Switch distributed control method and system of power distribution cabinet based on internet of things

Abstract

The application discloses a switch distributed control method and system of a power distribution cabinet based on an Internet of Things, and the method comprises the following steps: constructing a multi-level power distribution topology network; deploying a hierarchical distributed control unit; establishing an Internet of Things communication network covering power distribution cabinets at all levels to realize point-to-point communication between control units at all levels; designing a hierarchical distributed control decision, including: designing a main control unit responsible for global load balancing, energy distribution, and safety strategy formulation and issuance, designing a regional control unit responsible for regional load scheduling, energy optimization, and exception handling in the region, and designing a terminal control unit responsible for local load control, energy consumption execution, and real-time safety protection; based on the hierarchical distributed control decision, coordinating control units at all levels to execute a multi-level collaborative control process of load balancing, energy optimization, and safety protection. The application can solve the defects of collaborative control, efficient energy utilization, and adaptive capacity in the existing power distribution system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution control, in particular to a switch distributed control method and system for power distribution cabinet based on Internet of Things. BACKGROUND

[0002] With the continuous development of power systems, its scale continues to expand, and the demand for electricity is increasingly complex. The traditional power distribution system usually adopts a centralized control architecture, which is managed by a central control unit. This architecture has played an important role in the stable operation of the power system in a certain period of time, promoted the orderly progress of power distribution, and met the relatively simple electricity demand at that time. However, with the change of the times, the power system is facing more challenges and higher requirements, and the limitations of the centralized control architecture have gradually emerged.

[0003] The centralized control architecture relies on the central control unit to manage and dispatch all power distribution switches, and realizes the distribution and control of electricity through centralized processing and decision-making. However, the centralized control architecture has a high risk of single-point failure, and once the central control unit fails, the entire power distribution system may be paralyzed; the system response speed is slow, and it is difficult to quickly respond to sudden changes in electricity demand; the scalability is poor, and it is difficult to adapt to the continuous expansion of the power system scale and the increasing complexity of electricity demand.

[0004] In recent years, distributed control technology has gradually attracted attention in power distribution systems. Distributed control is to distribute control functions to multiple control units, each of which is responsible for managing power distribution switches in a specific area or at a specific level, and each control unit works cooperatively through a communication network. However, the existing distributed control scheme lacks coordination between control units, each control unit often operates independently, lacks effective coordination mechanisms, and is difficult to achieve global optimization control; the degree of intelligence is limited, most control units only perform simple switch operations, lack intelligent decision-making capabilities based on data analysis; the system adaptability is not strong, and it is difficult to dynamically adjust the control strategy according to changes in the electricity environment and user demand; energy utilization efficiency is low, and there is a lack of fine management of energy consumption, leading to energy waste; the security mechanism is not perfect, and in abnormal situations, there is a lack of cooperative response mechanism between distributed nodes, affecting system security. SUMMARY

[0005] In order to solve the problems of cooperative control, intelligent sensing and control, energy efficient utilization, and self-adaptive ability in existing power distribution systems, the present application provides a switch distributed control method and system for power distribution cabinet based on Internet of Things.

[0006] In a first aspect, the present application provides a switch distributed control method for power distribution cabinet based on Internet of Things, comprising:

[0007] The multi-level power distribution topology network is constructed, including: deploying a first topology layer of a first power distribution cabinet, deploying a second topology layer of a plurality of second power distribution cabinets, and deploying a third topology layer of a plurality of terminal power distribution cabinets; each power distribution cabinet integrates a communication gateway and an environmental sensor, and is correspondingly equipped with a topology node and a power distribution switch;

[0008] The hierarchical distributed control unit is deployed, including: a master control unit deployed in the first power distribution cabinet, a regional control unit deployed in the second power distribution cabinet, and a terminal control unit deployed in the third power distribution cabinet;

[0009] The Internet of Things communication network covering all levels of power distribution cabinets is established to realize point-to-point communication between control units at all levels;

[0010] Based on the constructed multi-level power distribution topology network and the hierarchical distributed control unit, a hierarchical distributed control decision is designed, including: the master control unit is responsible for global load balancing, energy distribution, and security policy formulation and issuance; the regional control unit is responsible for regional load scheduling, energy optimization, and exception handling within the region; and the terminal control unit is responsible for local load control, energy consumption execution, and real-time safety protection;

[0011] Based on the hierarchical distributed control decision, a multi-level collaborative control process is coordinated to execute load balancing, energy optimization, and safety protection by the control units at all levels, including: a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

[0012] By adopting the above scheme, the multi-level power distribution topology network is constructed, the hierarchical distributed control unit is deployed, the Internet of Things communication network is established, the hierarchical distributed control decision is designed, and the multi-level collaborative control process is executed, realizing distributed control of the power distribution system, dynamically adjusting the power supply strategy according to actual power consumption demand, reducing unnecessary energy consumption, and at the same time ensuring power safety.

[0013] Preferably, based on the hierarchical distributed control decision, the multi-level collaborative control process for executing load balancing by the control units at all levels includes:

[0014] The terminal control unit is used to collect terminal load data and environmental data in real time, compare the terminal load data with a dynamic load threshold preset based on the environmental data, obtain terminal load overrun data, or obtain terminal overrun data through a deep learning algorithm; the regional control unit is used to aggregate regional terminal load data, analyze and determine whether the regional terminal load triggers a same-level load coordination condition, the same-level load coordination condition refers to that the regional terminal load can be coordinately scheduled to eliminate terminal load overrun; when the same-level load coordination condition is triggered, a greedy algorithm is used to obtain an optimal strategy for coordinating the regional terminal load to eliminate terminal load overrun; when the same-level load coordination condition is not triggered, a regional terminal load gap is calculated and a regional load demand is updated and reported;

[0015] Based on the regional load demand reported by the regional control unit and the global power supply capacity, the main control unit generates a global load quota allocation strategy by using a linear programming algorithm and presetting the load priority of each region, and distributes it to the regional control unit; then the regional control unit decomposes the regional load quota based on the preset terminal load priority, and distributes it to the terminal control unit; then the terminal control unit adjusts the terminal operating power by controlling the switch of the power distribution cabinet according to the decomposed load quota.

[0016] By adopting the above scheme, the terminal control unit monitors the terminal load condition and reports it, the regional control unit analyzes whether the same level load coordination condition is triggered, and when it is triggered, the optimal strategy is obtained by using the greedy algorithm to eliminate the overrun, and when it is not triggered, the gap is calculated and reported, the main control unit generates a global load quota allocation strategy according to the reported demand and global power supply capacity, the regional control unit decomposes and distributes it, and the terminal control unit adjusts the terminal operating power according to the quota, which specifically realizes the balanced distribution of load in the power distribution system, and improves the stability and reliability of power supply.

[0017] Preferably, based on the hierarchical distributed control decision, the control instructions of each level of control unit are coordinated to complete the control instruction interaction to execute the multi-level collaborative control process of energy optimization, which includes:

[0018] The terminal control unit collects the terminal real-time energy consumption and calculates the energy efficiency parameter in real time, compares it with the preset energy efficiency parameter, obtains the terminal energy efficiency abnormal data or predicts the terminal energy consumption data by using a deep learning algorithm and obtains the energy efficiency abnormal data; the regional control unit summarizes the regional terminal energy consumption data and energy efficiency abnormal data, analyzes and judges whether the regional terminal energy consumption data triggers the same level energy consumption coordination condition, the same level energy consumption coordination condition refers to the terminal energy efficiency abnormality can be eliminated by coordinating the compensation energy consumption data through the regional terminal energy consumption; when the same level coordination condition is triggered, the optimal strategy of regional terminal energy consumption coordination is obtained by using a multi-objective optimization algorithm to eliminate the terminal energy efficiency abnormality; when the same level coordination condition is not triggered, the regional terminal energy efficiency optimization demand is calculated and the regional energy consumption demand is updated and reported;

[0019] Combined with the peak and valley electricity price of the day and the regional energy consumption demand, the main control unit formulates a global energy distribution strategy by using an energy distribution algorithm, and distributes it to the regional control unit; then the regional control unit formulates a corresponding regional terminal energy distribution strategy according to the global energy distribution strategy and the energy consumption characteristics of the terminals in the region, and distributes it to the terminal control unit; then the terminal control unit adjusts the terminal power operation mode in each period by controlling the switch of the power distribution cabinet according to the corresponding regional terminal energy distribution strategy.

