CN116566048A - Wisdom power consumption safety monitoring system - Google Patents
Wisdom power consumption safety monitoring system Download PDFInfo
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- CN116566048A CN116566048A CN202310501169.4A CN202310501169A CN116566048A CN 116566048 A CN116566048 A CN 116566048A CN 202310501169 A CN202310501169 A CN 202310501169A CN 116566048 A CN116566048 A CN 116566048A
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- monitoring system
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 56
- 238000005070 sampling Methods 0.000 claims abstract description 101
- 239000000779 smoke Substances 0.000 claims abstract description 37
- 230000005611 electricity Effects 0.000 claims abstract description 27
- 238000003062 neural network model Methods 0.000 claims abstract description 12
- 210000002569 neuron Anatomy 0.000 claims description 49
- 230000004913 activation Effects 0.000 claims description 47
- 230000006870 function Effects 0.000 claims description 41
- 230000009466 transformation Effects 0.000 claims description 7
- 230000001681 protective effect Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 description 27
- 238000010586 diagram Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000000034 method Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00036—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers
- H02J13/0004—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers involved in a protection system
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Alarm Systems (AREA)
Abstract
The application provides an wisdom electricity consumption safety monitoring system, include: the intelligent cloud platform comprises a cable temperature sensor, a smoke sensor, a residual current sensor, a three-phase voltage sensor, a three-phase current sensor, an intelligent gateway and a cloud platform; the cloud platform is based on cable temperature sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic, and a neural network model is adopted for comprehensive analysis to generate a safety monitoring result.
Description
Technical Field
The invention relates to the field of intelligent electricity utilization, in particular to an intelligent electricity utilization safety monitoring system.
Background
With the rapid development of power systems and power distribution network informatization, electric energy is a highly dependent energy source. The intelligent electricity utilization safety monitoring system can know relevant data of the circuit and the electricity utilization device through each acquisition terminal and monitor and analyze various factors causing the electric fire in real time on line, so that potential safety hazards of the electric fire are eliminated, and the electricity utilization safety is comprehensively guaranteed.
At present, the existing intelligent electricity safety monitoring system is mainly designed by adopting models such as BP neural network, convolutional neural network, deep long-short time memory network and the like. The BP neural network has good nonlinear mapping capability, self-learning and self-adapting capability, generalization capability and fault tolerance capability, and is widely applied. However, in order to improve the nonlinear fitting capability and monitoring accuracy of the BP neural network, the BP neural network has the problems of a large number of neurons, a complex structure and a slow self-adaptive learning rate.
In summary, how to effectively realize intelligent electricity safety monitoring, reduce the structural complexity, improve the learning efficiency and the accuracy of safety monitoring, and become the technical problem to be solved in the present technology.
Disclosure of Invention
In view of the above problems, the present application has been proposed, in which only three or four neurons are used in the hidden layer, to simplify the structure of the BP neural network and improve the adaptive learning rate of the BP neural network. Meanwhile, each neuron adopts different activation functions, so that the advantages of the different activation functions are combined, and the monitoring precision of the BP neural network is improved. Further, each activation function also shares e x The self-adaptive learning rate of the BP neural network is greatly improved.
The application provides an wisdom electricity consumption safety monitoring system, include: the intelligent cloud platform comprises a cable temperature sensor, a smoke sensor, a residual current sensor, a three-phase voltage sensor, a three-phase current sensor, an intelligent gateway and a cloud platform;
the cable temperature sensor, the smoke sensor, the residual current sensor, the three-phase voltage sensor and the three-phase current sensor acquire cable temperature, smoke, residual current, three-phase voltage and three-phase current in a target environment to obtain cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic;
the intelligent gateway receives cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic for summarizing, and transmits the cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic to the cloud platform;
the cloud platform carries out comprehensive analysis by adopting a neural network model based on the cable temperature sampling signals, the residual current sampling signals, the three-phase voltage sampling signals Ua, ub and Uc and the three-phase current sampling signals Ia, ib and Ic to generate a safety monitoring result;
the hidden layer of the neural network model employs at least three different activation functions.
Further, the neural network model comprises an input layer, an implicit layer and an output layer;
the input layer is composed of eight neurons, and each neuron receives a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic respectively and transmits the signals to the hidden layer;
the hidden layer is composed of three neurons, eight input ends of each neuron respectively receive a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic transmitted by the input layer, and three hidden outputs are generated after summation operation and activation transformation are carried out;
the output layer is composed of a neuron, receives three hidden outputs, and generates a safety monitoring result after summation operation and activation transformation.
Further, the activation function of the first neuron in the hidden layer is specifically:
where x is the input of the activation function.
Further, the activation function of the second neuron in the hidden layer is specifically:
further, the activation function of the third neuron in the hidden layer is specifically:
further, the hidden layer is further provided with a fourth neuron.
Further, the activation function of the fourth neuron in the hidden layer is specifically:
F 4 (x)＝ln(1+e x )
further, the cloud platform also judges whether to adopt the protection measures or not based on the safety monitoring result and the smoke sampling signal.
