CN103487723B - Fault diagnosis method of electric power system and system - Google Patents

Abstract

The present invention provides a power system fault diagnosis method and system. The method includes the following steps: receiving alarm information generated after a power system fault occurs, the alarm information including timing information; determining the fault area according to the alarm information; Each faulty element is modeled according to the logical relationship between each faulty element in the faulty area and the corresponding protection and circuit breaker action, and the weighted fuzzy temporal Petri net fault diagnosis model of the faulty element is established; according to the weighted fuzzy temporal Petri net The inference analysis of the fault diagnosis model constructs the corresponding matrix for inference operation and diagnoses the faulty components. The power system fault diagnosis method and system of the present invention effectively improve the fault tolerance and accuracy of fault diagnosis in the power system.

Description

Power System Fault Diagnosis Method and System

technical field

The invention relates to the technical field of power system safety processing, in particular to a power system fault diagnosis method and a power system fault diagnosis system.

Background technique

Power system fault diagnosis is to use the alarm information generated after the fault occurs to quickly and accurately locate the fault component, provide decision support for the dispatcher, thereby quickly eliminate the fault, restore the system to the normal operating state, and improve the operating stability of the system. A lot of research work has been done in the direction of power system fault diagnosis at home and abroad, and a variety of fault diagnosis methods have been proposed. Among them, the Petri net fault diagnosis method with simple model and clear physical meaning has been widely used.

In the traditional method of applying Petri nets to power system fault diagnosis, aiming at the uncertain problems such as protection and circuit breaker refusal or misoperation, the fault tolerance of fault diagnosis is improved by establishing a fuzzy Petri net reasoning model. However, the above method only uses the received action information of protection and circuit breakers. When complex faults occur accompanied by refusal or malfunction of protection and circuit breakers and distortion of alarm information itself, correct diagnosis results may not be obtained.

Contents of the invention

Based on this, the present invention provides a power system fault diagnosis method and system, which can improve the accuracy of fault diagnosis.

To achieve the above object, the present invention adopts the following technical solutions:

A power system fault diagnosis method, comprising the following steps:

receiving alarm information generated after a power system failure occurs, and the alarm information includes timing information;

determining the fault area according to the alarm information;

Each faulty element is modeled according to the logical relationship between each faulty element in the faulty area and the corresponding protection and circuit breaker action, and the weighted fuzzy sequential Petri net fault diagnosis model of the faulty element is established;

According to the inference analysis of the weighted fuzzy time-series Petri net fault diagnosis model, a corresponding matrix is constructed for inference operation, and the faulty element is diagnosed.

A power system fault diagnosis system, comprising:

The alarm information receiving module is used to receive the alarm information generated after the power system failure occurs, and the alarm information includes timing information;

A fault area determining module, configured to determine the fault area according to the alarm information;

The model building module is used to model each fault element according to the logical relationship between each fault element in the fault area and the corresponding protection and circuit breaker action, and establishes a weighted fuzzy sequential Petri net fault diagnosis model of the fault element;

The reasoning operation module is used for constructing a corresponding matrix to perform reasoning operations according to the reasoning analysis of the weighted fuzzy time-series Petri net fault diagnosis model, and to diagnose faulty components.

It can be seen from the above scheme that the power system fault diagnosis method and system of the present invention comprehensively consider the delay constraint characteristics between the protection and the circuit breaker action and the possibility of malfunction and refusal of the protection and circuit breaker, Based on the existing fault diagnosis model, a power system weighted fuzzy time series Petri net fault diagnosis model which can take into account the delay constraint is proposed, and the fault diagnosis is carried out according to the weighted fuzzy time series Petri net fault diagnosis model. Since the fault diagnosis model adopted in the present invention can deal with the delay constraint problem, the fault tolerance and accuracy of fault diagnosis in the power system are effectively improved.

