CN106571638A - Method for judging type of low-frequency oscillation - Google Patents

Abstract

The invention provides a method for discriminating low-frequency oscillation types, the method comprising: setting a sliding window and an oscillation energy threshold; calculating the oscillation energy of the unit in each time period, and determining the target unit; calculating the oscillation of the target unit in each time period Energy spatial distribution entropy and per unit standard deviation indicators; entropy difference classification indicators are calculated according to the oscillation energy spatial distribution entropy and per unit standard deviation indicators of the target unit; low-frequency oscillation types are judged based on entropy difference classification indicators. The technical solution provided by the invention essentially reveals the motion characteristics of low-frequency oscillations, intuitively reflects the difference characteristics of different types of low-frequency oscillations, and has more obvious adaptability and differentiation effects, and proposes a standard deviation index to reflect the percentage of vibration energy of the unit Size; through the entropy difference classification criterion to comprehensively characterize the energy distribution characteristics of different types of low-frequency oscillations, and use the sliding window to dynamically reflect the change of oscillation energy over time, thereby realizing accurate and effective distinction of different types of low-frequency oscillations.

Description

A Discrimination Method of Low Frequency Oscillation Type

technical field

The invention relates to a method, in particular to a low-frequency oscillation type discrimination method.

Background technique

Low frequency oscillation is the power swing in the tie-line after the power system suffers a disturbance. The dynamic instability of the system is caused by the divergent oscillation caused by insufficient damping or even negative damping after the disturbance. The main cause of instability is the insufficient electrical damping of the system or the lack of suitable active coordination, which is usually caused by the following disturbances: (1) machine cut-off; (2) transmission line failure or protection malfunction; (3) circuit breaker equipment accident; (4) loss of load. Disturbance phenomena generally go through the process of generation, propagation, and dissipation, and new disturbances may be caused during the propagation process. At the same time, the operation for disturbance is itself a disturbance. Therefore, these situations are often not isolated, but interrelated, presenting multiple phenomena in time and space. This is the actual physical background for the existence of multiple perturbations. The deteriorating interaction will eventually lead to system instability, disassembly, and large-scale power outages.

In recent years, many low-frequency oscillation events have occurred in domestic and foreign power grids, seriously threatening the safe and stable operation of the power grid, and restricting the power transmission capacity of the power grid. Low-frequency oscillation refers to the oscillation of the generator's rotor angle, speed, and related electrical quantities, such as line power, bus voltage, etc., with approximately equal amplitude or increased amplitude. Because the oscillation frequency is low, it is generally 0.1-2.5Hz.

With the continuous expansion of the scale of the interconnected power grid and the application of high-magnification fast excitation systems, the relative swing between the rotors of the generator is prone to occur under small disturbances, causing low-frequency oscillations with weak or negative damping in the power system, which seriously restricts the safety and stability of the power grid. run. When low-frequency oscillation occurs in the power system, it is very important to accurately determine the type of oscillation to determine the cause of the oscillation and take suppression measures. Therefore, the classification and identification of low-frequency oscillation has attracted more and more attention from operators and researchers. According to the cause and scope of oscillation, low-frequency oscillation can be divided into local oscillation, interval oscillation and local-interval coupling oscillation. The suppression measures corresponding to different types of low-frequency oscillations are significantly different.

In the prior art, when analyzing low-frequency oscillation with the concept of energy, it is found that low-frequency oscillation is a special form of motion of the power system. The oscillation process is accompanied by the propagation and conversion of oscillation energy. When low-frequency oscillation occurs in the power system, oscillation energy is generated. The negative damping generator set is an oscillation energy source. After the local oscillation or interval oscillation occurs, the distribution of the oscillation energy source in space is relatively fixed, and the related units gradually become stable with the release of oscillation energy over time. After local-interval coupling oscillation occurs, the oscillation energy source gradually transitions from a small number of units in the local oscillation mode to many units in the interval oscillation mode, and the spatial distribution of system oscillation energy experiences a transition process from centralized to decentralized over time. However, only relying on the time-scale distribution of oscillation energy to determine the type of low-frequency oscillation, although this method can essentially reveal the motion characteristics of low-frequency oscillation, there is no clear type boundary division, and it cannot intuitively reflect the different characteristics of different types of low-frequency oscillations.