[0020] By adopting the above scheme, the terminal control unit is used to monitor terminal energy consumption reporting, the regional control unit is used to analyze whether to trigger the same level energy consumption coordination condition, the optimal strategy is triggered to eliminate energy efficiency abnormalities, and the energy consumption demand is reported if it is not triggered. The main control unit formulates a global energy distribution strategy in combination with peak and valley electricity prices and reported demand, the regional control unit further decomposes and issues the strategy, and the terminal control unit adjusts the terminal power operation mode according to the strategy. The energy optimization distribution and utilization are specifically realized, the terminal power can be dynamically adjusted according to different time periods and energy consumption conditions, and the energy utilization efficiency is improved.

[0021] Preferably, based on the hierarchical distributed control decision, the control instruction interaction of each level control unit is coordinated to execute the multi-level cooperative control process for safety protection, which includes:

[0022] The terminal control unit is used to collect terminal electrical parameters, equipment state parameters and environmental data in real time, to judge whether a fault occurs and the fault type and level through a deep learning algorithm, to obtain or predict the terminal fault and the fault type and level, and to upload them to the regional control unit. The regional control unit is used to judge whether a fault diffusion occurs and the fault diffusion degree through a deep learning algorithm according to the received terminal fault and the fault type and level and the local protection condition. When it is judged that a fault diffusion occurs and spreads to other areas, the regional control unit is used to continue cross-verification of regional level data and fault positioning, and to upload the fault positioning and fault diffusion results to the main control unit.

[0023] The main control unit is used to obtain a global safety isolation strategy through a deep learning algorithm based on the fault positioning and fault diffusion results, and to issue it to the regional control unit. The regional control unit is used to obtain a corresponding regional terminal safety isolation strategy through a deep learning algorithm based on the fault positioning and fault diffusion results and the global regional safety isolation strategy, and to issue it to the terminal control unit. The terminal control unit is used to adjust whether the terminal is isolated according to the corresponding regional terminal safety isolation strategy by controlling the switch of the power distribution cabinet.

[0024] By adopting the above scheme, the terminal control unit is used to monitor terminal faults and types and levels in real time. The regional control unit is used to match a preset regional isolation strategy and estimate whether it leads to fault diffusion, to perform cross-verification of data and fault positioning when fault diffusion occurs, and to improve the accuracy of fault judgment. The main control unit is used to match a global safety isolation strategy according to the fault positioning and diffusion results. The regional control unit is used to further match a terminal safety isolation strategy. The terminal control unit is used to control the switch to adjust whether the terminal is isolated according to the strategy. When a fault occurs, the fault terminal is effectively isolated to ensure the safety of electricity use.

[0025] Preferably, in the multi-level cooperative control process in which the control units at different levels perform load balancing, energy optimization and safety protection, it is determined whether conflicts exist in different dimension multi-level cooperative control processes at the same time, and the different dimension multi-level cooperative control processes with conflicts are identified and obtained; it is determined from the obtained different dimension multi-level cooperative control processes with conflicts whether a fault level exists that is predicted to occur, and if so, the multi-level cooperative control process for safety protection is preferentially executed;

[0026] If not, for each dimension multi-level cooperative control process, the corresponding dimension process preset core scoring indicator is evaluated, and the score of each dimension multi-level cooperative control process is obtained; wherein the preset core scoring indicator of the multi-level cooperative control process for load balancing includes a quantitative indicator of the proportion of the difference between the current terminal load and the terminal load limit in different difference preset ranges; the preset core scoring indicator of the multi-level cooperative control process for energy optimization includes a quantitative indicator of the economic expenditure range in which the current terminal energy efficiency abnormal economic expenditure is eliminated; the preset core scoring indicator of the multi-level cooperative control process for safety protection includes a quantitative indicator of the proportion of fault levels; according to the scores obtained from the different dimension multi-level cooperative control processes with conflicts, the priority of the multi-level cooperative control processes for load balancing, energy optimization and safety protection is set from high to low according to the scores, and the multi-level cooperative control process with high priority is preferentially executed.

[0027] By using the above scheme, when the control units at different levels perform the multi-level cooperative control process for load balancing, energy optimization and safety protection, it is determined whether conflicts exist in different dimension cooperative control processes and the conflicting processes are identified; when a fault with a serious fault level exists, the safety protection process is preferentially executed to ensure system safety; when no serious fault exists, the processes are evaluated by preset core scoring indicators, the priority is set according to the scores, and the process with high priority is preferentially executed, thereby ensuring that the control processes can be reasonably arranged under different control requirements.

[0028] Preferably, it further comprises:

[0029] In the multi-level cooperative control process of coordinating the control units at different levels to perform load balancing, energy optimization and safety protection, when a regional control unit is used to execute the control process, a determination is made as to whether a preset key decision scenario is triggered, including: adjusting a preset key terminal load quota scenario, adjusting a preset key terminal energy consumption data scenario, and obtaining a preset key terminal safety isolation strategy scenario; a verification instruction is sent to the upper and lower control units of the regional control unit, and if the upper-level control unit verifies that there is no conflict between the load quota adjustment, terminal energy consumption data adjustment, isolation of the preset key terminal and the regional load quota, regional terminal energy distribution and regional safety isolation strategy issued by the upper-level control unit, and if the lower-level control unit verifies that the load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal can be executed by the lower-level control unit, a verification success is sent back to the regional control unit, and the process of load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal is continued using the regional control unit; otherwise, a verification failure is sent back to the regional control unit, and the process of load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal is continued using the regional control unit.

[0030] By using the above scheme, when the regional control unit executes the key decision, the verification of the upper and lower control units is performed to ensure that the decision does not conflict with the strategies at different levels and is conditional for execution by the lower level, thereby improving the accuracy and reliability of the multi-level cooperative control process and avoiding control errors caused by decision conflicts.

[0031] In a second aspect, the application provides a switch distributed control system for power distribution cabinets based on the Internet of Things, comprising:

[0032] A power distribution topology construction module is used to construct a multi-level power distribution topology network, including: a first topology layer deploying a first power distribution cabinet, a second topology layer deploying a plurality of second power distribution cabinets, and a third topology layer deploying a plurality of terminal power distribution cabinets; each power distribution cabinet integrates a communication gateway and an environmental sensor, and is equipped with a topology node and a power distribution switch;

[0033] A control unit deployment module is used to deploy layered distributed control units, including: a main control unit deployed in the first power distribution cabinet, a regional control unit deployed in the second power distribution cabinet, and a terminal control unit deployed in the third power distribution cabinet;

[0034] A communication network establishment module is used to establish an Internet of Things communication network covering power distribution cabinets at different levels to realize point-to-point communication between control units at different levels;

[0035] The control decision design module is configured to design a hierarchical distributed control decision based on the constructed multi-level power distribution topology network and the hierarchical distributed control unit, including: designing a master control unit responsible for global load balancing, energy distribution, and security policy formulation and issuing, designing a regional control unit responsible for regional load scheduling, energy optimization, and exception handling in the region, and designing a terminal control unit responsible for local load control, energy consumption execution, and real-time security protection.

[0036] The collaborative control execution module is configured to coordinate the multi-level collaborative control processes of load balancing, energy optimization, and security protection executed by the control units at different levels based on the hierarchical distributed control decision, including: a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

[0037] By adopting the above scheme, distributed control of the power distribution cabinet switch is achieved, the control strategy can be dynamically adjusted according to the power consumption environment and demand, the adaptability and intelligent degree of the system are enhanced, and power consumption safety is ensured and energy utilization efficiency is improved through multi-level collaborative control.

[0038] In a third aspect, the present application provides a computer readable storage medium, comprising a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the method as described above when the computer program is running.

[0039] In a fourth aspect, the present application provides a computer device, comprising a memory, a processor, and a program stored on the memory and executable, wherein the program is executed by the processor to implement the steps of the method as described above.

