CN102520373A - Distinguishing method of direct current magnetic biasing of power transformer based on vibration analysis - Google Patents

Abstract

The invention discloses a distinguishing method of direct current magnetic biasing of a power transformer based on vibration analysis, which includes obtaining a corresponding numeric symbol sequence by collecting vibration signals on the surface of an oil tank of the transformer and embedding the vibration signals into an m-dimensional space through time delay, and constructing corresponding characteristic function through a column diagram of the sequence and then distinguishing the direct current magnetic biasing according to the characteristic quantity. Therefore, the distinguishing method can distinguish whether abnormal strong vibration of the power transformer is caused by the direct current magnetic biasing or not and the severe degree of the magnetic biasing, is not required to be electrically connected with the transformer and accordingly has no interference effect on operation of the transformer. The distinguishing method is intuitive and simple to operate after being written into software program and has no special requirements for operators, and the operators can operate after simple training. Simultaneously, the distinguishing method is high in distinguishing accuracy, easy to implement and popularize, low in implementation cost and capable of being applied to online detection and online state detection and fault diagnosis of the power transformer.

Description

A kind of method of discrimination of the power transformer DC magnetic biasing based on vibration analysis

Technical field

The invention belongs to the input analysis technical field, be specifically related to a kind of method of discrimination of the power transformer DC magnetic biasing based on vibration analysis.

Background technology

Large-scale power transformer is a node important in the electrical network, and the safe operation of large-scale power transformer is extremely important for the reliability service of electrical network.The running status that adopts vibration analysis method on-line monitoring transformer is a research focus in recent years, and its main points are to detect transformer through the tank surface vibration signal of analyzing power transformer whether unusual or fault take place.When transformer generation abnormal vibrations and noise, vibration can significantly increase.The strong vibration meeting of transformer influences the safe operation of transformer, and winding and the iron core etc. of transformer are partly broken down, and also surrounding enviroment is produced noise pollution simultaneously.Therefore, the reason of searching transformer abnormal vibrations and noise helps " suiting the remedy to the case ", in time takes measures to safeguard the safe and stable operation of transformer.

DC magnetic biasing is the recurrent a kind of abnormal conditions of neutral ground power transformer, the vibration and the noise that produce when vibration that transformer in service produces when DC magnetic biasing occurring and noise normally move far above transformer.The essence of DC magnetic biasing is that the magnetic flux of transformer fe in-core makes the interior magnetic flux maximum value of iron core obviously increase owing to occur direct current biasing under the effect of neutral point direct current electric current, and inspires the strong vibration of iron core.When DC magnetic biasing occurring, the exciting current in the winding also can significantly increase, and makes basket vibration increase.Because the abnormal vibrations of operating transformer also comprised various physical construction fault state such as transformer overexcitation process, winding and core slackness distortion, the vibration that accurate differentiation DC magnetic biasing causes from transformer strong vibration signal is necessary.The magnetic bias research to power transformer at present concentrates on relevant electrical quantity analytically basically, but only from the abnormal vibrations and the noise of electrical quantity analysis transformer limitation is arranged.

Existing magnetic bias method of discrimination generally is DC component whether to occur from neutral point current to differentiate; But electric parameter and transformer device structure relevance are little; The vibration of electrical quantity and power transformer does not have clear and definite funtcional relationship, only can not provide magnetic bias whether transformer device structure is impacted from the neutral point current judgement.In addition, comparatively difficulty and can cause certain influence to transformer operation of electric measurement is carried out at the power transformer scene, such as the measurement of neutral point current since the fixed form (being fixed on cement wall or the bar) of neutral ground bar often be difficult for measuring.

Summary of the invention

To the above-mentioned technological deficiency of existing in prior technology; The invention provides a kind of method of discrimination of the power transformer DC magnetic biasing based on vibration analysis, whether the unusual strong vibration that can differentiate power transformer is caused by DC magnetic biasing and the order of severity of magnetic bias.

A kind of method of discrimination of the power transformer DC magnetic biasing based on vibration analysis comprises the steps:

(1) vibration signal on power transformer fuel tank surface is obtained in sampling, and described vibration signal is carried out normalization handle;

(2) vibration signal after the normalization is embedded in the m-dimensional space through time-delay, obtains the space oscillations signal of m dimension, and then make up the numeric character sequence of space oscillations signal;

(3) set up corresponding histogram according to described numeric character sequence, and then make up the characteristic of correspondence function according to described histogram;

(4), differentiate in the power transformer whether have DC magnetic biasing according to described fundamental function; If there is DC magnetic biasing, then confirm the intensity grade of DC magnetic biasing according to the vibration signal after the described normalization.

