CN112512110B - Wireless transmitting power prediction control method for intelligent substation reliability demand constraint - Google Patents

Wireless transmitting power prediction control method for intelligent substation reliability demand constraint Download PDF

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CN112512110B
CN112512110B CN202011302469.2A CN202011302469A CN112512110B CN 112512110 B CN112512110 B CN 112512110B CN 202011302469 A CN202011302469 A CN 202011302469A CN 112512110 B CN112512110 B CN 112512110B
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孙伟
韦康
张磊
李奇越
李帷韬
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Hefei University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/22—TPC being performed according to specific parameters taking into account previous information or commands
    • H04W52/223—TPC being performed according to specific parameters taking into account previous information or commands predicting future states of the transmission
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/242—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account path loss
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/245—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account received signal strength
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses a wireless transmitting power prediction control method for reliability requirement constraint of an intelligent substation, which comprises the following steps: firstly, a wireless signal propagation state space equation with state observation is constructed by analyzing the relation between reliability, signal-to-noise ratio and transmitting power and utilizing a loss model and a Kalman filter of wireless signal propagation; secondly, carrying out expected signal-to-noise ratio compensation by estimating a confidence interval of signal-to-noise ratio fluctuation in real time; and finally, defining a quadratic cost function taking the difference value between the transmitting power and the actual signal-to-noise ratio as a target and the expected signal-to-noise ratio, and realizing the optimal control of the transmitting power through a dynamic programming algorithm. The method and the device can solve the problem of coordination between the transmitting power of the wireless sensor network and the wireless communication reliability in the environment of the transformer substation, thereby ensuring the wireless communication reliability and reducing the transmitting power of the node as much as possible.

Description

Wireless transmitting power prediction control method for intelligent substation reliability demand constraint
Technical Field
The invention belongs to the field of wireless communication network data communication, and particularly relates to a wireless transmitting power prediction control method for intelligent substation reliability requirement constraint, which is used for realizing coordination of reliability and energy consumption while ensuring the reliability of wireless data communication.
Background
With the development of science and technology and the promotion of the Internet of things of electric power, more and more transformer substations adopt an intelligent operation management mode. The development of wireless communication enables the interaction between the measuring terminal and background information to be realized, wherein the ZigBee wireless communication technology is widely applied to smart grid communication by the characteristics of low power consumption, low cost, reliability, safety, confidentiality and the like, and provides technical support for realizing wireless distributed monitoring of the environment temperature and humidity of the transformer substation and evaluating the running state of equipment. By applying the wireless sensor network, compared with the traditional transformer substation, the intelligent transformer substation does not need to distribute on-site survey of inspectors when monitoring the operation environment of the transformer substation, and the problems that the traditional transformer substation is long in patrol work time, low in work efficiency, and wastes human resources are solved. Therefore, the application of the wireless sensor network in the intelligent substation has great development significance and prospect.
Reliability is an important indicator of the performance of a communication system, and refers to the degree of reliability with which information is received within a given channel. The data communication of the wireless sensor network is interfered by the external environment, the reliability of the communication is influenced, and the factors influencing the reliability comprise: the distance between communication nodes, whether a communication channel propagation path is shielded, the electromagnetic noise interference of external environment electronic equipment and the like. In order to ensure the reliability of communication, the communication signal strength can be improved by improving the node transmission power, so that the reliability of communication is improved, however, in the wireless communication process, when the node adopts higher transmission power, the interference of electromagnetic wave signals on communication channels of other nodes is increased, the communication