Description of drawings

Fig. 1 is a schematic flow chart of a power system fault diagnosis method in an embodiment of the present invention;

Fig. 2 is a schematic flow chart of a fault diagnosis model based on weighted fuzzy temporal Petri nets in an embodiment of the present invention;

Fig. 3 is the schematic diagram of the weighted fuzzy time series Petri net fault diagnosis model of busbar in the embodiment of the present invention;

Fig. 4 is the schematic diagram of the weighted fuzzy time series Petri net fault diagnosis model of line in the embodiment of the present invention;

5 is a schematic diagram of IEEE New England 10-machine 39-node system in the embodiment of the present invention;

Fig. 6 is a schematic structural diagram of a power system fault diagnosis system in an embodiment of the present invention.

detailed description

Referring to shown in Fig. 1, a kind of power system fault diagnosis method comprises the following steps:

Step S101, receiving alarm information generated after a power system failure occurs, the alarm information includes timing information, and then proceeds to step S102.

Step S102, determine the fault area according to the alarm information, and then go to step S103.

As a better embodiment, the process of determining the fault area may specifically include: using a breadth-first search algorithm to determine the fault area.

Step S103, modeling each fault element according to the logical relationship between each fault element in the fault area and the corresponding protection and circuit breaker action, establishing a weighted fuzzy sequential Petri net fault diagnosis model of the fault element, and then entering step S104.

Step S104, according to the inference analysis of the weighted fuzzy time-series Petri net fault diagnosis model, construct a corresponding matrix to perform inference operation, and diagnose the faulty element.

As a better embodiment, after diagnosing the faulty element, the following steps may also be included:

In step S105, reverse reasoning is performed on the diagnosed faulty components to obtain the malfunction and refusal of the protection and circuit breaker.

In another embodiment, the fault diagnosis model of the present invention can be divided into the following four layers: 1), determining the power outage area (that is, the fault area); 2), establishing a component fault diagnosis model; 3), reasoning based on matrix calculation; 4) Protection and circuit breaker action evaluation. The specific process is shown in Figure 2:

1. According to the network topology and the circuit breaker displacement information provided by the supervisory control and data acquisition (SCADA), use breadth-first search to determine the fault area. If the faulty area contains only one element, then the element is the faulty element; if the faulty area includes two or more elements, a weighted fuzzy temporal Petri network model is established for each element to judge the faulty element;

2. Construct a weighted fuzzy sequential Petri network diagnostic model of components based on the logical relationship between faulty components and corresponding protection and circuit breaker actions, and then fuse them according to the topological relationship between components;

3. Based on the developed weighted fuzzy sequential Petri net diagnosis model, reasoning analysis is realized through matrix operations, and finally faulty components are diagnosed;

4. Reverse reasoning is adopted for the diagnosed fault components to judge the malfunction and refusal of protection and circuit breakers.

The fault diagnosis steps of the present invention will be described in detail below.

1. Mathematical description of weighted fuzzy temporal Petri net:

1), define the weighted fuzzy temporal Petri net as an eleven-tuple:

S _WFTPN ={P,T,I,O,A _cc ,ΔT _min ,ΔT _max ,U,T _h ,W,M} (1)

In the formula: P={p ₁ ,p ₂ ,…,p _n } is a place set; T={t ₁ ,t ₂ ,…,t _m } is a transition set; it is used to represent inference rules; I:P→ T is the mapping reflecting places to transitions; I=[δ _ij ] is an n×m matrix; when p _i is the input of t _j (that is, there is a directed arc from p _i to t _j ), δ _ij =1; otherwise δ _ij =0; O:T→P reflects the mapping from transition to place; O=[γ _ij ] is an m×n matrix; when p _j is the output of t _i (there is a directed arc from t _i to p _j ) when γ _ij =1; otherwise γ _ij =0; A _cc =[a _ij ] is an n×n matrix; it represents the path from the general place to the destination place; when the place path of p _i passes through p _j , a _ij = 1; otherwise, a _ij =0; ΔT _min =[Δτ _1min ,Δτ _2min ,…,Δτ _nmin ] is the minimum delay constraint for the place and front transition; ΔT _max =[Δτ _1max ,Δτ _2max, …,Δτ _nmax ] is the maximum time-delay constraint of place and front transition; if Δτ _min =Δτ _max =0; the transition is activated instantaneously; U=[μ ₁ ,μ ₂ ,…,μ _m ] is the confidence vector of transition; if for Any j has μ _j =1; the model is a simple Petri net without fuzzy variables; T _h =[λ ₁ ,λ ₂ ,…,λ _m ] is the transitional ignition threshold vector; W=diag(w ₁ ,w ₂ ,…,w _n ) is the weight matrix of the input arc; it reflects the degree of influence of the preconditions on the rules; its value is related to the event type represented by the library; M=[α(p ₁ ),α(p ₂ ) ,…,α(p _n )] is the place confidence vector; α(p _i ) represents the confidence degree of place p _i .

