CN105629101B - A kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm - Google Patents
A kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm Download PDFInfo
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 16
- 238000009825 accumulation Methods 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 10
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Abstract
The invention discloses a kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm, it is directed to more power module parallel application systems, each power module is circularized with joint form arrangement and compares graph structure, according to symmetrical comparison model, the complete ant system containing pheromones and heuristic information is obtained.On this basis, the present invention relatively schemes with ant group algorithm one direction traversal annular, and continuous by pheromones corrects renewal, completes system-level failure diagnostic process, the fast and effective malfunction that must judge each node.The present invention is the method for diagnosing faults of module level, can quickly and accurately carry out the fault diagnosis of more power module parallel systems.
Description
Technical field
The invention belongs to power electronic equipment fault diagnosis technology field, and in particular to a kind of more work(based on ant group algorithm
The method for diagnosing faults of rate wired in parallel system.
Background technology
Increasingly deficient and environment the deterioration increasingly of traditional energy, is greatly promoted the development of new energy, new energy hair
The scale of electricity also quickly rises.But wind-powered electricity generation, solar power generation itself intrinsic randomness, Intermittent Features, determine its rule
Modelling development, which will necessarily bring peak load regulation network and system safety operation, to be significantly affected, it is necessary to has advanced energy storage technology to prop up
Support.Contain dc bus and ac bus in multiple-energy-source energy-storage system, wherein each generation of electricity by new energy end and energy-storage travelling wave tube end lead to
Cross multiple DC-DC converter power modules in parallel and be connected to dc bus, then dc bus passes through the multiple of parallel connection
DC-AC inverter power module is connected to ac bus, and ac bus is connected to individual loads or grid side.
Fault Diagnosis of Power Electronic Circuits is to ensure the important leverage and key of this reliably working of multiple-energy-source energy-storage system
Technology, towards the application scenario of more power module parallels, the fault diagnosis technology of module level can fast and effeciently position failure mould
Block, technical support is provided for follow-up faults-tolerant control strategy.For more power module parallel systems of existing communication link, upper strata control
Device processed can control the distribution of the operation of modules and power by communications command, realize the flexible configuration of power.
Wodule level fault diagnosis substantially increases the reliability and maintainability of multiple-energy-source energy-storage system.It is but accurate due to module
Model is difficult to determine, it is desirable to by carrying out fault diagnosis there are certain difficulty based on the method for concrete model, thus, is based on
The ant group algorithm of swarm intelligence is more suitable for the application of Practical Project.
Ant group algorithm has the characteristics that concurrency, positive feedback, robustness, has been successfully applied to solve many complexity
Combinatorial optimization problem.This method is used to solve famous traveling salesman problem first by Italian scholar Macro Dorigo etc.,
Subsequent many scholars solve quadratic assignment problem, queens Problem etc. using ant colony optimization algorithm successively.Solving, these are more multiple
In terms of the problem of miscellaneous, compared to traditional optimization algorithm, ant colony optimization method shows good performance.Therefore, attempt to use
Ant group algorithm carries out system-level fault diagnosis, and has obtained good effect.
The content of the invention
The present invention provides a kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm, it is by ant
Group's algorithm is applied in Power Electronic Circuit Methods for Diagnosing System Level Malfunctions, and this method is only uploaded a small amount of electric by power module
Characteristic quantity data, establishes annular relatively graph structure, fault diagnosis, anti-interference just can must be quick and precisely carried out using ant group algorithm
By force, reliability is high, is adapted to the application of Practical Project.
A kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm, includes the following steps：
(1) in acquisition system each power module electric characteristic amount, then each power module is connected successively with joint form
Loop configuration is connected into, the electric characteristic amount by comparing adjacent node determines to obtain the disease of system
(2) current state of each node is initialized：Normal or failure；For any node according to diseaseCalculate the section
Point for normal condition and malfunction probability, and then according to the current state of the described probability updating node, according to this from the
One node starts to complete an ant colony traversal after traveling through all nodes one by one；
(3) ant colony traversal is performed repeatedly, is calculated after each ant colony traversal and is updated each node in normal condition and failure
The pheromones accumulated under state, and then judge whether termination ant colony time according to the pheromones that each node is accumulated under current state
Go through, if terminating, the current state of each node is corresponded to the working status of each power module.