quality of nearby nodes is affected, the energy consumption of the transmitting node equipment is increased, and the lower transmission power may not meet the requirement of data communication reliability. Therefore, the two mutually restricted problems of communication reliability requirement and node transmission power control in the wireless sensor network become a key problem for the application of the wireless sensor network in the transformer substation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a wireless transmitting power prediction control method for the reliability requirement constraint of an intelligent substation, so that the transmitting power of a wireless communication equipment node can be better controlled, and the service life of the communication equipment can be prolonged while the transmission reliability of the communication equipment is ensured.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a wireless transmitting power prediction control method for intelligent substation reliability demand constraint, which is characterized by comprising the following steps of:
step one, according to the reliability index requirement P of wireless communication data of the transformer substationsCalculating the corresponding communication error rate PeFurther, the signal-to-noise ratio S of the corresponding communication expectation is obtained;
step two, constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation;
step 2.1, the wireless communication receiving node periodically collects the received signal strength P in wireless communicationrAnd background noise Pn；
Step 2.2, constructing an approximate logarithmic distance path loss discrete model of the communication signal-to-noise ratio of the signal receiving node and the transmitting power of the transmitting node of the wireless sensor network at the kth moment by using the formula (1):
Figure GDA0003440519470000021
in formula (1): pr(k) Indicating the signal reception strength, P, at the k-th instantSNR(k) Representing the signal-to-noise ratio, P, at time ksent(k) Representing the transmission power, PL (d), of a transmitting node in wireless communication at time k0) Is a reference distance d0A loss value of the communication propagation process of time, d represents the distance from the transmitting node to the receiving nodeN (k) denotes the path loss exponent at time k, Xσ(k) For the influence of multipath effects on the received signal at the k-th instant, Pn(k) Representing the background noise at time k;
step 2.3, making the k moment propagation path loss
Figure GDA0003440519470000022
Constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation by using an equation (2):
Figure GDA0003440519470000023
in formula (2): x (k +1) represents the snr at the k +1 th time, x (k) represents the actual snr at the k th time, u (k) represents the amount of change in the transmission power of the transmitting node in the wireless communication between the k +1 th time and the k th time, w (k) represents the amount of discrete system environment disturbance at the k th time, and w (k) ═ Δ PL (k +1), Δ Pn(k+1)]Where Δ PL (k +1) represents the amount of change between propagation path loss PL (k +1) at the k +1 th time and propagation path loss PL (k) at the k-th time, and Δ Pn(k +1) represents the noise floor P at the k +1 th timen(k +1) and the k-th time background noise Pn(k) Z (k) represents a measurement value of the signal-to-noise ratio at the k-th time, and v (k) represents a measurement noise at the k-th time; a is the coefficient of the actual signal-to-noise ratio x (k) of the state equation, BuIs a coefficient of the transmission power variation u (k), BwThe coefficient is the disturbance amount w (k) of the discrete system environment, and C is the coefficient of the actual signal-to-noise ratio x (k) of the observation equation;
thirdly, constructing a linear quadratic Gaussian controller through a Kalman filter, and resetting the expected signal-to-noise ratio in a confidence interval mode;
step 3.1, filtering the formula (2) through a Kalman filter to obtain an optimal signal-to-noise ratio estimation value at the kth moment shown in the formula (3):
Figure GDA0003440519470000024
in formula (3):
Figure GDA0003440519470000025
the posterior signal-to-noise ratio at the k moment is represented as the optimal signal-to-noise ratio estimated value at the k moment after filtering,
Figure GDA0003440519470000026
the prior signal-to-noise ratio at the k moment is represented, namely the predicted value of the signal-to-noise ratio at the k moment, L (k) represents the Kalman filtering gain at the k moment, and z (k) represents the measured value of the signal-to-noise ratio at the k moment;
step 3.2, introducing a method for statistically setting a confidence interval, performing data analysis on the optimal signal-to-noise ratio estimated value x at the first m moments of the k moment to obtain a standard deviation sigma of the optimal signal-to-noise ratio estimated value x at the first m moments of the k moment, and calculating the confidence interval by using a formula (4) when the confidence coefficient is alpha:
Pm{x＞e-zασ}＝α (4)