Petri nets can be represented by place nodes P={p ₁ ,p ₂ ,…,p _n }, transition nodes T={t ₁ ,t ₂ ,…,t _m } and directed arcs. Directed arcs include input arcs and output arcs. Input arcs point to transitions from places, while output arcs point to places from transitions. Among them, a place is represented by a circle "○", and a transition is represented by a vertical bar "|". The biggest difference between the structure of the weighted fuzzy Petri net and the simple Petri net is that the weight of the input arc, the confidence of the place, the confidence of the transition, and the transition process of the probability value are considered. In addition, the weighted fuzzy Petri net place value represents the probability value of the event represented by the place. Even if the transition is activated, the input place value remains unchanged, which still represents the confidence of the place, and will not be transferred out of the place as the transition occurs. Place. On the basis of the weighted fuzzy Petri net model, the delay constraint between places is considered, which is the weighted fuzzy time series Petri net model. This model can deal with delay constraints and improve the fault tolerance and accuracy of the model.

2) Inference analysis of transition timing constraints, the forward set and backward set ^of transition t∈T are respectively defined ^as t={p|δ _pt =1}; t ={p|γ _tp =1}.

The action delay constraint of a component store is the delay interval for two stores (sequential actions) ^· t and t ^· connected on both sides of the same transition t with Perform association constraints, that is, ΔT _t =[Δτ _tmin ,Δτ _tmax ], which is reflected in the connection transition of these two component libraries.

Forward Reasoning: Known and ΔT _t =[Δτ _tmin ,Δτ _tmax ], the action delay constraint ^of the successor component storehouse of component storehouse t can be obtained:

Backward Reasoning: Known and ΔT _t =[Δτ _tmin ,Δτ _tmax ], we can obtain the action delay constraint ^of the precursor component place of component place t :

3) For the weighted fuzzy Petri net, its fuzzy reasoning depends on the set of fuzzy rules R={R ₁ ,R ₂ ,…,R _m }:

R _i : Ifd _j Thend _k (CF=μ _i )(i=1,2,…,m) (4)

In the formula: d _j and d _k are propositions containing fuzzy variables, and the values of their confidence α(d _j ) and α(d _k ) are in the interval [0, 1], which is reflected in the Petri net as the Confidence degree; μ _i ∈ [0,1] indicates the value of the confidence degree of the rule R _i , which is reflected in the transition confidence degree in the Petri net.

In the reasoning process of weighted fuzzy temporal Petri nets, delay constraints need to be considered at the same time. Assume that A, B and C are all m×n matrices, and D is an m×q matrix, and E is a q×n matrix; definition: (1) Addition operator ⊕: C=A⊕B, then c _ij =max( a _ij ,b _ij ); (2) comparison operator : Then when a _ij ≥ b _ij , c _ij =1, otherwise c _ij =0; (3) direct multiplication operator e: C=AeB then c _ij =a _ij b _ij ; (4) multiplication operator : but (5) Matrix multiplication g: C=DgE, then

The inference process of the weighted fuzzy temporal Petri net is used to obtain a stable network state, that is, the state where the value of the place confidence matrix M no longer changes with iterations. Alerts are first filtered by delay constraints. Assuming that the confidence matrix M ^k is obtained in the kth iteration, the reasoning process for obtaining the k+1th confidence matrix ^Mk+1 is as follows:

Step1: Define the place path matrix as A _cc . According to the timing constraint reasoning analysis, the minimum cumulative delay constraint matrix ΣΔT _min and the maximum cumulative delay constraint matrix ΣΔT _max corresponding to the place channel are obtained, both of which are n×1 vectors:

ΣΔT _min =(A _cc g(ΔT _min ) ^T ) ^T (5)

ΣΔT _max =(A _cc g(ΔT _max ) ^T ) ^T (6)

