CN112788142B - Intelligent edge Internet of things gateway supporting multi-sensor access - Google Patents
Intelligent edge Internet of things gateway supporting multi-sensor access Download PDFInfo
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Abstract
The invention relates to an intelligent edge internet of things gateway supporting multi-sensor access, which at least comprises a data sensing layer, an intelligent edge computing management layer, a cloud platform layer and a human-computer interaction layer, wherein at least one data source node in the data sensing layer transmits the acquired data to the intelligent edge computing management layer through a preset communication networking scheme, the intelligent edge computing management layer performs data processing and computing on the data acquired by the at least one data source node and uploads the processed necessary data to the cloud platform layer, and the cloud platform layer is used for cooperatively processing the necessary data obtained by processing a plurality of gateway devices in the intelligent edge computing management layer and feeding back the processed results to the human-computer interaction layer.
Description
Technical Field
The invention relates to the technical field of power equipment, in particular to an intelligent edge Internet of things gateway supporting multi-sensor access.
Background
With the gradual development of the internet of things technology, the artificial intelligence technology and the high-speed communication technology, various solutions related to the smart grid are endless, but still, a plurality of pain points which need to be solved urgently exist:
firstly, although the gradual development and application of the internet of things technology enable a large-scale digital sensor to be widely deployed in power grid equipment, the collection of power grid data in the scene is realized. Millions of power distribution equipment, electrical quantity sensors and state quantity sensors are connected to an internet of things, massive heterogeneous power distribution and utilization data are generated, huge pressure is brought to a communication channel and a main station storage and calculation system by collection, transmission and calculation of the power distribution and utilization data, equipment in different areas is relatively independent, how to quickly connect to power grid equipment in different areas and types is achieved, and the problem that large-scale real-time collection of heterogeneous data is still required to be solved is achieved;
secondly, the artificial intelligence technology represented by the deep learning network can greatly improve the processing and analyzing capability of mass data, and is a brain for realizing the core function of the smart grid. However, artificial intelligence techniques require significant computing resources, which ordinary grid devices do not have. Although the brain can be driven to operate by using elastic computing resources provided by cloud computing, the service delay is still high, and sensitive data cannot be processed, so that real-time analysis and privacy protection of massive data cannot be well realized.
In addition, implementation and deployment of a high-speed communication technology (such as 5G communication) can guarantee transmission of sensor data in a power scene, provide stable network connection, and timely feed back final judgment and decision. However, the high-speed communication technology can only provide a high-speed and stable network connection mode, and the effect of the technology is limited under the condition that the internet of things technology and the artificial intelligence technology have the pain points. Furthermore, terrain and area limitations result in remote locations of parts of the power grid equipment, deployed sensors may not be within the coverage of the high speed communication base station, and the power operators are unable to continuously collect such data.
For example, a cloud computing-based smart grid detection platform proposed in patent document with publication number CN106972636B in the prior art mainly includes a master monitoring computer and a slave monitoring computer, where the slave monitoring computer is in online communication with the master monitoring computer through the internet; the input end of the slave monitoring computer is connected with a power supply and a clock, and the output end of the slave monitoring computer is connected with the printer; the slave monitoring computer is respectively connected with the human-computer interface, the communication server and the communication gateway through the Ethernet switch; the human-computer interface is respectively connected with the microcomputer protection device and the temperature and humidity controller through an RS485 bus, the communication server is respectively connected with the power instrument and the motor protector through the RS485 bus, and the communication gateway is respectively connected with the intelligent circuit breaker, the four-remote unit and the direct-current screen through the RS485 bus.
Furthermore, on the one hand, due to the differences in understanding to those skilled in the art; on the other hand, since the inventor has studied a lot of documents and patents when making the present invention, but the space is not limited to the details and contents listed in the above, however, the present invention is by no means free of the features of the prior art, but the present invention has been provided with all the features of the prior art, and the applicant reserves the right to increase the related prior art in the background.
Disclosure of Invention
Although the solution proposed to the smart grid at present widely deploys a large-scale digital sensor into the grid equipment to realize the acquisition of grid data, however: millions of power distribution equipment, electrical quantity sensors and state quantity sensors are connected into an internet of things, massive heterogeneous power distribution and utilization data are generated, huge pressure is brought to a communication channel and a main station storage and calculation system by collection, transmission and calculation of the power distribution and utilization data, service delay of cloud calculation is high, sensitive data cannot be processed, besides, the positions of parts of power grid equipment are remote due to terrain and region limitation, deployed sensors may not be in the coverage range of a high-speed communication base station, and power operators cannot continuously collect the data.
Aiming at the defects of the prior art, the invention provides the intelligent edge Internet of things gateway supporting multi-sensor access, the traditional technical scheme that all computing tasks related to data analysis are transferred to a cloud computing platform to be executed is changed, compared with cloud computing, edge computing is closer to a data source, and data transmission time can be saved. In the intelligent edge internet of things gateway provided by the invention, necessary data are finely uploaded through the edge computing function of the intelligent edge computing management layer, the pressure of processing mass data by a cloud is reduced, and the operating efficiency of a cloud platform and the maximum access quantity of expansion equipment are further improved. The intelligent edge computing management layer can perform real-time online monitoring control on each data source node, provide intelligent micro application, realize the data acquisition and simple discrimination functions of the data source nodes, and also realize the operation and maintenance functions of the equipment, such as distribution transformer energy efficiency analysis, energy consumption analysis, electric energy quality analysis, automatic inspection, non-invasive load identification, demand side power supply resource optimization scheduling, state maintenance and the like. The intelligent edge internet-of-things gateway can predict the short-term load of the power grid, provides a local prediction mechanism for the power spot market, and improves the efficiency and the cost for centralized operation and maintenance by clustering intelligent operation and maintenance.