[0040] In summary, the present application has the following beneficial effects:

[0041] 1. Constructing a multi-level power distribution topology network, deploying a hierarchical distributed control unit, establishing an Internet of Things communication network, designing a hierarchical distributed control decision, coordinating the multi-level collaborative control processes of load balancing, energy optimization, and security protection executed by the control units at different levels, implementing collaborative control of the control units at different levels, realizing intelligent perception, remote control, and data analysis of the power distribution switch based on the Internet of Things, achieving efficient utilization of energy on the premise of ensuring power consumption safety, dynamically adjusting the allocation strategy according to user demand, and improving the adaptive ability;

[0042] 2. When the control units at different levels execute the multi-level collaborative control processes, conflicts between different processes are effectively handled to ensure that safety is prioritized in the event of a serious fault, and the priority of each process is reasonably set according to the preset core scoring index, making the system run more stably and efficiently, and ensuring the safe and reliable operation of the power distribution system and the rational use of energy;

[0043] 3. In the process of coordinating the multi-level collaborative control of the load balancing, energy optimization and safety protection of the control units at all levels, when the regional control unit makes key decisions, the consistency of the decisions with the overall strategy and the feasibility of the execution of the lower level are ensured through the verification of the upper and lower control units, and control conflicts are avoided. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 Flow chart of the switch distributed control method of the power distribution cabinet based on the Internet of Things described in the specific embodiment;

[0045] Figure 2 Device deployment diagram of the multi-level power distribution topology network in the switch distributed control method of the power distribution cabinet based on the Internet of Things described in the specific embodiment;

[0046] Figure 3 Topological principle diagram of the multi-level power distribution topology network in the switch distributed control method of the power distribution cabinet based on the Internet of Things described in the specific embodiment;

[0047] Figure 4 Structural schematic diagram of the switch distributed control system of the power distribution cabinet based on the Internet of Things described in the specific embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0049] As Figure 1 shown, the present application discloses a switch distributed control method of a power distribution cabinet based on the Internet of Things, which specifically comprises: constructing a multi-level power distribution topology network, deploying a hierarchical distributed control unit, establishing an Internet of Things communication network, designing a hierarchical distributed control decision, and coordinating the execution of a multi-level collaborative control process by the control units at all levels. Among them, the multi-level power distribution topology network provides a physical basis for distributed control, the hierarchical distributed control unit realizes the decentralization and hierarchy of control functions, the Internet of Things communication network guarantees the data interaction between control units, the hierarchical distributed control decision guides the work of the control units at all levels, and the multi-level collaborative control process ensures the realization of the overall goal of the system, achieving the effect of improving the energy utilization efficiency, safety and adaptability of the power distribution system. The present application will be further described in detail below.

[0050] S1, constructing a multi-level power distribution topology network.

[0051] Specifically, a hierarchical power distribution network structure composed of a first topology layer, a second topology layer and a third topology layer is established to form a physical basis for distributed control. As Figure 2 and Figure 3As shown, a primary power distribution cabinet (box) is deployed at the primary topology layer as the source of the entire topology structure, responsible for receiving the power supply from the upper level and distributing power to the lower level topology layer. A plurality of secondary power distribution cabinets (boxes) are deployed at the secondary topology layer, each equipped with a topology node and a power distribution switch, connecting the primary power distribution cabinet (box) and the tertiary topology layer. A plurality of terminal power distribution boxes are deployed at the tertiary topology layer, each equipped with a topology node and a power distribution switch, directly connected to the terminal load.

[0052] S2, deploy a hierarchical distributed control unit.

[0053] Specifically, deploying a hierarchical distributed control unit includes deploying a master control unit in the primary power distribution cabinet, deploying a regional control unit in the secondary power distribution cabinet, and deploying a terminal control unit in the tertiary power distribution cabinet. The hierarchical relationship between the control units is established, the upper level control unit can cover the decision of the lower level control unit and issue decision instructions to the lower level control unit, but the lower level control unit can operate independently when communication is interrupted.

[0054] Among them, the master control unit is responsible for formulating and issuing global control strategies; Specifically, by collecting data from all levels of control units, global load balancing, energy distribution and safety strategy formulation are performed, and the strategy is issued to each regional control unit. The regional control unit is responsible for the control of the power distribution switch in a specific area, has a certain autonomous decision-making ability, and uses embedded controllers and corresponding control algorithms for local analysis; Specifically, according to the strategy of the master control unit and the terminal data collected, load scheduling, energy optimization and exception handling in the region are performed, and relevant data is reported to the master control unit. The terminal control unit is responsible for directly controlling the power distribution switch control, and then controlling the power supply state of the terminal load and local protection, specifically according to the instructions of the regional control unit and local data, local load control and real-time safety protection are performed to ensure the normal operation of the terminal load. The internal of each level of power distribution cabinet (box) is integrated with intelligent molded case switches, intelligent switches, communication gateways, environmental sensors and other elements to form a local control unit.

[0055] S3, establish an Internet of Things communication network covering all levels of power distribution cabinets to realize point-to-point communication between all levels of control units.

[0056] Specifically, as shown in Figure 2 The Internet of Things sensing and communication network covering all levels of power distribution cabinets (boxes) is constructed to realize data acquisition, transmission and sharing. Intelligent switches are deployed in all levels of power distribution cabinets (boxes) to realize real-time acquisition of current, voltage, power, frequency, temperature and power factor. Environmental sensors are deployed inside the power distribution cabinet (box) to monitor the temperature, humidity, smoke and other environmental parameters inside the power distribution cabinet (box). Through the communication gateway, all levels of power distribution cabinets (boxes) are connected to the Internet of Things to realize real-time uploading of data and issuing of instructions.

[0057] A distributed data storage mechanism is established, preset key power distribution data is set, and the preset key power distribution data is stored locally while being uploaded to the cloud to ensure data reliability and consistency. Point-to-point communication between control units is achieved, including point-to-point communication between control units of different levels and point-to-point communication between control units of the same level. The main control unit, the regional control unit and the terminal control unit are connected through communication to form a data closed loop.

[0058] S4, based on the constructed multi-level power distribution topology network and the hierarchical distributed control unit, a hierarchical distributed control decision is designed.

[0059] Specifically, the main control unit is responsible for global load balancing, energy distribution, and security policy formulation and issuance. In terms of global load balancing, the main control unit calculates the optimal load distribution scheme based on the load conditions of each level of power distribution cabinet and the demand of terminal load, ensuring the load balancing of each region. In terms of energy distribution, the main control unit formulates a reasonable energy distribution strategy based on the supply of energy and the demand for energy consumption, improving energy utilization efficiency. In terms of security policy formulation, the main control unit establishes a perfect security policy model to monitor and evaluate the security state of the system in real time, formulates corresponding security policies and issues them to each level of control unit.

[0060] The regional control unit is responsible for regional load scheduling, energy optimization and exception handling. Regional load scheduling adjusts the state of power distribution switch according to the change of terminal load in the region to ensure the stability of load in the region. Energy optimization analyzes the energy consumption in the region and adopts energy-saving measures to reduce energy waste. Exception handling responds and handles the abnormal situation in the region in a timely manner.

[0061] The terminal control unit is responsible for local load control, energy consumption execution and real-time safety protection. Through regional load scheduling scheme and regional energy distribution strategy, the on-off of power distribution switch is controlled to realize individual power supply and energy consumption execution of terminal load. Real-time safety protection monitors the electrical parameters and equipment state of terminal load to discover and handle safety hazards in time, ensuring the safe operation of terminal load.

[0062] S5, based on the hierarchical distributed control decision, the multi-level collaborative control process of load balancing, energy optimization and safety protection is coordinated.

[0063] Specifically, based on the control framework provided by the above hierarchical distributed control decision, control information interaction is achieved through coordination of control units at all levels, so as to realize hierarchical collaborative control in the dimensions of load balancing, energy optimization and safety protection, and achieve the effect of improving the energy utilization efficiency, safety and adaptability of the power distribution system.

[0064] The hierarchical collaborative control process of the load balance, energy optimization, and safety protection dimensions includes top-down control, bottom-up feedback, and peer coordination. The hierarchical collaborative control process of each dimension includes a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process.

[0065] In the bottom-up perception and early warning collaborative control process, the terminal control unit collects terminal load data, environmental data, electrical parameters, and device state parameters in real time and uploads these information to the regional control unit. The regional control unit aggregates and analyzes these information and uploads the analysis information to the main control unit. The main control unit performs global evaluation and decision-making based on the information uploaded by the regional control unit and formulates a corresponding control strategy.

[0066] In the top-down decision and scheduling collaborative control process, the main control unit issues the formulated control strategy to the regional control unit, which formulates a control scheme for the region based on the strategy of the main control unit and issues it to the terminal control unit. The terminal control unit executes the corresponding control operation according to the instructions of the regional control unit to achieve control over the terminal load.

