaching index, motor instability index or reactive power margin evaluation index), it can only be concluded that the system has both power angle instability and voltage instability, and it cannot identify which instability form is dominant, and it cannot distinguish which control mode should be the main control strategy. The comprehensive evaluation method proposed in this embodiment can provide such dominant stability identification, and the evaluation values ​​calculated based on the method proposed in this embodiment are shown in Table 1.

[0155] Table 1. Case 1 indicator identification results

[0156]

[0157] Table 1 shows that as the fault duration increases, the system instability mode gradually evolves from voltage instability to power angle instability. During this change, the power angle instability gradually increases, and the voltage instability gradually decreases. The above identification results are verified by taking corresponding control measures: taking power angle instability as an example, the generator is cut off; for voltage instability, reactive power compensation is used.

[0158] For Example 1, due to voltage instability, a maximum voltage is used immediately after the fault is eliminated. VCPI Qi For example 3, since the power angle instability is the main instability mode, after immediate removal, the maximum U i The fault of the generator 35 can be eliminated. Figures 9 to 11 , both control devices have restored the system to a stable state, indicating that the identification method is feasible.

[0159] In case 2, a three-phase short circuit fault occurred on bus 15, and the fault s As shown in Table 2, according to the above identification method, the dominant instability factor of the system is voltage instability. Figure 12 to Figure 14 , the 33rd generator with the largest voltage value is cut off, and the voltage value of bus 15 is still lower than 0.8 pu, local voltage instability; if part of the load on bus 15 is removed, the power angle and voltage are restored. This case is a typical example of power angle instability caused by motor instability. It can be seen that the result of this identification method is correct.

[0160] Table 2 Indicator identification results for Case 2

[0161]

[0162] Embodiment 3

[0163] Please refer to Figure 2 A system-dominant instability morphology recognition terminal based on a fuzzy evaluation function comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the system-dominant instability morphology recognition method based on a fuzzy evaluation function of embodiment one or two is implemented.

[0164] In summary, the method and terminal for identifying the dominant instability form of the system based on the fuzzy evaluation function provided by the present invention calculate the instability identification index, wherein the instability identification index includes the generator instability index, the power angle stability margin index, the voltage collapse proximity index, the motor instability index and the reactive power margin index, which can comprehensively consider the acceleration kinetic energy and power angle of the generator, distinguish between the relatively decelerated units and the generators that have a major impact on the transient power angle instability of the system, and improve the accuracy of voltage instability identification through the transient voltage stability evaluation index. According to the instability identification index, the power angle instability index and the voltage instability index are normalized based on the fuzzy membership function; the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are calculated based on the normalization processing result; the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability are input into the objective function to obtain the value of the objective function, and the dominant instability form of the system is identified according to the value of the objective function. Therefore, based on the voltage instability and power angle instability mechanism, combined with five stability analysis indicators, a method for identifying the dominant instability form based on fuzzy recognition function is proposed. It can identify the dominant instability form of the system and make the selection of control strategy after instability more efficient, which is difficult to achieve with a single stability analysis method.

[0165] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying system-dominant instability patterns based on fuzzy evaluation functions, characterized in that: Includes steps: Calculating instability identification indicators, wherein the instability identification indicators include a generator instability indicator, a power angle stability margin indicator, a voltage critical collapse indicator, a motor instability indicator, and a reactive power margin indicator; According to the instability identification index, normalization processing of the power angle instability index and the voltage instability index is performed based on a fuzzy membership function; Calculate the fuzzy comprehensive evaluation value of power angle stability and the fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result; Inputting the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability into the objective function to obtain the value of the objective function, and identifying the dominant instability form of the system according to the value of the objective function; Calculating the fuzzy comprehensive evaluation value of power angle stability and the fuzzy comprehensive evaluation value of voltage stability according to the normalization processing result includes: Calculate the fuzzy comprehensive evaluation value of power angle stability y 1 and fuzzy comprehensive evaluation value of voltage stability y 2: y 1 = w 1 s 1 + w 2 s 2； y 2 = w 3 s 3 + w 4 s 4 + w 5 s 5； In the formula, s 1 represents the membership degree of the generator instability index, s 2 represents the degree of membership of the power angle stability margin index, s 3 represents the membership degree of the voltage critical collapse index, s 4 represents the degree of membership of the motor instability index, s 5 represents the membership degree of reactive margin index, w 1 and w 2 represent the weights of the generator instability index and the power angle stability margin index, w 3. w 4 and w 5 represents the weights of voltage critical collapse index, motor instability index and reactive power margin index respectively; The corresponding weights are determined according to the relative values ​​of the five instability indicators: Fuzzy comprehensive evaluation value of power angle stability y 1. If s i =max{ s 1, s 2}, i =1,2, then w i =0.6, otherwise w i =0.4; Fuzzy comprehensive evaluation value of voltage stability y 2. If s j =max{ s 3, s 4, s 5}, j =3,4,5, then w j =0.4, otherwise w j =0.3; Inputting the fuzzy comprehensive evaluation value of the power angle stability and the fuzzy comprehensive evaluation value of the voltage stability into the objective function to obtain the value of the objective function includes: Set the objective function to a discrete function, and y 1, y 2) The plane is divided into multiple areas, and a corresponding Z value: ； According to the value of the objective function, the dominant instability forms of the system are identified as follows: If the value of the objective function is greater than 0, the system tends to be unstable at the power angle; If the value of the objective function is less than 0, the system tends to experience voltage instability; If the value of the objective function is equal to 0, it means that the power angle instability and voltage instability are strongly coupled; If the value of the objective function is equal to NaN, it means that the system is in a critical state of instability, and either power angle control or voltage control can restore the system to stability.

