CN111199254A - High-voltage electrical equipment real-time detection method based on RBF neural network and Bayesian network - Google Patents
High-voltage electrical equipment real-time detection method based on RBF neural network and Bayesian network Download PDFInfo
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- CN111199254A CN111199254A CN201911399155.6A CN201911399155A CN111199254A CN 111199254 A CN111199254 A CN 111199254A CN 201911399155 A CN201911399155 A CN 201911399155A CN 111199254 A CN111199254 A CN 111199254A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres
- G01K11/324—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres using Raman scattering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/003—Environmental or reliability tests
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The embodiment of the invention provides a high-voltage electrical equipment real-time detection method based on an RBF neural network and a Bayesian network, which comprises the following steps: step S1, collecting contact temperature data of the high-voltage electrical equipment by using a temperature sensor, combining the current, the resistance and the voltage of the high-voltage electrical equipment as fault data to be diagnosed, and then carrying out normalization pretreatment; step S2, basic probability distribution is carried out by using three RBF neural networks, and fault data to be diagnosed are accurately classified; and step S3, performing fault diagnosis on the high-voltage electrical equipment by using the Bayesian network. According to the RBF neural network and Bayesian network-based real-time detection method for the high-voltage electrical equipment, on the basis of temperature measurement of the high-voltage electrical equipment by using the Raman fiber sensor, an information fusion technology is used in a high-voltage electrical equipment temperature monitoring and early warning system with temperature as a main parameter and current and the like as auxiliary parameters, so that the reliability of diagnosis is effectively improved, and the uncertainty of diagnosis is reduced.
Description
Technical Field
The invention relates to the field of on-line monitoring and fault diagnosis of electrical equipment in a smart grid environment, in particular to a high-voltage electrical equipment real-time detection method based on a RBF neural network and a Bayesian network.
Background
With the rapid development of modern power systems, higher requirements are also put on the safe, stable and reliable operation of the equipment. In enterprises of metallurgy, chemical engineering and electric power, high-voltage electrical equipment such as high-voltage transformers, high-voltage switches and high-voltage cable buses are prone to faults due to the characteristics of high voltage, large load, long operation time and the like. If a fault occurs, an immeasurable negative impact occurs, which seriously affects the production of the enterprise and also accompanies considerable economic loss.
When the high-voltage electrical equipment runs, the high-voltage electrical equipment generates partial heat due to circuit loss, but when some parts of the high-voltage electrical equipment are in poor contact or have faults, the temperature of the parts rises rapidly, so that the physical and electrical properties of the high-voltage electrical equipment are seriously damaged, and the normal production of an enterprise group is influenced. In high-voltage, heavy-duty enterprises, especially those who use electricity such as chemical industry, forging, metallurgy, etc., most of the operational failures are caused by the excessive temperatures of the joints and contacts of the high-voltage electrical equipment. If the faults are not checked and diagnosed in time, the damage degree of the equipment is seriously expanded, so that the normal production of an enterprise is influenced, and serious economic loss is caused. In order to guarantee normal production operation of enterprises, high-voltage electrical equipment needs to be subjected to troubleshooting in time. Therefore, temperature monitoring of joints and contacts of high voltage electrical equipment is particularly important.
When a temperature change fault of high-voltage electrical equipment occurs, the gradual change process of temperature change in normal times is changed into temperature sudden change. The contingency and the burstiness of the temperature fault determine the fatality of the temperature-dependent fault, and once the fault occurs, great economic loss is caused. At present, temperature detection means under a high-pressure environment is very limited, particularly temperature change characteristic points are in a closed state or a special environment, and online real-time monitoring and analysis and computer system integration cannot be realized on closed equipment, ultrahigh-pressure equipment and high-pressure liquid by conventional hands. The direct detection of the temperature of the contact or the joint of the high-voltage switch, in particular to a totally-enclosed electrical appliance, is always a difficult problem in the temperature change fault monitoring of high-voltage electrical equipment.
