CN106921979B - Method for constructing wireless monitoring system of wind turbine generator - Google Patents

Method for constructing wireless monitoring system of wind turbine generator Download PDF

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CN106921979B
CN106921979B CN201710155483.6A CN201710155483A CN106921979B CN 106921979 B CN106921979 B CN 106921979B CN 201710155483 A CN201710155483 A CN 201710155483A CN 106921979 B CN106921979 B CN 106921979B
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CN106921979A (en
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傅质馨
骆阳
袁越
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Hohai University HHU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/14—Routing performance; Theoretical aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a method for constructing a wireless monitoring system of a wind turbine generator, which constructs the wireless monitoring system of the wind turbine generator from two aspects of node deployment and routing protocol. Considering that the distribution of typical faults of the wind turbine generator has certain spatiality, a space regular tetrahedron node deployment scheme is provided. The node deployment scheme is that a regular tetrahedron theory is applied to node deployment of a monitoring system, and monitoring deployment is performed on a gear box, a generator and a transmission shaft of a cabin with emphasis; in order to better improve the routing protocol performance of the wireless monitoring system, an LEACH algorithm based on improved cluster head selection is provided. The algorithm considers the residual energy of the nodes and the geometric distance of the nodes, and uses the residual energy of the nodes and the geometric distance of the nodes as parameters of a cluster head selection threshold value calculation formula to improve the performance of a monitoring system. The algorithm can effectively balance the energy consumption of the monitoring system nodes and prolong the optimal operation time of the system.

Description

Method for constructing wireless monitoring system of wind turbine generator
Technical Field
The invention relates to a method for constructing a wireless monitoring system of a wind turbine generator, which constructs a space node deployment scheme to make node deployment more consistent with a model of an actual generator. Meanwhile, a cluster head election method of the LEACH protocol is improved, so that the coverage of the monitoring system is improved in practical application.
Background
Wind power generation is a clean and environment-friendly power generation mode with high electricity generation utilization hours, and is widely popularized by governments and power generation enterprises. However, most wind turbines are located in regions with severe environments, faults are easy to occur, accessibility is poor, manual maintenance is difficult, operation and maintenance costs of the wind turbines are greatly increased, and economic benefits are reduced. The rapid increase of the installed capacity of the wind turbine generator brings great challenges to the operation and maintenance of the wind turbine generator. Therefore, it is necessary to monitor the operation state of the wind turbine in real time. A Wireless monitoring system based on a Wireless Sensor Network (WSN) technology has the advantages of real-time uninterrupted monitoring, strong dynamic performance, simple and easy installation of facilities and the like. The wind turbine generator set is monitored in real time by using the unique advantages of the wireless sensor network, the efficiency of a monitoring system is improved, and meanwhile, the operation cost can be greatly reduced. Therefore, the WSN technology has a good application prospect in monitoring the running state of the wind turbine generator. However, the wireless monitoring system has some defects, such as the sensor node fails due to too fast energy consumption. The failure of the node is directly reflected as the reduction of the coverage of the monitoring system, so that the monitoring range of the system is reduced, the monitoring accuracy is reduced, and even the system is paralyzed. Therefore, it is necessary to construct a wind turbine monitoring system with reliable performance.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a construction method of a wireless monitoring system of a wind turbine generator. And (3) trying to break the node deployment scheme through a plane, and constructing a space node deployment scheme to enable the node deployment to be more consistent with a model of an actual unit. Meanwhile, a cluster head election method of the LEACH protocol is improved, so that the coverage of the monitoring system is improved in practical application.