in formula (4): e represents the mean value of the optimal SNR estimated values x at the first m moments of the kth moment, zαExpressing a fraction bit corresponding to the confidence coefficient alpha in the standard Gaussian distribution cumulative distribution function; pmWhen the confidence coefficient of the optimal signal-to-noise ratio estimation value x is alpha, the lower boundary of the interval is e-zαThe probability of σ;
step 3.3, obtaining the expected signal-to-noise ratio R (k) compensated at the kth moment by using the formula (5):
R(k)＝S+zασ(k) (5)
in the formula (5), zασ (k) represents a discrete system SNR compensation value at the kth time, and σ (k) represents a standard deviation at m times from x (k), x (k-1),. cndot., x (k-m + 1);
step four, constructing a target optimization function, and solving the optimal transmitting power of the transmitting node of the wireless communication equipment through a dynamic programming algorithm;
step 4.1, a linear quadratic Gaussian control method is adopted, and the variable quantity of the node transmitting power and the difference value of the actual signal-to-noise ratio and the expected signal-to-noise ratio after compensation are used asConstructing a quadratic cost function V shown in a formula (6) for a performance control targett(x (t)) and as a target optimization function:
Figure GDA0003440519470000031
in formula (6): x (t) represents the actual signal-to-noise ratio at the time t, x (N) represents the actual signal-to-noise ratio at the time N, R (N) represents the compensated expected signal-to-noise ratio at the time N, Q and R respectively represent the relative weights of the state deviation and the input deviation, and Q and R are positive definite matrixes; e represents the mean value; n represents a control time domain;
step 4.2, initializing the transmitting power P at the t-th momentsent(t), the actual signal-to-noise ratio x (t) at the t moment, and the covariance M (t) of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimated value at the t moment;
step 4.3, optimizing the objective function V by using the formula (7)N(x (N)) the actual snr at time N is converted to an optimal snr estimate:
Figure GDA0003440519470000032
in formula (7): tr (-) represents the trace of the matrix, M (N) represents the covariance of the actual SNR and the optimal SNR estimated value at the Nth moment;
Figure GDA0003440519470000041
representing the optimal SNR estimation value at the Nth moment;
step 4.4, dividing the target optimization function in the formula (6) into a single-stage optimization problem in N-t steps, and solving by adopting a reverse thinking method of dynamic programming, so as to obtain an optimal solution of the transmission power variation u (k) and a quadratic general formula V of the target optimization function shown in the formula (8)k(x(k))：
Figure GDA0003440519470000042
In formula (8): p (k) represents a symmetric matrix of the quadratic form general at time k and has:
Figure GDA0003440519470000043
in formula (8): q (k) represents a constant term of the quadratic form formula at time k, and has:
Figure GDA0003440519470000044
in formula (10):
Figure GDA0003440519470000045
representing the covariance of the predicted signal-to-noise ratio and the actual signal-to-noise ratio at the k +1 moment, and M (k +1) representing the covariance of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimation value at the k +1 moment;
and 4.5, obtaining a symmetric matrix P (N) -Q of the quadratic form general formula at the Nth moment from the formula (7), and performing recursive calculation on the formula (8) to obtain symmetric matrices P (N-1) and P (N-2) · · P (2) of the quadratic form general formula from the Nth moment to the t +1 th moment so as to obtain a wireless communication transmission power input increment u (t) from the t moment to the N-1 th moment, and u (t +1) · · u (N-1) to realize the optimal control of the transmission power of the transmission node.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention obtains the lower bound of the expected signal-to-noise ratio by utilizing the reliability requirement of wireless communication through the relation between the reliability and the signal-to-noise ratio, establishes a system state space model with state observation of the signal-to-noise ratio and the transmitting power, and obtains the relation between two performance indexes of the reliability and the transmitting power, thereby meeting the reliability requirement of communication by controlling the transmitting power of the wireless communication equipment node.
2. The method and the device perform the expected signal-to-noise ratio compensation by estimating the confidence interval of the signal-to-noise ratio fluctuation in real time, and ensure that the reliability requirement in the communication process can still be met under the condition that the signal-to-noise ratio has stronger randomness in the wireless communication process.
3. According to the invention, the Kalman filter is adopted to filter the signal-to-noise ratio observation value, the LQG controller and the dynamic programming algorithm are utilized to predict and control the transmitting power of the wireless communication equipment, the transmitting power of the equipment is controlled more accurately, the transmitting power is reduced under the condition of meeting the reliability requirement in the communication process, and the service life of the equipment is prolonged.
