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This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establish ing the power transformers faults identifications and captur ing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Today the electric networks become a large and more complex with big data receives from a lot of events in different sections, power transformer is one of the most important section in power systems. Any fault in the transformer can cause a severe outage, which therefore necessitates continuous monitoring and diagnostics of its operation. In this sense, any faults caused in power transformers will produce a lot of alarms, some of which are uncertain, incomplete and misinformed, thus, it is necessary to develop a good method to help dispatchers evaluate where the faults are and which transformer fail. However transformer fault diagnosis decision-making based on dissolved and free gas analysis (DGA) diagnostic methods may give conflict analysis results and complicate the final decision making by operators [

In fact, intelligent fault diagnosis systems are necessary to deal with changes in typology of power network to fast diagnose the fault stat and location of power transformers faults [

In recent years, artificial intelligence approaches have been proposed with high performance programs and in developing more smart diagnostic techniques for power transformers based on DGA methods, such as support vector machine [

However, these approaches are using several techniques for detecting transformer faults based gases concentrations in the oil and DGA is recognized as the most informative method. This method involves sampling the oil and testing the sample to measure the concentration of the dissolved gases. The standards are associated with sampling, testing, and analyzing the results such as the standard IEC 60599 [

As a newly attractive research field of computer science, fuzzy reasoning spiking neural P systems (FRSN P systems), formally introduced by Hong Peng 2013 [

In this paper, FRSN P systems are introduced as diagnostic technique to tackle the power transformer faults based on DGA, and can be viewed as a directed graph; reasoning steps and transmits pulses from input proposition neurons to the output proposition neurons under the control of firing/spiking mechanism of neurons [

Furthermore, this method uses the IEC ratio gases as input signature to FRSN P systems diagnosis model to establish the fault reasoning results with confidence levels, based on confidence levels for different fault types of transformer can get decision which one faulty. In addition, fault diagnosis process is expressed by assume the initial parameters of FRSN P systems model with linguistic terms to give operators more accuracy to describe the degree of uncertainty fault information [

This paper is organized as follows. Section 2 provides the definitions of FRSN P systems. Section 3 presents power transformer DGA based on FRSN P systems and fault diagnosis model. Section 4 discusses the test results. Finally, conclusions and proposals for future work are given in Section 5.

A FRSN P system with degree m ≥ 1 is a construct of the form [

where:

1.

2.

where:

A.

B.

C.

where:

a) E is a regular expression.

b)

3. syn is a directed graph of synapses between neurons, where:

4.

According to their usage in this study, neurons in FRSN P systems are classified into four types of neurons;

1. Proposition neurons

In this kind of neuron, If neuron as input proposition neuron in P, then

2. General rule neurons

If neuron as general rule neuron in P, then the pulse value equals the pulse value received from their presynaptic proposition neuron [

As shown in

3. And rule neurons

If neuron as and rule neuron in P, then the pulse value equals all pulse values received from their presynaptic proposition neurons based on logical and operation [

As shown in

4. Or rule neurons

If neuron as or rule neuron in P, then the pulse value equals all pulse values received from their presynaptic proposition neurons based on logical or operation [

As shown in

We defined some matrices, reasoning processes and execution rules as follows [

1)

2)

3)

4)

5)

In order to represent the execution rules of FRSN P systems formally, we introduce some fuzzy matrix operations [

1)

2)

3)

Dissolved gas analysis (DGA) is powerful technique has been used to identify the incipient power oil transformers faults. In this technique can be identified according to the gases concentrations dissolved in oil of transformer, hydrogen (H_{2}), (CH_{4}), (C_{2}H_{6}), (C_{2}H_{4}), (C_{2}H_{2}), various interpretative DGA methods has been established, such as Gas key method, IEC ratio method, and the graphical representation method [

In this study we propose adaptive IEC ratio (AIEC ratio) method as first incipient diagnosis of the possible faults of oil transformer, in order to identifying the fault types based incipient possible faults diagnosed by IEC ratio method, we use the ratio of gases as input data to FRSN P systems diagnosis model and the output fuzzy reasoning results as shown in

In IEC ratio method, five gases, H_{2}, CH_{4}, C_{2}H_{2}, C_{2}H_{4} and C_{2}H_{6}, as concentration gases in oil transformer. From these gases produce three ratios [

From the operator expert knowledge, in real word fault diagnosis events, in this study linguistic terms are always used to express the fault types related with gas concentrations ratio, such as (C_{2}H_{2})/(C_{2}H_{4}) very low, low, medium, high and very high in the transformer oil.