[0067] One specific embodiment takes into account the need to adapt different control logic and optimization algorithms for different types of collaborative control, such as the real-time requirements of load balancing, the economic requirements of energy optimization, and the reliability requirements of safety protection. It further refines the control logic by using appropriate perception and early warning algorithms, decision and scheduling algorithms from the load, energy, and safety dimensions, and coordinates the execution of the multi-level collaborative control process of the corresponding dimension by the control units at different levels to achieve more optimized collaborative control. The method includes:

[0068] First, the core goal of load collaborative control is global load balance, regional load matching, and terminal load stability. Based on hierarchical distributed control decision, the multi-level collaborative control process of load balance executed by the control units at different levels includes:

[0069] The terminal control unit is used to collect terminal load data (such as load type, current power, and adjustable performance) and environmental data (such as temperature and humidity) in real time, compare the collected terminal load data with a dynamic load threshold value preset based on the environmental data, wherein the dynamic load threshold value is dynamically adjusted according to the environmental data and can be obtained through historical data learning or expert experience setting, for example, the current power of B2 is 35kW, and the load threshold value is 30kW when the current environmental temperature exceeds 27°. According to the comparison result, terminal load out-of-limit data is obtained. In addition, in order to further avoid the load out-of-limit situation, the historical terminal load data is learned through the deep learning algorithm (such as LSTM algorithm) embedded in the terminal control unit to predict the change trend of the terminal load, and the terminal out-of-limit data is obtained. The terminal load data containing the obtained or predicted terminal load out-of-limit data is uploaded to the regional control unit.

[0070] The regional control unit is used to summarize the regional terminal load data, analyze and determine whether the regional terminal load triggers a same-level load coordination condition, which refers to the regional terminal load being coordinately scheduled to eliminate the terminal load out-of-limit, that is, determining the out-of-limit value of the current regional terminal load out-of-limit and the load redundancy of other terminals in the adjacent region, and determining whether the load redundancy of other terminals in the adjacent region can offset the out-of-limit value of the current regional terminal load out-of-limit. If the load redundancy of other terminals in the adjacent region can offset the out-of-limit value of the current regional terminal load out-of-limit, it is determined that the same-level load coordination is triggered. Considering that there are various coordination scheduling modes for the same-level load coordination, in order to obtain the optimal coordination scheduling decision, an algorithm embedded in the regional control unit is used, such as a greedy algorithm, a neural network algorithm, etc. In this embodiment, the greedy algorithm is used.

[0071] When the same-level load coordination condition is triggered, the optimal strategy for regional terminal load coordination is obtained based on the greedy algorithm to eliminate the terminal load out-of-limit, which specifically includes: initializing a candidate region, that is, based on the topological relationship, screening the regions directly connected with the current region and having a communication delay less than 100ns; defining a greedy index and sorting according to (available redundant power / transmission loss (which can be estimated according to the transmission distance and historical transmission loss)); iteratively selecting, preferentially requesting load support to the region with the highest greedy index, and if the corresponding region is insufficient in resources, turning to the next region, until the terminal load out-of-limit value is eliminated or all candidate regions are traversed; terminating the iteration; when the same-level load coordination condition is not triggered, calculating the regional terminal load gap and updating the regional load demand for reporting; the regional terminal load gap refers to the difference between the out-of-limit value of the current regional terminal load out-of-limit and the load redundancy of other terminals in the adjacent region.

[0072] The top-down collaborative control flow (decision and scheduling): based on the regional load demand and global power supply capacity reported by the regional control unit, the global load balancing distribution is completed by using the algorithm embedded in the main control unit to generate the global load distribution decision, which specifically includes: generating a global load quota allocation strategy by linear programming algorithm and presetting the load priority of each region, and issuing it to the regional control unit; for example: design a linear programming algorithm to minimize the weighted combination of global load fluctuation and minimum cross-regional transmission loss, as follows:

[0073] wherein, is the actual power supply quota of the regional terminal; is the average quota of the whole regional terminal, is the loss rate of power transmission from region i to region j, which is a fixed parameter based on topology; is the transmission power from region i to region j, is the weight coefficient; wherein the constraint conditions include:

[0074] wherein, is the maximum power supply capacity; is the minimum necessary load and maximum carrying capacity of the corresponding region; is the load demand reported by the region; wherein, contains the load gap, and when there is no solution , that is, the sum of the regional load demand uploaded is greater than the maximum power supply capacity, then the regional load demand of the region with low priority is adjusted downward according to the preset load priority of each region in the priority order, so as to finally solve the regional load quota. Then use the regional control unit to decompose the regional load quota based on the preset terminal load priority and issue it to the terminal control unit; specifically, according to the load characteristics of each terminal (such as the priority of the terminal load) and the load demand of each terminal in the region, the order matching is carried out, and on the basis of meeting the corresponding load of the basic power supply of each terminal, the redundant load is allocated according to the priority order, so as to decompose and obtain the load quota of each terminal. Then use the terminal control unit to adjust the terminal operating power according to the decomposed load quota by controlling the switch of the power distribution cabinet, such as reducing the B2 air conditioner from 40kW to 20kW, then adjusting the air conditioner switch to 50% power.

[0075] Second, the energy collaborative control takes economy and efficiency as the core, which is specifically based on hierarchical distributed control decision, and coordinates the multi-level collaborative control flow of load balancing executed by each level of control unit, which specifically includes:

[0076] The bottom-up cooperative control process (perception and early warning): the terminal control unit collects terminal real-time energy consumption and calculates energy efficiency parameters (active power / reactive power) in real time, compares them with preset energy efficiency parameters (which can be dynamically adjusted according to the service life of regional terminal equipment), and obtains terminal energy efficiency abnormal data, i.e. terminals with low energy efficiency. In addition, in order to further avoid terminal energy efficiency abnormalities, the terminal control unit learns from historical terminal energy consumption data through a deep learning algorithm (such as the LSTM algorithm) to predict the trend of terminal energy consumption, obtain terminal energy consumption and obtain energy efficiency abnormal data, and upload terminal energy consumption data containing obtained or predicted terminal energy efficiency abnormal data to the regional control unit.

[0077] The regional control unit aggregates regional terminal energy consumption data and energy efficiency abnormal data, analyzes and determines whether the regional terminal energy consumption data triggers a same-level energy consumption coordination condition, which refers to the compensation of energy consumption data through regional terminal energy consumption to eliminate terminal energy efficiency abnormalities, i.e. determining the active / reactive power compensation value corresponding to the current regional terminal energy efficiency optimization requirement, and the redundant active / reactive power compensation value of other terminals in the adjacent region, determining whether the power resource redundancy of other terminals in the adjacent region can offset the current regional terminal energy efficiency abnormality, i.e. meeting the active / reactive power compensation value corresponding to the energy efficiency optimization requirement, if it can offset the active / reactive power compensation value corresponding to the current regional terminal energy efficiency optimization requirement, it is determined that the same-level energy consumption coordination is triggered. For example, the power factor of B region is low (0.85) during peak period, and there is redundant reactive compensation resource (15 kvar) in adjacent A region, which is sent to B region to make B power factor improve to 0.9 to meet the energy efficiency requirement.

[0078] Upon triggering the same-level coordination condition, energy coordination pays more attention to energy optimization than load matching, and then selects the optimal strategy for regional terminal energy consumption coordination through a multi-objective optimization algorithm to eliminate terminal energy efficiency abnormalities, including: defining the objective function, including: minimizing cross-regional line loss: , i is the adjacent region of the current region, is the compensation value transmitted from the i region to the current region, is the line loss rate of the i region to the current region; minimizing the total cost of coordination: , is the energy consumption cost per unit compensation; maximizing power supply stability: , To obtain the weighted comprehensive objective function for the current regional voltage after compensation, the decision variables (compensation combinations for terminals in different adjacent regions) and constraints (compensation to meet the current regional deficit) are determined. The algorithm solves the problem, outputs and filters the Pareto solution set, and obtains the optimal strategy for regional terminal energy consumption coordination. If the same-level coordination condition is not triggered, the regional terminal energy efficiency optimization requirement is calculated and the regional energy consumption requirement is updated and reported. The regional terminal energy efficiency optimization requirement refers to the difference between the active / reactive power compensation value corresponding to the current regional terminal energy efficiency optimization requirement and the active / reactive power redundancy (sum) of other terminals in adjacent regions.