The intelligent edge Internet of things gateway supporting multi-sensor access at least comprises a data sensing layer, an intelligent edge computing management layer, a cloud platform layer and a man-machine interaction layer, wherein at least one data source node in the data sensing layer transmits acquired data to the intelligent edge computing management layer through a preset communication networking scheme, the intelligent edge computing management layer performs data processing and computing on the data acquired by the at least one data source node and uploads necessary data obtained after processing to the cloud platform layer, and the cloud platform layer is used for cooperatively processing the necessary data obtained by processing a plurality of gateway devices in the intelligent edge computing management layer and feeding back results obtained by processing to the man-machine interaction layer. The communication networking scheme refers to networking modes such as Ethernet based on a wired form and Zigbee, GPRS, 3G/4G/5G, wiFi, bluetooth, loRa and the like based on a wireless form.
According to a preferred embodiment, the intelligent edge computing management layer includes a distribution energy efficiency analysis module configured to: establishing a power distribution loss analysis model based on data acquired from the data perception layer to calculate and obtain real-time power distribution energy efficiency; processing the dynamic change of the real-time power distribution energy efficiency, and analyzing to obtain an energy efficiency operation optimization scheme for outputting to a data sensing layer; and enabling the data perception layer to control the corresponding energy equipment to execute energy efficiency operation optimization according to the received energy efficiency operation optimization scheme.
Preferably, the mass data of the plurality of data source nodes may include high-voltage cabinet related data (voltage, current, power, switch state quantity, switch control, small ground line selection, moving and static contact temperature, circuit breaker mechanical characteristic parameters, protection constant value, online partial discharge monitoring), transformer related data (oil temperature, oil level, gas, voltage, vibration, transformer temperature (dry type transformer)), and the like,Low-voltage cabinet related data (voltage, current, active power, reactive power, power factor, three-phase unbalanced harmonic, switching state quantity, switching control, moving and static contact temperature, automatic topology generation), box transformer/distribution room related data (temperature, video monitoring, water smoke sensing (for distribution room), fan (for box transformer), dehumidifier (for box transformer), industrial air conditioner (for box transformer) or compensation device (compensation capacity, compensation switching situation)). Preferably, the data collected from the data perception layer at least includes total loss of each energy device at the current moment and total actual power utilization power of the load side at the current moment. The load side refers to the equipment or the power transmission line connected with the outgoing line of which the user terminal adopts the arc extinguishing measure. The real-time power distribution efficiency of each energy device can be calculated using the following formula:
in the formula, T (t) Real-time power distribution efficiency, D, for each energy device at the current moment (t) For the total loss of each energy facility at the present moment, l (t) The total power of the load side is actually used at the current moment. Preferably, at least one energy efficiency operation optimization scheme is preconfigured in the distribution and transformation energy efficiency analysis module, the distribution and transformation energy efficiency analysis module establishes a real-time power distribution energy efficiency curve corresponding to each energy device and analyzes big data of the power distribution energy efficiency curve to calculate the degree of influence of a plurality of device parameters on each energy efficiency curve and obtain the energy efficiency operation optimization scheme including the optimal value of at least one device parameter. When the real-time power distribution efficiency is continuously lower than the target power distribution efficiency within a preset time, the distribution transformation efficiency analysis module outputs an efficiency operation optimization scheme to the data perception layer, and the corresponding energy equipment in the data perception layer can adjust the equipment parameters to enable the equipment parameters to reach an optimal value, so that the operation optimization of the efficiency is realized.
According to a preferred embodiment, the intelligent edge computing management layer comprises an energy consumption analysis module configured to: acquiring original discrete data of each energy device from a data sensing layer, fitting the original discrete data according to a preset division period, and calculating to obtain at least one real-time change curve of continuous data along with time; generating a real-time change curve of the actual power consumption of each energy device along with time and/or a real-time change curve of the total energy consumption of the device along with time according to at least one real-time change curve of the continuous data along with time; and monitoring the abnormality of each energy device in the data perception layer based on the real-time change curve. Therefore, under the condition that data abnormity occurs in the real-time change curve, a prompt can be sent to a manager in time.
According to a preferred embodiment, the intelligent edge computing management layer comprises a power quality analysis module configured to: collecting real-time line measurement information of each line measurement point from a data perception layer, and identifying and obtaining an electric energy quality transient event generated by the monitored alternating current and the occurrence time of the electric energy quality transient event; the method comprises the steps of obtaining action information of switching equipment and/or relay protection equipment in a power grid associated with a power quality transient event; and feeding back the action information and the electric energy quality transient event to the cloud platform layer and/or the human-computer interaction layer in the real-time line measurement information in an information labeling mode. In the operation process of the power system, various power quality events generally occur, which brings adverse effects to the normal operation of the power system. The power quality events are divided into transient events and steady-state events, wherein abnormal events lasting for 10ms to 1min are determined as power quality transient events. The monitoring method of the electric energy quality transient event can adopt a half-wave effective value algorithm, namely, the half-wave effective value in the cycle wave of the monitored alternating current waveform is obtained, whether the half-wave effective value exceeds a set threshold value or not is judged, and the transient event is determined to occur if the half-wave effective value exceeds the threshold value.
According to a preferred embodiment, the intelligent edge computing management layer comprises an automatic inspection robot configured to: when a temporary detection task including at least one target detection point from a cloud platform layer and/or a man-machine interaction layer is received, intelligently planning a traveling route according to one or more of the geographic position of the temporary detection task, the position distribution of the target detection point in a pre-storage environment map and the importance level of the target detection point; in the daily inspection period without the temporary detection task, an advancing route is intelligently planned according to the position distribution of key point positions in the pre-stored environment map, and the acquired data to be processed is processed in a queue-inserting mode according to the importance levels of the detection points contained in the data.
