High-energy-efficiency sensor access method for transformer substation

Abstract

The invention provides a high-energy-efficiency sensor access method for a transformer substation, and the method achieves the high-energy-efficiency sensor access through the reasonable steps of network deployment, energy consumption model establishment, dormancy algorithm execution and the like, and achieves the efficient access and data transmission of sensors in the transformer substation. The invention discloses a high-energy-efficiency sensor access method for a transformer substation. The method comprises the following steps: step 1, preliminarily deploying a clustering type wireless sensor network; 2, establishing an energy consumption model EC of a wireless sensor node, namely cluster head energy consumption ETC and non-cluster head energy consumption ERC; 3, executing a clustering type dormancy algorithm; and 4, the cluster head node receives data of other non-cluster head wireless sensor nodes in the cluster head alternative set, collects the data and sends the data to a base station (BS).

Description

Technical Field

[0001] The present invention relates to the technical field of electric power monitoring, and in particular to a high-energy-efficiency sensor access method for a transformer substation. Background Art

[0002] In the context of the construction of new power systems, substations are a key part of the power system, and their safe and stable operation is crucial to the power system. In order to detect power equipment failures in a timely manner and avoid accidents, it is necessary to install a variety of status monitoring sensors. In order to better achieve the perception effect, these sensors need to be able to be installed on the surface of the object being measured, so there are requirements for the size, power supply, and communication method of the sensors.

[0003] For example, for the application of partial discharge sensors and in-station trench water immersion sensors in GIS (gas-insulated metal-enclosed switch) in substations, the sensors must transmit signals back via wireless communication. This requires that battery-powered sensors have as long working time as possible (generally 3-5 years), which requires sensor communication to use a more energy-efficient method to extend working time.

[0004] To address the above issues, this patent proposes an energy-efficient clustered sleep algorithm, which sets different working modes according to the remaining energy of the sensor to save sensor energy and improve energy efficiency.

[0005] Compared with traditional single-hop communication, cluster communication selects a cluster head as the central aggregation node within the range. Sensor nodes except the cluster head within the range send data to the cluster head, which is then aggregated and sent to the base station. Traditional clustering algorithms do not consider the remaining energy of the sensor, and select cluster heads for high-energy and low-energy sensor nodes according to the same criteria, which causes some sensor nodes to run out of energy early, resulting in data loss.

[0006] This patent content optimizes the selection of cluster head nodes according to the remaining energy of the sensor, thereby extending the life cycle of the entire wireless sensor network. Summary of the invention

[0007] The present invention aims to address the technical defects of the prior art and provide a high-energy-efficiency sensor access method for substations. The method achieves high-energy-efficiency sensor access and efficient access and data transmission of sensors in substations through reasonable network deployment, energy consumption model establishment, and sleep algorithm execution.

[0008] The present invention provides the following technical solution: a high energy efficiency sensor access method for a substation, the method comprising the following steps:

[0009] Step 1: Initial deployment of clustered wireless sensor network;

[0010] Step 2: Establish the energy consumption model E of the wireless sensor node C , that is, cluster head energy consumption E TC and non-cluster head energy consumption E RC ;

[0011] Step 3: Execute clustered sleep algorithm;

[0012] Step 3-1, energy decision: In the energy decision stage, an energy threshold is set, and the remaining energy of the wireless sensor is compared with the energy threshold. The wireless sensor nodes with energy levels higher than the energy threshold are grouped as cluster head candidate sets.

[0013] Step 3-2, cluster head selection: select the cluster head node with the highest residual energy from the cluster head candidate set as the cluster head node;

[0014] Step 3-3, non-cluster head node wireless sensor node action; the wireless sensor that is not selected as the cluster head node, if it is in the cluster head candidate set, executes step 3-4, otherwise executes step 3-5;

[0015] Step 3-4, data transmission: the wireless sensor node sends data to the cluster head node.