In order to accurately identify the type of low-frequency oscillation, it is necessary to provide a method for identifying the type of low-frequency oscillation based on the entropy of the time-space distribution of oscillation energy to meet the needs of the prior art.

Contents of the invention

In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a method for discriminating the type of low-frequency oscillation, the method comprising:

(1) Set the sliding window and oscillation energy threshold;

(2) Determine the target unit according to the calculated oscillation energy of the unit in each time period;

(3) Calculate the oscillation energy spatial distribution entropy and standard deviation index of the target unit in each time period;

(4) Calculate the entropy difference classification index by using the oscillation energy spatial distribution entropy and standard deviation index of the target unit;

(5) Judging the type of low-frequency oscillation according to the classification index of entropy difference.

Preferably, the step (1) setting the sliding window includes: setting the initial moment, the sliding length and the sliding step.

Preferably, the oscillation energy E _Gi of the step (2) is shown in the following formula:

In the formula, t1, t2: time period; ΔP _i : the variation of generator output active power relative to the steady-state value; Δf _i : frequency offset.

Preferably, the determination of the target unit in the step (2) includes: determining the target unit according to whether the oscillation energy E _Gi is greater than the oscillation energy threshold.

Preferably, the oscillation energy spatial distribution entropy S _OE of the target unit within each time period of the step (3) is shown in the following formula:

In the formula, η _i : the percentage of unit i in the system oscillation energy, N: the number of units.

Preferably, the percentage η _i of the system oscillation energy of the unit i is shown in the following formula:

In the formula, E _Gi : the oscillation energy of the unit; E _Σ : the total oscillation energy of the unit.

Preferably, the standard deviation index σ _{p u} of the step (3) is shown in the following formula:

In the formula, σ: standard deviation of oscillation energy of the unit; μ: average value of oscillation energy of the unit; N: number of units; E _Gi : oscillation energy of the unit.

Preferably, the standard deviation σ of the oscillation energy of the unit is shown in the following formula:

The average value μ of the oscillation energy of the unit is shown in the following formula:

In the formula, N: the number of units; E _Gi : the oscillation energy of the units.

Preferably, the step (4) entropy difference classification index D _Σ is shown in the following formula:

In the formula, S _OE : the spatial distribution entropy of oscillation energy; E _Gi : the oscillation energy of the unit; N: the number of units; μ: the average value of the oscillation energy of the unit.

Preferably, the determination of the type of low-frequency oscillation in step (5) includes: according to the entropy difference classification index _DΣ , the more dispersed the system oscillation energy is in the spatial distribution, it is divided into interval oscillation, local oscillation and coupled oscillation.

Compared with the closest prior art, the present invention has the following excellent effects:

(1) The technical solution provided by the present invention analyzes the problem of low-frequency oscillation from the perspective of energy, which can essentially reveal the motion characteristics of low-frequency oscillation. The space-time distribution characteristics of oscillation energy can intuitively reflect the difference characteristics of different types of low-frequency oscillations. Compared with other classification methods, it has better adaptability and more obvious distinguishing effect.

(2) The present invention applies the entropy theory to the discrimination of low-frequency oscillation types, and proposes an oscillation energy spatial distribution entropy index to quantitatively characterize the degree of aggregation of different types of low-frequency oscillation energy spatial distribution; proposes a standard deviation index to reflect the difference in the contribution of unit oscillation energy The energy distribution characteristics of different types of low-frequency oscillations are comprehensively characterized by entropy difference classification criteria, and the sliding window is used to dynamically reflect the change of oscillation energy over time, so that different types of low-frequency oscillations can be accurately and effectively distinguished.

Description of drawings

Fig. 1 is a flow chart of the method for discriminating the type of low-frequency oscillation of the present invention.

detailed description

In order to better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings and examples.

The present invention provides a method for discriminating the type of low-frequency oscillation, the method comprising:

(1) Setting the sliding window and the oscillation energy threshold: set the initial time W, the sliding window length 1, the sliding step g, and the oscillation energy threshold E _th .