The technical scheme is as follows: a method for constructing a wireless monitoring system of a wind turbine generator is used for constructing the wireless monitoring system of the wind turbine generator from two aspects of node deployment and routing protocols. Considering that the distribution of typical faults of the wind turbine generator has certain spatiality, the node deployment scheme is tried to break through a plane, and the space node deployment scheme is constructed, so that the node deployment is more consistent with a model of an actual generator. A space regular tetrahedron node deployment scheme is provided. The node deployment scheme is that a regular tetrahedron theory is applied to node deployment of a monitoring system, and monitoring deployment is performed on a gear box, a generator and a transmission shaft of a cabin with emphasis; based on the proposed space node deployment scheme, in order to better improve the routing protocol performance of the wireless monitoring system, the invention realizes the optimization of the monitoring system performance by improving the existing typical LEACH routing protocol in combination with the actual application occasions of the wind turbine generator. A LEACH algorithm based on improved cluster head selection is proposed. The algorithm considers the residual energy of the nodes and the geometric distance of the nodes, and takes the residual energy of the nodes and the geometric distance of the nodes as parameters of a cluster head selection threshold value calculation formula, so that the coverage of a monitoring system is improved in practical application.
Extending the conclusion of the plane to a spatial level, wherein the distance between the sensing radius of the node and the adjacent node needs to satisfy the following condition:
Figure BDA0001246854170000021
where R is the node perception radius and R is the neighboring node distance.
When the condition of the formula is met, the coverage of the monitoring system can be ensured to reach 100 percent, which is specifically proved as follows:
proposition: in regular tetrahedron ABCD, the edge length AB ═ r, AE is one high of the regular tetrahedron, and the regular tetrahedron center O is above the high AE, demonstrating the following formula:
Figure BDA0001246854170000022
and (3) proving that: o is the center of the regular tetrahedral ABCD, connecting OA, OB, OC, OD, and since O is the center, the regular tetrahedral ABCD is divided into four congruent small triangular pyramids O-ABC, O-ABD, O-ACD, O-BCD, wherein OA ═ OB ═ OC ═ OD. See fig. 1. Therefore:
Figure BDA0001246854170000023
AE is 4 OE.
According to the nature of regular tetrahedron, the projection of the vertex a on the base triangle BCD must BE the center of the triangle BCD, connecting BE and extending the intersection CD with F, from the nature of regular triangle, ∠ CFB is 90 °, F is the midpoint of the line segment CD, i.e. the midpoint of the line segment CD
Figure BDA0001246854170000024
According to the pythagorean theorem,
Figure BDA0001246854170000025
according to the property of the regular triangle,
Figure BDA0001246854170000026
namely, it is
Figure BDA0001246854170000027
In the triangular ABE, according to the Pythagorean theorem,
Figure BDA0001246854170000031
AE is 4OE, so
Figure BDA0001246854170000032
The invention provides an LEACH algorithm based on improved cluster head selection, and the main design concept comprises the following aspects:
1) in the case that the condition of the residual energy of the corresponding node is not considered when the cluster head in the LEACH protocol is elected, the invention tries to add the limiting condition of the residual energy of the node into an election algorithm, so that the probability that the node with more residual energy becomes the cluster head is higher, the probability that the node with less energy becomes the cluster head is reduced, and the further bipolar differentiation of the energy distribution of the node is prevented.
2) Aiming at the problem that the geometric distance from an election node to a sink point is not considered in the election of a cluster head of an LEACH protocol, the invention tries to fully consider the condition of the geometric distance from the node to the sink point in an election algorithm, so that the probability that the node far away from the sink point becomes the cluster head is lower.
For the two points, the invention is to make corresponding improvement from the threshold value T (n), and a new calculation formula of T (n) is proposed for the above situation:
Figure BDA0001246854170000033
wherein p is a cluster head nodeThe percentage value of the number of points to the total number of nodes is called cluster head expected value, G is the node set which is not selected as the cluster head in the latest 1/p round, KeIs an energy parameter related to the remaining energy of the current node, and likewise, KdIs a distance parameter related to the geometric distance from the current node to the sink point, and a and b are KeAnd KdAnd satisfies the relationship: a + b is 1.