[0016] Step 3-5, sleep; the wireless sensor nodes that are not in the cluster head candidate set enter the sleep state and do not send data;

[0017] Step 3-6: If all sensor nodes cannot be selected as cluster head nodes, the system network life cycle ends.

[0018] Step 4: The cluster head node receives data from other non-cluster head wireless sensor nodes in the cluster head candidate set, aggregates the data and sends it to the base station BS.

[0019] Further,

[0020] The energy threshold is set as the average residual energy of the wireless sensor node, that is:

[0021] (1),

[0022] in, Represents the energy threshold, which is the remaining energy of the i-th sensor node, satisfying .

[0023] Further,

[0024] The cluster head node , The cluster head node receives data from other non-cluster head wireless sensor nodes in the cluster head candidate set, aggregates the data and sends it to the base station BS.

[0025] Further,

[0026] The energy consumption of the wireless sensor includes an energy consumption model and energy receiving model They are respectively formula (2) and (3):

[0027] (2)

[0028] (3)

[0029] ,in The energy required to send a unit of data, in units of , The amount of data sent, in bits. is the sending distance in meters, is the energy magnification factor, in units of .

[0030] Further,

[0031] The energy consumption model E C , the formula is as follows:

[0032] (4)

[0033] in For the nth sensor node to the cluster head The distance is the distance from the cluster head to the base station BS.

[0034] Furthermore, in the cluster head selection process of step 3-2, priority evaluation is also included. In the cluster head candidate set, remaining energy, geographical location of wireless sensor nodes, and data communication quality are set to assign priorities to cluster head nodes.

[0035] Furthermore, in step 4, the following steps are also included:

[0036] Step 4-1: Data preprocessing: After the cluster head node receives data from the non-cluster head nodes in the cluster head candidate set, it preprocesses the data, including data denoising, data compression and data format conversion;

[0037] Step 4-2: Data verification and encryption: Verify the preprocessed data and encrypt the data using an encryption algorithm;

[0038] Step 4-3: Data upload: Upload the verified and encrypted data to the base station BS.

[0039] A computer readable medium stores a computer program, which implements a high energy efficiency sensor access method when executed by a processor.

[0040] The present invention discloses a high-energy-efficiency sensor access method for a substation, which is mainly used in a substation. By preliminarily deploying a clustered wireless sensor network, establishing an energy consumption model, executing a clustered sleep algorithm, and data collection and transmission, high-energy-efficiency sensor access is achieved. The method has the following advantages:

[0041] 1. Reasonably arrange wireless sensor nodes in the substation area to form a clustered network structure. This structure helps to reduce the communication distance between nodes, thereby reducing energy consumption and avoiding the high energy consumption caused by the sensor nodes sending data to the base station BS every time. Because the sensor nodes within the range may be far away from the base station BS, each transmission requires more energy, and the long distance will reduce the transmission reliability. Once the transmission fails, it may cause energy waste;

[0042] 2. Avoids the problem that high energy and low energy nodes have the same probability of being selected as cluster heads. Because cluster heads bear more transmission tasks, traditional clustering algorithms will cause low-energy sensor nodes to run out of energy prematurely, resulting in data loss. Cluster head nodes are responsible for receiving, processing and forwarding data, which improves data reliability and integrity. This solution takes the remaining energy of sensor nodes into consideration when selecting cluster heads, avoiding the problem of sensor nodes running out of energy prematurely. Through the clustering sleep algorithm, the energy consumption of non-cluster head nodes is effectively reduced, extending the life cycle of the network.

[0043] 3. This method can be flexibly deployed and adjusted according to the actual situation of the substation to meet different monitoring needs;

[0044] In summary, this high-energy-efficiency sensor access method achieves efficient access and data transmission of sensors in substations through reasonable network deployment, energy consumption model establishment, and execution of clustered sleep algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the clustered sleep algorithm of the present invention;

[0046] Figure 2 This is a performance simulation comparison chart. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0048] This patent proposes an energy-efficient clustered sleep algorithm, which sets different working modes according to the remaining energy of the sensor to save sensor energy and improve energy efficiency.