(2) Determine the target unit according to the calculated oscillation energy of the unit in each time period:

The oscillation energy E _Gi is shown in the following formula:

In the formula, t1, t2: time period; ΔP _i : the variation of generator output active power relative to the steady-state value; Δf _i : frequency offset.

The determination of the target unit is determined by whether the oscillation energy E _Gi is greater than the oscillation energy threshold E _th to determine the target unit,

When E _Gi >E _th , the unit is the target unit.

Low-frequency oscillation is a special form of motion in power systems, and the process of oscillation is accompanied by the propagation and conversion of oscillation energy. Using the concept of energy to analyze the problem of low-frequency oscillation can essentially reveal the motion characteristics of low-frequency oscillation. When low-frequency oscillation occurs in the power system, the negatively damped generator set that generates oscillation energy can be considered as the source of oscillation energy. After the oscillation, the oscillation energy injected into the system by the i-th generator set is:

E _Gi ＝∫(ΔP _Gi dΔθ _i +ΔQ _Gi d(ΔlnU _i ))＝

∫(ΔP _Gi 2πΔf _i dt+ΔQ _Gi d(ΔlnU _i ))

In the formula: ΔP _Gi ＝P _Gi -P _{Gi s} is the change of the active power output by the generator relative to the steady-state value; Δθ _i is the voltage phase angle offset of bus i; Δf _i ＝f _i -f ₀ is Frequency offset; ΔQ _Gi ＝Q _Gi -Q _{Gi s} , is the change of the reactive power output by the generator relative to the steady-state value; U _i is the voltage value of the bus i,

If the reactive power transmitted in the network and the change of node voltage are neglected, E _Gi can be approximately expressed as:

E _Gi ＝∫ΔP _Gi 2πΔf _i dt

When calculating the oscillation energy of each unit through the above formula, if the calculation result is positive, it means that the unit generates oscillation energy, has negative damping, and is an oscillation energy source; otherwise, it indicates that the unit is not an oscillation energy source.

Oscillatory energy space-time distribution characteristics:

In a certain period of time, the dynamic characteristics of the power system not only show changes in time, but also involve spatial distribution. Different types of low-frequency oscillations have different characteristics of time-space distribution of system oscillation energy due to different oscillation properties. Therefore, focusing on the variation characteristics of oscillation energy with time and space is of great significance for the identification of low-frequency oscillation types. According to the principle of energy conservation, the generation and consumption of oscillation energy maintain a dynamic balance relationship. In order to study the space-time distribution characteristics of oscillation energy more clearly, the oscillation energy in the present invention specifically refers to the oscillation energy produced by the model-related units.

1) Distribution of oscillation energy on a spatial scale: local mode oscillation energy is mainly generated by a small number of negatively damped units. From a spatial perspective, system oscillation energy will be concentrated in a small area of space; interval mode oscillation energy is generated by many units in the area , from the point of view of space, the oscillation energy of the system will be scattered everywhere in the space. The final form of local-interval coupling oscillation is an interval oscillation mode, but the oscillation energy of some units has been kept at a high level, so the spatial distribution characteristics of oscillation energy will be between local oscillation and interval oscillation. Local oscillations can be effectively identified by using the aggregation degree of the oscillation energy in the spatial distribution.

2) The distribution of oscillation energy on the time scale: After local oscillation or interval oscillation occurs, the spatial distribution of oscillation energy source is relatively fixed, and the oscillation energy emitted by each related unit gradually increases or tends to be stable with time. After local-interval coupling oscillation occurs, the oscillation energy source gradually transitions from a small number of units in the local oscillation mode to many units in the interval oscillation mode, and the spatial distribution of system oscillation energy will experience a transition process from centralized to decentralized over time. The spatial distribution of local-interval coupling mode oscillation energy spreads over time is the most obvious distinguishing feature from the other two types of low-frequency oscillations.

