A park power utilization equipment supervision system and method based on digital twinning

Abstract

The application belongs to the technical field of power equipment supervision, and discloses a park power equipment supervision system and method based on digital twinning. The application constructs a three-dimensional model of different types of equipment by using a three-dimensional modeling software, obtains physical parameters of each equipment, real-time operation parameters collected by a sensor and environmental parameters of each equipment, inputs the data into a digital twinning model construction platform, establishes a digital twinning model of the equipment, sets a data update frequency to ensure that the model can timely reflect actual state changes of the equipment, obtains running data in the model in real time, obtains state coefficients of each equipment through the running data, analyzes running conditions of each equipment according to the state coefficients, helps maintenance personnel to quickly locate and solve problems, and then obtains an optimal energy distribution scheme according to running conditions and energy consumption conditions of all equipment, so that optimal utilization of energy is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power equipment supervision, and particularly relates to a park power equipment supervision system and method based on digital twinning. BACKGROUND

[0002] With the significant improvement of people's living standards, people's dependence on electricity is becoming more and more important. Without the action of electricity, our life will be seriously affected, and even we cannot live normally. In daily life, our clothing, food, shelter and transportation all cannot leave the power equipment, so the safe supervision of power equipment is the most important link for power supply enterprises. Digital twin power grid creates a digital power grid corresponding to the physical entity power grid in the digital space, and reflects the state of the physical entity power grid in the real environment through holographic simulation, dynamic monitoring, real-time diagnosis and accurate prediction.

[0003] However, in the prior art, it is difficult to monitor and manage the equipment operation state in real time and accurately, potential fault hidden dangers cannot be found in time, which leads to an increase in equipment downtime, and at the same time, it is also difficult to dynamically adjust the power distribution strategy in real time to ensure that multiple power equipment in the park can operate normally. SUMMARY

[0004] The purpose of the present application is to provide a park power equipment supervision system and method based on digital twinning to solve the problems faced in the background art.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] A park power equipment supervision system based on digital twinning, comprising a data acquisition module, a digital twinning model construction module, an intelligent monitoring module and a power management module.

[0007] The data acquisition module acquires the operating parameters and environmental parameters of the power equipment through sensors and Internet of Things devices.

[0008] The digital twinning model construction module combines the physical parameters, operating parameters and environmental parameters of the equipment to construct a digital twinning model corresponding to the real equipment one by one, uses the data acquisition module to acquire the equipment operating parameters and environmental parameters in real time, constantly updates the digital twinning model, and ensures the high consistency of the model and the actual equipment state.

[0009] The intelligent monitoring module analyzes the equipment operation data based on the constructed digital twinning model.

[0010] The power management module is used to dynamically adjust the equipment power strategy according to the analysis result of the intelligent monitoring module.

[0011] As a further description of the present application, the specific working process of the data acquisition module includes:

[0012] All the electrical equipment in the park to be monitored is numbered, and the numbers are 1, 2, …, y in turn;

[0013] The running parameter time-varying data of each electrical equipment is collected in real time, the environmental parameter time-varying data of the environment where each electrical equipment is located is collected in real time, and the collected data is sent to the digital twin model in real time.

[0014] As a further description of the present application, the specific working process of the digital twin model construction module includes:

[0015] First, the electrical equipment in the park is numbered, and a three-dimensional model of each numbered equipment is constructed by using a three-dimensional modeling software;

[0016] Then, the physical parameters of each equipment, the real-time running parameters, load parameters and environmental parameters collected by sensors are collected, and these data are input into a digital twin model construction platform to establish a digital twin model of the equipment;

[0017] Finally, the data update frequency is set to ensure that the model can timely reflect the actual state change of the equipment.