The space oscillations signal of described m dimension is made up of n element, and each element is made up of m sampled point, and the element expression in the space oscillations signal is following:

x _s(i)＝{x(i)，x(i+u)，x(i+2u)，...，x[i+(m-1)u]} (1)

Wherein: x _s(i) be i element in the space oscillations signal, n is the sampled point number in the vibration signal, and x (i) counts for time-delay and is the practical experience value for the accekeration of i sampled point in the vibration signal after the normalization, u.

In the described step (2), make up the numeric character sequence of space oscillations signal through following formula;

R(τ)＝Θ[ε-||x _s(i)-x _s(i+τ)||] (2)

Wherein: R (τ) is the numeric character sequence, and Θ is the Heaviside function, x _s(i) be i element in the space oscillations signal, ε is distance threshold and is the practical experience value that τ counts for time-delay.

In the described step (3), make up the characteristic of correspondence function through following formula according to histogram;

$L\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{k=\min }^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}{{\mathrm{\Σ}}_{k=1}^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}---\left(3\right)$

Wherein: L (τ) is a fundamental function, P _τ(k) be the number of the array that is made up of continuous k 1 in the numeric character sequence, n is the sampled point number in the vibration signal, and τ counts for time-delay, and min is the minimum combination coefficient and is the practical experience value.

In the described step (4), whether exist the method for DC magnetic biasing to be in the differentiation power transformer:

1), calculates and confirm τ according to formula τ=f/F; Wherein, f is the SF of vibration signal, and F is the time-delay frequency and is the practical experience value that τ counts for time-delay;

2) with in τ, τ-1 and τ+1 difference substitution fundamental function, ask for three corresponding eigenwerts, and three eigenwerts are asked on average, if characteristic mean less than discrimination threshold, then shows to have DC magnetic biasing in the power transformer.

In the described step (4); The method of confirming the intensity grade of DC magnetic biasing according to the vibration signal after the normalization is: earlier the vibration signal after the normalization is carried out high-pass filtering and obtain the filtering vibration signal; Solve the degree value of DC magnetic biasing then through following formula, and then confirm the intensity grade of DC magnetic biasing according to described degree value;

$B\left(i\right)=\frac{\left|\right|x_1\left(i\right)\left|\right|}{\left|\right|x\left(i\right)\left|\right|}---\left(4\right)$

$B=\frac{1}{n}{\mathrm{\Σ}}_{i=1}^{n}B\left(i\right)---\left(5\right)$

Wherein: x (i) is the accekeration of i sampled point in the vibration signal after the normalization, x _l(i) be the accekeration of i sampled point in the filtering vibration signal, n is the sampled point number in the vibration signal, and B is the degree value of DC magnetic biasing.

Whether the unusual strong vibration that the present invention can differentiate power transformer is caused by DC magnetic biasing and the order of severity of magnetic bias, need not to be electrically connected with transformer, so to the noiseless influence of the operation of transformer; It is directly perceived simple that the inventive method is compiled into behind the software program operation, and operating personnel are not had specific (special) requirements, and training can be operated a little; The inventive method is differentiated the accuracy rate height simultaneously, is prone to implement to promote, and implementation cost is low, can be applicable to online detection and the on-line condition monitoring and the fault diagnosis of power transformer.

Description of drawings

Fig. 1 is a steps flow chart synoptic diagram of the present invention.

Fig. 2 is for adopting the field conduct diagnosis synoptic diagram of the inventive method.

Fig. 3 is the characteristic mean time dependent trend map of vibration signal in magnetic bias process of two measuring points of power transformer.

Fig. 4 is the amplitude-frequency response figure of the 200 rank FIR Hi-pass filters of 700Hz for cutoff frequency.

Fig. 5 is the vibration signal of four measuring points of the power transformer time dependent trend map of B value in magnetic bias process.

The oscillogram of the vibration signal of measuring point after normalization when Fig. 6 (a) is the serious magnetic bias of power transformer.

Fig. 6 (b) is the oscillogram of the vibration signal of Fig. 6 (a) through filtered filtering vibration signal.

Embodiment

In order to describe the present invention more particularly, method of discrimination of the present invention is elaborated below in conjunction with accompanying drawing and embodiment.

As shown in Figure 1, a kind of method of discrimination of the power transformer DC magnetic biasing based on vibration analysis comprises the steps:

(1) vibration signal on power transformer fuel tank surface is obtained in sampling, and vibration signal is carried out normalization handle.