In this proposed method, we use the linguistic terms to describe a degree of gas concentrations ratio to become more capable to use fuzzy knowledge with fuzzy numbers. We can use adaptive IEC ratio (AIEC ratio) to deal with FRSN P systems and graphically represents with fault diagnosis model from input proposition neurons by reasoning steps to reach the final rezoning results after computation halts in output proposition neurons.

Fault type Characteristic | Code |
---|---|

Low energy discharge | D_{1} |

High energy discharge | D_{2} |

Partial discharge | PD |

Thermal faults T < 300˚C | T_{1} |

Thermal faults 300˚C < T < 700˚C | T_{2} |

Thermal faults T > 700˚C | T_{3} |

Fault type | |||
---|---|---|---|

D_{1} | |||

D_{2} | |||

PD | |||

T_{1} | |||

T_{2} | |||

T_{3} |

Gas ratio | IEC 60599 limits | Fault type case | Linguistic terms (L.T) |
---|---|---|---|

PD, T_{1}, T_{2}, T_{3} | Very low (VL) | ||

NS | Low (L) | ||

D_{2} | Medium (M) | ||

D_{1}, D_{2} | High (H) | ||

D_{1} | Very High (VH) | ||

PD | Very low (VL) | ||

D_{1}, D_{2} | Low (L) | ||

D_{2} | Medium (M) | ||

T_{1}, T_{2}, T_{3} | High (H) | ||

T_{1}, T_{2}, T_{3} | Very High (VH) | ||

PD, T_{1} | Very low (VL) | ||

T_{1} | Low (L) | ||

D_{1}, T_{2} | Medium (M) | ||

D_{1}, D_{2}, T_{2} | High (H) | ||

D_{1}, D_{2}, T_{3} | Very High (VH) |

FRSN P systems diagnostic model based DGA shown in

In this graphical model, IEC ratio with fuzzy representation as linguistic terms can built the FRSN P systems diagnostic model as shown in

Three ratio

From the historical database of transformer we can use the confidence level of each fault dissolved gas to use it in the matrix calculations of proposed method based on their experience operator and also we have to certainty factor to represent the degree of confidence fault occurs.

The rule neurons with synapse input neurons, the confidence (0.8) and other rule neurons (1.0)

From the definition (P), we can use FRSN P systems to built fault diagnosis model for transformer based DGA ratio for all possible combinations of gases ratio based in AIEC ratio table, see

1.

2.

3.

where

a)

b)

c)

4. Syn. Shown in

5. in =

This section presents the test cases of power transformer tested data to perform the proposed method, fuzzy reasoning Spiking Neural P systems (FRSN P systems) and evaluation with comparative with method with the same cases.

From Tables 2-4 we can calculate the gas ratio and express by linguistic terms as shown in

No. | H_{2} | CH_{4} | C_{2}H_{6} | C_{2}H_{4} | C_{2}H_{2} |
---|---|---|---|---|---|

1 | 19.3 | 103 | 159 | 19 | 0.6 |

2 | 27 | 30 | 23 | 2.4 | 0.1 |

3 | 23 | 63 | 54 | 10 | 0.3 |

4 | 21 | 34 | 5 | 47 | 62 |

5 | 160 | 130 | 33 | 96 | 0.1 |

6 | 180 | 175 | 75 | 50 | 4 |

7 | 345 | 112.3 | 27.5 | 51.5 | 58.8 |

8 | 30.4 | 117 | 44.2 | 138 | 0.1 |

No. | C_{2}H_{2}/C_{2}H_{4} | CH_{4}/H_{2} | C_{2}H_{4}/C_{2}H_{6} | |||
---|---|---|---|---|---|---|

ratio | L.T | ratio | L.T | ratio | L.T | |

1 | 0.03 | VL | 5.33 | VH | 0.12 | VL |

2 | 0.04 | VL | 1.11 | H | 0.10 | VL |

3 | 0.03 | VL | 2.74 | VH | 0.19 | VL |

4 | 1.32 | H | 1.62 | H | 9.40 | VH |

5 | 0.001 | VL | 0.813 | M | 2.909 | H |

6 | 0.080 | VL | 0.972 | M | 0.666 | L |

7 | 1.142 | H | 0.326 | L | 1.873 | M |

8 | 0.0007 | VL | 3.849 | VH | 3.122 | H |

Each input proposition neurons will be assigned a truth degree value based on observation of the transformer history data, if the gas ratio limited values of AIEC ratio the of a transformer is actually observed, the input proposition neurons will have a truth degree value (0.9), otherwise truth degree value of non observed gases (0.1).