Further, when the safety monitoring result indicates that a fire disaster occurs and the smoke sampling signal indicates that smoke exists, judging that the fire disaster occurs, and adopting a protection measure; when the safety monitoring result indicates that a fire disaster occurs and the smoke sampling signal indicates that no smoke exists, the fire disaster is judged not to occur, but fire hidden danger exists, and engineering personnel are required to check.
The beneficial effects of this application are:
(1) The application provides an intelligent electricity safety monitoring system, which only adopts three or four neurons in an implicit layer, simplifies the structure of a BP neural network and improves the self-adaptive learning rate of the BP neural network. Meanwhile, each neuron adopts different activation functions, so that the advantages of the different activation functions are combined, and the monitoring precision of the BP neural network is improved.
(2) Sharing e for each activation function in implicit layer of the application x The self-adaptive learning rate of the BP neural network is greatly improved.
(3) The safety monitoring result and the smoke sampling signal are adopted to conduct protection measure judgment, false triggering of the protection measure can be prevented, and reliability of intelligent electricity safety monitoring is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of an intelligent electricity safety monitoring system provided by the present application;
fig. 2 is a block diagram of a neural network model provided in the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two, but does not exclude the case of at least one.
The application provides an intelligent electricity safety monitoring system, which only adopts three or four neurons in an implicit layer, simplifies the structure of a BP neural network and improves the self-adaptive learning rate of the BP neural network. Meanwhile, each neuron adopts different activation functions, so that the advantages of the different activation functions are combined, and the monitoring precision of the BP neural network is improved. Further, each activation function also shares e x The self-adaptive learning rate of the BP neural network is greatly improved.
The present application is further described below with reference to the drawings and specific examples.
Fig. 1 is a schematic diagram of an intelligent electricity safety monitoring system according to an embodiment of the present invention. As shown in fig. 1, an intelligent electricity safety monitoring system includes: cable temperature sensor, smoke sensor, residual current sensor, three-phase voltage sensor, three-phase current sensor, intelligent gateway, cloud platform.
The cable temperature sensor, the smoke sensor, the residual current sensor, the three-phase voltage sensor and the three-phase current sensor are used as sensing layers of the intelligent electricity safety monitoring system, and the cable temperature, the smoke, the residual current, the three-phase voltage and the three-phase current in the target environment are collected to obtain cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic.
The intelligent gateway is used as a network layer of the intelligent electricity safety monitoring system, and receives cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic for summarizing, and transmits the cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic to the cloud platform.
The cloud platform is used as an application layer of the intelligent electricity safety monitoring system, and based on cable temperature sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic, a neural network model is adopted for comprehensive analysis, a safety monitoring result is generated, and whether protection measures are adopted or not is judged based on the safety monitoring result and smoke sampling signals.
Further, in the present embodiment, the neural network model includes an input layer, an hidden layer, and an output layer.
The input layer is composed of eight neurons, and each neuron receives a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic respectively and transmits the signals to the hidden layer.
The hidden layer is composed of three neurons, eight input ends of each neuron respectively receive a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic transmitted by the input layer, and three hidden outputs are generated after summation operation and activation transformation are carried out.
The output layer is composed of a neuron, receives three hidden outputs, and generates a safety monitoring result after summation operation and activation transformation.
In the prior art, in order to improve generalization capability and nonlinear fitting capability of a neural network model, a large number of neurons and a complex model structure are required to be adopted, so that the problem of slow self-adaptive learning rate is caused.
In the hidden layer, only three neurons are adopted, so that the structure of the BP neural network is simplified, and the self-adaptive learning rate of the BP neural network is improved. Meanwhile, each neuron adopts different activation functions, so that the advantages of the different activation functions are combined, and the monitoring precision of the BP neural network is improved.
Specifically, in the hidden layer of the application, the first neuron, the second neuron and the third neuron respectively adopt three different activation functions, so that the advantages of the different activation functions are integrated, the application range and the defects of each activation function are overcome, and the monitoring precision of the BP neural network is improved.
Further, in the present application, the activation function in the first neuron is specifically:
where x is the input of the activation function.
The activation function is a continuous function, is a smooth S-shaped curve and is suitable for two-class output problems. But the output value range is asymmetric and the learning rate is slower.
The activation function in the second neuron is specifically:
the activation function curve is smoother, nonlinear fitting can be better realized, the problem of asymmetry of an output value range is solved, the learning rate can be enhanced, and the problem of gradient sinking exists.
The activation function in the third neuron is specifically:
the activation function curve is more gentle, the problems of slow learning rate and gradient hiding are alleviated, and the problem of asymmetrical output value range is also solved.
Therefore, the method integrates the advantages of three activation functions, can improve the self-adaptive learning rate, lighten the problems of gradient sinking and asymmetrical output value range, and improve the monitoring precision of the neural network model.
Further, in the present application, all three activation functions require e to be calculated x Thus three neurons in the hidden layer can share e with each other x Further enhancing the adaptive learning rate of the BP neural network.