[0018] As a further description of the present application, the specific process of the intelligent monitoring module includes:

[0019] Based on the digital twin model, the running parameter time-varying data of the xth electrical equipment and the environmental parameter time-varying data of the environment where the electrical equipment is located are obtained;

[0020] A mathematical model of the state coefficient of the xth electrical equipment is constructed, and the expression is:

[0021]

[0022] In the formula, S x is the mathematical model of the state coefficient of the xth electrical equipment, and x belongs to [1, y]; S xr is the running state coefficient of the xth electrical equipment, S xe is the environmental state coefficient of the xth electrical equipment, S xf is the fault state coefficient of the xth electrical equipment, S xL is the load state coefficient of the xth electrical equipment; n is the number of collected running parameters of the xth electrical equipment, i belongs to [0, n], S xi is the real-time value of the ith running parameter of the xth electrical equipment, S xi,min is the minimum value of the ith running parameter of the xth electrical equipment in a historical time period, and S xi,maxis the maximum value of the i-th operating parameter of the x-th electrical equipment in the historical time period, k xi is the weight coefficient of the i-th operating parameter; m is the number of environmental parameter items of the x-th electrical equipment collected, j belongs to [0, m], S xj is the real-time value of the j-th environmental parameter of the x-th electrical equipment, S xj,min is the minimum value of the j-th environmental parameter of the x-th electrical equipment in the historical time period, S xj,max is the maximum value of the j-th environmental parameter of the x-th electrical equipment in the historical time period, k xj is the weight coefficient of the j-th environmental parameter; N xf is the number of failures of the x-th electrical equipment in the historical time period, T x is the running time of the x-th electrical equipment in the historical time period; XL c is the current load value of the x-th electrical equipment, XL min is the minimum load value of the x-th electrical equipment in the historical time period, XL max is the maximum load value of the x-th electrical equipment in the historical time period,

[0023] As a further description of the present application, the

[0024] As a further description of the present application, the specific process of the intelligent monitoring module further includes:

[0025] The x-th electrical equipment state coefficient S x is compared with the x-th electrical equipment state coefficient threshold interval:

[0026] If S x belongs to (-∞, S1], the device state is poor and there is a risk of failure, if S x belongs to (S1, S2], the device state is general and needs to be maintained and checked, if S x belongs to (S2, S3], the device state is good and may be in potential failure, if S x belongs to (S3, +∞], the device state is excellent and runs normally.

[0027] As a further description of the present application, the working process of the power consumption management module includes:

[0028] Based on the digital twin model, the state coefficients of all electrical equipment in the park are calculated in turn, and the state coefficients of all electrical equipment are arranged in order from small to large. The lower the state coefficient of the electrical equipment, the higher the priority of the current device in energy distribution.

[0029] The x-th electrical equipment energy demand mathematical model is constructed, and the expression is:

[0030]

[0031] In the formula, μ and π are weight coefficients, E h is the energy consumption value of the previous day, E a is the average daily energy consumption value in the historical time period;

[0032] Compare with E T , E T is the total energy supply of the park, if is less than or equal to E T , there is no need to adjust the power consumption strategy of the equipment;

[0033] If is greater than E T , adjust the power consumption strategy of each device according to the priority order, and give priority to the energy demand of the device with high priority.

[0034] A park power consumption equipment supervision method based on digital twinning, the method comprises:

[0035] Step S1, collecting the operation parameters and environmental parameters of the power consumption equipment through sensors and Internet of Things equipment;

[0036] Step S2, constructing a digital twinning model;

[0037] Step S3, obtaining the state coefficient of the power consumption equipment based on the digital twinning model;

[0038] Step S4, comparing the state coefficient of the power consumption equipment with the state coefficient threshold interval of the power consumption equipment, and judging the operation state of each power consumption equipment;

[0039] Step S5, arranging the state coefficient of each power consumption equipment in order to obtain the energy distribution priority of the equipment;

[0040] Step S6, comparing the total energy demand of each power consumption equipment with the total energy supply of the park;

[0041] Step S7, if the total energy demand of each power consumption equipment is less than or equal to the total energy supply of the park, there is no need to adjust the power consumption strategy of the equipment;

[0042] Step S8, if the total energy demand of each power consumption equipment is greater than the total energy supply of the park, give priority to the energy demand of the device with high priority.