Determination of KeAnd KdThe method of (3) is as follows, K in formula (2)eIs an energy-related parameter. Simply put, K is the node's residual energy is relatively smalleThe value is also smaller, so that T (n) is smaller, the probability of the node being selected as a cluster head is smaller, and the energy consumption of the node is prevented from being exhausted too early, so that the condition of unbalanced system energy consumption is avoided. Briefly, KeThe value of (b) is positively correlated with the node residual energy.
With ErRepresenting node residual energy, E0To express the initial energy of the node, it is first easy to think of a scheme, let
Figure BDA0001246854170000034
Where K is a scaling factor, which makes it easy to adapt the energy parameter KeAnd the energy is positively correlated with the residual energy of the node. However, the nodes fail successively in the network operation process, and when the remaining energy of the surviving nodes is not enough, KeThe cluster node selection method is characterized in that all the cluster nodes are too small, the threshold is too small on a new threshold, and the condition that the number of the selected cluster nodes is lower than an expected value or even a special condition that one cluster node cannot be selected occurs.
Considering the fact that all the energy is uniformly in a gradually decreasing trend, another solution is conceivable: n is a radical ofaliveIndicates the number of surviving nodes, let KeAnd the ratio of the residual energy to the residual energy of all the survival nodes is formed, and the specific formula (3) is shown.
Figure BDA0001246854170000041
As can be seen from the formula (3),in any round, each node KeThe denominators are all the same, KeDirectly has positive correlation with the residual energy, and the expression can meet the required KeAnd (5) changing trend requirements.
For the same reason, K in the formula (2)dIs an energy-related parameter. By DtoBSRepresenting the geometric distance from each node to the sink point, assuming KdThe ratio of the average value of the geometric distance from all the surviving nodes to the sink point to the geometric distance from the node to the sink point is obtained. See formula (4):
Figure BDA0001246854170000042
as can be seen from formula (4), the values of the molecules are the same in any round, KdDirectly inversely related to the residual energy, the expression can satisfy the required KdAnd (5) changing trend requirements.
Has the advantages that: compared with the prior art, the wireless monitoring system of the wind turbine generator set is constructed from two aspects of monitoring system node deployment and routing protocols. The method is characterized in that a space regular tetrahedron node deployment scheme is provided, and the idea of the scheme is to combine a regular tetrahedron theory and the staggered distribution condition of actual wind turbine generator components to construct a space model. And then, an improved LEACH routing transmission protocol is provided on the basis of the deployment scheme, and simulation results show that the protocol has obvious improvement on balancing the overall energy consumption of the system, reducing the number of nodes with system failure and improving the coverage of the monitoring system. The research result of the invention provides a new idea for the construction of the node model of the wireless monitoring system of the wind turbine generator.
Drawings
FIG. 1 is a plan triangular deployment view;
FIG. 2 is a schematic top view of a nacelle;
FIG. 3 is a diagram of a triangular deployment scenario;
FIG. 4 is a comparison graph of coverage of different perceived radii; wherein (a) is a non-monitoring blind area, and (b) is a monitoring blind area;
FIG. 5 is a view of a tetrahedron configuration;
FIG. 6 is a spatial tetrahedral node deployment diagram;
FIG. 7 is a graph of coverage change;
FIG. 8 is a graph of weight comparison;
FIG. 9 is a diagram of the difference between the residual energy before and after;
FIG. 10 is a graph of failed node differences;
FIG. 11 is a coverage difference graph.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
1. Wireless monitoring system for wind turbine generator
Generally, a WSN-based wind turbine generator wireless monitoring system is composed of three major parts, which are: a state information acquisition system, an information transfer station and a control system.
The state information acquisition system acquires real-time data information through sensor nodes deployed at all positions of the wind turbine generator, acquires various parameters required by the wind turbine generator during operation, and gathers the parameters to a base station through a routing transmission protocol. All deployed sensor nodes and the base station jointly form a monitoring network. The information transfer station is used as an intermediate station for data transmission and used for subsequent expansion transmission. After receiving the real-time data from the transfer station, the central control system judges the running state of the wind turbine generator, and the method comprises the following steps: state monitoring, feature extraction, state recognition, state analysis, strategy formulation and the like.