Drawings
Fig. 1 is a schematic diagram of a wireless communication transmitting device node and a receiving device node;
FIG. 2 is a schematic diagram of the internal structure of a Kalman filter;
FIG. 3 is a schematic diagram of the structure of the LQG controller;
FIG. 4 is a graph of experimental simulation of signal-to-noise ratio under the control algorithm herein;
fig. 5 is a graph of experimental simulations of transmit power under the control algorithm herein.
Detailed Description
In the embodiment, a wireless transmission power prediction control method for intelligent substation reliability requirement constraint is characterized in that a laboratory indoor environment is used for simulating the intelligent substation indoor environment, a wireless communication sending node is used for routing inspection in a planned path in a laboratory and sending data, and a wireless communication receiving node is fixed at a specific indoor position; the reliability requirement constrained wireless transmission power prediction control method comprises the following steps:
step one, according to the reliability index requirement P of wireless communication data of the transformer substationsCalculating the corresponding communication error rate PeFurther, the signal-to-noise ratio S of the corresponding communication expectation is obtained;
step 1.1, selecting the reliability index requirement P of wireless communication data in the transformer substationsThe reliability P can be obtained from the equation (1) when the primary communication packet length l is 1000 bits 0.9999sSignal to noise ratio PSNRAnd bit error rate PeThe relationship of (1);
Figure GDA0003440519470000051
in formula (1): q (-) represents the tail integral function of the gaussian distribution,
Figure GDA0003440519470000053
representing the ratio of the data transmission speed to the noise bandwidth of the wireless transmission transceiver;
signal-to-noise ratio P calculable by equation (1)SNR9.5dbm, and the desired signal-to-noise ratio S9.5 dbm;
step two, constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation;
step 2.1, data are sent and received through a pair of wireless sensor nodes of CC2530 of TI company, the sending node is patrolled and examined in a planned path in a laboratory and sends data, meanwhile, the wireless receiving node is fixed at an indoor fixed position, and the received signal strength P in wireless communication is collected by taking 1s as a periodrAnd background noise PnThe experiment is carried out 500 times of data communication, and a wireless communication receiving device node and a wireless communication transmitting device node are shown in figure 1;
step 2.2, constructing an approximate logarithmic distance path loss discrete model of the communication signal-to-noise ratio of the signal receiving node and the transmitting power of the transmitting node of the wireless sensor network at the kth moment by using the formula (2):
Figure GDA0003440519470000052
in formula (2): pr(k) Indicating the signal reception strength, P, at the k-th instantSNR(k) Representing the signal-to-noise ratio, P, at time ksent(k) Representing the transmission power, PL (d), of a transmitting node in wireless communication at time k0) Is a reference distance d0A loss value of the communication propagation process, d represents the distance from a transmitting node to a receiving node, n (k) represents a path loss exponent, Xσ(k) For the influence of multipath effects on the received signal, Pn(k) Representing the background noise at time k;
step 2.3, making the k moment propagation path loss
Figure GDA0003440519470000061
Constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation by using the formula (3):
Figure GDA0003440519470000062
in formula (3): x (k +1) represents the snr at the k +1 th time, x (k) represents the actual snr at the k th time, u (k) represents the amount of change in the transmission power of the transmitting node in the wireless communication between the k +1 th time and the k th time, w (k) represents the amount of discrete system environment disturbance at the k th time, and w (k) ═ Δ PL (k +1), Δ Pn(k+1)]Where Δ PL (k +1) represents the amount of change between propagation path loss PL (k +1) at the k +1 th time and propagation path loss PL (k) at the k-th time, and Δ Pn(k +1) represents the noise floor P at the k +1 th timen(k +1) and the k-th time background noise Pn(k) Z (k) represents a measurement value of the signal-to-noise ratio at the k-th time, and v (k) represents a measurement noise at the k-th time; a is the coefficient of the actual signal-to-noise ratio x (k) of the state equation, BuIs a coefficient of the transmission power variation u (k), BwThe coefficient is the disturbance amount w (k) of the discrete system environment, and C is the coefficient of the actual signal-to-noise ratio x (k) of the observation equation; in this example, a is 1 and B isu＝1,Bw＝[-1-1],C＝1；