[0043] The beneficial effects of the present application are:

[0044] The present application is aimed at different types of equipment, using three-dimensional modeling software to construct its three-dimensional model, obtaining the physical parameters of each equipment and the real-time running parameters collected by the sensor and the environmental parameters of each equipment, inputting these data into the digital twin model construction platform, establishing the digital twin model of the equipment, at the same time, setting the data update frequency to ensure that the model can timely reflect the actual state change of the equipment, connecting the digital twin model with the intelligent monitoring software, obtaining the running data in the model in real time, obtaining the state coefficient of each equipment through the running data, and analyzing the running condition of each equipment according to the state coefficient, helping the maintenance personnel to quickly locate and solve the problem, then obtaining the optimal energy distribution scheme according to the running condition and energy consumption condition of all equipment, and the system automatically adjusts the running parameters of the equipment according to the scheme, such as adjusting the start time and running power of the equipment, realizing the optimized utilization of energy.

[0045] Of course, implementing any product of the present application does not necessarily need to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed for the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 The structure diagram of the geological surveying and mapping system based on the Internet of Things. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] Please refer to Figure 1 As shown in the figure, a park power equipment supervision system based on digital twin is disclosed, which comprises a data acquisition module, a digital twin model construction module, an intelligent monitoring module and a power management module.

[0050] The data acquisition module collects the running parameters and environmental parameters of the power equipment through sensors and Internet of Things equipment.

[0051] The digital twin model construction module combines the physical parameters, operation parameters and environmental parameters of the equipment to construct a digital twin model corresponding to the real equipment, uses the data acquisition module to collect the operation parameters and environmental parameters of the equipment in real time, and constantly updates the digital twin model to ensure high consistency between the model and the actual equipment state.

[0052] The intelligent monitoring module analyzes the equipment operation data based on the constructed digital twin model.

[0053] The power consumption management module is used to dynamically adjust the equipment power consumption strategy according to the analysis result of the intelligent monitoring module.

[0054] Through the above technical solution, the application provides a park power consumption equipment supervision system based on digital twin. First, the park power consumption equipment is numbered, and for different types of equipment, a three-dimensional model is constructed using three-dimensional modeling software to obtain the physical parameters of each equipment and the real-time operation parameters collected by sensors and the environmental parameters of each equipment. These data are input into the digital twin model construction platform to establish the digital twin model of the equipment. At the same time, the data update frequency is set to ensure that the model can timely reflect the actual state change of the equipment. The digital twin model is connected with the intelligent monitoring software to obtain the operation data in the model in real time. The state coefficient of each equipment is obtained through the operation data, and the operation of each equipment is analyzed according to the state coefficient to help maintenance personnel quickly locate and solve problems. Then, the optimal energy distribution scheme is obtained according to the operation and energy consumption of all equipment, and the system automatically adjusts the operation parameters of the equipment according to the scheme, such as adjusting the start time and operation power of the equipment, to realize the optimal utilization of energy.

[0055] As a further description of the application, the specific working process of the data acquisition module includes:

[0056] All the power consumption equipment in the supervised park is numbered, and the numbers are 1, 2, …, y in sequence.

[0057] The operation parameter time-varying data of each power consumption equipment is collected in real time, the environmental parameter time-varying data of the environment where each power consumption equipment is located is collected in real time, and the collected data is sent to the digital twin model in real time.

[0058] As a further description of the application, the specific working process of the digital twin model construction module includes:

[0059] First, the park power consumption equipment is numbered, and for different numbered equipment, a three-dimensional model is constructed using three-dimensional modeling software.

[0060] Then, the physical parameters of each device and the real-time running parameters, load parameters and environmental parameters collected through the sensor are collected, and the data is input into a digital twin model construction platform to establish a digital twin model of the device;

[0061] Finally, the data update frequency is set to ensure that the model can timely reflect the actual state change of the device.