[0079] Top-down collaborative control process (decision-making and scheduling): Combining daily peak and off-peak electricity prices and regional energy consumption demand, the main control unit formulates a global energy allocation strategy using an energy allocation algorithm and distributes it to regional control units. The global energy allocation strategy is constructed using a dual-objective optimization model, including: defining the objective function, which includes: minimizing the global electricity cost. , The power allocation for region i during time period t. The electricity price for time period t; Maximize the overall energy efficiency index: , , Let be the energy efficiency parameter of region i during time period t. For the line loss rate of i in time period t, a weighted objective function is obtained; constraints are defined, including: safety constraints (e.g., the total global power allocation is not greater than the power supply capacity and the power allocation of a single area is not greater than the corresponding limit power), demand constraints (e.g., the power allocation of a single area is not less than the rigid energy consumption demand), and energy efficiency constraints (e.g., the energy efficiency parameter of a single area is not less than the preset energy efficiency parameter, and the cross-regional transmission line loss rate is not less than the preset line loss rate); decision variables are defined, including: the power allocation of each area in time period t, and the active / reactive power compensation values ​​of each area in time period t; finally, the global energy allocation strategy is solved.

[0080] The recycling area control unit formulates a corresponding regional terminal energy distribution strategy according to a global energy distribution strategy and terminal energy consumption characteristics in the region, and distributes it to the terminal control unit; specifically including: quota decomposition of the global energy distribution strategy, splitting the regional total quota by time period (peak time, valley time), reserving 10% redundancy; decomposing the power quota according to the energy efficiency target; determine the terminal energy consumption characteristics in the region and label them, such as: non-adjustable key terminals (such as machine tools), adjustable important terminals (air conditioners), adjustable general terminals (lighting), and energy efficiency auxiliary terminals (reactive power compensation); based on the label, set the peak time distribution strategy, including: full allocation of non-adjustable key terminals, basic power allocation of adjustable important terminals (to ensure basic operation) and redundant allocation (according to the preset adjustment benefit priority of each terminal), suspension or compression allocation of adjustable general terminals when the peak time quota is tight, and on-demand input of energy efficiency auxiliary terminals; the valley time distribution strategy includes: basic power allocation of non-adjustable key terminals, full allocation of adjustable important terminals, supplementary allocation of adjustable general terminals, and on-demand input of energy efficiency auxiliary terminals.

[0081] The recycling terminal control unit adjusts the terminal switching power operation mode in each time period by controlling the power distribution cabinet switch according to the corresponding regional terminal energy distribution strategy.

[0082] Third, the safety collaborative control takes fast response, accurate isolation and global protection as the core, based on hierarchical distributed control decision, coordinates the control units at all levels to complete the control instruction interaction, to execute the multi-level collaborative control process of safety protection including:

[0083] Top-down collaborative control process (perception and early warning): use the terminal control unit to collect terminal electrical parameters, device state parameters and environmental data in real time, use the embedded deep learning algorithm to judge whether a fault occurs and the fault type and level, obtain the terminal fault and fault type and level that occurs; in order to further protect safety, use the deep learning algorithm to predict the terminal fault and fault type and level that occurs; upload the obtained or predicted terminal fault and fault type and level that occurs to the regional control unit, and start local protection when the fault level is serious and upload the local protection situation to the regional control unit.

[0084] The regional control unit continues to judge whether fault diffusion occurs and the degree of fault diffusion based on the received terminal fault, fault type and level, and local protection condition through a deep learning algorithm; specifically, an embedded deep learning algorithm can be selected to learn historical terminal faults, fault types and levels, local protection conditions, and fault diffusion and the degree of fault diffusion under corresponding conditions to obtain a fault diffusion and degree of fault diffusion judgment model, and finally determine whether fault diffusion occurs and the degree of fault diffusion; when it is determined that fault diffusion occurs and spreads to other regions, the regional control unit continues to request the terminal electrical parameters, equipment state parameters, and environmental data of other diffusion regions, and performs regional-level data cross-validation and fault positioning; specifically, an embedded deep learning algorithm can also be selected to learn the terminal electrical parameters, equipment state parameters, and environmental data of the current region and the diffusion region, and the corresponding historical fault positioning and fault diffusion results to generate a fault positioning and fault diffusion result judgment model, and then obtain the fault positioning and fault diffusion result and upload it to the main control unit.

[0085] Top-down collaborative control flow (decision and scheduling):

[0086] The main control unit obtains a global safety isolation strategy based on the fault positioning and fault diffusion result, and sends it to the regional control unit through a deep learning algorithm; specifically, a global safety isolation strategy acquisition model is generated by learning historical terminal fault positioning and fault diffusion results and the global safety isolation strategy given by experts under corresponding conditions, and then the corresponding global safety isolation strategy is obtained and sent to the regional control unit.

[0087] Based on the fault positioning and fault diffusion result and the global regional safety isolation strategy, the regional control unit obtains a corresponding regional terminal safety isolation strategy through a deep learning algorithm and sends it to the terminal control unit; similarly, a regional terminal safety isolation strategy acquisition model is generated by learning historical terminal fault positioning and fault diffusion results and the global safety isolation strategy, and then the corresponding regional terminal safety isolation strategy is obtained and sent to the regional control unit.

[0088] The terminal control unit adjusts whether the terminal is isolated according to the corresponding regional terminal safety isolation strategy by controlling the distribution cabinet switch.

[0089] In one specific embodiment, considering that the multi-layer collaborative control process at the same time often conflicts with the corresponding distribution switch control of the same terminal, and considering that only the switch control according to the priority order of different dimensions of collaborative control may lead to excessive safety redundancy or unnecessary loss, in order to realize conflict optimization, the method comprises:

[0090] In the multi-level collaborative control process of coordinating the control units at different levels to perform load balancing, energy optimization and safety protection, it is determined whether there is a conflict in the different dimension multi-level collaborative control processes at the same time, and the different dimension multi-level collaborative control processes with conflicts are identified and obtained. Specifically, the conflict identification compares whether the switches of the corresponding regulation power distribution cabinet of the different dimension multi-level collaborative control terminal conflict, such as whether the same switch is required to be opened and closed at the same time by different control strategies, to determine that there is a conflict.

[0091] From the different dimension multi-level collaborative control processes with conflicts, it is determined whether the obtained or predicted fault level is a serious fault level. If it is determined that there is, it indicates that there is a more serious safety problem, and the multi-level collaborative control of safety protection is given priority to perform the multi-level collaborative control process of safety protection.

[0092] If it is determined that there is not, there is no very serious safety problem, then for each dimension multi-level collaborative control process, the corresponding dimension process preset core scoring index is evaluated to obtain the score of each dimension multi-level collaborative control process. According to the scores obtained by the different dimension multi-level collaborative control processes with conflicts, the priority of the multi-level collaborative control process of load balancing, the multi-level collaborative control process of energy optimization and the multi-level collaborative control process of safety protection is set from high to low, and the multi-level collaborative control process with high priority is preferentially executed.

[0093] The preset core score index of the multi-level collaborative control process for performing load balancing includes: a quantitative index of a proportion of a difference between the current terminal load and the terminal load limit in different preset ranges of the difference, such as: the proportion of the difference being in 1kw-10kw, 10kw-100kw, and greater than 100kw is 2:7:1, the higher the proportion in 1kw-10kw, the higher the quantitative index score (80), the greater the current load coordination control demand, and the higher the proportion greater than 100kw, the lower the quantitative index score (30), the smaller the current load coordination control demand; in addition, in addition to the above score index, a quantitative index of a priority proportion of a terminal load with the highest proportion in the proportion of the difference between the current terminal load rate and the terminal load rate limit in different preset ranges of the difference can be set, such as: the terminal with the highest proportion is the terminal with the difference being in 10kw-100kw or greater than 100kw, the priority proportion (such as high priority for key terminals, medium priority for general terminals, and low priority for non-key terminals) in the part of the terminal is determined, the more the high priority proportion, the lower the quantitative index score (50), the smaller the current load coordination control demand, and the more the low priority proportion, the higher the quantitative index score (90), the greater the current load coordination control demand; the terminal with the highest proportion is the terminal with the difference being in 1kw-10kw, the priority proportion in the part of the terminal is determined, the more the high priority proportion, the higher the quantitative index score (90), the greater the current load coordination control demand, and the more the low priority proportion, the lower the quantitative index score (50), the smaller the current load coordination control demand; and then all the score indexes are obtained to perform weighted calculation of the preset core score of the multi-level collaborative control process for performing load balancing.