Thirdly, constructing a Linear Quadratic Gaussian (LQG) controller through a Kalman filter, and resetting the expected signal-to-noise ratio in a confidence interval mode;
step 3.1 variance Q of noise in Wireless communication Transmission Process1The signal-to-noise ratio observed noise variance V is 10. Filtering the formula (3) through a Kalman filter to obtain an optimal signal-to-noise ratio estimation value at the kth moment shown in the formula (4), wherein the Kalman filtering process is shown in FIG. 2;
Figure GDA0003440519470000063
in formula (4):
Figure GDA0003440519470000064
the posterior signal-to-noise ratio at the k moment is represented as the optimal signal-to-noise ratio estimated value at the k moment after filtering,
Figure GDA0003440519470000065
showing the prior signal-to-noise ratio at the k moment, namely the predicted value of the signal-to-noise ratio at the k moment, L (k) showing the Kalman filtering gain at the k moment, z (k) showing the measured value of the signal-to-noise ratio at the k moment,
Figure GDA0003440519470000066
represents the prior SNR covariance at time k, and M (k-1) represents the A posteriori SNR covariance at time k-1;
step 3.2, a method of introducing a statistical confidence interval is introduced, data analysis is performed on the optimal snr estimation value x at the first m moments of the k-th moment to obtain a standard deviation σ of the optimal snr estimation value x at the first m moments of the k-th moment, m is selected to be 10 to obtain an average value and a standard deviation of the optimal snr estimation value x at the k-th moment, and when the confidence coefficient is α, the confidence interval is calculated as shown in formula (5):
Pr{x＞e-zασ}＝α (5)
in formula (5): e represents the mean value of the optimal SNR x at the first m moments of the k moment, σ represents the standard deviation of the optimal SNR x at the first m moments of the k moment, and zαExpressing a fraction bit corresponding to the confidence coefficient alpha in the standard Gaussian distribution cumulative distribution function;
step 3.3, selecting confidence coefficient alpha as 0.989, and looking up the table to obtain zαThe desired snr r (k) compensated at time k is obtained using equation (6) at 2.29:
R(k)＝S+zασ(k) (6)
in the formula (6), zαSigma (k) represents the system signal-to-noise ratio compensation value at the time k, and sigma (k) is the standard deviation of m times from x (k), x (k-1),. cndot. (k-m + 1);
step four, constructing a target optimization function, and solving the optimal transmitting power of the transmitting node of the wireless communication equipment through a dynamic programming algorithm;
step 4.1, a linear quadratic Gaussian control method is adopted, a flow chart of the LQG controller is shown in fig. 3, the variable quantity of node transmitting power and the difference value of an actual signal-to-noise ratio and a post-compensation expected signal-to-noise ratio are used as performance control targets, and a quadratic cost function V shown in a formula (7) is constructedt(x (t)) and as an objective optimization function, the sum of the difference between the actual signal-to-noise ratio and the compensated expected signal-to-noise ratio from the t moment to the N moment and the variation of the transmission power is minimized by controlling the transmission power of the wireless communication node;
Figure GDA0003440519470000071
in formula (7): x (t) represents the actual signal-to-noise ratio at the time t, x (N) represents the actual signal-to-noise ratio at the time N, R (N) represents the compensated expected signal-to-noise ratio at the time N, Q and R respectively represent the relative weights of the state deviation and the input deviation, and Q and R are positive definite matrixes; e represents the mean value; n represents a control time domain; in the experimental process, Q is 0.5, and R is 0.5;
step 4.2, selecting t as 1, N as 500, initializing emission power Psent(1) Initializing an actual signal-to-noise ratio x (1) to 15, and initializing a covariance M (1) of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimation value to 10;
step 4.3, optimizing the objective function V by using the formula (8)N(x (N)) the actual snr at time N is converted to an optimal snr estimate:
Figure GDA0003440519470000072
in formula (8): tr (-) represents the trace of the matrix, M (N) represents the covariance of the actual SNR and the optimal SNR estimated value at the Nth moment;