The diseaseIt is made of i.e. n fiducial valueWhereinTable
Show the i-th node and fiducial value of its previous node on electric characteristic amount, if the electric characteristic amount of the i-th node and its previous node
It is identical, thenIf the i-th node is different from the electric characteristic amount of its previous node,I for natural number and
1≤i≤n, n are the number of power module in system.
Probability in the step (2) by the following formula calculate node for normal condition and malfunction：
Wherein：P (i, s) represents that the i-th node is s shape probability of states, and s=0 or 1,0 represents normal, and 1 represents failure；τ(i,
S) pheromones that ant discharges on the i-th node under s states are represented and 0≤τ (i, s)≤1, η (i, s) represents the i-th node in s shapes
Heuristic information and 0≤η (i, s)≤1, α and β under state are default weight coefficient, and i is for natural number and 1≤i≤n, n
The number of power module in system.
The expression formula of described information element τ (i, s) is as follows：
Wherein：WithRespectively diseaseIn i-th of fiducial value and i+1 fiducial value, σF(i,
S) represent when the i-th node is current state for s states other nodes the i-th node with its previous node on electric characteristic amount
Fiducial value, if the i-th node and its previous node are normal condition, σF(i, s)=0；If the i-th node and its previous node
One of them be normal condition another be malfunction, then σF(i, s)=1；If the i-th node and its previous node are failure
State, then σF(i, s)=0/1；σF(i+1, s) represents the i+1 when i+1 node is current state for s states other nodes
Node and fiducial value of its previous node on electric characteristic amount, if i+1 node and its previous node are normal condition,
σF(i+1, s)=0；If i+1 node and its previous node one of them be normal condition another be malfunction, σF(i+
1, s)=1；If i+1 node and its previous node are malfunction, σF(i+1, s)=0/1；| 0 ∩ 0 |=| 1 ∩ 1 |=
1, | 0 ∩ 1 |=| 1 ∩ 0 |=0, | 0 ∩ 0/1 |=| 1 ∩ 0/1 |=0.5.
The heuristic information η (i, s) is defined as follows：
If the i-th -1 node current state is normal and diseaseIn i-th of fiducial valueThen η (i, 0)=
1, η (i, 1)=0；
If the i-th -1 node current state is normal and diseaseIn i-th of fiducial valueThen η (i, 0)=
0, η (i, 1)=1；
If the i-th -1 node current state is failure and diseaseIn i-th of fiducial valueThen η (i, 0)=
0, η (i, 1)=1；
If the i-th -1 node current state is failure and diseaseIn i-th of fiducial valueThen η (i, 0)=
0.5, η (i, 1)=0.5.
Pass through the current state of relationship below more new node in the step (2)：
Wherein：S (i) is the current state of the i-th node, P (i, 0) and P (i, 1) be respectively the i-th node for normal condition with
The probability of malfunction, K are the default diminution factor, and rand is the random number between 0 to 1, and i is natural number and 1≤i≤n, n
For the number of power module in system.
Calculated in the step (3) by the following formula and update what each node was accumulated under normal condition and malfunction
Pheromones：
γ(i,s)r=ρ [γ (i, s)r-1+τ(i,s)r]
Wherein：τ(i,s)rRepresent the information that ant discharges on the i-th node under s states in the r times ant colony ergodic process
Element, s=0 or 1,0 represents normal, and 1 represents failure；γ(i,s)rWith γ (i, s)r-1After respectively the r times ant colony traversal and
The pheromones that the i-th node is accumulated under s states after the r-1 times ant colony traversal, γ (i, s)1=ρ τ (i, s)1, ρ is default
Volatility coefficient, r is traversal number, the number that i is natural number and 1≤i≤n, n are power module in system.
The step (3) if in relationship below set up, ant colony traversal terminates：
Wherein：δ is default convergence coefficient, γ (i, s (i))rThe i-th node is current after being traveled through for the r times ant colony
The pheromones accumulated under state s (i).