According to a preferred embodiment, the intelligent edge computing management layer comprises a load identification module configured to: acquiring original discrete data of each energy device from a data sensing layer, and performing noise reduction and time-frequency decomposition on the original discrete data to obtain load identification sample data; and inputting the load identification sample data into a pre-established neural network model to train and obtain a load identification model, so that the electric appliance use condition of the user can be calculated and obtained based on the load identification model.
According to a preferred embodiment, the intelligent edge computing management layer comprises a status troubleshooting module configured to: pre-establishing a fault symptom analysis model about each energy device, wherein the model comprises one or more of at least one state evaluation item, a maintenance scheme and a maintenance grade; performing state evaluation on each energy device through at least one state evaluation item based on the original discrete data of each energy device collected from the data sensing layer; and under the condition that the state evaluation result meets the fault symptom judgment condition, feeding back the state evaluation result and the corresponding maintenance scheme in the fault symptom analysis model to the cloud platform layer and/or the human-computer interaction layer.
According to a preferred embodiment, the intelligent edge computing management layer comprises a short-term load prediction module configured to: determining at least one important influence factor according to the change condition of the historical load data of each energy device collected from the data perception layer along with the at least one influence factor; constructing a short-term load prediction model based on the determined at least one important influence factor; the model is trained by using the change condition of the historical load data along with at least one influence factor to obtain a prediction model, so that the power grid load can be predicted in a short term based on the prediction model.
According to a preferred embodiment, the distribution energy efficiency analysis module may be configured to: acquiring actual power consumption and load rated power of each energy device and/or actual power consumption and distributed power supply rated power of each distributed power supply subunit from a data sensing layer; and/or establishing a distribution loss analysis model according to the data collected from the data perception layer.
According to a preferred embodiment, the distribution energy efficiency analysis module may be configured to: and feeding back the dynamic change of the real-time power distribution efficiency of each energy device to the cloud platform layer and/or the man-machine interaction layer in time in a dynamic curve mode.
Drawings
Fig. 1 is a simplified architecture diagram of an intelligent edge internet of things gateway provided by the present invention.
Detailed Description
With the access of more and more internet of things sensors, a new challenge is provided for a traditional cloud system adopting a central centralized architecture, and if sensor data, a large amount of novel unstructured data, energy efficiency problems and the like which are exponentially exploded all directly influence the intelligent power grid to fall to the ground or not. In order to solve the problem, the intelligent edge internet of things gateway supporting multi-sensor access is provided, or an intelligent edge internet of things gateway system supporting multi-sensor access is provided, the intelligent edge internet of things gateway system supports multiple internet of things sensor access, a large amount of sensing data can be processed and analyzed locally in real time, and intelligent operation and maintenance of a power grid are achieved. The intelligent edge internet of things gateway system forms a set of cloud edge collaborative optimization scheme based on technologies such as edge calculation and distributed calculation, replaces pure cloud computing by building distributed gateway equipment, reduces cloud computing amount, and improves response speed and accuracy.
The following discussion is a brief description of the related concepts and terms involved in the present application for the understanding of those skilled in the art.
As shown in fig. 1, in the present application, a system structure of the intelligent edge internet of things gateway may be divided into four parts from bottom to top, which are a data sensing layer, an intelligent edge computing management layer, a cloud platform layer, and a human-computer interaction layer. Each layer comprises a plurality of entities, and the entities refer to content and corresponding devices involved in sending and receiving information, for example, a plurality of data source nodes included in the data sensing layer, a plurality of gateway devices included in the edge computing layer, a plurality of servers included in the cloud platform layer, and a plurality of terminal devices included in the man-machine interaction layer. Each layer is used to implement different functions and communication between entities can be performed.
The data perception layer is mainly composed of a plurality of data source nodes together, each data source node comprises at least one sensing identification device (such as RFID labels, readers, various sensors, cameras, GPS, two-dimensional code labels, readers and other basic identifications and sensing devices) and/or a network (such as RFID network, sensor network and the like) composed of the sensing identification devices, and the data perception layer is used for realizing the collection, identification and collection of power grid information and sending the collected data to at least one gateway device in the intelligent edge computing management layer or the intelligent edge computing management layer.
The intelligent edge computing management layer is mainly composed of a plurality of gateway devices, on one hand, the gateway devices can communicate with sensing identification devices of various technical systems, and can acquire and compute multidimensional sensing data generated by sensing of a plurality of data source nodes and conversion of network protocols. The edge calculation layer is mainly used for quickly calculating and temporarily storing on-line voltage, network power distribution, power loss, impedance parameters, voltage drop, load flow calculation, electricity stealing detection and other multidimensional sensing data by means of a data fusion algorithm. On the other hand, the gateway device can finely upload necessary data through the edge computing function, the pressure of the cloud platform layer for processing mass data is reduced, and the operation efficiency of the cloud platform layer and the maximum access quantity of the expansion device are further improved. The necessary data mainly refers to data which are obtained by uploading all data according to a preset rule by a sensor after communication and are meaningful for operation and maintenance work and are used for an operator to check after screening of a gateway equipment communication protocol. For example, the data may be data (transformer body temperature, transformer gear, transformer oil level, transformer oil temperature, transformer operating noise, load percentage, oil sample monitoring data, etc.) about a transformer, data (cabinet busbar temperature/breaker (load switch) contact temperature, low-voltage load, relative humidity, breaker operation times, breaker mechanical characteristics, etc.) about a high-low voltage cabinet or data (input voltage, input Power factor, output voltage, output Power factor, UPS body temperature, etc.) about a UPS (uninterruptible Power Supply).