Step2: Check the timing consistency of the alarm information, and filter the alarms on this basis. Define F=[f ₁ ,f ₂ ,…,f _n ] as the delay constraint judgment vector of protection and circuit breaker, where the elements represent whether the corresponding places in the set of model places meet the delay constraints, f _i =1 and 0 (i = 1; 2; ...; n) denote compliance and non-compliance constraints, respectively. Comparing the delay information sets ΔT _mesmin and ΔT _mesmax of the protection and circuit breaker in the alarm information with the delay constraints, as shown in formula (7), we can get:

Step3: given the initial state M ⁰ , let k=0;

Step4: Calculate the weight of the input arc:

_Winarc =WgI (8)

Step5: Calculate the synthetic input credibility of the transition:

E ^k =M ^k gW _inarc (9)

Step6: Compare the synthetic input credibility of the transition with the threshold; get the set of transitions that meet the activation conditions:

Step7: Calculate the synthetic input credibility that can activate the transition:

H ^k =E ^k eG ^k (11)

Step8: Calculate the M ^k+1 calculated for the k+1th time of the place:

Step9: If M ^k+1 =M ^k , then the confidence matrix of the Petri net is stable, otherwise set k=k+1 and return to Step4.

2. Weighted fuzzy time-sequence Petri net fault diagnosis model. After a permanent fault occurs to a component in the power system, the relevant protection and circuit breaker will operate, and finally form one or more power outage areas, and the faulty component must be in the power outage area. The fault diagnosis model developed below is for the components included in the blackout area.

Taking the IEEE39 node system as an example, a weighted fuzzy time-series Petri net fault diagnosis model for busbars and lines is established. It is assumed that the main protection, near backup protection and far backup protection are configured at both ends of each line. It is assumed that each bus is equipped with primary protection and backup protection on connected lines. Use B, L, m, p and s to denote busbar, line, main protection, near backup protection and far backup protection respectively. For example, CB _(4)-14 means the circuit breaker on the side of line L4-14 close to bus B4, R _(4)-14m means the main protection on the side of line L4-14 close to bus B4, and so on.

1) The sequence of protection actions is main protection, near backup protection and far backup protection. Define their delays relative to the fault moment as with Define the delays of circuit breakers corresponding to various protections relative to the protection action time as with

2) The basic idea of establishing a fault diagnosis model for the busbar is: first establish the models of the main protection and backup protection in each connection direction, and then construct the comprehensive fault diagnosis model of the busbar. Similarly, the basic idea of establishing a fault diagnosis model for a line is: first establish various protection models at both ends of the line, and then construct a comprehensive line fault diagnosis model.

Taking bus B14 in Fig. 5 as an example, it is equipped with main protection R _B14m and remote backup protection R _(4)-14s , R _(13)-14s and R _14-(15)s of the connecting lines, and the The comprehensive fault diagnosis model of weighted fuzzy time series Petri net is shown in Fig.3. Taking line L4-14 as an example, it is equipped with main protection R _(4)-14m and R _4-(14)m , near backup protection R _(4)-14p and R _4-(14)p , far backup protection R _(3)-4s , R _4-(5)s , R _14-(15)s and R _(13)-14s , the weighted fuzzy time series Petri net fault diagnosis comprehensive model of the line is shown in Figure 4.

3) The parameter determination method, after constructing the fault diagnosis model, the next step is the reasoning process based on matrix operation, which is consistent with the fuzzy reasoning based on fuzzy rules. Among them, the relevant parameters are determined as follows:

①Change input arc weight

For each end of the line, consider the different effects of main protection, near backup protection and far backup protection on the line, and their cooperation with the corresponding circuit breakers. In this way, weights are assigned to the two input arcs from the protection place and the circuit breaker place to the transition, as shown in Table 1.

Table 1 Given protection and circuit breaker weights

②Given the initial value of confidence

For places corresponding to protection and circuit breaker alarms, the initial value of confidence is given, as shown in Table 2.

Table 2 Confidence levels for given protection and circuit breaker alarms

Considering that the received alarm information may be wrong or incomplete, the confidence level of the protection and circuit breaker not in the alarm is set to 0.2. From the perspective of fault tolerance, the confidence level of transition is set to 0.95, the threshold of transition is 0.2, the confidence level of transition place is 1, the threshold of transition transition is 0, and the delay of transition transition is 0.