(3) Calculate the oscillation energy spatial distribution entropy and standard deviation index of the target unit in each time period:

The degree of aggregation of the spatial distribution of oscillation energy reflects the relevant characteristics of the operating state of the system after an accident, and can reveal the internal energy distribution of the system after different types of oscillation accidents. As a measure of the chaos and disorder of the distribution state of a complex system, entropy can quantitatively characterize the spatial distribution characteristics of oscillation energy and describe the distinguishing characteristics of different types of low-frequency oscillations. It can be seen that using the spatial distribution entropy of oscillation energy as the basis for distinguishing different types of low-frequency oscillations has clear physical meaning.

The oscillation energy spatial distribution entropy S _OE of the target unit in each time period is expressed as follows:

In the formula, η _i : the percentage of unit i in the system oscillation energy, N: the number of units;

The percentage η _i of unit i in the oscillation energy of the system is shown in the following formula:

In the formula, E _Gi : the oscillation energy of the unit; E _Σ : the total oscillation energy of the unit.

The per-unit standard deviation index σ _{p u} is shown in the following formula:

In the formula, σ: standard deviation of oscillation energy of the unit; μ: average value of oscillation energy of the unit; N: number of units; E _Gi : oscillation energy of the unit.

The standard deviation σ of the vibration energy of the unit is shown in the following formula:

The average value μ of the oscillation energy of the unit is shown in the following formula:

In the formula, N: the number of units; E _Gi : the oscillation energy of the units.

The smaller the S _OE , the more concentrated the oscillation energy is in a few units, and this type of oscillation tends to be the oscillation between a few units relative to other units, which can be considered as a local oscillation; a larger S _OE indicates that the oscillation energy is more spatially distributed. Scattered, this type of oscillation is more inclined to the relative oscillation between the clusters, which can be considered as interval oscillation. And from the space-time distribution characteristics of oscillation energy, it can be seen that the S _{OE of local low-frequency oscillation and interval low-frequency oscillation remains basically unchanged with time, while the S OE of} local-interval coupled oscillation will remain unchanged after increasing with time.

(4) Calculate the entropy difference classification index by using the oscillation energy spatial distribution entropy and standard deviation index of the target unit

Mark:

After the occurrence of different types of low-frequency oscillation accidents, the standard deviation can be used to measure the dispersion of oscillation energy generated by the oscillation-related units, so as to quantify the difference rate of the oscillation energy contribution of the units. The oscillation energy emitted by each relevant unit will change with time. Selecting different time period lengths and initial moments to calculate the oscillation energy of the computer group will have an impact on the standard deviation of the oscillation energy emitted by each relevant unit. The influence of the mean value of the oscillation energy produced by the associated unit.

The per-unit standard deviation index of the oscillation energy of each relevant unit is:

The larger the σ _{p u} , the greater the difference in the contribution of the relevant units to the system oscillation energy. The system oscillation energy mainly comes from a small number of units with a large contribution. This type of low-frequency oscillation can be considered as a local low-frequency oscillation; σ _{p ·} When _u is small, it means that all relevant units share the system oscillation energy more evenly, and this type of low-frequency oscillation can be considered as interval oscillation. From the space-time distribution characteristics of system oscillation energy, it can be seen that the change trend of σ _{p u} of coupled oscillation will be opposite to that of S _OE , showing a trend of first decaying and then remaining unchanged over time.

The entropy difference classification index D _Σ is shown in the following formula:

In the formula, S _OE : the spatial distribution entropy of oscillation energy; E _Gi : the oscillation energy of the unit; N: the number of units; μ: the average value of the oscillation energy of the unit.

(5) Judging the type of low-frequency oscillation according to the classification index of entropy difference.

The judgment of the type of low-frequency oscillation includes: according to the entropy difference classification index _DΣ , the more dispersed the system oscillation energy is in the spatial distribution, it is divided into interval oscillation, local oscillation and coupling oscillation.

When D _Σ is larger, it means that the system oscillation energy is more dispersed in space, and the dispersion of oscillation energy of each related unit is smaller, which is an interval oscillation;

When _DΣ is smaller, it indicates that the system oscillation energy is concentrated in space and the contribution rate of oscillation energy of each related unit is quite different, and the oscillation energy mainly comes from a small number of units, which is a local oscillation;

When _DΣ undergoes a transition process from small to large, the spatial distribution characteristics of oscillation energy and the contribution rate of oscillation energy of each unit will change with time, which is a coupled oscillation.