Edge calculation is distinguished from limitation of a sampling frequency And a transmission mode of an RTU (Remote Terminal Unit)/measurement And control instrument, information of a common SCADA (Supervisory control And Data Acquisition) system (Data Acquisition And monitoring control) is refreshed once in 4-5S, namely, an S CADA system represents a system state of a power grid for several seconds, and edge calculation is a distributed calculation mode And refers to an operation program finished by utilizing an edge zone close to a Data source. The edge calculation inputs real-time small-scale data, and outputs a judgment result to regulate and control the electrical equipment to reach a stable state. The edge calculation layer settles most of the calculation at the edge close to the equipment, and a small part is connected with a far cloud. The self-healing capability of the power system when the communication link is in the failure can be improved.
The cloud platform layer is a physical server cluster based on scale, each cluster node is deployed in a backbone data center, and the cloud platform layer can independently provide computing, storage, bandwidth and other internet infrastructure services. The physical architecture mainly comprises a storage server cluster, a computing server cluster, an infrastructure management server and a network switch. The storage server cluster constructs a virtual resource pool, has super-large capacity and provides logical disk storage, non-structural data storage and integrated backup service for a cloud virtual machine in a node; the computing server cluster is integrated through a virtualization technology, and computing resources are generated and allocated by the control platform according to needs; the management server adopts a dual-computer hot standby mode to manage all the computing servers, the shared storage and the network of the whole node and simultaneously provides an API for managing the whole node externally; and the network switch is responsible for managing network segments, public network switching network segments, internal switching network segments, storage network segments and the like. The cloud platform layer can transmit data generated after processing to the man-machine interaction layer, the man-machine interaction layer supports an administrator to remotely view related data on the terminal equipment operated by the administrator at any place and any time, and/or the data perception layer and the edge computing layer are remotely managed. The cloud platform layer can fuse the electric quantity statistics, the loss analysis and the user equipment basic standing book information system together for describing the state of a specific station or specific equipment in a multi-dimensional mode, so that an operator can make effective coping decisions for the power problems.
The human-computer interaction layer mainly comprises a plurality of terminal devices, such as various terminals including smart phones, tablet computers, vehicle-mounted intelligent terminals, wearable devices, PCs, VR/AR devices and the like. At least one terminal device in the man-machine interaction layer can achieve information communication and interoperation between the terminal device and the cloud platform layer.
The system establishes a multidimensional data source node data fusion model by fusing a distributed intelligent power grid and a ubiquitous power Internet of things, constructs an intelligent comprehensive perception, information efficient processing and multi-link interconnection and intercommunication integrated power distribution Internet of things, simultaneously enhances the efficiency of the integrated power distribution Internet of things in the aspects of equipment data collection, data stable transmission, energy storage strategy analysis, power utilization safety monitoring and the like by introducing an edge computing mechanism into a regional power grid, and is favorable for realizing intelligent analysis and autonomous intelligent operation and maintenance of an intelligent edge Internet of things gateway through the edge computing generalization capability and reliable fusion between 5G wireless access network service and neural network deep learning.
Each data source node can be provided with one or more of an overhead line monitoring subsystem, an electric energy quality monitoring subsystem, a switching equipment partial discharge monitoring subsystem, a grounding resistance monitoring subsystem, a box transformer substation environment monitoring subsystem, a transformer fault monitoring subsystem, a through cable fault positioning monitoring subsystem and an intelligent patrol monitoring subsystem.
The edge computing layer comprises at least one gateway device, and the plurality of data source nodes are respectively in communication connection with the at least one gateway device. The edge computing layer can acquire and compute multi-dimensional sensing data generated by sensing of a plurality of data source nodes and conversion of network protocols. The edge calculation layer performs rapid calculation and temporary storage on multi-dimensional sensing data such as on-line voltage, network power distribution, power loss, impedance parameters, voltage drop, load flow calculation, electricity stealing detection and the like by means of a data fusion algorithm.
At the edge computing layer, the gateway device may build a device lifetime prediction knowledge base based on the massive data of the plurality of data source nodes that it collects. The parameters related to the service life of the equipment in the equipment service life prediction knowledge base are extracted and classified, and the specific stage of the service life of each equipment is calculated according to different stages of the service life of the equipment, so that the equipment can be classified into different categories based on the specific stage. And determining the use scene characteristic parameters of each device based on the obtained classification result, and carrying out primary device service life analysis based on the use scene characteristic parameters to establish a device service life model. And correcting the equipment life model based on an artificial intelligence learning algorithm, and establishing and classifying basic classification rules of the equipment life model so as to be suitable for correcting the equipment life model at each stage of the full life cycle of the equipment, thereby obtaining different types of equipment life models corresponding to different stages of the full life cycle of the equipment. At least one type of service life model can be matched and determined based on the basic parameters of the equipment, and relevant service life parameters are determined by matching with the service life prediction knowledge base and combining with the service life model analysis of the current type of equipment. Based on the method, the weak links of the operation and maintenance of the equipment in each period can be calculated through deep data mining and algorithm training, the early warning of the service life of the equipment is triggered in time, and the method is favorable for operators to carry out the work of overhauling and replacing elements in a targeted manner, so that the service life of the whole equipment is prolonged. The above parameters may refer to transformer related data (environmental temperature/humidity/noise, oil sample monitoring, insulating oil medium, initial value difference of winding short-circuit impedance, C2H2 content, furfural content, and the like), line related data (hardware temperature and line sag), high and low voltage cabinets (temperature, mechanical characteristics of a circuit breaker, resistance of a switching-on/off circuit, SF6 micro-water content, insulation resistance to ground, service life, open-circuit direct current resistance, and the like), or comprehensive protection device related data (device temperature, AD sampling consistency, secondary circuit integrity, communication interface state detection, insulation resistance, device temperature, message delay, sampling message packet loss rate, and the like).