[0049] Compared with traditional single-hop communication, cluster communication selects a cluster head as the central aggregation node within the range. Sensor nodes except the cluster head within the range send data to the cluster head, which is then aggregated and sent to the base station. Traditional clustering algorithms do not consider the remaining energy of the sensor, and select cluster heads for high-energy and low-energy sensor nodes according to the same criteria, which causes some sensor nodes to run out of energy early, resulting in data loss.

[0050] This patent content optimizes the selection of cluster head nodes according to the remaining energy of the sensor, thereby extending the life cycle of the entire wireless sensor network.

[0051] Specific embodiments include the following:

[0052] The system model is a simple single-hop network with N sensor nodes distributed in a limited area. Each sensor node is equipped with a battery with limited capacity. At initialization, the battery of each sensor node in the system is full, that is, 100% power. There is a base station BS in the system, which is responsible for collecting data from each sensor node. BS has a stable wired means, so there is no energy limit.

[0053] The energy consumption process of sensor nodes is divided into sending and receiving. The sending process is that the sensor node sends data to the receiver (which can be the cluster head or BS). Its energy consumption model is and energy receiving model The formula is as follows:

[0054]

[0055]

[0056] in The energy required to send a unit of data, in units of , The amount of data sent, in bits. is the sending distance in meters, and the BS coordinates are , is the energy magnification factor, in units of .

[0057] In a simple star network, each sensor node transmits data directly to the BS, so the total system energy consumption is:

[0058]

[0059] Since BS has no energy limit, its receiving energy consumption is not considered. is 0, so the total system energy consumption is:

[0060]

[0061] Considering the multi-hop communication model, it is assumed that each sensor node is arranged from far to near according to the distance from the BS. Farthest Recently, the multi-hop transmission model is

[0062]

[0063] The total energy consumption in multi-hop transmission is

[0064]

[0065] in is the distance between the i-th sensor node and the i+1-th sensor node. When i=N, is the distance between the Nth sensor node and the BS.

[0066] If clustering protocol is considered, a cluster head will be selected from N sensor nodes in each round of data transmission. , The role of the cluster head is to collect sensor data from other non-cluster heads, aggregate them, and send them to the BS. Therefore, in the clustering protocol, the system energy consumption can be expressed as the sum of the energy consumption of the cluster head and the energy consumption of the non-cluster head sensor nodes, that is, the following formula:

[0067] (7)

[0068] in For the nth sensor node to the cluster head The distance is the distance between the cluster head and the BS.

[0069] For the traditional low-power adaptive clustering algorithm, in each round of cluster head selection, the cluster head is selected in a random manner, and data aggregation and transmission are performed after the cluster head selection, that is, the cluster head selection satisfies the following formula.

[0070]

[0071] Its total energy consumption satisfies the formula:

[0072] For the enhanced low-power adaptive algorithm, compared with randomly selecting cluster heads, the system will first set an energy threshold ,

[0073]

[0074] in is the residual energy of the i-th sensor node, satisfying When the remaining energy of the sensor is greater than When , it can be selected as the candidate cluster head set, and then a sensor node is randomly selected as the cluster head in the candidate cluster head set. The enhanced low-power adaptive algorithm cluster head selection satisfies the following:

[0075] and

[0076] After the cluster head selection is completed, the total energy consumption of the system satisfies the formula:

[0077] Compared with the enhanced low-power adaptive algorithm, the clustered sleep algorithm proposed in this project also sets an energy threshold according to formula (8). When the remaining energy of the sensor is higher than the threshold, these sensor nodes are grouped into a cluster head candidate set. The sensor node with the highest remaining energy is randomly selected as the cluster head node in the set. Other sensor nodes that have not been selected as cluster heads transmit data to the cluster head node, and other sensor nodes with remaining energy lower than the threshold enter sleep without any data interaction. The cluster head selection of the enhanced low-power adaptive algorithm satisfies:

[0078]

[0079] in is a set of candidate cluster heads that satisfies:

[0080] Then the total system energy consumption of the clustered sleep algorithm satisfies .