The above are only embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the pending application of the present invention. within the scope of the claims.

Claims (10)

1. a kind of method of discrimination of low-frequency oscillation type, it is characterised in that methods described includes：

(1) sliding window and oscillation energy threshold value are set；

(2) target unit is determined according to the oscillation energy of unit in each time period for calculating；

(3) the oscillation energy spatial distribution entropy and standardization standard deviation requirement of target unit in each time period are calculated；

(4) entropy difference classification indicators are calculated with the oscillation energy spatial distribution entropy and standardization standard deviation requirement of target unit；

(5) low-frequency oscillation type is judged according to entropy difference classification indicators.

2. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that the step (1) arranges sliding window Including：Initial time, sliding length and sliding step are set.

3. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that the oscillation energy of the step (2) E_GiIt is shown below：

$E_{Gi}={\∫}_{t1}^{t2}{\mathrm{\ΔP}}_{i}2{\mathrm{\π\Δf}}_{i}dt$

In formula, t1, t2：Time period initial time, end time；ΔP_i：The change of generator active power of output Relative steady-state value Amount；Δf_i：Frequency offset.

4. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that step (2) the target unit It is determined that including：By oscillation energy E_GiWhether target unit is determined more than oscillation energy threshold value.

5. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that in the step (3) each time period The oscillation energy spatial distribution entropy S of target unit_OEIt is shown below：

$S_{OE}=-\underset{i=1}{\overset{N}{\Σ}}{\mathrm{\η}}_i{\mathrm{lg\η}}_i$

In formula, η_i：Unit i percentage, N in system oscillation energy：Unit quantity.

6. low-frequency oscillation type identification method as claimed in claim 5, it is characterised in that the unit i is in system oscillation energy Amount percentage η_iIt is shown below：

${\mathrm{\η}}_i=\frac{E_{Gi}}{E_{\mathrm{\Σ}}}$

In formula, E_Gi：The oscillation energy of unit；E_Σ：The global oscillation energy of unit.

7. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that the standardization mark of the step (3) Quasi- poor index σ_p·uIt is shown below：

${\mathrm{\σ}}_{p\·u}=\frac{\mathrm{\σ}}{\mathrm{\μ}}=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}{(\frac{E_{Gi}}{\mathrm{\μ}}-1)}^2}$

In formula, σ：The oscillation energy standard deviation of unit；μ：The oscillation energy mean value of unit；N：Unit quantity；E_Gi：Unit shakes Swing energy.

8. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that the oscillation energy standard of the unit Difference σ is shown below：

$\mathrm{\σ}=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}{(E_{Gi}-\mathrm{\μ})}^2}$

The oscillation energy average value mu of the unit is shown below：

$\mathrm{\μ}=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}{E}_{Gi}$

In formula, N：Unit quantity；E_Gi：The oscillation energy of unit.

9. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that step (4) the entropy difference class refers to Mark D_ΣIt is shown below：

$D_{\Σ}=\frac{S_{OE}}{{\mathrm{\σ}}_{p\·u}}=-\underset{i=1}{\overset{N}{\Σ}}\left(\frac{E_{Gi}}{E_{\mathrm{\Σ}}}\lg \frac{E_{Gi}}{E_{\mathrm{\Σ}}}\right)/\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}{(\frac{E_{Gi}}{\mathrm{\μ}}-1)}^2}$

In formula, S_OE：Oscillation energy spatial distribution entropy；E_Gi：The oscillation energy of unit；N：Unit quantity；μ：The oscillation energy of unit Mean value.

10. low-frequency oscillation type identification method as claimed in claim 1, it is characterised in that step (5) the low-frequency oscillation class The judgement of type includes：By entropy difference classification indicators D_ΣSystem oscillation energy is got over dispersion and be divided into interval in spatial distribution and is shaken Swing, local oscillation and coupled oscillations.