The invention mainly researches a wireless monitoring system configured on a wind turbine generator, and develops two aspects of node deployment and routing transmission protocol. Firstly, a space four-side node deployment scheme is provided. Then, on the basis of the scheme, an improved LEACH routing protocol is added to form a complete monitoring system scheme.
2. Node deployment scheme of wireless monitoring system of wind turbine generator
2.1 plane model
For the node deployment problem of the wireless monitoring system of the wind turbine generator, many documents are limited to relevant research on a plane node deployment scheme of the wireless monitoring system. However, in practical applications, the wind turbine sensor nodes are spatially distributed. Therefore, in order to better monitor the wind turbine generator comprehensively, the regular tetrahedron deployment model of the wireless monitoring system is constructed.
Regular triangle deployment is a typical deployment scheme for reducing redundant nodes, and the distances between all adjacent nodes in a network are equal by using a special structure of the regular triangle, so that the number of monitoring nodes is reduced, as shown in fig. 1.
Fig. 2 is a schematic top view of a nacelle, incorporating a triangular deployment scenario to form a nodal deployment scenario suitable for use in the present invention, and further elaborated below in conjunction with regular tetrahedron theory. The preliminary construction scheme is shown in FIG. 3.
2.2 theoretical basis of regular tetrahedron structure
The following work is carried out on the invention, with the support of relevant theories from a plane extension to a space level.
In the planar regular triangle deployment scheme, the node sensing radius and the distance between adjacent nodes need to have the following relation:
Figure BDA0001246854170000061
where R is the node perception radius and R is the neighboring node distance.
At this time, no coverage hole occurs in the system, as shown in fig. 4 (a). Once equation (1) is not satisfied, the situation of fig. 4(b) occurs, and it is obvious that a monitoring blind area occurs in the middle, and the coverage of the monitoring system is reduced to some extent.
The invention extends the conclusion of the plane to the spatial level, as shown in fig. 5, the sensing radius of the node and the distance between adjacent nodes need to satisfy the condition of formula (2):
Figure BDA0001246854170000062
where R is the node perception radius and R is the neighboring node distance.
When the condition of the formula (2) is satisfied, the coverage of the monitoring system can be ensured to reach 100%, which is specifically proved as follows:
proposition: in regular tetrahedron ABCD, the edge length AB ═ r, AE is one high of the regular tetrahedron, and the regular tetrahedron center O is above the high AE, demonstrating the following formula:
Figure BDA0001246854170000063
and (3) proving that: o is the center of the regular tetrahedral ABCD, connecting OA, OB, OC, OD, and since O is the center, the regular tetrahedral ABCD is divided into four congruent small triangular pyramids O-ABC, O-ABD, O-ACD, O-BCD, wherein OA ═ OB ═ OC ═ OD. See fig. 5. Therefore:
Figure BDA0001246854170000071
AE is 4 OE.
According to the nature of regular tetrahedron, the projection of the vertex a on the base triangle BCD must BE the center of the triangle BCD, connecting BE and extending the intersection CD with F, from the nature of regular triangle, ∠ CFB is 90 °, F is the midpoint of the line segment CD, i.e. the midpoint of the line segment CD
Figure BDA0001246854170000072
According to the pythagorean theorem,
Figure BDA0001246854170000073
according to the property of the regular triangle,
Figure BDA0001246854170000074
namely, it is
Figure BDA0001246854170000075
In the triangular ABE, according to the Pythagorean theorem,
Figure BDA0001246854170000076
AE is 4OE, so
Figure BDA0001246854170000077
2.3 space model
Based on the theory, the method combines the internal structure of the wind turbine generator cabin, and further expands the scheme on the basis of the formed plane regular triangle node deployment scheme (see fig. 3) to form a space regular tetrahedron node deployment scheme (see fig. 6).