[0081] The clustering protocol flow chart is as follows Figure 1 As shown:

[0082] Assume that at the beginning, the battery power of each sensor node is 100%.

[0083] Step 3-1, energy decision: In the energy decision stage, an energy threshold is set, and the remaining energy of the wireless sensor is compared with the energy threshold. The wireless sensor nodes with energy levels higher than the energy threshold are grouped as cluster head candidate sets.

[0084] Step 3-2, cluster head selection: select the cluster head node with the highest residual energy from the cluster head candidate set as the cluster head node;

[0085] Step 3-3, non-cluster head node wireless sensor node action; the wireless sensor that is not selected as the cluster head node, if it is in the cluster head candidate set, executes step 3-4, otherwise executes step 3-5;

[0086] Step 3-4, data transmission: the wireless sensor node sends data to the cluster head node.

[0087] Step 3-5, sleep; the wireless sensor nodes that are not in the cluster head candidate set enter the sleep state and do not send data;

[0088] In step 3-6, if all sensor nodes cannot be selected as cluster head nodes, the system network life cycle ends.

[0089] Figure 2 The performance simulation graph of the present invention and other transmission algorithms is given. The vertical axis of the simulation graph is the number of running sensor nodes, and the horizontal axis is the number of data transmission cycles. From the simulation graph, it can be seen that under the same number of running sensor nodes, the patented algorithm can provide the longest data transmission cycle, and the energy efficiency is higher than other clustering algorithms, which proves the advancement of the present invention.

[0090] Compared with traditional single-hop transmission, each sensor node in this patent needs to transmit data to the BS, and the longer transmission distance leads to higher energy consumption. In traditional multi-hop transmission, the system shortens the communication distance of each sensor node by relaying multiple hops, thus improving energy efficiency compared with traditional single-hop communication. However, in addition to the burden of transmitting its own data, the relay node also has to bear the aggregation data of the previous sensor node. Compared with traditional multi-hop communication, the traditional low-power adaptive clustering algorithm randomly selects a sensor node as the cluster head at each transmission. The cluster head node first aggregates the data of the sensor node and then transmits it to the BS. This avoids the problem of long transmission distance between sensor nodes and BS in traditional single-hop communication, and also avoids the problem of excessive energy consumption caused by a certain sensor node being the cluster head for a long time by randomly selecting each time. Compared with the traditional low-power adaptive clustering algorithm, this patent takes into account the residual energy of the sensor node. When the residual energy of the sensor is too low (lower than the specified energy threshold), it does not participate in energy transmission. Only when the energy of the sensor node is higher than the energy threshold will it enter the cluster head candidate set, and the node with the highest energy in the cluster head candidate set will be selected as the cluster head, and other nodes that have not been selected as cluster heads will send data. This patent gives priority to high-energy sensor nodes as cluster heads and transmits data, and low-energy nodes go into sleep first. In addition, this patent uses the average remaining energy of all sensor nodes in the system as the threshold. When a high-energy sensor node sends data and consumes energy, the threshold will be lowered, which increases the probability of the sensor node sending data in the next selection process, which not only improves energy efficiency, but also avoids the problem of a certain sensor node being unable to send data.

[0091] In the process of cluster head selection in step 3-2, a priority evaluation step can indeed be further included. This step aims to comprehensively consider multiple factors, such as remaining energy, geographical location of wireless sensor nodes, and data communication quality, to assign priorities to cluster head nodes.

[0092] Residual energy is an important criterion for evaluating cluster head node candidates. The higher the residual energy of a node, the better its stability and durability as a cluster head node. In the cluster head candidate set, the nodes can be initially screened and sorted according to their residual energy.

[0093] Combining the three factors of remaining energy, geographical location and data communication quality, a comprehensive evaluation index can be designed to quantify the priority of each candidate node.