The present invention is directed to more power module parallel application systems, and each power module is circularized ratio with joint form arrangement
Compared with graph structure, according to symmetrical comparison model, the complete ant system containing pheromones and heuristic information is obtained.It is basic herein
On, the present invention relatively schemes with ant group algorithm one direction traversal annular, and continuous by pheromones corrects renewal, completes system-level
Failure diagnostic process, the fast and effective malfunction that must judge each node.The present invention is the method for diagnosing faults of module level,
The fault diagnosis of more power module parallel systems can quickly and accurately be carried out.
Brief description of the drawings
Fig. 1 is the structure diagram of multiple-energy-source energy-storage system.
Fig. 2 is the step flow diagram of method for diagnosing faults of the present invention.
Fig. 3 is the schematic diagram of the annular comparative structure of the present invention.
Fig. 4 is the symmetrical relatively model schematic of the present invention.
Fig. 5 (a) is that parallel module number is 10, and malfunctioning module number is 2, system mode S=[0；0；1；1；0；0；0；0；0；
0], real system disease σF=[0；0；1；1；1；0；0；0；0；System disease pheromones convergence curve signal when 0].
Fig. 5 (b) is that parallel module number is 10, and malfunctioning module number is 2, system mode S=[0；0；1；1；0；0；0；0；0；
0], real system disease σF=[0；0；1；1；1；0；0；0；0；The diagnostic result schematic diagram of inventive algorithm when 0].
Embodiment
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings and embodiment is to technical scheme
It is described in detail.
Fig. 1 is the structure diagram of multiple-energy-source energy-storage system, and energy energy storage section includes fuel cell, photovoltaic cell, surpasses
The level low-voltage direct link such as capacitance and lithium battery, passes through the more power module parallel systems of DC-DC converter and high voltage direct current
Busbar is connected, and dc bus is connected with ac bus by the more power module parallel systems of direct-current-alternating-current converter, exchange
Busbar provides energy for load and carries out energy transmission with power grid.More power module parallel systems of the invention based on ant group algorithm
Method for diagnosing faults is not only suitable for the more power module parallel systems of DC-DC converter and is also applied for direct-current-alternating-current converter
More power module parallel systems.The present embodiment is carried out by application of the more power module parallel systems of direct-current-alternating-current converter
Illustrate.
As shown in Fig. 2, present embodiment method for diagnosing faults comprises the following steps：
(1) #2 master controllers gather the electricity of each DC/AC modules in the more power module parallel systems of direct-current-alternating-current converter
Gas characteristic value (voltage, electric current or other electrical quantity of such as module).
(2) each DC/AC modules are circularized with joint form arrangement and compares graph structure, obtained according to symmetrical comparison model
The disease of systemAnd the original state of each node is set at random.
Each DC/AC modules are circularized with joint form arrangement and are compared graph structure by 2.1, as shown in figure 3, #1DC/AC moulds
Block corresponds to No. 1 node in annular relatively graph structure, and #2DC/AC modules correspond to No. 2 nodes in annular relatively graph structure, with this
Analogize, #nDC/AC modules correspond to the n nodes in annular relatively graph structure, form an annular relatively graph structure.
2.2 Fig. 4 are symmetrical comparison model (node state just common 0 represents that failure is represented with 1), if two node shapes
State is normal, then electric characteristic amount is identical, comparative result 0；If two another normal failures of node state one,
Electric characteristic amount is different, comparative result 1；If two node states are failure, electric characteristic amount identical also may may be used
Can be different, comparative result is 0 or 1.The disease of system is obtained according to the symmetrical comparison model of Fig. 4Wherein
The original state of 2.3 initialization system nodes is normal condition, i.e. S=[0,0 ..., 0]1×n。
(3) ant group algorithm parameter is initialized, traversal annular relatively owns each ant on figure counterclockwise from No. 1 node
N node.