[0062] As a further description of the scheme of the application, the specific process of the intelligent monitoring module includes:

[0063] Based on the digital twin model, the running parameter time-varying data of the xth power utilization device and the environmental parameter time-varying data of the environment where the power utilization device is located are obtained;

[0064] A state coefficient mathematical model of the xth power utilization device is constructed, and the expression is:

[0065]

[0066] In the formula, S x is the state coefficient mathematical model of the xth power utilization device, x belongs to [1, y]; S xr is the running state coefficient of the xth power utilization device, S xe is the environmental state coefficient of the xth power utilization device, S xf is the fault state coefficient of the xth power utilization device, S xL is the load state coefficient of the xth power utilization device; n is the number of collected running parameter items of the xth power utilization device, i belongs to [0, n], S xi is the real-time value of the ith running parameter of the xth power utilization device, S xi,min is the minimum value of the ith running parameter of the xth power utilization device in the historical time period, S xi,max is the maximum value of the ith running parameter of the xth power utilization device in the historical time period, k xi is the weight coefficient of the ith running parameter; m is the number of collected environmental parameters of the xth power utilization device, j belongs to [0, m], S xj is the real-time value of the jth environmental parameter of the xth power utilization device, S xj,min is the minimum value of the jth environmental parameter of the xth power utilization device in the historical time period, S xj,max is the maximum value of the jth environmental parameter of the xth power utilization device in the historical time period, k xj is the weight coefficient of the jth environmental parameter; N xf is the number of faults of the xth power utilization device in the historical time period, T x is the running time length of the xth power utilization device in the historical time period; XL c is the current load value of the xth power utilization device, XL minXL is the minimum load value of the xth electrical equipment in the historical time period, max XL is the maximum load value of the xth electrical equipment in the historical time period,

[0067] As a further description of the present application, the specific process of the intelligent monitoring module further comprises:

[0068] As a further description of the present application, the specific process of the intelligent monitoring module further comprises:

[0069] The xth electrical equipment state coefficient S x is compared with the xth electrical equipment state coefficient threshold interval:

[0070] If S x belongs to (-∞, S1], the equipment state is poor, and there is a risk of failure, if S x belongs to (S1, S2], the equipment state is general, and maintenance inspection is required, if S x belongs to (S2, S3], the equipment state is good, and there may be potential failure, if S x belongs to (S3, +∞], the equipment state is excellent, and the operation is normal.

[0071] Through the above technical solution, the present embodiment provides a method for monitoring the state of the equipment based on the digital twin model, first, the real-time running data and the historical data of the equipment (such as current, voltage, vibration frequency, temperature, etc.) are obtained, different monitoring parameters are selected according to the equipment, then the equipment running state coefficient is calculated through the formula , the real-time data and the historical technical data of the environment in which the equipment is located are obtained, then the equipment environment state coefficient is calculated through the formula , the fault parameters and the use time of the equipment are obtained, the fault state coefficient of the equipment is calculated through the formula , the load state coefficient of the equipment is calculated through the formula based on the load data of the equipment and the physical parameters of the equipment, finally, the state coefficient of the equipment is calculated through the formula S x = α * S xr + β * S xe + γ * S xf + δ * S xL weighted summation.

[0072] Example

[0073] Alpha is the weight of the operation parameter score, usually high, beta is the weight of the environment parameter score, usually medium, gamma is the weight of the historical failure score, usually low, delta is the weight of the load parameter score, usually the lowest, alpha+beta+gamma+delta=1, the running state coefficient S of a certain x equipment xr =0.7, the environment state coefficient S xe =0.8, the failure state coefficient S xf =0.5, the load state coefficient S xL =0.9, alpha=0.4, beta=0.3, gamma=0.2, delta=0.1;

[0074] Substitute the mathematical model to get S x =0.7*0.4+0.8*0.3+0.5*0.3+0.9*0.2=0.85, then compare 0.85 with the threshold interval set by the system to judge the running state of the equipment.

[0075] As a further description of the scheme of the application, the working process of the power consumption management module includes:

[0076] Based on the digital twin model, the state coefficients of all power consumption equipment in the park are calculated in turn, and the state coefficients of all power consumption equipment are arranged in order from small to large. The lower the state coefficient of the power consumption equipment, the higher the energy distribution priority of the current equipment.