In a slowly-varying load system, the temperature is used as a diagnosis parameter singly, and the monitoring problem of temperature variation faults can be effectively judged. However, the problem of temperature change fault monitoring in irregular load systems is difficult to solve, and the traditional method for detecting multi-source information usually processes data generated by various sensors respectively or simply adds the data. Firstly, doing so increases the workload of information processing; secondly, the collocation and combination of various sensors cannot be fully reflected in the data. Therefore, the traditional detection mode greatly wastes information resources, and the precision and the robustness of fault detection are seriously reduced.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a high-voltage electrical equipment real-time detection method based on an RBF neural network and a Bayesian network.
The embodiment of the invention provides a high-voltage electrical equipment real-time detection method based on an RBF neural network and a Bayesian network, which comprises the following steps:
step S1, collecting contact temperature data of the high-voltage electrical equipment by using a temperature sensor, combining the current, the resistance and the voltage of the high-voltage electrical equipment as fault data to be diagnosed, and then carrying out normalization pretreatment;
step S2, basic probability distribution is carried out by using three RBF neural networks, and fault data to be diagnosed are accurately classified; the characteristic data information processed by the first RBF neural network is as follows: actual temperature characteristics of the high voltage electrical equipment contacts; the characteristic data information processed by the second RBF neural network is as follows: monitoring the temperature difference between the object and the previous moment; the third RBF neural network processes characteristic data of current, resistance and voltage;
and step S3, analyzing the evolution path of the operation state of the high-voltage electrical equipment and the probability of the occurrence of different situations by using a Bayesian network, measuring the relevance between the multidimensional characteristic index and different faults, constructing a time-varying scoring function, integrating characteristic information with different timeliness, quantifying the fuzzy state of the occurrence of the faults, and performing fault diagnosis on the high-voltage electrical equipment.
Further, in step S1, the normalization preprocessing of the data to be diagnosed adopts a demodulation method of comparing the anti-stokes light with the stokes light.
Furthermore, the number of input neurons of the RBF neural network is determined according to the number of devices to be monitored, and the output neurons are correspondingly free of faults, short-circuit faults, large in dielectric loss, poor in insulation and damp in the interior.
Further, in step S2, the three RBF neural networks are used to accurately classify the fault data to be diagnosed, and then three feature subsets are respectively formed, and then normalization preprocessing is performed.
Further, the temperature sensor in step S1 is a raman fiber sensor.
The RBF neural network and Bayesian network-based real-time detection method for the high-voltage electrical equipment, provided by the embodiment of the invention, utilizes the characteristics of strong anti-interference capability, high voltage resistance, reliable transmission, good safety and the like of the optical fiber, and uses the distributed Raman optical fiber temperature sensor as a temperature monitoring means of the high-voltage electrical equipment, thereby better solving the temperature monitoring problem of temperature change characteristic points in a high-voltage environment. The method and the system for online temperature measurement of the high-voltage electrical equipment adopt the RBF neural network with self-learning, self-adaption and fault-tolerant capabilities to perform basic probability distribution, train the neural network by using a large amount of actual operation data, accurately classify online monitored fault data by using generalization capabilities of the neural network, play a role of a field expert and improve the system identification rate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting high-voltage electrical equipment in real time based on an RBF neural network and a bayesian network according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an RBF neural network and a bayesian network in the method according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Fig. 1 is a flowchart of a real-time detection method for high-voltage electrical equipment based on an RBF neural network and a bayesian network according to an embodiment of the present invention, and as shown in fig. 1, the online temperature measurement method for high-voltage electrical equipment includes the following steps:
step S1, collecting contact temperature data of the high-voltage electrical equipment by using the Raman fiber sensor, combining the electrical parameters of the high-voltage electrical equipment as fault data to be diagnosed, and then carrying out normalization pretreatment;
the Raman fiber sensor utilizes the characteristics of strong anti-interference capability, high voltage resistance, reliable transmission, good safety and the like of the optical fiber, and takes the distributed Raman fiber temperature sensor as a temperature monitoring means of high-voltage electrical equipment, and has the advantages of high voltage resistance, strong anti-interference capability, high transmission reliability and good electrical insulation. The characteristics are particularly suitable for severe environments where high-voltage electrical equipment is located, and the temperature monitoring problem of temperature change characteristic points in various high-voltage environments can be solved.