The gateway equipment can train the historical data of the sensor into digital characteristic models aiming at different fault phenomena through machine learning based on a plurality of fault characteristics appearing in the running process of the equipment, and forms the basis for accurately judging the fault state of the equipment in the future by combining the records processed by experts, so that the autonomous intelligent diagnosis and judgment are realized. Based on the method, the evaluation of the current state, the diagnosis of problems occurring in the past and the prediction of future trends can be realized, and the analysis result is given, so that various possibilities are simulated, and more comprehensive decision support is provided. Through data deep mining and algorithm training, the service life early warning of the equipment is triggered in time, the operation and maintenance attention of different electrical equipment in different periods is improved, the operation condition of the electrical equipment is evaluated, a scientific operation and inspection plan is formulated, and shutdown and production halt caused by unnecessary annual inspection pre-test work are reduced. The operation and maintenance attention mainly refers to the attention degree of operation and maintenance personnel to the operation state of components in the box transformer substation.
Preferably, the gateway device may intelligently counter-control various devices based on its edge calculation of the collected mass data of the plurality of data source nodes, so that the box transformer returns to an optimal operating state. The inverse control operation can comprise automatic gear shifting of a transformer, automatic control of a reactive power compensation device, automatic control of an air conditioner, automatic control of an industrial dehumidifier, automatic starting and stopping of a box transformer exhaust fan, triggering out-of-limit alarm when power data is abnormal and the like. The issuing and controlling process can be as follows: after the temperature uploaded by the sensor is judged by the gateway equipment, if the temperature reaches the temperature alarm threshold value, the gateway equipment indicates the air conditioner to be started and simultaneously sends alarm information to a monitoring system of the air conditioner.
The gateway equipment can support the access of various communication networks such as Ethernet, WIFI, 3G, 4G, 5G eMBB/uRLLC and the like, and can ensure the uninterrupted connection of the communication networks. Each gateway device supports access to multiple types of cloud platforms, such as currently mainstream industrial cloud platforms, including general, siemens, schneider, aristolocos, microsoft, amazon, and huayayun. Each gateway device is compatible with various device mainstream industrial real-time Ethernet protocols and industrial bus protocols, such as PROFIBUS-DP protocol, MODBUS TCP/RTU protocol, PR OFINET protocol, OPC UA protocol and PROFINET protocol.
The gateway device can compare the TOC value (Total Owni ng Cost, comprehensive energy efficiency Cost) of the existing transformer in the continuous operation period with the TOC value of the novel transformer to be updated in the period. And determining the existing transformer as an abnormal loss point when the TOC value of the existing transformer is relatively higher.
The TOC value can be obtained using the following computational expression:
TOC＝CI+(A×P 0 +B×P k ) /1000, wherein: TOC refers to the total cost of ownership, yuan, of the transformer; CI refers to the initial cost of the transformer equipment, dollars, which can typically be the purchase price of the transformer; p is 0 Refers to the rated no-load loss, W, of the transformer; p k Refers to the rated load loss of the transformer, W; a is equivalent initial cost of unit no-load loss of the transformer, yuan/KW; and B is the equivalent initial cost of unit load loss of the transformer, yuan/KW.
The above calculation expression can be simplified without considering the additional loss cost, the average annual incremental cost of the power supply network, and assuming that the load rate of the transformer is constant during the economic trial period as follows:
TOC＝CI+K pv ×E×(H py ×P 0 +τ×β 0 2 ×P k ) /1000, wherein: e is the average selling price of the transformer user, yuan/KWh; n is the economic service life of the transformer, generally 20 years, or the service life T of the existing transformer which can continue to operate; k pv The current value coefficient of the continuous n-year cost with the discount rate of i is referred to; i is the annual discount rate which is not lower than the loan profit rate value of the bank in the same year; h py The annual charging hours of the transformer are generally 8760h; τ refers to the annual maximum load loss hours, h; beta is a 0 The initial load factor and the per unit value of the transformer are referred.
The initial cost of the existing transformer can be obtained by adopting the following calculation expression:
CI＝V O0 -V OT /(1+i) T wherein, T means the continuous operation life of the existing transformer; v O0 Means the available price of the existing transformer in the decision-making year, namely the transformerA previous value; v OT The residual value from the prior transformer to the end of the T year, namely the net residual value, is equivalent to the scrap recovery price (the maintenance cost of the T year is properly deducted); i means annual discount rate.
The initial cost of the new transformer to be updated can be obtained by adopting the following calculation expression:
CI＝V N -V NT /(1+i) T in the formula, V N Means the purchase cost of a new transformer; v NT The residual value of the new transformer to the end of the T year; the other parameters are as above. Wherein, V NT Can be reduced according to the evaluation age V NT ＝V N -T(V N -V Nn ) The estimation is performed as/n. V Nn The net residual value after the economic operation time limit (n years) of the new transformer is referred to, namely the scrapping and recycling price.
The gateway equipment comprises one or more of a distribution transformer energy efficiency analysis module, an energy consumption analysis module, an electric energy quality analysis module, an automatic inspection robot, a load identification module, a state maintenance module and a short-term load prediction module. The intelligent edge computing management layer has one or more equipment operation and maintenance functions of distribution transformer energy efficiency analysis, energy consumption analysis, electric energy quality analysis, automatic inspection, non-intrusive load identification, demand side power resource optimal scheduling and state maintenance.
The distribution transformation energy efficiency analysis module can collect actual power consumption and load rated power of each energy device, and actual power consumption and distributed power rated power of each distributed power supply subunit from the data perception layer. The distribution transformer energy efficiency analysis module establishes a distribution loss analysis model based on data collected from the data perception layer, and can calculate real-time distribution energy efficiency of the distribution system under device loss and line loss based on the model. The distribution transformer energy efficiency analysis module feeds back dynamic changes of real-time power distribution energy efficiencies of all energy devices to the cloud platform layer and/or the man-machine interaction layer in time in a dynamic curve mode. The distribution transformation energy efficiency analysis module is used for carrying out big data analysis on the real-time distribution energy efficiency curves of the energy devices and calculating to obtain the influence degrees of a plurality of parameters on the energy efficiency curves and the optimized values corresponding to the parameters. The distribution transformer energy efficiency analysis module outputs an energy efficiency operation optimization scheme to the data perception layer based on at least one parameter which does not reach the optimization value. The data perception layer can control the energy source equipment corresponding to the received energy efficiency operation optimization scheme to execute energy efficiency operation optimization based on at least one parameter optimization value obtained by analyzing the received energy efficiency operation optimization scheme.