[0094] The preset core score index of the multi-level collaborative control process for performing energy optimization includes: a quantitative index of an economic expenditure range of an economic expenditure for eliminating a current terminal energy efficiency anomaly, that is, a range of an economic expenditure for compensating active / reactive power corresponding to the current terminal energy efficiency anomaly is quantified, the greater the range of different economic expenditures, the higher the energy optimization difficulty, and the lower the quantitative index score; correspondingly, in addition to the economic expenditure, a quantitative index of an energy consumption level range of energy consumption of the current terminal energy efficiency anomaly can be designed, that is, an energy consumption level range of compensating active / reactive power corresponding to the current terminal energy efficiency anomaly is quantified, the greater the range of different energy consumption levels, the higher the energy optimization demand, and the higher the quantitative index score; and then all the score indexes are obtained to perform weighted calculation of the preset core score index of the multi-level collaborative control process for performing energy optimization.

[0095] The preset core score indicators of the multi-level collaborative control flow of the security protection include: a fault level proportion quantitative indicator, which determines the proportion of the current fault level being general or serious, different proportions correspond to different quantitative scores, and a higher quantitative score is set for a larger proportion of serious; a security isolation strategy influence terminal quantity quantitative indicator can also be set, such as quantifying the range of the number of isolated terminals involved in the security isolation strategy based on the range of the number of preset isolated terminals, and the larger the range of the number of preset isolated terminals, the higher the security protection required and the higher the quantitative indicator score. Then, all the score indicators are obtained to perform weighted calculation on the preset core score indicators of the multi-level collaborative control flow of the security protection.

[0096] In one specific embodiment, to ensure that the operation of the regional control unit does not conflict with the strategy issued by the upper level and meets the conditional execution of the lower level, the accuracy and reliability of the multi-level collaborative control flow are improved, and the effective implementation of load balancing, energy optimization and security protection control is guaranteed. The method further comprises:

[0097] In the multi-level collaborative control flow of coordinating the control units at all levels to perform load balancing, energy optimization and security protection, when the control flow is executed by the regional control unit, it is determined whether a preset key decision scenario is triggered, and a verification instruction is sent to the upper and lower control units of the regional control unit. When the verification by the upper and lower control units is successful, the control collaborative flow is allowed to continue to be executed by the regional control unit. When the verification by the upper and lower control units fails, the control collaborative flow is allowed to be re-executed by the regional control unit.

[0098] Specifically, the preset key decision scenario includes: an adjustment of a preset key terminal load quota scenario (i.e. involving adjustment of the load quota of a key terminal), an adjustment of a preset key terminal energy consumption data scenario (i.e. involving adjustment of the energy consumption of a key terminal), and an acquisition of a preset key terminal security isolation strategy scenario (i.e. involving security isolation operation of a key terminal).

[0099] Specifically, the condition for successful verification by the upper and lower control units includes: the upper control unit verifies that the load quota adjustment, terminal energy consumption data adjustment, isolation of preset key terminals, and the regional load quota, regional terminal energy distribution, and regional safety isolation strategy issued by the upper control unit do not conflict, and the lower control unit verifies that the load quota adjustment, terminal energy consumption data adjustment, isolation of preset key terminals, and the lower control unit has conditions for execution, i.e. the load quota adjustment does not exceed the preset load threshold, the terminal energy consumption adjustment does not exceed the operating energy consumption threshold, and the key terminal is allowed to be isolated, etc. When the condition for successful verification by the upper and lower control units is met, the verification success is returned to the regional control unit, and the process of load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals is continued using the regional control unit. Otherwise, the verification failure is returned to the regional control unit, and the process of load quota adjustment, terminal energy consumption data adjustment, and isolation of preset key terminals is re-acquired using the regional control unit.

[0100] In one specific embodiment, in order to better cope with different power consumption scenarios and sudden situations and ensure stable operation of the power distribution system, when the preset applicable scenario of the corresponding dimension jump control is triggered, the jump control process can be executed instead of the original multi-level collaborative control process to improve the flexibility and timeliness of control. The method further comprises:

[0101] In the multi-level collaborative control process of coordinating the load balancing, energy optimization, and safety protection of the control units at all levels, when the preset applicable scenario of the corresponding dimension jump control of load balancing, energy optimization, and safety protection is triggered, including: one-level to three-level jump control preset applicable scenario, three-level to one-level jump control preset applicable scenario; then the jump control process of coordinating the load balancing, energy optimization, and safety protection of the control units at all levels is selected to replace the original multi-level collaborative control process, including: bottom-up perception and early warning jump control process and top-down decision and scheduling jump control process.

[0102] Specifically, for the preset applicable scenario of the corresponding dimension jump control of load balancing. Wherein, the one-level to three-level jump control preset applicable scenario of the corresponding dimension, such as: extreme load scenario exceeding the processing capacity of the regional control unit, i.e. the regional terminal load is instantaneously over-quota by 90%, then the bottom-up perception and early warning jump control process is adopted, specifically including: using the terminal control unit to collect terminal load data in real time; using the regional control unit to aggregate regional terminal load data and analyze whether the regional terminal load is instantaneously over-quota by 80%, if the regional terminal load is instantaneously over-quota by 90%, then no same-level load coordination judgment is made, and the regional load demand is directly updated and reported according to the regional terminal load; and the subsequent self-top-down collaborative control process in the original load balancing multi-level collaborative control process is executed.

[0103] The third to first level jump control preset applicable scene of the corresponding dimension, such as a preset terminal load scene, that is, control needs to be performed according to a globally balanced preset terminal load, a top-down decision and scheduling jump control process is adopted, and specifically includes: a preset terminal load distribution strategy is directly generated by the main control unit, and is distributed to the terminal control unit; and then, the terminal control unit adjusts terminal operating power by controlling the distribution cabinet switch according to the decomposed load quota.

[0104] Specifically, for triggering energy optimization corresponding dimension jump control preset applicable scene, the first to third level jump control preset applicable scene of the corresponding dimension, such as an extreme energy consumption scene exceeding the processing capacity of the regional control unit, that is, the regional terminal energy efficiency optimization demand exceeds 90% of the power supply capacity, a bottom-up perception and early warning jump control process is adopted, and specifically includes:

[0105] The terminal control unit collects terminal real-time energy consumption and calculates energy efficiency parameters in real time; the regional control unit aggregates regional terminal energy consumption data and energy efficiency parameters, analyzes and judges the elimination of regional terminal energy efficiency abnormal data, and calculates the regional terminal energy efficiency optimization demand when the corresponding energy efficiency optimization demand exceeds 90% of the power supply capacity, and updates the regional energy consumption demand and reports it to the main control unit to replace the upload to the regional control unit for the same level energy consumption collaborative condition trigger judgment; and the subsequent self-top-down collaborative control process in the original energy optimization multi-level collaborative control process is executed.

[0106] The third to first level jump control preset applicable scene of the corresponding dimension, such as power grid power limiting, directly cutting off non-critical terminals; a top-down decision and scheduling jump control process, specifically including: combining the peak and valley electricity prices and the regional key terminal energy consumption demand, the main control unit formulates a global energy distribution strategy by using an energy distribution algorithm, and distributes it to the terminal control unit; and then, the terminal control unit adjusts the terminal in each period switching power operation mode according to the corresponding regional terminal energy distribution strategy by controlling the distribution cabinet switch.

[0107] Specifically, for triggering safety protection corresponding dimension jump control preset applicable scene, the first to third level jump control preset applicable scene of the corresponding dimension, such as a super serious type of fault, a bottom-up perception and early warning jump control process is adopted, and specifically includes: the terminal control unit collects terminal electrical parameters, equipment state parameters and environmental data in real time, judges whether a fault occurs and the fault type and level by using a deep learning algorithm, obtains or predicts the terminal fault and the fault type and level, and starts local protection when the fault type is a super serious type and the upload to the regional control unit is overdue; the deep learning algorithm is used to judge whether a fault diffusion occurs and the fault diffusion degree, and the fault location and fault diffusion result are uploaded to the main control unit; and the subsequent self-top-down collaborative control process in the original safety protection multi-level collaborative control process is executed.

[0108] Wherein, the third to first level jump control preset applies to the scene such as monitoring the regional control unit failure, then the decision and scheduling jump control flow from top to bottom, specifically including: using the main control unit to obtain the terminal safety isolation strategy based on the fault location and fault diffusion result through the deep learning algorithm and issuing to the terminal control unit; then using the terminal control unit to adjust the terminal isolation or not through the control switch of the distribution cabinet according to the corresponding regional terminal safety isolation strategy.