③Delay constraints

Define the main protection, near backup protection, far backup protection, and the delay constraints of their corresponding circuit breakers as ΔT _mr ∈ [10,40], ΔT _pr ∈ [300,500], ΔT _sr ∈ [600,1100], ΔT _mc ∈[40,60], ΔT _pc ∈[20,40], ΔT _sc ∈[40,100], the unit is ms.

The fault diagnosis model developed takes into account the delay constraints between component failure and protection action, and between protection action and circuit breaker tripping. From the model, it can be seen that circuit breakers and protection places directly reach the bus and lines through transitions. In this way, the assignment of ΔT _min and ΔT _max of the circuit breaker's place should be the delay constraint obtained by forward reasoning relative to the time when the fault occurs.

3. The adaptability and fault tolerance of the fault diagnosis model:

1) Adaptability to network topology changes. The existing fault diagnosis model based on Petri net often needs to rebuild the Petri net model when the structure of the power system changes (for example, a new line is added or an existing line is out of service), and the workload is heavy. And the weighted fuzzy time-series Petri net model in the present invention, after setting up the main protection of busbar, back-up protection, and the sub-model of line main protection, near back-up protection, far back-up protection, carry out simple fusion again and form comprehensive diagnosis model, structure concise. When there is a new busbar or the existing busbar is out of operation, for the model that has been built, only the model of the relevant busbar needs to be added or deleted. Since the model structure and reasoning process of each busbar are similar, this can Quickly make corrections to network topology changes. For the line model, the situation is similar to the bus model, but considering that a line may also be responsible for the backup/remote backup protection of the related bus or line, when a new line is added or an existing line is out of operation, it is not only necessary to add or delete the line It is also necessary to adjust the backup/remote backup protection of the corresponding busbar and line, that is, to add or delete the corresponding protection sub-model. The developed fault diagnosis model can quickly adapt to network topology changes and has high computational efficiency.

2) Fault tolerance when the alarm information is incorrect. The fault diagnosis model based on time-series fuzzy Petri nets established in the embodiment of the present invention can take into account the misoperation/rejection of protection and circuit breakers, as well as the situation that the alarm information is not completely correct, by analyzing the confidence of candidate fault components. It has better fault tolerance. In addition, according to the configuration relationship and action logic of components, protections, and circuit breakers, reverse reasoning can be used to determine the malfunction/rejection of protection and circuit breakers.

Next, in view of the possible misoperation or refusal of protection and circuit breakers in the fault diagnosis, and uncertain factors such as untimely upload, distortion or loss during the alarm upload process, the present invention uses the IEEE39 node system shown in Figure 5 Take an example to verify the developed fault diagnosis model. Assume that the alarm information with time sequence is received: protection R _B14m (20ms), R _(4)-14s (750ms) and R _14-(15)s (371ms) action; circuit breaker CB _(14)-15 (73ms), CB _(14)-13 (81ms) and CB _(4)-14 (87ms) skipped.

1) Faulty area identification. Based on the received alarm information, the fault area is determined through breadth-first search, and the possible fault components are B14 and L4-14.

2) Model each component in the fault area. Establish the weighted fuzzy time series Petri net fault diagnosis model of bus B14 and line L4-14 as shown in Fig. 3 and Fig. 4.

3) Reasoning analysis based on matrix operation. According to the inference analysis of the weighted fuzzy temporal Petri net, the corresponding matrix is constructed for inference operation:

①Matrix operation is used to reason and analyze the weighted fuzzy time-series Petri net model of bus B14:

Firstly, the received alarms are screened according to the delay constraints. The place path matrix, the minimum cumulative delay constraint vector and the maximum cumulative delay constraint vector corresponding to the place path are respectively:

(Note: Only one of t ₁ , t ₃ and t ₅ can be counted for the place path of _{place RB14m} , because the three transitions are equivalent.)

ΣΔT _min =(A _cc g(ΔT _min ) ^T ) ^T =[640,640,640,50,50,50,10,600,600,600,0,0,0,0]

ΣΔT _max =(A _cc g(ΔT _max ) ^T ) ^T =[1200,1200,1200,100,100,100,40,1100,1100,1100,0,0,0,0]

Afterwards, the timing consistency is checked. Comparing the protection and circuit breaker delay information sets ΔT _mesmin and ΔT _mesmax in the alarm information with the delay constraints, the protection and circuit breaker decision vectors that satisfy the constraints are obtained (f _i =0 means that the timing is inconsistent):

In this way, the effective alarm information screened by the delay constraint is: protection _RB14m (20ms) and R _(4)-14s (750ms) action; circuit breaker CB _(14)-15 (73ms), CB _(14)-13 (81ms) and CB _(4)-14 (87ms) action trip.