The wind turbine generator is a UP82-1500 IIIA fan produced by national electricity integration power limited company, and the length, width and height of a cabin of the wind turbine generator are as follows: 10200mm 3800 mm. The wind turbine generator operation fault report shows that the pitch-variable fault, the current-variable fault, the gear box fault and the generator fault are the most prominent in detail and need important monitoring.
2.4 simulation analysis
Simulation analysis on a MATLAB platform shows that when the formula (2) is satisfied, the coverage can reach 100% (the R is 1.35m, and the corresponding perception radius critical value R is 0.83 m); as R gradually decreases, the coverage decreases as shown in fig. 7.
The simulation result shows the correctness of the calculation result of the regular tetrahedron theory. When R is 0.83m, the coverage can be approximately 100% within the allowable range of the error, so that in the subsequent operation of configuring the transmission protocol, the initial coverage can be ensured to meet the requirement by setting the sensing radius to exceed 0.83m, and therefore the initial sensing radius is set to be 0.9 m.
3. Routing protocol of wireless monitoring system of wind turbine generator
The environmental-friendly enterprises are valued by various countries, so that the wind power industry is developed unprecedentedly, and the development of a wireless monitoring system of a wind turbine generator is driven. There are many points of entry for improving monitoring systems, such as: route self-healing, node transmission path repair, and node repair using a dormancy mechanism. Based on the space node deployment scheme provided in the previous section, the performance of the monitoring system is optimized by improving the existing typical LEACH routing protocol in combination with the actual application occasions of the wind turbine generator.
3.1 improvement of Cluster head election method
The LEACH protocol has several significant drawbacks:
1) the cluster head election mode is over simple and randomized, the probability that all nodes become cluster head nodes is the same, and as the cluster heads consume more energy than common nodes, when the nodes with less energy are elected as the cluster heads, the energy consumption is aggravated, and the nodes fail prematurely.
2) The geometric distance from the node to the sink point is not considered when the cluster head elects, and the geometric distance between the cluster head and the sink point directly influences the energy consumption when the cluster head transmits data to the sink point, so that the LEACH protocol is not suitable for large-scale occasions.
In view of these disadvantages, the present invention will propose improvements, and the main design concept includes the following aspects:
1) in the case that the condition of the residual energy of the corresponding node is not considered when the cluster head in the LEACH protocol is elected, the invention tries to add the limiting condition of the residual energy of the node into an election algorithm, so that the probability that the node with more residual energy becomes the cluster head is higher, the probability that the node with less energy becomes the cluster head is reduced, and the further bipolar differentiation of the energy distribution of the node is prevented.
2) Aiming at the problem that the geometric distance from an election node to a sink point is not considered in the election of a cluster head of an LEACH protocol, the invention tries to fully consider the condition of the geometric distance from the node to the sink point in an election algorithm, so that the probability that the node far away from the sink point becomes the cluster head is lower.
For the two points, the invention is to make corresponding improvement from the threshold value T (n), and a new calculation formula of T (n) is proposed for the above situation:
Figure BDA0001246854170000081
wherein p is the percentage value of the number of cluster head nodes to the total number of nodes, called cluster head expectation value, G is the node set which is not selected as the cluster head in the latest 1/p round, KeIs an energy parameter related to the remaining energy of the current node, and likewise, KdIs a distance parameter related to the geometric distance from the current node to the sink point, and, in addition, aB is KeAnd KdAnd satisfies the relationship: a + b is 1.
3.2KeAnd KdIs determined
K in the formula (3)eIs an energy-related parameter. Simply put, K is the node's residual energy is relatively smalleThe value is also smaller, so that T (n) is smaller, the probability of the node being selected as a cluster head is smaller, and the energy consumption of the node is prevented from being exhausted too early, so that the condition of unbalanced system energy consumption is avoided. Briefly, KeThe value of (b) is positively correlated with the node residual energy.