[0109] As shown in Figure 4 The embodiment discloses a switch distributed control system of power distribution cabinet based on Internet of Things, comprising:

[0110] The power distribution topology construction module 101 is used for constructing a multi-level power distribution topology network, including: deploying a first-level topology layer of a first-level power distribution cabinet, deploying a second-level topology layer of a plurality of second-level power distribution cabinets, and deploying a third-level topology layer of a plurality of terminal power distribution cabinets; each power distribution cabinet is integrated with a communication gateway and an environment sensor, and is correspondingly provided with a topology node and a power distribution switch;

[0111] The control unit deployment module 102 is used for deploying a hierarchical distributed control unit, including: a main control unit deployed in the first-level power distribution cabinet, a regional control unit deployed in the second-level power distribution cabinet, and a terminal control unit deployed in the third-level power distribution cabinet;

[0112] The communication network establishment module 103 is used for establishing an Internet of Things communication network covering all levels of power distribution cabinets to realize point-to-point communication between all levels of control units;

[0113] The control decision design module 104 is used for designing a hierarchical distributed control decision based on the constructed multi-level power distribution topology network and the hierarchical distributed control unit, including: designing the main control unit to be responsible for global load balancing, energy distribution, and safety strategy formulation and issuance, designing the regional control unit to be responsible for regional load scheduling, energy optimization, and exception handling, and designing the terminal control unit to be responsible for local load control, energy consumption execution, and real-time safety protection;

[0114] The cooperative control execution module 105 is used for coordinating all levels of control units to execute a multi-level cooperative control flow of load balancing, energy optimization, and safety protection based on the hierarchical distributed control decision, including: a multi-level perception and early warning cooperative control flow from bottom to top and a multi-level decision and scheduling cooperative control flow from top to bottom.

[0115] A specific embodiment further comprises:

[0116] The cooperative control execution optimization module 106 is configured to, in the multi-level cooperative control process in which the control units at all levels perform load balancing, energy optimization and safety protection, determine whether there is a conflict between different dimension multi-level cooperative control processes at the same time, and identify and obtain the different dimension multi-level cooperative control processes in conflict; according to the different dimension multi-level cooperative control processes in conflict, determine whether there is a fault level that is a serious fault level in the obtained or predicted fault, and if there is, execute the multi-level cooperative control process for safety protection in priority; if there is not, evaluate each dimension multi-level cooperative control process according to the preset core score index of the corresponding dimension process, and obtain the score corresponding to each dimension multi-level cooperative control process; according to the scores obtained by the different dimension multi-level cooperative control processes in conflict, set the priority of the multi-level cooperative control process for load balancing, the multi-level cooperative control process for energy optimization and the multi-level cooperative control process for safety protection in the order of high to low score, and execute the multi-level cooperative control process with high priority in priority.

[0117] The cooperative control execution optimization module 106 is further configured to, in the multi-level cooperative control process in which the control units at all levels perform load balancing, energy optimization and safety protection, when the control process is executed by the regional control unit, determine whether a preset key decision scenario is triggered, and if so, send a verification instruction to the upper and lower control units of the regional control unit; if the upper control unit verifies that there is no conflict between the load quota adjustment, terminal energy consumption data adjustment, isolation of the preset key terminal and the regional load quota, regional terminal energy distribution and regional safety isolation strategy issued by the upper control unit, and if the lower control unit verifies that the load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal can be executed by the lower control unit, send a verification success message to the regional control unit, and continue to execute the load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal by the regional control unit; otherwise, send a verification failure message to the regional control unit, and continue to obtain the load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal by the regional control unit.

[0118] The embodiment of the present application further discloses a computer readable storage medium.

[0119] Specifically, the computer readable storage medium stores a computer program capable of being loaded and executed by a processor, such as the switch distributed control method of the power distribution cabinet based on the Internet of Things described above, and the computer readable storage medium includes various storage program code media, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0120] The embodiment of the present application further discloses a computer device.

[0121] Specifically, the computer device comprises a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to execute the above-mentioned switch distributed control method for the power distribution cabinet based on the Internet of Things.

[0122] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and drawings) can be replaced by other equivalent or similar features, unless specifically stated otherwise. That is, each feature is only an example of a series of equivalent or similar features, unless specifically stated otherwise.

Claims

1. A switch distributed control method for an Internet of Things-based power distribution cabinet, characterized in that, Comprise: Build a multi-level power distribution topology network, comprising: deploying a first-level power distribution cabinet first-level topology layer, deploying a plurality of second-level power distribution cabinets second-level topology layer and deploying a plurality of terminal power distribution cabinets third-level topology layer; Each power distribution cabinet integrates a communication gateway and an environmental sensor, and is equipped with a topology node and a power distribution switch; Deploy hierarchical distributed control units, including: deploying a master control unit in the first-level power distribution cabinet, deploying a regional control unit in the second-level power distribution cabinet and deploying a terminal control unit in the third-level power distribution cabinet; Establish an Internet of Things communication network covering all levels of power distribution cabinets to realize point-to-point communication between all levels of control units; Based on the multi-level power distribution topology network and the hierarchical distributed control unit built, design a hierarchical distributed control decision, including: design the master control unit to be responsible for global load balancing, energy distribution and security policy formulation and issuance, design the regional control unit to be responsible for regional load scheduling, energy optimization and exception handling in the region, and design the terminal control unit to be responsible for local load control, energy consumption execution and real-time safety protection; Based on the hierarchical distributed control decision, coordinate the multi-level collaborative control process of all levels of control units to execute load balancing, energy optimization and safety protection, including: multi-level bottom-up perception and early warning collaborative control process and multi-level top-down decision and scheduling collaborative control process; Based on the hierarchical distributed control decision, coordinate the multi-level collaborative control process of all levels of control units to complete control instruction interaction to execute safety protection, including: using the terminal control unit to collect terminal electrical parameters, equipment state parameters and environmental data in real time, using a deep learning algorithm to determine whether a fault occurs and the fault type and level, obtaining or predicting the terminal fault and the fault type and level that occurs and uploading to the regional control unit, and starting local protection when the fault level is determined to be a serious type; Using the regional control unit to determine whether a fault diffusion occurs and the fault diffusion degree based on the terminal fault and the fault type and level received and the local protection condition, using a deep learning algorithm; When it is determined that a fault diffusion occurs and spreads to other regions, continue to use the regional control unit to cross-verify regional-level data and locate the fault, and upload the fault location and fault diffusion result to the master control unit; Using the master control unit to obtain a global safety isolation strategy based on the fault location and fault diffusion result and issuing it to the regional control unit through a deep learning algorithm; Based on the fault location and fault diffusion result and the global regional safety isolation strategy, use the regional control unit to obtain a corresponding regional terminal safety isolation strategy through a deep learning algorithm and issue it to the terminal control unit; Use the terminal control unit to adjust whether to isolate the terminal according to the corresponding regional terminal safety isolation strategy by controlling the power distribution cabinet switch; The multi-level cooperative control process for coordinating the load balancing of the control units at different levels based on hierarchical distributed control decisions comprises: collecting terminal load data and environmental data in real time by the terminal control unit, comparing the terminal load data with a dynamic load threshold preset based on the environmental data, and obtaining terminal load overrun data or terminal overrun data predicted by a deep learning algorithm; summarizing regional terminal load data by the regional control unit, analyzing and determining whether the regional terminal load triggers a same-level load coordination condition, which refers to eliminating terminal load overrun through coordinated scheduling of the regional terminal load; if the same-level load coordination condition is triggered, obtaining an optimal regional terminal load coordination strategy to eliminate terminal load overrun by combining a greedy algorithm; if the same-level load coordination condition is not triggered, calculating a regional terminal load gap and updating regional load demand for reporting; based on the regional load demand reported by the regional control unit and the global power supply capacity, generating a global load quota allocation strategy by the main control unit through a linear programming algorithm and a preset load priority of each region, and distributing the strategy to the regional control unit; then, decomposing the regional load quota based on the preset terminal load priority by the regional control unit, and distributing the quota to the terminal control unit; and finally, adjusting the terminal operating power by controlling the distribution cabinet switch according to the decomposed load quota by the terminal control unit. 2.The switch distributed control method of the power distribution cabinet based on the Internet of Things according to claim 1, characterized in that, The multi-level cooperative control process for coordinating the control instruction interaction of the control units at different levels to perform energy optimization based on hierarchical distributed control decisions comprises: collecting terminal real-time energy consumption and calculating energy efficiency parameters by the terminal control unit in real time, comparing the energy efficiency parameters with preset energy efficiency parameters, obtaining terminal energy efficiency abnormal data or terminal energy consumption data and energy efficiency abnormal data predicted by a deep learning algorithm; summarizing regional terminal energy consumption data and energy efficiency abnormal data by the regional control unit, analyzing and determining whether the regional terminal energy consumption data triggers a same-level energy consumption coordination condition, which refers to eliminating terminal energy efficiency abnormality through coordinated compensation of the regional terminal energy consumption; if the same-level coordination condition is triggered, obtaining an optimal regional terminal energy consumption coordination strategy to eliminate terminal energy efficiency abnormality by a multi-objective optimization algorithm; if the same-level coordination condition is not triggered, calculating regional terminal energy efficiency optimization demand and updating regional energy consumption demand for reporting; combining the peak-valley electricity price of the day and the regional energy consumption demand, generating a global energy distribution strategy by the main control unit through an energy distribution algorithm, and distributing the strategy to the regional control unit; then, formulating corresponding regional terminal energy distribution strategies by the regional control unit according to the global energy distribution strategy and the energy consumption characteristics of the terminals in the region, and distributing the strategies to the terminal control unit; and finally, adjusting the terminal power operation mode in each period by controlling the distribution cabinet switch according to the corresponding regional terminal energy distribution strategy by the terminal control unit. 3.The switch distributed control method of the power distribution cabinet based on the Internet of Things according to claim 1, characterized in that, In the multi-level collaborative control process of coordinating the execution of load balancing, energy optimization and safety protection by control units at different levels, it is determined whether there is a conflict in different dimension multi-level collaborative control processes at the same time, and the different dimension multi-level collaborative control processes with conflicts are identified and obtained; according to the different dimension multi-level collaborative control processes with conflicts, it is determined whether there is a fault level that is a serious fault level, and if so, the multi-level collaborative control process of safety protection is executed preferentially; If there is no conflict, each dimension multi-level collaborative control process is evaluated according to the corresponding dimension process preset core scoring index to obtain the corresponding score of each dimension multi-level collaborative control process; wherein the preset core scoring index of the multi-level collaborative control process for executing load balancing includes: a quantitative index of the proportion of the difference between the current terminal load and the terminal load limit in different difference preset ranges; the preset core scoring index of the multi-level collaborative control process for executing energy optimization includes: a quantitative index of the economic expenditure range in which the current terminal energy efficiency anomaly economic expenditure is eliminated; the preset core scoring index of the multi-level collaborative control process for executing safety protection includes: a quantitative index of the proportion of faults of each level; according to the scores obtained from the different dimension multi-level collaborative control processes with conflicts, the priority of the multi-level collaborative control processes for executing load balancing, energy optimization and safety protection is set from high to low according to the scores, and the multi-level collaborative control process with high priority is executed preferentially.