As shown in Figure 2; This embodiment utilizes piezoelectric acceleration vibration transducer 2 to be installed in power transformer 1 tank surface (positions such as the weld seam, reinforcement, tank wall edge of fuel tank are avoided in the fixed position as far as possible) of load running; Utilize the vibration signal on signal pickup assembly 3 sampling power transformer fuel tank surfaces, supply computing machine 4 analyzing and diagnosings; SF is 8000Hz, and the continuous sampling time is 1 second, so sampled point number n=8000 in the vibration signal.

(2) vibration signal after the normalization is embedded in the m-dimensional space through time-delay, obtains the space oscillations signal of m dimension;

The space oscillations signal of m dimension is made up of n element, and each element is made up of m sampled point, and the element expression in the space oscillations signal is following:

x _s(i)＝{x(i)，x(i+u)，x(i+2u)，...，x[i+(m-1)u]} (1)

Wherein: x _s(i) be i element in the space oscillations signal, x (i) counts for time-delay for the accekeration of i sampled point in the vibration signal after the normalization, u; U=20 in this embodiment, m=3.

Make up the numeric character sequence of space oscillations signal through following formula;

R(τ)＝Θ[ε-||x _s(i)-x _s(i+τ)||] (2)

Wherein: R (τ) is the numeric character sequence, and Θ is the Heaviside function, and ε is a distance threshold, and τ counts for time-delay; 2-norm calculation mode is adopted in ε in this embodiment=0.25, the norm calculation in the formula 2.

(3) set up corresponding histogram according to the numeric character sequence, and then make up the characteristic of correspondence function through following formula according to histogram;

$L\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{k=\min }^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}{{\mathrm{\Σ}}_{k=1}^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}---\left(3\right)$

Wherein: L (τ) is a fundamental function, P _τ(k) be the number of the array that is made up of continuous k 1 in the numeric character sequence, min is the minimum combination coefficient; Min=3 in this embodiment.

(4), calculate and confirm τ according to formula τ=f/F; Wherein, f is the SF of vibration signal, and F is the time-delay frequency; F=8000 in this embodiment, F=100, then calculate τ=80.

In the fundamental function with τ, τ-1 and τ+1 difference substitution formula 3, ask for three corresponding eigenwerts, and three eigenwerts are asked on average, if characteristic mean less than discrimination threshold, then shows to have DC magnetic biasing in the power transformer; Discrimination threshold is 0.2 in this embodiment.Fig. 3 for the 500KV power transformer when a DC magnetic biasing takes place gradually, put the characteristic mean L that data recorded in continuous two hours (1 second vibration data of the 60 seconds continuous acquisition in every interval) calculates according to two vibration detection _ATime dependent trend map; Provided the DC component that flows into the power transformer neutral point current among Fig. 3 simultaneously, the neutral point current DC component is not 0 promptly to be magnetic bias when taking place, as can be seen from the figure L _AProvided the transformer DC magnetic bias situation exactly.

If have DC magnetic biasing in the power transformer, then utilize the FDAtool kit in the Matlab software to design a FIR Hi-pass filter earlier.Fig. 4 is the amplitude-frequency response figure of the 200 rank FIR Hi-pass filters of 700Hz for the cutoff frequency that adopts the design of FDAtool kit.

Vibration signal after utilizing the FIR Hi-pass filter to normalization earlier carries out filtering and obtains the filtering vibration signal, solves the degree value of DC magnetic biasing then through following formula, and then confirms the intensity grade of DC magnetic biasing according to degree value;

$B\left(i\right)=\frac{\left|\right|x_1\left(i\right)\left|\right|}{\left|\right|x\left(i\right)\left|\right|}---\left(4\right)$

$B=\frac{1}{n}{\mathrm{\Σ}}_{i=1}^{n}B\left(i\right)---\left(5\right)$

Wherein: x (i) is the accekeration of i sampled point in the vibration signal after the normalization, x _l(i) be the accekeration of i sampled point in the filtering vibration signal, n is the sampled point number in the vibration signal, and B is the degree value of DC magnetic biasing; Norm calculation in the formula 4 adopts 1-norm calculation mode.