The RBF neural network and Bayesian network-based real-time detection method for the high-voltage electrical equipment is based on the temperature measurement of the high-voltage electrical equipment by using the Raman fiber sensor, and the information fusion technology is used in a high-voltage electrical equipment temperature monitoring and early warning system which takes temperature as a main parameter and current and the like as auxiliary parameters (the contact temperature data of the high-voltage electrical equipment is combined with the electrical parameters of the high-voltage electrical equipment as data to be diagnosed), so that the diagnosis reliability is effectively improved, and the diagnosis uncertainty is reduced.
In step S1 of the embodiment, the electrical parameters of the high-voltage electrical device include: current, resistance and voltage.
In step S1 of the embodiment, a demodulation method of comparing anti-stokes light with stokes light is adopted for normalization preprocessing of data to be diagnosed;
the neural network can learn and train more efficiently by carrying out normalization preprocessing on the fault data to be diagnosed, and in addition, the normalization preprocessing adopts a demodulation method comparing anti-Stokes light with Stokes light, so that a foundation is laid for information fusion.
As shown in fig. 2, in step S2, a large amount of actual operation data is used to train the RBF neural network, and then basic probability distribution is performed to accurately classify the fault data to be diagnosed;
the RBF (radial basis function) neural network is a three-layer neural network which comprises an input layer, a hidden layer and an output layer. The transformation from the input space to the hidden layer space is non-linear, while the transformation from the hidden layer space to the output layer space is linear. The basic idea of the RBF network is: the RBF is used as the base of the hidden unit to form the hidden layer space, so that the input vector can be directly mapped to the hidden space without being connected through the weight. When the center point of the RBF is determined, the mapping relation is determined. The mapping from the hidden layer space to the output space is linear, that is, the output of the network is the linear weighted sum of the hidden unit outputs, and the weight here is the network adjustable parameter. The hidden layer is used for mapping the vector from p with a low dimension to h with a high dimension, so that the low-dimension linearity can be linearly separable from the high dimension, and the concept of the kernel function is mainly used. Thus, the mapping of the network from input to output is non-linear, whereas the network output is linear for the adjustable parameters. The weight of the network can be directly solved by a linear equation system, thereby greatly accelerating the learning speed and avoiding the local minimum problem.
The method adopts the RBF neural network with self-learning, self-adaption and fault-tolerant capabilities to carry out basic probability distribution, trains the neural network by using a large amount of actual operation data, accurately classifies fault data monitored on line by using the generalization capability of the neural network, plays the role of a field expert and improves the system identification rate.
In step S2 of the embodiment, there are three RBF neural networks, and the feature data information processed by the first RBF neural network is: actual temperature characteristics of the high voltage electrical equipment contacts; the characteristic data information processed by the second RBF neural network is as follows: monitoring the temperature difference between the object and the previous moment; the third RBF neural network processes characteristic data of current, resistance and voltage;
in step S2 of the embodiment, three RBF neural networks are used to accurately classify the fault data to be diagnosed, and then three feature subsets are respectively formed, and then normalization preprocessing is performed.
In step S2 of the embodiment, the number of input neurons of the RBF neural network is determined according to the number of devices to be monitored, and the output neurons correspond to five states: f0 (no fault), 0000; f1 (short circuit fault), 0001; f2 (large dielectric loss), 0010; f3 (insulation failure), 0100; f4 (inside wet), 1000.
And step S3, analyzing the evolution path of the operation state of the high-voltage electrical equipment and the probability of the occurrence of different situations by using a Bayesian network, measuring the relevance between the multidimensional characteristic index and different faults, constructing a time-varying scoring function, integrating characteristic information with different timeliness, quantifying the fuzzy state of the occurrence of the faults, and performing fault diagnosis on the high-voltage electrical equipment.