[0077] The energy demand mathematical model of the xth power consumption equipment is constructed, and the expression is:

[0078]

[0079] In the formula, mu and pi are weight coefficients, E h is the energy consumption value of the previous day, E a is the average daily energy consumption value in the historical time period;

[0080] Compare E with E T , E T is the total energy supply of the park, if E is less than or equal to E T , there is no need to adjust the power consumption strategy of the equipment;

[0081] If E is greater than E T , adjust the power consumption strategy of each equipment according to the priority order, and give priority to guarantee the energy demand of the equipment with high priority.

[0082] Through the above technical scheme, the application provides a power consumption equipment power distribution strategy. First, based on the load parameters and historical energy consumption parameters of each equipment, the formula The energy demand mathematical model of each electrical equipment is calculated, then, the state coefficients of all electrical equipment are arranged in order from small to large, the lower the state coefficient of electrical equipment, the higher the priority of energy distribution of the current equipment, finally, the E T E T is the total supply of park energy, if is less than or equal to E T , there is no need to adjust the power consumption strategy of the equipment; if is greater than E T , the power consumption strategy of each equipment is adjusted according to the priority order, and the energy demand of the equipment with high priority is preferentially guaranteed.

[0083] Examples

[0084] The park can allocate 950KW of power, has five electrical equipment, A, B, C, D and E, S A = 0.3, S B = 0.5, S C = 0.6, S D = 0.8 and S E = 0.9, therefore, the priorities of A, B, C, D and E are A > B > C > D > E, the energy demands of A, B, C, D and E are 100KW, 150KW, 200KW, 250KW and 300KW, the power consumption demands of A, B, C and D are preferentially guaranteed to be allocated 100KW, 150KW, 200KW and 250KW, and the remaining 250KW of energy is allocated to E, and the operation power or operation time of equipment E is adjusted so that E can stably operate under the supply of 250KW of energy.

[0085] It should be noted that the minimum supply energy of each equipment needs to be obtained in advance, and under the principle of priority allocation, the allocated energy of each equipment needs to be higher than the minimum supply energy.

[0086] A park electrical equipment supervision method based on digital twinning, the method comprises:

[0087] Step S1, collecting the operation parameters and environmental parameters of the electrical equipment through sensors and Internet of Things devices;

[0088] Step S2, constructing a digital twinning model;

[0089] Step S3, obtaining the state coefficient of the electrical equipment based on the digital twinning model;

[0090] Step S4, comparing the state coefficient of the electrical equipment with the state coefficient threshold interval of the electrical equipment, and judging the operation state of each electrical equipment;

[0091] Step S5, arranging the state coefficients of each electrical equipment in order to obtain the energy distribution priority of the equipment;

[0092] Step S6, comparing the total energy demand of each electrical equipment with the total energy supply of the park;

[0093] Step S7, if the total energy demand of each electrical equipment is less than or equal to the total energy supply of the park, there is no need to adjust the equipment power consumption strategy;

[0094] Step S8, if the total energy demand of each electrical equipment is greater than the total energy supply of the park, the energy demand of the equipment with high priority is preferentially guaranteed.

[0095] It should be noted that the threshold, threshold interval and coefficient set in the present application are empirical values, and all calculations in the present application are dimensionless calculations, and do not need to be described in detail.

[0096] The above is only an example and description of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present application, which should belong to the protection scope of the present application.

Claims

1. A digital twin-based monitoring system for electrical equipment in a park, characterized in that, include: The module includes a data acquisition module, a digital twin model construction module, an intelligent monitoring module, and an electricity management module. The data acquisition module collects operating parameters and environmental parameters of electrical equipment through sensors and IoT devices; The digital twin model building module combines the physical parameters, operating parameters and environmental parameters of the equipment to build a digital twin model that corresponds one-to-one with the real equipment. The data acquisition module collects the operating parameters and environmental parameters of the equipment in real time and continuously updates the digital twin model to ensure a high degree of consistency between the model and the actual equipment status. The intelligent monitoring module analyzes equipment operation data based on the constructed digital twin model; The power management module is used to dynamically adjust the power consumption strategy of the equipment based on the analysis results of the intelligent monitoring module. The operation process of the electricity management module includes: Based on the digital twin model, the state coefficients of all electrical equipment in the park are calculated in turn. The state coefficients of all electrical equipment are arranged in ascending order. The lower the state coefficient of the electrical equipment, the higher the energy allocation priority of the current equipment. Construct a mathematical model for the energy demand of the x-th electrical device, expressed as: ； In the formula, and These are the weighting coefficients. This is the energy consumption value from the previous day. This represents the average daily energy consumption over a historical period. Let x be the current load value of the x-th electrical device. This represents the maximum load value of the x-th electrical device within the historical time period. Will and Compare, For the total energy supply of the park, if Less than or equal to No need to adjust the equipment's power consumption strategy; like Greater than The power consumption strategy of each device is adjusted according to priority, and the energy needs of high-priority devices are guaranteed first.