The place set of the Petri net model is:

p={CB _(4)-14 ,CB _(13)-14 ,CB _14-(15) ,CB _4-(14) ,CB _(14)-13 ,CB _(14)-15 ,R _B14m ,R _{( 4)-14s} ,R _(13)-14s ,R _14-(15)s ,L4-14,L13-14,L14-15,B14}

The transition set is:

T={t ₁ ,t ₂ ,t ₃ ,t ₄ ,t ₅ ,t ₆ ,t ₇ }

The input matrix of the transition:

Transition's output matrix:

Change the confidence vector:

U=[0.95,0.95,0.95,0.95,0.95,0.95,1]

Transition activation threshold vector:

T _h =[0.2,0.2,0.2,0.2,0.2,0.2,0]

Enter a vector of arc weights:

W=diag(0.50,0.50,0.50,0.40,0.40,0.40,0.60,0.50,0.50,0.50,0.33,0.33,0.33,0)

The initial state of a given place is:

M ⁰ =[0.65,0.20,0.20,0.20,0.85,0.85,0.90,0.70,0.20,0.20,0.00,0.00,0.00,0.00]

Using the matrix reasoning process, the confidence level of bus B14 failure is 0.771.

②Matrix operation is used to reason and analyze the weighted fuzzy time-series Petri net model of line L4-14:

Firstly, the received alarms are screened according to the delay constraints. The place path matrix, the minimum cumulative delay constraint vector and the maximum cumulative delay constraint vector corresponding to the place path are respectively:

ΣΔT _min =(A _cc g(ΔT _min ) ^T ) ^T =[640,640,640,640,320,320,50,50,10,10,300,300,600,600,600,600,0,0,0,0,0]ΣΔT _max =(A _cc g(ΔT _max ) ^T ) ^T =[1200,1200,1200,1200,540,540,100,100,40,40,500,500,1100,1100,1100,1100,0,0,0,0,0]

Afterwards, the timing consistency is checked. Comparing the protection and circuit breaker delay information sets ΔT _mesmin and ΔT _mesmax in the alarm information with the delay constraints, the protection and circuit breaker decision vectors that satisfy the constraints are obtained (f _i =0 means that the timing is inconsistent):

In this way, the effective alarm information screened by the delay constraint is: protection _RB14m (20ms) and R _(4)-14s (750ms) action; circuit breaker CB _(14)-15 (73ms), CB _(14)-13 (81ms) and CB _(4)-14 (87ms) action trip.

The place set of the Petri net model is:

p={CB _4-(5) ,CB _(3)-4 ,CB _14-(15) ,CB _(13)-14 ,CB′ _(4)-14 ,CB′ _4-(14) ,CB _{(4 )-14} ,CB _4-(14) ,R _(4)-14m ,R _4-(14)m ,

R _(4)-14p ,R _4-(14)p ,R _4-(5)s ,R _(3)-4s ,R _14-(15)s ,R _(13)-14s ,p ₁ ,p ₂ ,L(4)-14,L4-(14),L4-14}

The transition set is:

T={t ₁ ,t ₂ ,t ₃ ,t ₄ ,t ₅ ,t ₆ ,t ₇ ,t ₈ ,t ₉ ,t ₁₀ ,t ₁₁ }

The input matrix of the transition:

Transition's output matrix:

Delay constraint vector:

_ΔTmin =[640,640,640,640,320,320,50,50,10,10,300,300,600,600,600,600,0,0,0,0,0]

ΔT _max =[1200,1200,1200,1200,540,540,100,100,40,40,500,500,1100,1100,1100,1100,0,0,0,0,0] transition confidence vector:

U=[0.95,0.95,0.95,0.95,0.95,0.95,0.95,0.95,1,1,1]

Transition activation threshold vector:

T _h =[0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0,0,0]

Enter a vector of arc weights:

W=diag(0.50,0.50,0.50,0.50,0.45,0.45,0.40,0.40,0.60,0.60,0.55,0.55,0.50,0.50,0.50,0.50,1,1,0.50,0.50,0

Using the matrix reasoning process, the confidence level of the failure of line L4-14 is 0.219.