[0094] For example, a weight coefficient can be assigned to each factor, and a comprehensive score of each candidate node can be calculated based on these coefficients. The higher the score, the higher the priority of the node.

[0095] In summary, by introducing the priority evaluation link in the cluster head selection process, multiple factors can be comprehensively considered to optimize the cluster head node selection strategy, which helps to improve the energy efficiency, stability and reliability of the entire wireless sensor network.

[0096] Step 4 also involves the processing flow after the cluster head node receives the data, which specifically includes data preprocessing, data verification and encryption, and the operation of uploading the data to the base station BS.

[0097] Step 4: Cluster head node data processing and uploading

[0098] Step 4-1: Data preprocessing

[0099] After the cluster head node successfully receives data from non-cluster head nodes in the cluster head candidate set, the first task is to preprocess the data. Data preprocessing is a key step to ensure data quality and subsequent analysis accuracy, and specifically includes the following sub-steps:

[0100] Data denoising: When collecting data, sensors may be affected by environmental noise, equipment failure, or transmission errors, which may cause the data to contain noise or outliers. Data denoising aims to identify and remove these noisy data to restore the true appearance of the data.

[0101] Data compression: In order to reduce the cost of data transmission and storage while maintaining the main characteristics and integrity of the data, data needs to be compressed. Data compression technology can achieve efficient representation of data by reducing redundant information in the data.

[0102] Data format conversion: Different sensors may produce data in different formats, which may lead to compatibility issues in the subsequent processing and analysis of the data. Therefore, data format conversion is the process of converting data into a unified or standard format to ensure the readability and processability of the data.

[0103] Step 4-2: Data verification and encryption

[0104] After completing data preprocessing, the data needs to be verified and encrypted to ensure its accuracy and security.

[0105] Data verification: Data verification is the process of checking the integrity and consistency of data. By comparing the checksum or hash value of the data, it is possible to detect whether the data has been erroneous or tampered with during transmission. This step is critical to ensuring the reliability of the data.

[0106] Data encryption: To protect the privacy and security of data, data needs to be encrypted. The encryption algorithm can convert data into an incomprehensible ciphertext form, and only authorized users with the corresponding decryption key can decrypt and access the data. This step helps prevent data from being stolen or misused by unauthorized third parties during transmission.

[0107] Step 4-3: Data upload

[0108] After completing data preprocessing, verification and encryption, the cluster head node uploads the prepared data to the base station BS. Data upload is the process of transmitting data from the cluster head node to the base station through wireless or wired communication. In order to ensure the reliability and efficiency of data transmission, the following measures can be taken:

[0109] Select the appropriate communication protocol: According to the characteristics and requirements of the network, select the appropriate communication protocol (such as TCP / IP, Zigbee, etc.) for data transmission. These protocols can provide guarantees in terms of reliability, security, and efficiency of data transmission.

[0110] Optimize data transmission strategy: formulate reasonable data transmission strategy based on factors such as data urgency, size, and network bandwidth. For example, urgent or important data can be transmitted first; for large data volumes or non-urgent data, batch transmission or compressed transmission can be adopted to reduce transmission costs and improve efficiency.

[0111] Monitor data transmission status: During the data transmission process, it is necessary to monitor the transmission status in real time and handle possible abnormal situations. For example, when data transmission interruption or error is detected, measures such as retransmission or error correction can be taken to ensure the integrity and accuracy of the data.

[0112] In summary, step 4 involves the processing flow after the cluster head node receives the data, including data preprocessing, data verification and encryption, and data upload to the base station BS. These steps together constitute the key links to ensure data quality, security and reliability, and provide a solid foundation for subsequent data analysis and application.

[0113] A computer readable medium stores a computer program, which implements a high energy efficiency sensor access method when executed by a processor.

[0114] The clustering algorithm proposed in this patent has the following advantages:

[0115] (1) It avoids the high energy consumption caused by the sensor nodes sending data to the BS every time. Because the sensor nodes within the range may be far away from the BS, each transmission requires more energy. In addition, the longer distance will reduce the transmission reliability. Once the transmission fails, it may cause energy waste.