Figure GDA0003440519470000081
representing the optimal SNR estimation value at the Nth moment; r (N) represents the compensated expected signal-to-noise ratio at the Nth moment;
step 4.4, dividing the target optimization function of the formula (7) into N-t single-stage optimization problems, and solving by adopting a reverse thinking method of dynamic programming, so as to obtain the optimal solution of the transmission power variation u (k) and the quadratic general formula V of the target optimization function shown in the formula (9)k(x(k))：
Figure GDA0003440519470000082
In formula (9): p (k) represents a symmetric matrix of the quadratic form general at time k and has:
Figure GDA0003440519470000083
in formula (9): q (k) represents a constant term of the quadratic form formula at time k, and has:
Figure GDA0003440519470000084
in formula (11):
Figure GDA0003440519470000085
representing the covariance of the predicted signal-to-noise ratio and the actual signal-to-noise ratio at the k +1 moment, and M (k +1) representing the covariance of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimation value at the k +1 moment;
and 4.5, obtaining a symmetric matrix P (N) ═ Q and P (N) ═ 0.5 of the quadratic form general formula at the Nth time from the formula (8), and performing recursive calculation on the formula (9) to obtain symmetric matrices P (N-1) and P (N-2) · · P (2) of the quadratic form general formula from the Nth time to the t +1 th time, so as to obtain wireless communication transmission power input increments u (t) from the t-th time to the N-1 th time, and u (t +1) · · u (N-1) to realize the optimal control of the transmission power of the transmission node.
Fig. 4 shows the value of the estimated value of the optimal snr for 500 communications, and fig. 5 shows the transmit power of the transmitting node for 500 communications, when the transmitting node moves or there is obstruction interference between the communicating nodes, the actual snr is substantially above the expected snr. In 500 measurements, there are only 6 times when the snr is lower than the expected value, and the ratio r to get the actual snr higher than the expected snr is 0.988, which basically meets the set target with confidence α of 0.989. The experimental result shows that the LQG control algorithm can control the transmitting power in real time when external disturbance occurs, the lower bound of the actual signal-to-noise ratio is achieved to be above the expected signal-to-noise ratio, and the transmitting power is reduced when the external disturbance is small. The dynamic planning method based on the LQG controller can realize the increment of the transmitting power of the nodes controlled layer by layer, realize the coordination between the transmitting power of the wireless sensor network and the reliability of wireless communication in the environment of the transformer substation, ensure the reliability of the wireless communication and reduce the transmitting power of the nodes as much as possible.

Claims (1)

1. A wireless transmitting power prediction control method for intelligent substation reliability demand constraint is characterized by comprising the following steps:
step one, according to the reliability index requirement P of wireless communication data of the transformer substationsCalculating the corresponding communication error rate PeFurther, the signal-to-noise ratio S of the corresponding communication expectation is obtained;
step two, constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation;
step 2.1, the wireless communication receiving node periodically collects the received signal strength P in wireless communicationrAnd background noise Pn；
Step 2.2, constructing an approximate logarithmic distance path loss discrete model of the communication signal-to-noise ratio of the signal receiving node and the transmitting power of the transmitting node of the wireless sensor network at the kth moment by using the formula (1):
Figure FDA0003440519460000011
in formula (1): pr(k) Indicating the signal reception strength, P, at the k-th instantSNR(k) Representing the signal-to-noise ratio, P, at time ksent(k) Indicating transmission of transmitting node in wireless communication at time kRadio power, PL (d)0) Is a reference distance d0A time-dependent communication propagation loss value, d represents the distance from the transmitting node to the receiving node, n (k) represents a path loss exponent at the k-th time, Xσ(k) For the influence of multipath effects on the received signal at the k-th instant, Pn(k) Representing the background noise at time k;