The energy consumption analysis module collects original discrete data of each energy device from the data perception layer. And the energy consumption analysis module fits the original discrete data according to a preset division period and calculates to obtain at least one real-time change curve of the continuous data along with time. And the energy consumption analysis module generates real-time change curves of actual power consumption of each energy device and total energy consumption of the device along with time according to at least one real-time change curve of continuous data along with time. And monitoring the abnormality of each energy device in the data perception layer based on the real-time change curve.
The electric energy quality analysis module collects real-time line measurement information of each line measurement point from the data perception layer. And identifying the transient event of the power quality of the monitored alternating current and the occurrence moment of the transient event. Action information of a switching device and/or a relay protection device in the power grid associated with the power quality transient event is obtained. And feeding back the action information and the electric energy quality transient event to the cloud platform layer and/or the human-computer interaction layer in the real-time line measurement information in an information labeling mode.
When a temporary detection task including at least one target detection point from a cloud platform layer and/or a man-machine interaction layer is received, the automatic inspection robot intelligently plans a traveling route according to one or more of the geographic position of the automatic inspection robot, the position distribution of the target detection points in a pre-storage environment map and the importance level of the target detection points. In the daily inspection period without the temporary detection task, the automatic inspection robot intelligently plans an advancing route according to the position distribution of key point positions in the pre-stored environment map, and performs queue-insertion type processing on the acquired data to be processed according to the importance levels of the detection points contained in the data. The queue type processing means that the data to be processed with higher grade collected in real time can be preferentially reacted and processed.
Because most areas still distribute the electrical room for the tradition at present, it does not possess the function of independently gathering relevant data and the automatic upload of data to distribute electrical room partial equipment yet, to this, the technical scheme who utilizes the cloud server to carry out data processing among the prior art is because it extremely depends on the bottom data, to traditional distribution electrical room, or require the manpower increase to patrol and examine the frequency in real time and timely errorless upload work, there is the problem of increase technical staff's work load by a wide margin, or the function that stops to distribute the electrical room carries out equipment replacement upgrading to intelligent distribution electrical room, there is the problem of the power consumption demand of with high costs and seriously influence a large amount of users. Aiming at the defects of the prior art, the solution capable of giving consideration to both the traditional power distribution room and the intelligent power distribution room is provided, wherein on one hand, the automatic inspection robot is adopted to automatically inspect and intelligently acquire and upload equipment data in the traditional power distribution room, the original configuration of the traditional power distribution room is not required to be changed in assembly and use of the automatic inspection robot, the workload of technical personnel is greatly reduced, the automatic inspection robot is particularly suitable for the condition that the room temperature is not suitable for the technical personnel to enter the room for inspection in summer or winter, and the high cost and the influence on the power consumption demand of a user are avoided; on the other hand, the automatic inspection robot is combined with intelligent equipment such as intelligent glasses, the operation of technicians entering the power distribution room maintenance equipment can be detected and assisted, and the work safety is guaranteed while the maintenance efficiency of the technicians is improved.
The gateway device further comprises at least one intelligent glasses, wherein the intelligent glasses are used for being worn by technicians when the technicians receive the need of entering the power distribution room for device maintenance tasks, and real-time technical support can be provided. At least two monitoring devices are arranged at the entrance of the power distribution room.
First supervisory equipment detectable technical staff wears intelligent glasses according to the operation standard, sends the suggestion to this technical staff under the condition that detects not wearing intelligent glasses. The first monitoring equipment can identify and acquire the equipment connection code of the intelligent glasses worn by the technician. First supervisory equipment can locate and be located the gate side, and the distribution room door is opened to technical staff's accessible key or the mode of punching the card or biological identification, and when entering, first supervisory equipment is located technical staff side and discerns the scanning to it.
The second monitoring device can check the identity of the at least one technical person based on the power distribution personnel management database and the device connection code of the intelligent glasses. A prompt is issued to the technician upon detection of identity non-compliance. The second monitoring equipment is positioned in front of the technician to perform identification scanning on the technician, so that the identity information of the technician can be acquired. The power distribution personnel management database stores technical personnel information which corresponds to the equipment connection codes of the intelligent glasses one by one. The technical staff information corresponding to the equipment connecting code of the intelligent glasses can be replaced through a personal authorization or superior authorization mode.
Under the condition that the operation specification is met and the identity is checked successfully, the second monitoring device can find out the intelligent glasses in the open state in a surrounding connectable device table updated in real time based on the device connecting code, the second monitoring device sends the identity information checked successfully to the intelligent glasses for safety verification, the safety verification is passed, and the second monitoring device is in communication connection with the intelligent glasses.
The monitoring equipment in communication connection with the intelligent glasses can acquire the dynamic key of the intelligent glasses. Under the condition that the operation specification is met and the identity is checked successfully, the monitoring equipment transmits the equipment connection code and the dynamic key of the intelligent glasses to at least one inspection robot in the power distribution room. At least one inspection robot can be in communication connection with the intelligent glasses within the effective time of the dynamic key. The inspection robot can also find out the intelligent glasses in the open state in a surrounding connectable device table updated in real time based on the device connection code, send the dynamic key to the inspection robot for safety verification, and the safety verification is in communication connection with each other.