2. The system-dominant instability morphology identification method based on fuzzy evaluation function according to claim 1 is characterized in that: The normalization processing of the power angle instability index and the voltage instability index based on the fuzzy membership function according to the instability identification index includes: The generator instability index and voltage critical collapse index are normalized using the ascending semi-trapezoidal fuzzy membership function. The ascending semi-trapezoidal distribution expression is: ； In the formula, g 1(x) represents a rising semi-trapezoidal distribution function, x Indicates the input index value, which includes the generator instability index and the voltage critical collapse index. a and b Respectively represent the lower and upper limits of the distribution function determined by each index value.

3. The system-dominant instability morphology identification method based on fuzzy evaluation function according to claim 1 is characterized in that: The normalization processing of the power angle instability index and the voltage instability index based on the fuzzy membership function according to the instability identification index also includes: The power angle stability margin index, motor instability index and reactive power margin evaluation index are normalized using the descending semi-trapezoidal fuzzy membership function. The expression of descending semi-trapezoidal distribution is: ； In the formula, g 2( x ) represents a descending semi-trapezoidal distribution function, x Indicates the input index value, c and d Respectively represent the lower limit and upper limit of the distribution function determined by each indicator value.

4. The system-dominant instability morphology identification method based on fuzzy evaluation function according to claim 1 is characterized in that: The computational instability identification index includes: Calculation of generator instability index U i : ； In the formula, sign (·) represents the sign function, t 0 means the generator instability time, t bi Indicates i The first minimum time of the kinetic energy of the generator, ( t ) indicates the i Generator t The rotor angle at time V KEi ( t bi ) indicates the i Generators in t bi The kinetic energy relative to the center of inertia is , T i represents the inertia time constant of the i-th generator, Indicates i The rotor of the generator is t bi The speed of the moment; Calculate the power angle stability margin index η : ； In the formula, A dec represents the area of ​​the generator deceleration zone, A acc Represents the area of ​​the generator acceleration zone.

5. The system-dominant instability morphology identification method based on fuzzy evaluation function according to claim 1 is characterized in that: The calculation of instability identification indicators also includes: Calculating the voltage critical collapse index VCPI : VCPI =max{| VCPI Pi |,| VCPI Qi |}; ； ； In the formula, ΣΔ Q gi Represents the increment of all reactive power in the system, Δ P Li represents the active power increment of the i-th load node, Δ Q Li represents the reactive power increment of the i-th load node, h represents the system algebraic variables, h Including the voltage amplitude and phase angle of the generator node, the voltage amplitude and voltage phase angle of the load node, J acobi The Jacobian matrix representing the system of algebraic equations; Calculation of motor instability index S L : ； In the formula T e represents the electromagnetic torque, T m represents the mechanical torque, s Indicates the motor slip rate; Calculate the reactive power margin index VQ: The VQ curve of the critical load bus is calculated by the continuous power flow equation. The bottom of the VQ curve of the critical load bus is Q= ,satisfy , define the current operating point as Q', define the distance from the current operating point to the bottom of the VQ curve as VQ = - Q'.

6. A system-dominant instability morphology recognition terminal based on fuzzy evaluation function, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the system-dominated instability morphology recognition method based on fuzzy evaluation function as described in any one of claims 1 to 5 when executing the computer program.

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