4. The switch distributed control method of the power distribution cabinet based on the Internet of Things according to claim 3, characterized in that, Also includes: In the multi-level collaborative control process of coordinating the execution of load balancing, energy optimization and safety protection by control units at different levels, when the control process is executed by the regional control unit, it is determined whether a preset key decision scenario is triggered, including: adjusting the preset key terminal load quota scenario, adjusting the preset key terminal energy consumption data scenario, obtaining the preset key terminal safety isolation strategy scenario; if so, a verification instruction is sent to the upper and lower control units of the regional control unit, and if the upper control unit verifies that there is no conflict between the load quota adjustment, terminal energy consumption data adjustment, isolation of the preset key terminal and the regional load quota, regional terminal energy distribution and regional safety isolation strategy issued by the upper control unit, and if the lower control unit verifies that the load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal can be executed by the lower control unit, the verification success is returned to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal is continued to be executed by the regional control unit; otherwise, the verification failure is returned to the regional control unit, and the process involving load quota adjustment, terminal energy consumption data adjustment and isolation of the preset key terminal is continued to be re-acquired by the regional control unit.

5. A switch distributed control system of an Internet of Things based power distribution cabinet, characterized in that, It includes: A power distribution topology construction module is used to construct a multi-level power distribution topology network, including: deploying a first-level power distribution cabinet to form a first-level topology layer, deploying a plurality of second-level power distribution cabinets to form a second-level topology layer, and deploying a plurality of terminal power distribution cabinets to form a third-level topology layer; each power distribution cabinet is integrated with a communication gateway and an environment sensor, and is equipped with a topology node and a power distribution switch. The control unit deployment module is configured to deploy a hierarchical distributed control unit, including a master control unit deployed in a primary power distribution cabinet, a regional control unit deployed in a secondary power distribution cabinet, and a terminal control unit deployed in a tertiary power distribution cabinet. The communication network establishment module is configured to establish an Internet of Things communication network covering the power distribution cabinets at all levels to realize point-to-point communication between the control units at all levels. The control decision design module is configured to design a hierarchical distributed control decision based on the constructed multi-level power distribution topology network and the hierarchical distributed control unit, including designing the master control unit to be responsible for global load balancing, energy distribution, and security policy formulation and issuance, designing the regional control unit to be responsible for regional load scheduling, energy optimization, and exception handling, and designing the terminal control unit to be responsible for local load control, energy consumption execution, and real-time security protection. The collaborative control execution module is configured to coordinate the control units at all levels to execute a multi-level collaborative control process of load balancing, energy optimization, and security protection based on the hierarchical distributed control decision, including a multi-level bottom-up perception and early warning collaborative control process and a multi-level top-down decision and scheduling collaborative control process. The terminal control unit is configured to collect terminal electrical parameters, device state parameters, and environmental data in real time, determine whether a fault occurs and the type and level of the fault through a deep learning algorithm, obtain or predict the terminal fault and the type and level of the fault, and upload them to the regional control unit, and start local protection when the fault level is determined to be a serious type. The regional control unit is configured to determine whether a fault spreads and the degree of fault spreading through a deep learning algorithm based on the terminal fault, the type and level of the fault, and the local protection. When it is determined that a fault spreads and spreads to other regions, the regional control unit is configured to continue to perform regional-level data cross-validation and fault positioning, and upload the fault positioning and fault spreading results to the master control unit. The master control unit is configured to obtain a global security isolation strategy through a deep learning algorithm based on the fault positioning and fault spreading results, and issue the global security isolation strategy to the regional control unit. The regional control unit is configured to obtain a corresponding regional terminal security isolation strategy through a deep learning algorithm based on the fault positioning and fault spreading results and the global regional security isolation strategy, and issue the corresponding regional terminal security isolation strategy to the terminal control unit. The terminal control unit is configured to adjust whether to isolate the terminal according to the corresponding regional terminal security isolation strategy by controlling the power distribution cabinet switch. The terminal control unit is used to collect terminal load data and environment data in real time, compare the terminal load data with a dynamic load threshold based on the environment data, and obtain terminal load overrun data or terminal overrun data predicted by a deep learning algorithm; the regional control unit is used to aggregate regional terminal load data, analyze and determine whether the regional terminal load triggers a peer load coordination condition, which refers to a condition that the terminal load overrun can be eliminated by coordinated scheduling of the regional terminal load; if the peer load coordination condition is triggered, the optimal strategy for regional terminal load coordination is obtained by combining a greedy algorithm to eliminate the terminal load overrun; if the peer load coordination condition is not triggered, the regional terminal load gap is calculated and the regional load demand is updated and reported; based on the regional load demand reported by the regional control unit and the global power supply capacity, the main control unit is used to generate a global load quota allocation strategy by a linear programming algorithm and a preset load priority of each region, and the strategy is distributed to the regional control unit; then, the regional control unit is used to decompose the regional load quota based on a preset terminal load priority, and the decomposed load quota is distributed to the terminal control unit; finally, the terminal control unit is used to adjust the terminal operating power by controlling the switch of the power distribution cabinet according to the decomposed load quota.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the method of any one of claims 1 to 4 when the computer program is running.

7. A computer device, comprising: The computer device comprises a memory, a processor and a program stored on the memory and executable by the processor, and the program is executed by the processor to implement the steps of the method of any one of claims 1 to 4.

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