Fig. 5 the time dependent trend map of B value that the DC magnetic biasing process calculates from 4 measuring points occurs for the 500KV power transformer corresponding to Fig. 3.The as can be seen from the figure reasonable degree that reflects magnetic bias of B value.During beginning, DC current is little, and the transformer bias degree is lighter, and the B value is generally less than 0.25; And, show that then transformer has got into moderate magnetic bias state in a period of time B＞0.25 subsequently; When in serious geomagnetic storm or transformer power up, observing the state that serious magnetic bias appears in transformer, corresponding B value is often greater than 0.4; According to the magnetic bias degree, can take corresponding measure to safeguard the safe and stable operation of transformer.Fig. 6 has provided 220KV the power transformer vibration signal (a) after the measuring point normalization and comparison of wave shape of filtering vibration signal (b) when serious magnetic bias takes place, and this moment, the B value was about 0.518.

Claims (6)

1. the method for discrimination based on the power transformer DC magnetic biasing of vibration analysis comprises the steps:

(1) vibration signal on power transformer fuel tank surface is obtained in sampling, and described vibration signal is carried out normalization handle;

(2) vibration signal after the normalization is embedded in the m-dimensional space through time-delay, obtains the space oscillations signal of m dimension, and then make up the numeric character sequence of space oscillations signal;

(3) set up corresponding histogram according to described numeric character sequence, and then make up the characteristic of correspondence function according to described histogram;

(4), differentiate in the power transformer whether have DC magnetic biasing according to described fundamental function; If there is DC magnetic biasing, then confirm the intensity grade of DC magnetic biasing according to the vibration signal after the described normalization.

2. the method for discrimination of the power transformer DC magnetic biasing based on vibration analysis according to claim 1; It is characterized in that: the space oscillations signal of described m dimension is made up of n element; Each element is made up of m sampled point, and the element expression in the space oscillations signal is following:

x _s(i)＝{x(i)，x(i+u)，x(i+2u)，...，x[i+(m-1)u]} (1)

Wherein: x _s(i) be i element in the space oscillations signal, n is the sampled point number in the vibration signal, and x (i) counts for time-delay for the accekeration of i sampled point in the vibration signal after the normalization, u.

3. the method for discrimination of the power transformer DC magnetic biasing based on vibration analysis according to claim 1 is characterized in that: in the described step (2), make up the numeric character sequence of space oscillations signal through following formula;

R(τ)＝Θ[ε-||x _s(i)-x _s(i+τ)||] (2)

Wherein: R (τ) is the numeric character sequence, and ε is a distance threshold, and τ counts for time-delay.

4. the method for discrimination of the power transformer DC magnetic biasing based on vibration analysis according to claim 1 is characterized in that: in the described step (3), make up the characteristic of correspondence function according to histogram through following formula;

$L\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{k=\min }^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}{{\mathrm{\Σ}}_{k=1}^{n-\mathrm{\τ}}k{P}_{\mathrm{\τ}}\left(k\right)}---\left(3\right)$

Wherein: L (τ) is a fundamental function, P _τ(k) be the number of the array that is made up of continuous k 1 in the numeric character sequence, min is the minimum combination coefficient.

5. whether the method for discrimination of the power transformer DC magnetic biasing based on vibration analysis according to claim 1 is characterized in that: in the described step (4), differentiate in the power transformer and exist the method for DC magnetic biasing to be:

1), calculates and confirm τ according to formula τ=f/F; Wherein, f is the SF of vibration signal, and F counts for time-delay for time-delay frequency, τ;

2) with in τ, τ-1 and τ+1 difference substitution fundamental function, ask for three corresponding eigenwerts, and three eigenwerts are asked on average, if characteristic mean less than discrimination threshold, then shows to have DC magnetic biasing in the power transformer.

6. the method for discrimination of the power transformer DC magnetic biasing based on vibration analysis according to claim 1; It is characterized in that: in the described step (4); The method of confirming the intensity grade of DC magnetic biasing according to the vibration signal after the normalization is: earlier the vibration signal after the normalization is carried out high-pass filtering and obtain the filtering vibration signal; Solve the degree value of DC magnetic biasing then through following formula, and then confirm the intensity grade of DC magnetic biasing according to described degree value;

$B\left(i\right)=\frac{\left|\right|x_1\left(i\right)\left|\right|}{\left|\right|x\left(i\right)\left|\right|}---\left(4\right)$

$B=\frac{1}{n}{\mathrm{\Σ}}_{i=1}^{n}B\left(i\right)---\left(5\right)$

Wherein: x (i) is the accekeration of i sampled point in the vibration signal after the normalization, x _l(i) be the accekeration of i sampled point in the filtering vibration signal, B is the degree value of DC magnetic biasing.

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