Aiming at the problem of how to quickly determine the position of equipment to be maintained after a technician enters a power distribution room, the technical scheme that an automatic inspection robot is used as a technical staff guide is adopted in the prior art, but the automatic inspection robot needs to perform daily inspection work, the time of the technician reaching the power distribution room cannot be predicted, the technician needs to wait for the inspection robot to go to an inlet from the position of the technician when arriving, the inspection work is interrupted, and the maintenance time of the technician is prolonged. In this regard, in the present application, the second monitoring device obtains the device maintenance task information corresponding to the technician in the power distribution personnel management database while performing identity verification. Based on the position of the equipment to be maintained in the power distribution room in the equipment maintenance task information, the monitoring equipment transmits the current power distribution room map with the route marks to the intelligent glasses so as to indicate technicians to go directly to the equipment to be maintained. Therefore, the inspection task of the automatic inspection robot is not required to be interrupted, a technician can clearly determine a traveling route after entering a power distribution room, and time consumed by equipment maintenance is saved.
Although the technical scheme of detecting the operation of a technician by using an automatic inspection robot to realize the standardized operation has been proposed in the prior art, the automatic inspection robot moves along a rail and is blocked in front of a power distribution cabinet by the technician, so that the view of the robot is greatly blocked, the operation of the technician cannot be detected at all, and whether the operation is standardized or not cannot be judged. Therefore, in the application, the automatic inspection robot is automatically connected with the intelligent glasses in a pairing mode, when the technical staff perform equipment maintenance, the automatic inspection robot can acquire the camera data acquired by the intelligent glasses in real time, the operation information of the technical staff can be obtained through processing and analyzing based on the equipment maintenance task information and the real-time camera data, and the detection of the operation specifications of the technical staff is achieved. The automatic inspection robot can acquire panel information on equipment to be maintained, can monitor whether the power distribution cabinet is in a required power-on state or a power-off state in real time, and immediately sends out a warning to a technician or sends out a warning through intelligent glasses if the technician still operates and monitors that the power distribution cabinet is changed from the power-off state to the power-on state. The intelligent glasses can directly indicate the operation position, the sequence, the attention and the like of a technician by utilizing the augmented reality technology, and simultaneously visually analyze the operated condition in real time to judge whether the operation accords with the operation specification. Technicians can select to check the information acquired by the automatic inspection robot through the intelligent glasses. Therefore, the position of the automatic inspection robot is not limited by the visual field, and better operation safety guarantee can be realized by combining with the intelligent glasses.
Generally, equipment maintenance needs two technicians to cooperate, one technician is in the eminence through the ladder and is responsible for the inside of inspection switch board or with monitoring devices 'line access switch board, another technician is responsible for observing monitoring devices' display data on ground, needs two people to make a round trip to check operation or recite display data, and the problem of misreading or reciting appears easily in work efficiency low and, has great potential safety hazard. In contrast, in the application, each technician wears smart glasses, and the smart glasses can acquire the device connection codes of other technicians participating in maintenance through the device maintenance task information and can be in communication connection with each other after passing the security verification. Each technician can obtain the operating condition of other technicians or the display data of ground monitoring equipment on own intelligent glasses, and the work efficiency can be effectively enhanced through the oral check and the interactive display of the intelligent glasses, and the problem of untimely check or error is greatly avoided. When a single technician performs equipment maintenance operation, the monitoring equipment and the intelligent glasses of the monitoring equipment can be preferentially connected in a pairing mode, so that monitoring data in the monitoring equipment can be directly reflected to the interactive display of the intelligent glasses. The technical staff can control the operations such as opening and closing of the monitoring equipment by inputting the operation instructions to the intelligent glasses, so that the operation that the monitoring equipment needs to move up and down and back and forth when a single technical staff carries out equipment maintenance operation is avoided.
The load identification module collects original discrete data of each energy device from the data perception layer, and carries out noise reduction processing and time-frequency decomposition on the original discrete data to obtain load identification sample data. And inputting the load identification sample data into a pre-established neural network model to train to obtain a load identification model. And the load identification module calculates and obtains the electric appliance use condition of the user based on the load identification model.
A fault symptom analysis model related to each energy device is established in advance in the condition maintenance module. The model includes one or more of at least one condition evaluation item, a service plan, and a service level. And based on the original discrete data of each energy device collected from the data perception layer, the state overhaul module carries out state evaluation on each energy device through at least one state evaluation item. And under the condition that the state evaluation result meets the fault symptom judgment condition, feeding back the state evaluation result and the corresponding maintenance scheme in the fault symptom analysis model to the cloud platform layer and/or the man-machine interaction layer.
The short-term load prediction module determines at least one important influence factor according to the change situation of the historical load data of each energy device collected from the data perception layer along with the at least one influence factor. A short-term load prediction model is constructed based on the determined at least one significant impact factor. And training the model by using the change condition of the historical load data along with at least one influence factor to obtain a prediction model. So that the short-term load prediction module can perform short-term prediction on the load of the power grid based on the prediction model.
It should be noted that the above-mentioned embodiments are exemplary, and that those skilled in the art, having benefit of this disclosure, may devise various solutions which are within the scope of this disclosure and are within the scope of the invention. It should be understood by those skilled in the art that the present specification and figures are illustrative only and are not limiting upon the claims. The scope of the invention is defined by the claims and their equivalents. The present description contains several inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", each indicating that the respective paragraph discloses a separate concept, the applicant reserves the right to submit divisional applications according to each inventive concept.