step 2.3, making the k moment propagation path loss
Figure FDA0003440519460000012
Constructing a discrete wireless signal propagation state space equation with signal-to-noise ratio observation by using an equation (2):
Figure FDA0003440519460000013
in formula (2): x (k +1) represents the snr at the k +1 th time, x (k) represents the actual snr at the k th time, u (k) represents the amount of change in the transmission power of the transmitting node in the wireless communication between the k +1 th time and the k th time, w (k) represents the amount of discrete system environment disturbance at the k th time, and w (k) ═ Δ PL (k +1), Δ Pn(k+1)]Where Δ PL (k +1) represents the amount of change between propagation path loss PL (k +1) at the k +1 th time and propagation path loss PL (k) at the k-th time, and Δ Pn(k +1) represents the noise floor P at the k +1 th timen(k +1) and the k-th time background noise Pn(k) Z (k) represents a measurement value of the signal-to-noise ratio at the k-th time, and v (k) represents a measurement noise at the k-th time; a is the coefficient of the actual signal-to-noise ratio x (k) of the state equation, BuIs a coefficient of the transmission power variation u (k), BwThe coefficient is the disturbance amount w (k) of the discrete system environment, and C is the coefficient of the actual signal-to-noise ratio x (k) of the observation equation;
thirdly, constructing a linear quadratic Gaussian controller through a Kalman filter, and resetting the expected signal-to-noise ratio in a confidence interval mode;
step 3.1, filtering the formula (2) through a Kalman filter to obtain an optimal signal-to-noise ratio estimation value at the kth moment shown in the formula (3):
Figure FDA0003440519460000021
in formula (3):
Figure FDA0003440519460000022
the posterior signal-to-noise ratio at the k moment is represented as the optimal signal-to-noise ratio estimated value at the k moment after filtering,
Figure FDA0003440519460000023
the prior signal-to-noise ratio at the k moment is represented, namely the predicted value of the signal-to-noise ratio at the k moment, L (k) represents the Kalman filtering gain at the k moment, and z (k) represents the measured value of the signal-to-noise ratio at the k moment;
step 3.2, introducing a method for statistically setting a confidence interval, performing data analysis on the optimal signal-to-noise ratio estimated value x at the first m moments of the k moment to obtain a standard deviation sigma of the optimal signal-to-noise ratio estimated value x at the first m moments of the k moment, and calculating the confidence interval by using a formula (4) when the confidence coefficient is alpha:
Pm{x＞e-zασ}＝α (4)
in formula (4): e represents the mean value of the optimal SNR estimated values x at the first m moments of the kth moment, zαExpressing a fraction bit corresponding to the confidence coefficient alpha in the standard Gaussian distribution cumulative distribution function; pmWhen the confidence coefficient of the optimal signal-to-noise ratio estimation value x is alpha, the lower boundary of the interval is e-zαThe probability of σ;
step 3.3, obtaining the expected signal-to-noise ratio R (k) compensated at the kth moment by using the formula (5):
R(k)＝S+zασ(k) (5)
in the formula (5), zασ (k) represents a discrete system SNR compensation value at the kth time, and σ (k) represents a standard deviation at m times from x (k), x (k-1),. cndot., x (k-m + 1);
step four, constructing a target optimization function, and solving the optimal transmitting power of the transmitting node of the wireless communication equipment through a dynamic programming algorithm;
step 4.1, constructing a quadratic cost function V shown in the formula (6) by adopting a linear quadratic Gaussian control method and taking the node transmitting power variation and the difference value between the actual signal-to-noise ratio and the expected signal-to-noise ratio after compensation as a performance control targett(x (t)) and as a target optimization function:
Figure FDA0003440519460000024
in formula (6): x (t) represents the actual signal-to-noise ratio at the time t, x (N) represents the actual signal-to-noise ratio at the time N, R (N) represents the compensated expected signal-to-noise ratio at the time N, Q and R respectively represent the relative weights of the state deviation and the input deviation, and Q and R are positive definite matrixes; e represents the mean value; n represents a control time domain;
step 4.2, initializing the transmitting power P at the t-th momentsent(t), the actual signal-to-noise ratio x (t) at the t moment, and the covariance M (t) of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimated value at the t moment;