With ErRepresenting node residual energy, E0To express the initial energy of the node, it is first easy to think of a scheme, let
Figure BDA0001246854170000091
Where K is a scaling factor, which makes it easy to adapt the energy parameter KeAnd the energy is positively correlated with the residual energy of the node. However, the nodes fail successively in the network operation process, and when the remaining energy of the surviving nodes is not enough, KeThe cluster node selection method is characterized in that all the cluster nodes are too small, the threshold is too small on a new threshold, and the condition that the number of the selected cluster nodes is lower than an expected value or even a special condition that one cluster node cannot be selected occurs.
Considering the fact that all the energy is uniformly in a gradually decreasing trend, another solution is conceivable: n is a radical ofaliveIndicates the number of surviving nodes, let KeAnd the ratio of the residual energy to the residual energy of all the survival nodes is formed, and the specific formula (4) is shown.
Figure BDA0001246854170000092
As can be seen from equation (4), in any round, each node KeThe denominators are all the same, KeDirectly has positive correlation with the residual energy, and the expression can meet the required KeAnd (5) changing trend requirements.
For the same reason, K in the formula (3)dIs andan energy-related parameter. By DtoBSRepresenting the geometric distance from each node to the sink point, assuming KdThe ratio of the average value of the geometric distance from all the surviving nodes to the sink point to the geometric distance from the node to the sink point is obtained. See formula (5):
Figure BDA0001246854170000093
as can be seen from formula (5), the values of the molecules are the same in any round, KdDirectly inversely related to the residual energy, the expression can satisfy the required KdAnd (5) changing trend requirements.
4. Simulation analysis
4.1 evaluation index
In order to evaluate the effectiveness of the proposed method, the following evaluation indexes are proposed:
1) optimal run time, i.e. the time at which a failed node first appears: before no failure node occurs, all nodes are in a normal monitoring state, and the monitoring quality is the highest at the moment, so that the optimal running time of the monitoring system is obtained. The number of first failed rounds of the different schemes is recorded for system performance comparison.
2) System stable working time, i.e. 70% coverage time: it is considered that when the coverage of the system is reduced to below 70%, the system cannot be monitored comprehensively and effectively, so that the performance of the monitoring system is compared by recording the number of rounds of 70% coverage of different schemes in the subsequent simulation process. The percentage may be adjusted according to the actual application requirements.
4.2 determining weights
For the selection of the weights a and b, the optimal weight solution is obtained through experiments. And for a, taking values of a from 0.1, 0.2, and 0.9, and based on a limiting condition that a + b is 1, corresponding b values are 0.9, 0.8, and 0.1, performing MATLAB simulation on the 9 groups of weight values, and obtaining a weight solution most suitable for node deployment according to a specific simulation result.
The above 9 sets of weights are simulated respectively, and the number of rounds of the respective first failure node and the number of rounds of 70% coverage are recorded, so as to form fig. 9. For ease of notation, only the values of a are labeled, and the values of b are not labeled.
As is apparent from fig. 9, when a is 0.8 and b is 0.2, the optimum operating time of the system is longest and the stable operating time of the system is longest, so this weight is used as the optimum weight of the solution of the present invention. That is, in equation (3), the weights of a and b are set to 0.8 and 0.2, respectively.
4.3 simulation results
The parameters of the LEACH protocol of the present invention are shown in Table 1.