Each rule neurons with a certainty factor, which describes the confidence level based on experience of operator, in these cases,

Case 1#: The observed gases data are listed in

From

The inference procedures are described step by step as follows:

where

where

where

where

where

At t = 0

At t = 1

At t = 2

Thus, the FRSN P system computation halts and the reasoning fault diagnosis results is

The reasoning results indicate the PD confidence (0.08), D_{1} confidence (0.08), D_{2} confidence (0.08), T_{1} confidence (0.72), T_{2} confidence (0.08) and T_{3} confidence (0.08).

So T_{1} with highest confidence level and greater than threshold (0.50) is thermal faults T < 300◦C, and results for other cases are listed in

In these cases, comparative studies of FRSN P systems with ratio support vector machine method (SVMR) and graphical support vector machine (SVMG), considered the same cases fault situations, the status tested gas data of transformer for eight tested cases are shown in _{1}) with confidence level (0.72), case studies (4, 5, 6) their isn’t fault with confidence level ( 0.08), case (7) is High energy discharge (D_{2}) and case (8) is Thermal faults 300 < T < 700◦C (T_{2}) fault with confidence level ( 0.72).

In this study, the FRSN P systems technique has combined strength of uncertainty

Cases | FRSN P systems Diagnosis Results | ||
---|---|---|---|

Fault type | CF | Fault state | |

1 | D_{1}, D_{2}, PD, T_{2}, T_{3} | (0.08) | No |

T_{1} | (0.72) | Yes | |

2 | D_{1}, D_{2}, PD, T_{2}, T_{3} | (0.08) | No |

T_{1} | (0.72) | Yes | |

3 | D_{1}, D_{2}, PD, T_{2}, T_{3} | (0.08) | No |

T_{1} | (0.72) | Yes | |

4 | D_{1}, D_{2}, PD,T_{1}, T_{2}, T_{3} | (0.08) | No |

5 | D_{1}, D_{2}, PD,T_{1}, T_{2}, T_{3} | (0.08) | No |

6 | D_{1}, D_{2}, PD,T_{1}, T_{2}, T_{3} | (0.08) | No |

7 | D_{1}, PD,T_{1}, T_{2}, T_{3} | (0.08) | No |

D_{2} | (0.72) | Yes | |

8 | D_{1}, D_{2}, PD,T_{1}, T_{3} | (0.08) | No |

T_{2} | (0.72) | Yes |

Case No. | SVM | FRSN P systems | |
---|---|---|---|

SVMR | SVMG | ||

1 | T_{1} | T_{2} | T_{1} |

2 | No fault | T_{1} | T_{1} |

3 | T_{1} | T_{2} | T_{1} |

4 | No fault | D_{2} | No fault |

5 | T_{2} | No fault | No fault |

6 | No fault | T_{2} | No fault |

7 | D_{1} | D_{2} | D_{2} |

8 | T_{2} | T_{3} | T_{2} |

processing, rule-based reasoning, symbolic representation, and parallel computing. It makes transformer fault diagnosis based on DGA more accurate, fast and adaptive to system changes.

Especially, the reasoning process can be visualized in a form of graphical representation of FRSN P systems. The rule base and parameters are saved in matrix forms and the whole reasoning process is implemented by fuzzy matrix operations.

The aim of this study is to adaptive IEC Ratio with fuzzy representation and construct FRSN P systems diagnosis model to deal with fault transformers based on (IEC 60599) DGA as signature. Thus, the diagnosis model can be represent fuzzy production rules, dynamic reasoning algorithm and firing mechanism to diagnosis six types of fault transformer. Moreover, the practical test cases of transformer fault diagnosis are used to evaluate the proposed method.

This paper proposes FRSN P systems and tests its validity and feasibility in transformer fault diagnosis and comparing results with support vector machine (SVMR/SVMG) methods for the same fault cases.

Future work will focus on verifying the performance superiority of FRSN P systems, compared with other diagnosis methods; it can be integrated with other analysis applications comprehensive analysis.

This paper is partly supported by National Science Foundation of China (51577115).

Yahya, Y., Qian, A. and Yahya, A. (2016) Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems. Journal of Intelligent Learning Systems and Applications, 8, 77-91. http://dx.doi.org/10.4236/jilsa.2016.84007