3.1 No. i-th node failure states judge that ant is judged by the pheromones on i nodes and heuristic information
The current state of node, i node state probability functions are defined as:
Wherein：S is the state of node, and normal is two parameters that 0 failure is 1, α and β is ant group algorithm, α=0.9, β=
0.1, τ (i, s) is the pheromones under i node s states, and 0≤τ (i, s)≤1, η (i, s) is the inspiration under i node s states
Formula information, 0≤η (i, s)≤1.
The pheromones that 3.2 ants discharge on i nodes are defined as：
Wherein：τ (i) is the pheromones of i nodes, represents node i and neighborhood of nodes i-1, the comparative result that i+1 is produced with
Known diseaseMiddle opposite position comparative result compares, wherein identical number.Since the state of each node is normal
(being represented with 0) or failure (being represented with 1), therefore two kinds of pheromones are employed in present embodiment, normal information element τ (i, 0)
With fault message element τ (i, 1), and | 0 ∩ 0 |=| 1 ∩ 1 |=1, | 0 ∩ 1 |=| 1 ∩ 0 |=0, | 0 ∩ 0/1 |=| 1 ∩ 0/1 |=
0.5。
3.3 heuristic informations also have two kinds of " normal " and " failure ", are respectively η (i, 0) and η (i, 1), and i nodes inspire
Formula information definition is as follows：
1. if node i-1 is judged as proper node, and in known diseaseSo heuristic letter of node i
Cease and be：η (i, 0)=1, η (i, 1)=0.
2. if node i-1 is judged as proper node, and in known diseaseSo heuristic letter of node i
Cease and be：η (i, 0)=0, η (i, 1)=1.
3. if node i-1 is judged as failure node, and in known diseaseSo heuristic letter of node i
Cease and be：η (i, 0)=0, η (i, 1)=1.
4. if node i-1 is judged as failure node, and in known diseaseSo heuristic letter of node i
Cease and be：η (i, 0)=0.5, η (i, 1)=0.5.
3.4 current i node states decision rules are as follows：
Wherein：S (i) is the state of i nodes, and 0 represents normal, 1 representing fault, and rand is the random number between 0 to 1, K
For the diminution factor less than 1, K=2/3 in the present embodiment.
The renewal of 3.5 current i node informations elements：γ(i,s)r=ρ [γ (i, s)r-1+ τ (i, s)], wherein s for 0 or
1, γ (i, s)rRepresent to iterate over the pheromones accumulated under rear i nodes s states, γ (i, s) the r timesr-1For the r-1 times repeatedly
The pheromones accumulated after generation traversal under i node s states；In order to avoid the unlimited accumulation of pheromones so that algorithm can be forgotten
The poor selection made before, will there is certain volatilization after each iteration of pheromones, and wherein ρ is volatilization parameter, this reality
Apply ρ=0.5 in example.
3.6 crawl into next node, repeat step 3.1 to 3.5, until all n nodes in annular relatively figure counterclockwise
All by ant flag state, ant completes once to travel through.
(4) ant finds a fault set F, and the disease σ compatible with fault set F after once traveling throughF；Pheromones
Concept can be generalized to whole system, and it is as follows to define system disease pheromones：
Wherein：N is node number, system disease pheromones is normalized, therefore can regard system disease pheromones as
The matching degree of fault set F.WhenWhen, the fault set that ant is found is exactly the physical fault collection of system.In order to add
The diagnosis speed of fast inventive algorithm, on the premise of diagnostic accuracy is not influenced, can relax algorithm end condition.This reality
Apply in example, algorithm end condition is as follows：
I.e. when system disease pheromones are more than 0.98, algorithm terminates, and ant stops traversal annular and relatively schemes, and judgement is
System state is final result.
Fig. 5 (a) is that parallel module number is 10, and malfunctioning module number is 2, system mode S=[0；0；1；1；0；0；0；0；0；
0]；Real system disease σF=[0；0；1；1；1；0；0；0；0；System disease convergence curve when 0].It can be seen that ant passes through
After crossing 13 traversals, system disease pheromones are more than 0.98, and algorithm terminates.Fig. 5 (b) is that parallel module number is 10, malfunctioning module
Number is 2, system mode S=[0；0；1；1；0；0；0；0；0；0]；Real system disease σF=[0；0；1；1；1；0；0；0；0；0]
When algorithm diagnostic result.Black small circle is the virtual condition of system node, and solid black lines diagnose for the algorithm of system node
State, both fit like a glove, and illustrate the accuracy of the method for the present invention.