[0116] (2) Avoids the problem that high-energy and low-energy nodes have the same probability of being selected as cluster heads. Because cluster heads bear more transmission tasks, traditional clustering algorithms will cause low-energy sensor nodes to run out of energy prematurely, resulting in data loss. This scheme takes the remaining energy of sensor nodes into account when selecting cluster heads, avoiding the problem of sensor nodes running out of energy prematurely and improving the network life cycle.

[0117] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the field without departing from the purpose of the present invention. These changes involve related technologies well known to those skilled in the art, which all fall within the scope of protection of the patent of the present invention.

[0118] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A high energy efficiency sensor access method for a substation, characterized in that: The method comprises the following steps: Step 1: Initial deployment of clustered wireless sensor network; Step 2: Establish the energy consumption model E of the wireless sensor node C , that is, cluster head energy consumption E TC and non-cluster head energy consumption E RC ; Step 3: Execute clustered sleep algorithm; Step 3-1, energy decision: In the energy decision stage, an energy threshold is set, and the remaining energy of the wireless sensor is compared with the energy threshold. The wireless sensor nodes with energy levels higher than the energy threshold are grouped as cluster head candidate sets. Step 3-2, cluster head selection: select the cluster head node with the highest residual energy from the cluster head candidate set as the cluster head node; Step 3-3, non-cluster head node wireless sensor node action; the wireless sensor that is not selected as the cluster head node, if it is in the cluster head candidate set, executes step 3-4, otherwise executes step 3-5; Step 3-4, data transmission: the wireless sensor node sends data to the cluster head node; Step 3-5, sleep; The wireless sensor nodes that are not in the cluster head candidate set enter the dormant state and do not send data; Step 3-6: If all sensor nodes cannot be selected as cluster head nodes, the system network life cycle ends. Step 4: The cluster head node receives data from other non-cluster head wireless sensor nodes in the cluster head candidate set, aggregates the data and sends it to the base station BS.

2. A high energy efficiency sensor access method for a substation according to claim 1, characterized in that: The energy threshold is set as the average residual energy of the wireless sensor node, that is: （1）， in, Represents the energy threshold, which is the remaining energy of the i-th sensor node, satisfying .

3. A high energy efficiency sensor access method for a substation according to claim 2, characterized in that: The cluster head node , The cluster head node receives data from other non-cluster head wireless sensor nodes in the cluster head candidate set, aggregates the data and sends it to the base station BS.

4. A high energy efficiency sensor access method for a substation according to claim 3, characterized in that: The energy consumption of the wireless sensor includes an energy consumption model and energy receiving model They are respectively formula (2) and (3): （2） (3), where The energy required to send a unit of data, in units of , The amount of data sent, in bits. is the sending distance in meters, is the energy magnification factor, in units of .

5. A high energy efficiency sensor access for a substation according to any one of claims 1 to 4 The method is characterized in that The energy consumption model E C , the formula is as follows: （4）， in For the nth sensor node to the cluster head The distance is the distance from the cluster head to the base station BS.

6. A high energy efficiency sensor access method for a substation according to claim 5, characterized in that: The process of cluster head selection in step 3-2 also includes priority evaluation. In the cluster head candidate set, the remaining energy, the geographical location of the wireless sensor node, and the data communication quality are set to assign priorities to the cluster head nodes.

7. A high energy efficiency sensor access method for a substation according to claim 6, characterized in that: In step 4, the following steps are also included: Step 4-1: Data preprocessing: After the cluster head node receives data from the non-cluster head nodes in the cluster head candidate set, it preprocesses the data, including data denoising, data compression and data format conversion; Step 4-2: Data verification and encryption: Verify the preprocessed data and encrypt the data using an encryption algorithm; Step 4-3: Data upload: Upload the verified and encrypted data to the base station BS.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the high-energy-efficiency sensor access method according to any one of claims 1 to 7 is implemented.

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