4), protection and circuit breaker action evaluation

After the faulty components are judged through the above steps, reverse reasoning is now carried out. It can be seen that after the bus B14 fails, the main protection _RB14m acts _, triggering circuit breaker CB _{( 14 )-15} ; ₎ refuses to move, so the corresponding backup protection R _(4)-14s moves to trip the circuit breaker CB _(4)-14 , thereby isolating the fault. In this way; the evaluation result is that the circuit breaker CB _4-(14) refuses to move.

In order to better verify the developed fault diagnosis model, various fault scenarios are also tested in the embodiment of the present invention. Table 3 lists the diagnostic results of some fault scenarios. The calculation example results show that the method proposed by the present invention can deal with the complex fault situations where protection and circuit breakers have incorrect actions and alarm information is wrong.

Table 3 Example test results

The same as the above-mentioned power system fault diagnosis method, the present invention also provides a power system fault diagnosis system, as shown in Figure 6, including:

The alarm information receiving module 101 is configured to receive alarm information generated after a power system failure occurs, and the alarm information includes timing information;

A failure area determination module 102, configured to determine the failure area according to the alarm information;

The model building module 103 is used to model each fault element according to the logical relationship between each fault element in the fault area and the corresponding protection and circuit breaker action, and establishes a weighted fuzzy sequential Petri net fault diagnosis model of the fault element;

The reasoning operation module 104 is used for constructing a corresponding matrix and performing reasoning operation according to the reasoning analysis of the weighted fuzzy time-series Petri net fault diagnosis model, and diagnosing the faulty element.

As a better embodiment, the power system fault diagnosis system may also include:

The action evaluation module is used to perform reverse reasoning on the diagnosed faulty components after diagnosing the faulty components, and obtain the malfunction and refusal of protection and circuit breakers.

As a better embodiment, the fault area determining module may use a breadth-first search algorithm to determine the fault area.

Other technical features of the above-mentioned power system fault diagnosis system are the same as those of the power system fault diagnosis method of the present invention, and will not be repeated here.

It can be seen from the above scheme that the power system fault diagnosis method and system of the present invention comprehensively consider the delay constraint characteristics between the protection and the circuit breaker action and the possibility of malfunction and refusal of the protection and circuit breaker, Based on the existing fault diagnosis model, a power system weighted fuzzy time series Petri net fault diagnosis model which can take into account the delay constraint is proposed, and the fault diagnosis is carried out according to the weighted fuzzy time series Petri net fault diagnosis model. Since the fault diagnosis model adopted in the present invention can deal with the delay constraint problem, the fault tolerance and accuracy of fault diagnosis in the power system are effectively improved.

The above-mentioned embodiments only express several implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (6)