In another embodiment of the present application, the hidden layer of the BP neural network may further set a fourth neuron, where eight input ends of the fourth neuron respectively receive the cable temperature sampling signal T, the residual current sampling signal Ires, the three-phase voltage sampling signals Ua, ub, uc, and the three-phase current sampling signals Ia, ib, ic transferred by the input layer, and generate a fourth hidden output after performing a summation operation and an activation transformation.
The activation function in the fourth neuron is specifically:
F 4 (x)＝ln(1+e x )
the activation function curve is relatively flat, the derivative is smaller, and the silent neuron can be avoided, so that the BP neural network can avoid the silent neuron.
Further, in another embodiment of the present application, the cloud platform further determines whether to adopt a protection measure based on the security monitoring result and the smoke sampling signal, specifically:
when the safety monitoring result indicates that a fire disaster occurs and the smoke sampling signal indicates that smoke exists, judging that the fire disaster occurs, and adopting protective measures.
When the safety monitoring result indicates that a fire disaster occurs and the smoke sampling signal indicates that no smoke exists, the fire disaster is judged not to occur, but fire hidden danger exists, and engineering personnel are required to check.
The safety monitoring result and the smoke sampling signal are adopted to conduct protection measure judgment, false triggering of the protection measure can be prevented, and reliability of intelligent electricity safety monitoring is improved.
In this application, intelligent electricity utilization safetyThe full monitoring system only adopts three or four neurons in the hidden layer, simplifies the structure of the BP neural network, and improves the self-adaptive learning rate of the BP neural network. Meanwhile, each neuron adopts different activation functions, so that the advantages of the different activation functions are combined, and the monitoring precision of the BP neural network is improved. Further, each activation function also shares e x The self-adaptive learning rate of the BP neural network is greatly improved.
While the foregoing description illustrates and describes the preferred embodiments of the present application, it is to be understood that this application is not limited to the forms disclosed herein, but is not to be construed as an exclusive use of other embodiments, and is capable of many other combinations, modifications and environments, and adaptations within the scope of the inventive concept described herein, through the foregoing teachings or through the skill or knowledge of the relevant arts. And that modifications and variations which do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.
Claims (9)
1. An intelligent electrical safety monitoring system, comprising: the intelligent cloud platform comprises a cable temperature sensor, a smoke sensor, a residual current sensor, a three-phase voltage sensor, a three-phase current sensor, an intelligent gateway and a cloud platform;
the cable temperature sensor, the smoke sensor, the residual current sensor, the three-phase voltage sensor and the three-phase current sensor acquire cable temperature, smoke, residual current, three-phase voltage and three-phase current in a target environment to obtain cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic;
the intelligent gateway receives cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic for summarizing, and transmits the cable temperature sampling signals, smoke sampling signals, residual current sampling signals, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic to the cloud platform;
the cloud platform carries out comprehensive analysis by adopting a neural network model based on the cable temperature sampling signals, the residual current sampling signals, the three-phase voltage sampling signals Ua, ub and Uc and the three-phase current sampling signals Ia, ib and Ic to generate a safety monitoring result;
the hidden layer of the neural network model employs at least three different activation functions.
2. The intelligent electricity safety monitoring system of claim 1, wherein the neural network model comprises an input layer, an hidden layer, and an output layer;
the input layer is composed of eight neurons, and each neuron receives a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic respectively and transmits the signals to the hidden layer;
the hidden layer is composed of three neurons, eight input ends of each neuron respectively receive a cable temperature sampling signal T, a residual current sampling signal Ires, three-phase voltage sampling signals Ua, ub and Uc and three-phase current sampling signals Ia, ib and Ic transmitted by the input layer, and three hidden outputs are generated after summation operation and activation transformation are carried out;
the output layer is composed of a neuron, receives three hidden outputs, and generates a safety monitoring result after summation operation and activation transformation.
3. The intelligent electricity safety monitoring system according to claim 2, wherein the activation function of the first neuron in the hidden layer is specifically:
where x is the input of the activation function.
4. A smart electricity security monitoring system as claimed in claim 3, wherein the activation function of the second neuron in the hidden layer is specifically:
5. the intelligent electricity safety monitoring system according to claim 4, wherein the activation function of the third neuron in the hidden layer is specifically:
6. the intelligent electricity usage safety monitoring system according to claim 5, wherein the hidden layer is further provided with a fourth neuron.
7. The intelligent electricity safety monitoring system according to claim 6, wherein the activation function of the fourth neuron in the hidden layer is specifically:
F 4 (x)＝ln(1+e x )
8. the intelligent electricity safety monitoring system according to claim 1, wherein the cloud platform further determines whether to take protective measures based on the safety monitoring results and the smoke sampling signals.
9. The intelligent electrical safety monitoring system according to claim 8, wherein when the safety monitoring result indicates that a fire is occurring and the smoke sampling signal indicates that smoke is present, determining that a fire has occurred, adopting a protective measure; when the safety monitoring result indicates that a fire disaster occurs and the smoke sampling signal indicates that no smoke exists, the fire disaster is judged not to occur, but fire hidden danger exists, and engineering personnel are required to check.
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