Claims (9)
1. An intelligent edge Internet of things gateway supporting multi-sensor access is characterized by at least comprising a data sensing layer, an intelligent edge computing management layer, a cloud platform layer and a man-machine interaction layer, wherein,
at least one data source node in the data perception layer transmits the acquired data to the intelligent edge computing management layer through a preset communication networking scheme,
the intelligent edge computing management layer processes and computes data collected by at least one data source node, and uploads necessary data obtained after processing to the cloud platform layer,
the cloud platform layer is used for cooperatively processing necessary data processed by a plurality of gateway devices in the intelligent edge computing management layer and feeding back the processed results to the human-computer interaction layer;
the intelligent edge computing management layer comprises an automatic inspection robot which is configured to:
when a temporary detection task including at least one target detection point from a cloud platform layer and/or a man-machine interaction layer is received, intelligently planning a traveling route according to one or more of the geographic position of the temporary detection task, the position distribution of the target detection point in a pre-storage environment map and the importance level of the target detection point;
in the routine inspection period without a temporary detection task, an advancing route is intelligently planned according to the position distribution of key point positions in a pre-stored environment map, and the acquired data to be processed is processed in a queue-inserting manner according to the important grades of detection points contained in the data;
the gateway equipment also comprises at least one intelligent glasses, wherein the intelligent glasses are used for being worn by technicians when the technicians receive the equipment maintenance tasks and need to enter a power distribution room, so that real-time technical support is provided, and the power distribution room is provided with at least two monitoring devices;
the monitoring equipment in communication connection with the intelligent glasses can acquire the dynamic key of the intelligent glasses, and transmits the equipment connection code and the dynamic key of the intelligent glasses to at least one inspection robot in the power distribution room under the conditions that the equipment connection code and the dynamic key of the intelligent glasses meet the operation specification and the identity is successfully checked;
when a technician performs equipment maintenance, the automatic inspection robot acquires the camera data acquired by the intelligent glasses in real time, can process and analyze the camera data based on the equipment maintenance task information and the real-time camera data to obtain the operation information of the technician, realizes the detection of the operation standard of the technician, can directly indicate the operation position, the sequence and the attention of the technician by the intelligent glasses by using the augmented reality technology, performs real-time visual analysis on the operated condition, and judges whether the operation meets the operation standard.
2. The intelligent edge internet of things gateway of claim 1, wherein the intelligent edge computing management layer comprises a distribution energy efficiency analysis module configured to:
establishing a power distribution loss analysis model based on data acquired from the data perception layer to calculate and obtain real-time power distribution energy efficiency;
processing the dynamic change of the real-time power distribution energy efficiency, and analyzing to obtain an energy efficiency operation optimization scheme for outputting to a data sensing layer;
and the data perception layer can control the corresponding energy equipment to execute energy efficiency operation optimization according to the received energy efficiency operation optimization scheme.
3. The intelligent edge internet of things gateway of claim 1 or 2, wherein the intelligent edge computing management layer comprises an energy consumption analysis module configured to:
acquiring original discrete data of each energy device from a data sensing layer, fitting the original discrete data according to a preset division period, and calculating to obtain at least one real-time change curve of continuous data along with time;
generating real-time change curves of actual power consumption of each energy device and/or real-time change curves of total energy consumption of the device along with time according to at least one real-time change curve of continuous data along with time;
and monitoring the abnormality of each energy device in the data perception layer based on the real-time change curve.
4. The intelligent edge internet of things gateway of claim 3, wherein the intelligent edge computing management layer comprises a power quality analysis module configured to:
collecting real-time line measurement information of each line measurement point from a data perception layer, and identifying and obtaining an electric energy quality transient event generated by the monitored alternating current and the occurrence time of the electric energy quality transient event;
acquiring action information of switching equipment and/or relay protection equipment in a power grid associated with the power quality transient event;
and feeding back the action information and the electric energy quality transient event to the cloud platform layer and/or the human-computer interaction layer in the real-time line measurement information in an information labeling mode.
5. The intelligent edge internet of things gateway of claim 4, wherein the intelligent edge computing management layer comprises a load identification module configured to:
acquiring original discrete data of each energy device from a data perception layer, and performing noise reduction and time-frequency decomposition on the original discrete data to obtain load identification sample data;
and inputting the load identification sample data into a pre-established neural network model to train and obtain a load identification model, so that the electric appliance use condition of the user can be calculated and obtained based on the load identification model.
6. The intelligent edge internet of things gateway of claim 5 wherein the intelligent edge computing management layer includes a stateful overhaul module configured to:
pre-establishing a fault symptom analysis model about each energy device, wherein the model comprises one or more of at least one state evaluation item, a maintenance scheme and a maintenance grade;
performing state evaluation on each energy device through at least one state evaluation item based on the original discrete data of each energy device collected from the data perception layer;
and under the condition that the state evaluation result meets the fault symptom judgment condition, feeding back the state evaluation result and the corresponding maintenance scheme in the fault symptom analysis model to the cloud platform layer and/or the human-computer interaction layer.
7. The intelligent edge internet of things gateway of claim 6, wherein the intelligent edge computing management layer comprises a short term load prediction module configured to:
determining at least one important influence factor according to the change condition of the historical load data of each energy device collected from the data perception layer along with the at least one influence factor;
constructing a short-term load prediction model based on the determined at least one significant impact factor;
the model is trained by using the change condition of the historical load data along with at least one influence factor to obtain a prediction model, so that the power grid load can be predicted in a short term based on the prediction model.
8. The intelligent edge internet of things gateway of claim 2, wherein the distribution energy efficiency analysis module is configured to:
acquiring actual power consumption and load rated power of each energy device and/or actual power consumption and distributed power supply rated power of each distributed power supply subunit from a data sensing layer; and/or
And establishing a distribution loss analysis model according to the data collected from the data perception layer.
9. The intelligent edge internet of things gateway of claim 8, wherein the distribution energy efficiency analysis module is configured to feed back dynamic changes of the real-time distribution energy efficiency of each energy device to the cloud platform layer and/or the man-machine interaction layer in time in the form of dynamic curves of the real-time distribution energy efficiency at different moments.
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