1. A power system fault diagnosis method, is characterized in that, comprises the following steps: receiving alarm information generated after a power system failure occurs, and the alarm information includes timing information; determining the fault area according to the alarm information; Each faulty element is modeled according to the logical relationship between each faulty element in the faulty area and the corresponding protection and circuit breaker action, and the weighted fuzzy sequential Petri net fault diagnosis model of the faulty element is established; According to the inference analysis of the weighted fuzzy temporal Petri net fault diagnosis model, the corresponding matrix is constructed to carry out inference operation, and the fault element is diagnosed; Wherein, the weighted fuzzy temporal Petri net fault diagnosis model includes an eleven-tuple: S _WFTPN ={P,T,I,O,A _cc ,ΔT _min ,ΔT _max ,U,T _h ,W,M}; In the formula, P＝{p ₁ ,p ₂ ,…,p _n } is a place set, T＝{t ₁ ,t ₂ ,…,t _m } is a transition set, which is used to represent inference rules, I:P→ T is the mapping reflecting places to transitions, I=[δ _ij ] is an n×m matrix, when p _i in the place set is the input of t _j in the transition set, the δ _ij =1, otherwise the The above δ _ij =0, O:T→P reflects the mapping from transition to places, O=[γ _ij ] is an m×n matrix, when p _j in the set of places is the output of t _i in the set of transitions, The γ _ij =1, otherwise the γ _ij =0, A _cc =[a _ij ] is an n×n matrix, which represents the path from the general place to the destination place, when the place path of p _i passes through p _j , the a _ij = 1; otherwise, the a _ij = 0, ΔT _min = [Δτ _1min ,Δτ _2min ,…,Δτ _nmin ] is the minimum delay constraint of the place and the front transition, ΔT _max = [ Δτ _1max ,Δτ _2max ,…,Δτ _nmax ] are the maximum delay constraints of the place and the front transition, if Δτ _min ＝Δτ _max ＝0, the transition is activated instantaneously, U=[μ ₁ ,μ ₂ ,…,μ _m ] is the confidence vector of the transition, T _h =[λ ₁ ,λ ₂ ,…,λ _m ] is the ignition threshold vector of the transition, W=diag(w ₁ ,w ₂ ,…,w _n ) is the weight of the input arc Value matrix, reflecting the degree of influence of the preconditions on the rules, M=[α(p ₁ ),α(p ₂ ),…,α(p _n )] is the location confidence vector. 2. The power system fault diagnosis method according to claim 1, further comprising the step of: Reverse reasoning is carried out on the diagnosed fault components to obtain the malfunction and refusal of protection and circuit breakers. 3. The power system fault diagnosis method according to claim 1 or 2, wherein the process of determining the fault area comprises: using a breadth-first search algorithm to determine the fault area. 4. A power system fault diagnosis system, characterized in that, comprising: The alarm information receiving module is used to receive the alarm information generated after the power system failure occurs, and the alarm information includes timing information; A fault area determining module, configured to determine the fault area according to the alarm information; The model building module is used to model each fault element according to the logical relationship between each fault element in the fault area and the corresponding protection and circuit breaker action, and establishes a weighted fuzzy sequential Petri net fault diagnosis model of the fault element; Reasoning operation module, for reasoning analysis according to described weighted fuzzy time-series Petri net fault diagnosis model, constructs corresponding matrix and carries out reasoning operation, diagnoses fault element; Wherein, the weighted fuzzy temporal Petri net fault diagnosis model includes an eleven-tuple: S _WFTPN ={P,T,I,O,A _cc ,ΔT _min ,ΔT _max ,U,T _h ,W,M}; In the formula, P＝{p ₁ ,p ₂ ,…,p _n } is a place set, T＝{t ₁ ,t ₂ ,…,t _m } is a transition set, which is used to represent inference rules, I:P→ T is the mapping reflecting places to transitions, I=[δ _ij ] is an n×m matrix, when p _i in the place set is the input of t _j in the transition set, the δ _ij =1, otherwise the The above δ _ij =0, O:T→P reflects the mapping from transition to places, O=[γ _ij ] is an m×n matrix, when p _j in the set of places is the output of t _i in the set of transitions, The γ _ij =1, otherwise the γ _ij =0, A _cc =[a _ij ] is an n×n matrix, which represents the path from the general place to the destination place, when the place path of p _i passes through p _j , the a _ij = 1; otherwise, the a _ij = 0, ΔT _min = [Δτ _1min ,Δτ _2min ,…,Δτ _nmin ] is the minimum delay constraint of the place and the front transition, ΔT _max = [ Δτ _1max ,Δτ _2max ,…,Δτ _nmax ] are the maximum delay constraints of the place and the front transition, if Δτ _min ＝Δτ _max ＝0, the transition is activated instantaneously, U=[μ ₁ ,μ ₂ ,…,μ _m ] is the confidence vector of the transition, T _h =[λ ₁ ,λ ₂ ,…,λ _m ] is the ignition threshold vector of the transition, W=diag(w ₁ ,w ₂ ,…,w _n ) is the weight of the input arc Value matrix, reflecting the degree of influence of the preconditions on the rules, M=[α(p ₁ ),α(p ₂ ),…,α(p _n )] is the location confidence vector. 5. The power system fault diagnosis system according to claim 4, further comprising: The action evaluation module is used to perform reverse reasoning on the diagnosed faulty components after diagnosing the faulty components, and obtain the malfunction and refusal of protection and circuit breakers. 6. The power system fault diagnosis system according to claim 4 or 5, characterized in that the fault area determination module uses a breadth-first search algorithm to determine the fault area.