The Bayesian network, also called belief network, is an extension of Bayes method, and is one of the most effective theoretical models in uncertain knowledge expression and reasoning field at present. Since its introduction by Pearl in 1988, it has become a hot spot of research in recent years. A bayesian network is a Directed Acyclic Graph (DAG) consisting of nodes representing variables and Directed edges connecting these nodes. The nodes represent random variables, the directed edges among the nodes represent the mutual correlation system (the father node points to the son node), the relation strength is expressed by conditional probability, and the prior probability is used for expressing information without the father node. The node variables may be abstractions of any problem, such as: test values, observations, opinion polls, etc. The method is applicable to expressing and analyzing uncertain and probabilistic events, and to making decisions that are conditionally dependent on a variety of control factors, and can make inferences from incomplete, inaccurate, or uncertain knowledge or information.
In summary, the method for detecting the high-voltage electrical equipment in real time based on the RBF neural network and the bayesian network provided by the embodiment of the invention uses the characteristics of strong anti-interference capability, high voltage resistance, reliable transmission, good safety and the like of the optical fiber, and uses the distributed raman optical fiber temperature sensor as a temperature monitoring means of the high-voltage electrical equipment, thereby better solving the temperature monitoring problem of the temperature change characteristic point in the high-voltage environment. The method and the system for online temperature measurement of the high-voltage electrical equipment adopt the RBF neural network with self-learning, self-adaption and fault-tolerant capabilities to perform basic probability distribution, train the neural network by using a large amount of actual operation data, accurately classify online monitored fault data by using generalization capabilities of the neural network, play a role of a field expert and improve the system identification rate.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A high-voltage electrical equipment real-time detection method based on an RBF neural network and a Bayesian network is characterized by comprising the following steps:
step S1, collecting contact temperature data of the high-voltage electrical equipment by using a temperature sensor, combining the current, the resistance and the voltage of the high-voltage electrical equipment as fault data to be diagnosed, and then carrying out normalization pretreatment;
step S2, basic probability distribution is carried out by using three RBF neural networks, and fault data to be diagnosed are accurately classified; the characteristic data information processed by the first RBF neural network is as follows: actual temperature characteristics of the high voltage electrical equipment contacts; the characteristic data information processed by the second RBF neural network is as follows: monitoring the temperature difference between the object and the previous moment; the third RBF neural network processes characteristic data of current, resistance and voltage;
and step S3, analyzing the evolution path of the operation state of the high-voltage electrical equipment and the probability of the occurrence of different situations by using a Bayesian network, measuring the relevance between the multidimensional characteristic index and different faults, constructing a time-varying scoring function, integrating characteristic information with different timeliness, quantifying the fuzzy state of the occurrence of the faults, and performing fault diagnosis on the high-voltage electrical equipment.
2. The real-time detection method for the high-voltage electrical equipment based on the RBF neural network and the Bayesian network as recited in claim 1, wherein in the step S1, a demodulation method comparing anti-Stokes light with Stokes light is adopted for normalization preprocessing of data to be diagnosed.
3. The real-time detection method for the high-voltage electrical equipment based on the RBF neural network and the Bayesian network as recited in claim 2, wherein the number of input neurons of the RBF neural network is determined according to the number of equipment to be monitored, and the output neurons correspond to five states of no fault, short circuit fault, large dielectric loss, poor insulation and internal moisture.
4. The real-time detection method for the high-voltage electrical equipment based on the RBF neural network and the Bayesian network as recited in claim 1, wherein in the step S2, three RBF neural networks are utilized to accurately classify fault data to be diagnosed, then three feature subsets are respectively formed, and then normalization preprocessing is performed.
5. The method for detecting high-voltage electrical equipment based on the RBF neural network and the Bayesian network in real time as claimed in claim 1, wherein the temperature sensor in the step S1 is a Raman fiber sensor.
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| CN112798142A (en) * | 2020-12-28 | 2021-05-14 | 哈尔滨工业大学 | Brillouin optical fiber sensor strain and temperature two-stage rapid identification method based on Bayesian updating and random simulation |
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| US20100114806A1 (en) * | 2008-10-17 | 2010-05-06 | Lockheed Martin Corporation | Condition-Based Monitoring System For Machinery And Associated Methods |
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| CN112798142A (en) * | 2020-12-28 | 2021-05-14 | 哈尔滨工业大学 | Brillouin optical fiber sensor strain and temperature two-stage rapid identification method based on Bayesian updating and random simulation |
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