TABLE 1 LEACH protocol operating parameters
Parameter(s) Description of the invention Value (unit: J)
E0 Initial energy of node 2
Etx Energy loss of transmitting unit message 50×10-9
Erx Energy loss of receiving unit message 50×10-9
Efs Free space energy 10×10-12
Emp Attenuating spatial energy 0.0013×10-12
Eds Multipath fading energy 5×10-9
(1) Total energy remaining of node
As shown in fig. 10, the difference is the difference between the residual energy of the system after the improvement of the LEACH protocol and the residual energy before the improvement. It can be seen that the graph shows a trend of ascending first and then descending, which indicates that the improvement protocol has a significant effect on the overall macroscopic regulation and control of energy in the early stage, i.e., when the same number of nodes work. When more than 3000 rounds are reached, because failure nodes appear in succession without improving the protocol, the nodes in the working state are reduced, the energy consumption is naturally reduced, and all the nodes of the system adding the improved protocol are in the working period at the moment, the energy consumption is large, so that the system presents a descending trend. The final energies are all depleted and their difference approaches zero. The figure mainly illustrates that when the same number of nodes work, the improved LEACH protocol can well macroscopically regulate and control the total energy consumption, reasonable application of energy is achieved in the early stage, and the occurrence of failed nodes is delayed.
(2) Number of failed nodes
As shown in fig. 10, the difference is the difference between the failed node before modification and the modified node. The figure focuses on the portion of the abscissa cut off to 5500 wheels, since 5500 wheels are not referenced for system coverage below 70% backwards. It can be seen that the number of failed nodes in the monitoring system using the modified LEACH protocol is less than that in the equivalent unmodified monitoring system before the coverage is reduced to 70%, with a maximum of 9 differences, which means an 18% performance increase for a system consisting of 50 sensors. For the trend changes in the figures, a brief explanation is made here: compared with a system with an improved protocol, a system without the improved protocol has the advantages that the energy consumption of a single node is not accurately controlled, the latter has a good protection effect on nodes with lower energy, the probability that the nodes become large-end energy consumption is greatly reduced, namely cluster heads, so that the energy is effectively balanced, the service life of each node is prolonged as much as possible, when the energy of the nodes is close to the limit, the nodes only become common nodes, the energy consumption is reduced, and the number of failure rounds of the nodes of the latter is more concentrated than that of the nodes of the former. In the simulation, the system nodes of the improved protocol fail from 4000 rounds, and it is obvious that the trend of the curve decreases and the difference value decreases from 4000 rounds in fig. 10. Although the number of the system node failure rounds of the improved protocol is late, the system node failure rounds are relatively concentrated, so that the node failure rate is very high at this time, the difference value in the graph shows an obvious descending trend until a certain negative value, and finally the number of the two failure nodes is always 50, so that the simulation has a rising trend at the final stage, and the final difference value is always zero.
(3) Coverage degree
As shown in fig. 11, the difference is the difference between the coverage after improvement and the coverage before improvement. The figure focuses on the abscissa cut-off to 5500 rounds because 5500 rounds backward system coverage is below 70% without reference. As is evident from fig. 11, the coverage of the protocol-improved system is improved over the previous one, and particularly, the coverage is different by more than 10% from the 4000 to 4400 rounds. Therefore, the improved LEACH protocol monitoring system has a good buffering self-healing effect on coverage, can maximize the working reliability of the monitoring system, and improves the performance of the monitoring system. For the trend change in the figure, a brief explanation is made here: the reason is greatly related to the number of the failed nodes, the difference value of the number of the failed nodes in fig. 10 is rapidly reduced from 4000 to 5500 rounds, the direct influence caused by the node failure is the reduction of the coverage, which is shown in fig. 11, the same node is rapidly reduced from 4000 to 5500 rounds, and the coverage difference value even reaches a negative value because the number of the failed nodes is too many, so that the negative value appears in fig. 11. However, the coverage of both systems is lower than 70%, and the situation is not in the actual application range, and only appears in simulation experiments, and only simple analysis is performed here.
(4) Critical phase
For the system critical phases, they are recorded in table 2. The number of first failure node rounds and the number of 70% coverage rounds of the system before improvement are respectively as follows: 3069. 5193; the two data of the improved monitoring system respectively correspond to: 4031. 5238 it is also provided. The 962 round is delayed in respect of the first failed node occurrence, which means that the time for optimum operation of the system using the improved LEACH protocol is increased by 31.35% compared to before.