The above-mentioned description to embodiment is understood that for ease of those skilled in the art and using this hair
It is bright.Person skilled in the art obviously easily can make above-described embodiment various modifications, and described herein
General Principle is applied in other embodiment without by performing creative labour.Therefore, the invention is not restricted to above-described embodiment,
Those skilled in the art disclose according to the present invention, and the improvement and modification made for the present invention all should be in the protections of the present invention
Within the scope of.
Claims (8)
1. a kind of method for diagnosing faults of more power module parallel systems based on ant group algorithm, includes the following steps：
(1) in acquisition system each power module electric characteristic amount, then each power module is in turn connected into joint form
Loop configuration, the electric characteristic amount by comparing adjacent node determine to obtain the disease of system
(2) current state of each node is initialized：Normal or failure；For any node according to diseaseThe node is calculated as just
The probability of normal state and malfunction, and then according to the current state of the described probability updating node, according to this from first section
Point starts to complete an ant colony traversal after traveling through all nodes one by one；
(3) ant colony traversal is performed repeatedly, is calculated after each ant colony traversal and is updated each node in normal condition and malfunction
The pheromones of lower accumulation, and then judge whether that terminating ant colony travels through according to the pheromones that each node is accumulated under current state, if
Terminate, then the current state of each node is to correspond to the working status of each power module.
2. method for diagnosing faults according to claim 1, it is characterised in that：Pass through the following formula in the step (2)
Calculate node is the probability of normal condition and malfunction：
<mrow>
<mi>P</mi>
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Wherein：P (i, s) represents that the i-th node is s shape probability of states, and s=0 or 1,0 represents normal, and 1 represents failure；τ (i, s) table
Show the pheromones that ant discharges on the i-th node under s states and 0≤τ (i, s)≤1, η (i, s) represents the i-th node under s states
Heuristic information and 0≤η (i, s)≤1, α and β be default weight coefficient, i is system for natural number and 1≤i≤n, n
The number of middle power module.
3. method for diagnosing faults according to claim 2, it is characterised in that：The expression formula of described information element τ (i, s) is such as
Under：
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Wherein：WithRespectively diseaseIn i-th of fiducial value and i+1 fiducial value, σF(i, s) table
Show the i-th node and ratio of its previous node on electric characteristic amount when the i-th node is current state for s states other nodes
Compared with value, if the i-th node and its previous node are normal condition, σF(i, s)=0；If the i-th node and its previous node are wherein
One be normal condition another be malfunction, then σF(i, s)=1；If the i-th node and its previous node are failure shape
State, then σF(i, s)=0/1；σF(i+1, s) represents the i+1 section when i+1 node is current state for s states other nodes
Point and fiducial value of its previous node on electric characteristic amount, if i+1 node and its previous node are normal condition, σF
(i+1, s)=0；If i+1 node and its previous node one of them be normal condition another be malfunction, σF(i+
1, s)=1；If i+1 node and its previous node are malfunction, σF(i+1, s)=0/1；| 0 ∩ 0 |=| 1 ∩ 1 |=
1, | 0 ∩ 1 |=| 1 ∩ 0 |=0, | 0 ∩ 0/1 |=| 1 ∩ 0/1 |=0.5.
4. method for diagnosing faults according to claim 2, it is characterised in that：The definition of the heuristic information η (i, s) is such as
Under：
If the i-th -1 node current state is normal and diseaseIn i-th of fiducial valueThen η (i, 0)=1, η (i,
1)=0；
If the i-th -1 node current state is normal and diseaseIn i-th of fiducial valueThen η (i, 0)=0, η (i,
1)=1；
If the i-th -1 node current state is failure and diseaseIn i-th of fiducial valueThen η (i, 0)=0, η (i,
1)=1；
If the i-th -1 node current state is failure and diseaseIn i-th of fiducial valueThen η (i, 0)=0.5, η
(i, 1)=0.5.