step 4.3, optimizing the objective function V by using the formula (7)N(x (N)) the actual snr at time N is converted to an optimal snr estimate:
Figure FDA0003440519460000031
in formula (7): tr (-) represents the trace of the matrix, M (N) represents the covariance of the actual SNR and the optimal SNR estimated value at the Nth moment;
Figure FDA0003440519460000032
representing the optimal SNR estimation value at the Nth moment;
step 4.4, dividing the target optimization function in the formula (6) into a single-stage optimization problem in N-t steps, and solving by adopting a reverse thinking method of dynamic programming, so as to obtain an optimal solution of the transmission power variation u (k) and a quadratic general formula V of the target optimization function shown in the formula (8)k(x(k))：
Figure FDA0003440519460000033
In formula (8): p (k) represents a symmetric matrix of the quadratic form general at time k and has:
Figure FDA0003440519460000034
in formula (8): q (k) represents a constant term of the quadratic form formula at time k, and has:
Figure FDA0003440519460000035
in formula (10):
Figure FDA0003440519460000036
representing the covariance of the predicted signal-to-noise ratio and the actual signal-to-noise ratio at the k +1 moment, and M (k +1) representing the covariance of the actual signal-to-noise ratio and the optimal signal-to-noise ratio estimation value at the k +1 moment;
and 4.5, obtaining a symmetric matrix P (N) -Q of the quadratic form general formula at the Nth time from the formula (7), and performing recursive calculation on the formula (8) to obtain symmetric matrices P (N-1) and P (N-2) … P (2) of the quadratic form general formula from the Nth-1 time to the t +1 time, so as to obtain wireless communication transmission power input increments u (t) from the t time to the N-1 time, and u (t +1) … u (N-1) to realize optimal control of transmission power of the transmission node.
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CN113959447B (en) * 2021-10-19 2023-06-27 北京京航计算通讯研究所 Relative navigation high noise measurement identification method, device, equipment and storage medium
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001091320A1 (en) * 2000-05-20 2001-11-29 Robert Bosch Gmbh Method for regulating the transmitting power of a master station, and corresponding master station
WO2008022555A1 (en) * 2006-08-16 2008-02-28 Huawei Technologies Co., Ltd. A method,system and appratus for determining transmission antenna
CN103179596A (en) * 2013-03-12 2013-06-26 浙江大学 Method for optimizing total power consumption for LQG (linear quadratic Gaussian) control and communication
CN108830007A (en) * 2018-06-28 2018-11-16 电子科技大学 A kind of radar transmission power distribution method based on secondary cost function
CN109982422A (en) * 2019-04-09 2019-07-05 合肥工业大学 A kind of adaptive wireless sensor network transmission power control method
CN110764409A (en) * 2019-10-12 2020-02-07 山东科技大学 Optimal linear quadratic Gaussian control method for discrete system
CN111182564A (en) * 2019-12-26 2020-05-19 合肥工业大学 Wireless link quality prediction method based on LSTM neural network

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001091320A1 (en) * 2000-05-20 2001-11-29 Robert Bosch Gmbh Method for regulating the transmitting power of a master station, and corresponding master station
WO2008022555A1 (en) * 2006-08-16 2008-02-28 Huawei Technologies Co., Ltd. A method,system and appratus for determining transmission antenna
CN103179596A (en) * 2013-03-12 2013-06-26 浙江大学 Method for optimizing total power consumption for LQG (linear quadratic Gaussian) control and communication
CN108830007A (en) * 2018-06-28 2018-11-16 电子科技大学 A kind of radar transmission power distribution method based on secondary cost function
CN109982422A (en) * 2019-04-09 2019-07-05 合肥工业大学 A kind of adaptive wireless sensor network transmission power control method
CN110764409A (en) * 2019-10-12 2020-02-07 山东科技大学 Optimal linear quadratic Gaussian control method for discrete system
CN111182564A (en) * 2019-12-26 2020-05-19 合肥工业大学 Wireless link quality prediction method based on LSTM neural network

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control;A SK Annamalai等;《IET Conference on Control and Automation 2013: Uniting Problems and Solutions》;20130930;全文 *
Optimal LQG control based on the prediction for a robot system over wireless network;Wei Wang等;《2011 International Conference on Electric Information and Control Engineering》;20110527;全文 *
智能电网可靠性需求约束下无线发射功率模型预测控制;孙伟等;《电力***自动化》;20191218(第03期);全文 *
用卡尔曼滤波器估计目标机动加速度;雷虎民等;《飞行力学》;20021030(第03期);全文 *

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