2. The park electrical equipment monitoring system based on digital twin as described in claim 1, characterized in that, The specific working process of the data acquisition module includes: All electrical equipment in the park to be regulated will be numbered sequentially as 1, 2, ..., y; The system collects real-time data on the changes in operating parameters of each electrical device over time, as well as real-time data on the changes in environmental parameters of the environment in which each electrical device is located, and sends the collected data to the digital twin model in real time.

3. A park electrical equipment monitoring system based on digital twins according to claim 2, characterized in that, The specific working process of the digital twin model construction module includes: First, the electrical equipment in the park is numbered, and for each numbered piece of equipment, a 3D model is built using 3D modeling software. Then, the physical parameters of each device, as well as the real-time operating parameters, load parameters, and environmental parameters collected by sensors, are collected and input into the digital twin model building platform to establish a digital twin model of the device. Finally, set the data update frequency to ensure that the model can reflect changes in the actual status of the equipment in a timely manner.

4. A digital twin-based monitoring system for industrial park electrical equipment according to claim 3, characterized in that, The specific processes of the intelligent monitoring module include: Based on the digital twin model, obtain the data on the time-varying operation parameters of the xth electrical device and the time-varying environmental parameters of the environment in which the electrical device is located; Construct a mathematical model for the state coefficient of the x-th electrical device, with the following expression: ； In the formula, Let x be the state coefficient mathematical model for the x-th electrical device, where x belongs to ; Let x be the operating status coefficient of the x-th electrical device. Let x be the environmental condition coefficient of the x-th electrical device. Let x be the fault state coefficient of the xth electrical equipment. Let be the load state coefficient of the x-th electrical device; n is the number of operating parameter items collected for the x-th electrical device, and i belongs to . , Let be the real-time value of the i-th operating parameter of the x-th electrical device. This represents the minimum value of the i-th operating parameter of the x-th electrical device within a historical time period. This represents the maximum value of the i-th operating parameter of the x-th electrical device within a historical time period. The weight coefficient for the i-th running parameter; Let j be the number of environmental parameter items collected for the x-th electrical device, and j belong to... , This represents the real-time value of the j-th environmental parameter for the x-th electrical device. Let j be the minimum value of the j-th environmental parameter of the x-th electrical device within the historical time period. This represents the maximum value of the j-th environmental parameter for the x-th electrical device within a historical time period. The weighting coefficient for the j-th environmental parameter; This represents the number of failures of the x-th electrical device within a historical time period. The runtime of the xth electrical device within the historical time period; Let x be the current load value of the x-th electrical device. This represents the minimum load value of the x-th electrical device within a historical time period. This represents the maximum load value of the x-th electrical device within a historical time period. , .

5. A campus electrical equipment monitoring system based on digital twins according to claim 4, characterized in that, The current load value = Minimum load value = Maximum load value = .

6. A campus electrical equipment monitoring system based on digital twins according to claim 5, characterized in that, The specific process of the intelligent monitoring module also includes: The state coefficient of the xth electrical device Compare with the threshold range of the state coefficient of the xth electrical device: like belong The equipment is in poor condition and there is a risk of failure. belong The equipment is in fair condition and requires maintenance and inspection. belong The equipment is in good condition, but may have a potential malfunction. belong The equipment is in excellent condition and operating normally.

Images