5. method for diagnosing faults according to claim 1, it is characterised in that：Pass through following relation in the step (2)
The current state of formula more new node：
Wherein：S (i) is the current state of the i-th node, and P (i, 0) and P (i, 1) are respectively that the i-th node is normal condition and failure
Shape probability of state, K are the default diminution factor, and rand is the random number between 0 to 1, and i is natural number and 1≤i≤n, n are to be
The number of power module in system.
6. method for diagnosing faults according to claim 1, it is characterised in that：Pass through the following formula in the step (3)
Calculate and update the pheromones that each node is accumulated under normal condition and malfunction：
γ(i,s)r=ρ [γ (i, s)r-1+τ(i,s)r]
Wherein：τ(i,s)rRepresent the pheromones that ant discharges on the i-th node under s states in the r times ant colony ergodic process, s=0
Or 1,0 represents normal, and 1 represents failure；γ(i,s)rWith γ (i, s)r-1After respectively the r times ant colony traversal and the r-1 times
The pheromones that the i-th node is accumulated under s states after ant colony travels through, γ (i, s)1=ρ τ (i, s)1, ρ is default volatilization system
Number, r is traversal number, the number that i is natural number and 1≤i≤n, n are power module in system.
7. method for diagnosing faults according to claim 6, it is characterised in that：The step (3) if in relationship below
Set up, then ant colony traversal terminates：
<mrow>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mi>&gamma;</mi>
<msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>s</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mi>r</mi>
</msub>
</mrow>
<mi>n</mi>
</mfrac>
<mo>></mo>
<mi>&delta;</mi>
</mrow>
Wherein：δ is default convergence coefficient, γ (i, s (i))rThe i-th node is in current state s after being traveled through for the r times ant colony
(i) pheromones accumulated under.
8. according to the method for diagnosing faults described in claim 1,3 or 4, it is characterised in that：The diseaseBy n fiducial value
Composition isWhereinRepresent the i-th node with its previous node on electric characteristic
The fiducial value of amount, if the i-th node is identical with the electric characteristic amount of its previous node,If the i-th node is previous with it
The electric characteristic amount of node is different, thenThe number that i is natural number and 1≤i≤n, n are power module in system.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US6014598A (en) * | 1996-06-28 | 2000-01-11 | Arcelik A.S. | Model-based fault detection system for electric motors |
| CN101520662A (en) * | 2009-02-18 | 2009-09-02 | 嘉兴学院 | Process industrial dispersion type equipment failure diagnosis system for process industrial dispersion type equipment |
| CN102163300A (en) * | 2011-04-20 | 2011-08-24 | 南京航空航天大学 | Method for optimizing fault diagnosis rules based on ant colony optimization algorithm |
| CN104484547A (en) * | 2014-11-03 | 2015-04-01 | 中国船舶重工集团公司第七一二研究所 | Electric propulsion system fault diagnosis method and system based on ant colony algorithm |
| CN105067956A (en) * | 2015-08-26 | 2015-11-18 | 云南电网有限责任公司电力科学研究院 | Anti-colony-algorithm-based distribution network fault positioning method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US6014598A (en) * | 1996-06-28 | 2000-01-11 | Arcelik A.S. | Model-based fault detection system for electric motors |
| CN101520662A (en) * | 2009-02-18 | 2009-09-02 | 嘉兴学院 | Process industrial dispersion type equipment failure diagnosis system for process industrial dispersion type equipment |
| CN102163300A (en) * | 2011-04-20 | 2011-08-24 | 南京航空航天大学 | Method for optimizing fault diagnosis rules based on ant colony optimization algorithm |
| CN104484547A (en) * | 2014-11-03 | 2015-04-01 | 中国船舶重工集团公司第七一二研究所 | Electric propulsion system fault diagnosis method and system based on ant colony algorithm |
| CN105067956A (en) * | 2015-08-26 | 2015-11-18 | 云南电网有限责任公司电力科学研究院 | Anti-colony-algorithm-based distribution network fault positioning method |
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