The number of 70% coverage rounds is only increased by 45 rounds, and although the stable working time is not increased much, from the previous comparison, we have found that by the process before 70% coverage, the coverage of the monitoring system applying the improved LEACH protocol is higher than that of the previous monitoring system under the same running time, which means that the monitoring of the system is more comprehensive and the performance is more excellent.
TABLE 2 Key stage round number before and after improvement
Figure BDA0001246854170000121

Claims (2)

1. A method for constructing a wireless monitoring system of a wind turbine generator is characterized by comprising the following steps: constructing a wind turbine generator set wireless monitoring system from two aspects of node deployment and routing protocol; a space regular tetrahedron node deployment scheme is provided; the node deployment scheme is that a regular tetrahedron theory is applied to node deployment of a monitoring system, and monitoring deployment is performed on a gear box, a generator and a transmission shaft of a cabin with emphasis; the node deployment scheme extends the conclusion of the plane to a spatial level, and the distance between the node sensing radius and the adjacent node needs to satisfy the following condition:
Figure FDA0002335033300000011
wherein R is the node perception radius, and R is the distance between adjacent nodes;
based on the node deployment scheme, an LEACH algorithm based on improved cluster head selection is provided; the LEACH algorithm includes the following aspects:
1) the condition of the residual energy of the corresponding node is not considered when a cluster head is elected in an LEACH protocol, the limiting condition of the residual energy of the node is added into an election algorithm, the probability that the node with more residual energy becomes the cluster head is higher, the probability that the node with less energy becomes the cluster head is reduced, and the further bipolar differentiation of the energy distribution of the node is prevented;
2) the condition of the geometric distance from the node to the sink point is fully considered in an election algorithm, so that the probability that the node farther away from the sink point becomes a cluster head is lower;
for the two aspects, improved from the threshold value T (n), a new calculation formula of T (n) is provided:
Figure FDA0002335033300000012
wherein p is the percentage value of the number of cluster head nodes to the total number of nodes, called cluster head expectation value, G is the node set which is not selected as the cluster head in the latest 1/p round, KeIs an energy parameter related to the remaining energy of the current node, and likewise, KdIs a distance parameter related to the geometric distance from the current node to the sink point, and a and b are KeAnd KdAnd satisfies the relationship: a + b is 1;
the LEACH algorithm takes the residual energy of the nodes and the geometric distance of the nodes into consideration, and takes the residual energy of the nodes and the geometric distance of the nodes as parameters of a cluster head selection threshold value calculation formula, so that the coverage of a monitoring system is improved in practical application.
2. The method for constructing the wireless monitoring system of the wind turbine generator set according to claim 1, characterized in that: determination of KeAnd KdThe method of (3) is as follows, K in formula (2)eIs an energy-related parameter, KeThe value of (2) is positively correlated with the node residual energy;
with ErRepresenting node residual energy, E0Representing the initial energy of the node, NalivePresentation memoryNumber of active nodes, order KeAnd (3) obtaining the ratio of the residual energy to the residual energy of all the survival nodes, wherein the formula is shown as the following formula:
Figure FDA0002335033300000021
as can be seen from equation (3), in any round, each node KeThe denominators are all the same, KeDirectly has positive correlation with the residual energy, and the expression can meet the required KeA trend of change requirement;
k in the formula (2)dIs an energy-related parameter; by DtoBSRepresenting the geometric distance from each node to the sink point, assuming KdThe ratio of the average value of the geometric distances from all surviving nodes to the sink point to the geometric distance from the node to the sink point is obtained; see formula (4):
Figure FDA0002335033300000022
as can be seen from formula (4), the values of the molecules are the same in any round, KdDirectly inversely related to the residual energy, the expression can satisfy the required KdAnd (5) changing trend requirements.
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