CN104331551A - Grid frequency disturbance recovery process output method - Google Patents
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Abstract
The invention provides a grid frequency disturbance recovery process output method. The method comprises the steps: acquiring grid input data; acquiring grid output data according to a weight and the grid input data; acquiring grid frequency disturbance recovery process output according to the grid input data, the weight and the grid output data; acquiring an error function according to the grid output data and a set desired output value; inputting the grid input data and a set initial weight to a power grid, then, acquiring an initial error function value, setting a target range of the error function, and adjusting the initial weight until the error function value is in the target range if the initial error function value is outside the target range, wherein the weight obtained through last adjustment is a target weight, and the initial weight is the target weight if the initial error function value is in the target range. The method has the advantages that frequency change circumstances after a set fault occurs can be predicted, and real-time reserve capacity and UHV (Extra-High Voltage) high-power transmission power matched check on the power grid is realized.
Description
Technical Field
The invention relates to the field of power grid management, in particular to an output method for a power grid frequency disturbance recovery process.
Background
In recent years, a plurality of large-area power failure accidents occur continuously in the world, and serious economic loss and social impact are caused. The reasons for causing heavy power failure are numerous, and the method relates to each link of power grid planning, construction, scheduling operation and management, wherein the reserve adequacy of a power grid is an important factor. The shortage of the reserve capacity can cause the instability of the frequency of the power grid when high power is lost, and the system frequency breakdown can be caused when the power grid is serious, so that the timely grasping of the reserve capacity has important significance for the safe and stable operation of the power grid.
There are many methods for calculating the total amount of reserve abroad, and in summary, the method for determining the total amount of reserve mainly has the following three forms:
1) the N-1 criterion determines the spare capacity of the system: when the system has an accident, the load is prevented from being cut off as much as possible.
2) A certain percentage of the load etc. empirical estimation
3) Probabilistic method: and (4) solving the probability of a certain capacity shortage of the system, and further solving the required spare capacity according to the reliability index required by the system.
Although the estimation of the reserve capacity by the method can meet the basic requirements, the reserve capacity required by the system cannot be accurately given in real time according to the running characteristics of the power grid. The spare capacity calculated through reliability simulation is often large in margin and not economical enough, and the calculation through an empirical method is too extensive and lacks scientificity.
Data mining is an emerging edge discipline, which refers to a process of mining useful knowledge from a large amount of data stored in a database, a data warehouse, or other information base, and has attracted considerable attention in recent years from the chinese academia and the industry. Classification and prediction are the two most prominent forms of data analysis that can be used to extract models that can describe sets of important data or predict trends in future data. For estimating the frequency disturbance process and determining the spare capacity problem, firstly, the data is analyzed and processed by using a classification method, and the logical relationship between the data is determined by rule extraction. The data comprises input information related to grid faults and frequency changes, such as total load before fault, loss power, direct current input power before fault, reserve capacity, comprehensive frequency effect coefficients, recovery frequency estimation coefficients and frequency before fault, which form a nonlinear relation with frequency disturbance changes; the output information is frequency data which changes constantly, and the whole frequency recovery process needs to be expressed visually and clearly. In the aspect of rule extraction, a black box method capable of well reflecting the nonlinear relation is effective. And the data prediction is used for predicting future values of the data objects, and the future frequency disturbance process can be estimated by utilizing a mathematical model established by the previous data classification, so that an auxiliary decision is provided for the standby management problem.
Disclosure of Invention
The invention aims to provide an output method for a power grid frequency disturbance recovery process, which is used for predicting the frequency change condition of a power grid after a certain set fault, and particularly solves the problems that the traditional frequency estimation method has off-line errors and the estimation result precision is reduced under the condition that direct-current high power is introduced.
In order to achieve the above object, the present invention provides an output method for a grid frequency disturbance recovery process, which is applied to a grid having multiple layers of data units and at least two data nodes on each layer, and comprises the following steps:
measuring to obtain power grid input data related to power grid faults and frequency changes;
inputting the power grid input data into a power grid, connecting any two data nodes through a weight, receiving the output information of the previous layer by the data node of each layer and outputting the output information to each node of the next layer, and acquiring the power grid output data according to the power grid input data and the weight by taking the weight and the power grid output data as variables;
obtaining the output of the power grid frequency disturbance recovery process according to the power grid input data, the weight and the power grid output data;
obtaining an error function of the power grid according to the power grid output data and a set expected output value;
inputting the power grid input data and the set initial weight value into the power grid to obtain initial power grid output data, combining the expected output value to obtain an initial error function value, setting a target range of an error function, and comparing the initial error function value with the target range of the error function: if the initial error function value falls outside the target range, adjusting the initial weight by adopting a gradient steepest descent method, wherein the gradient steepest descent method is to adjust the initial weight along the negative gradient direction of the weight so that the adjustment amount of the weight is in direct proportion to the gradient descent of the error function, and performing iterative operation by taking the sum of the initial weight and the adjustment amount of the weight as a new weight so as to minimize the adjusted error function value until the error function value falls within the target range, and the weight obtained by the last adjustment is the target weight; and if the initial error function value falls within the target range, the initial weight value is the target weight value.
Further, the power grid input data is xnAnd N is 1,2 … N, the power grid is provided with an input layer, a hidden layer and an output layer, the input data of the power grid is input into the input layer, the output data of the power grid is obtained through the output of the hidden layer and the output layer, the hidden layer is provided with L-layer data units, wherein the number of the nodes on the L-th layer is NlAnd L, for the kth node of the L-1 layer, obtaining a weighted summation value u of the kth node according to the power grid input data and the weight valuekThe output data of the kth node is ykAnd is and
yk＝f(uk)，
wherein k is more than 0 and less than Nl-1A is a set first coefficient, wknInputting data x for the kth node relative to the gridnThe weight of (2).
Further, the j node of the l layer is weighted to have a summation value ofAnd is
Wherein j is more than 0 and less than Nl，0＜s＜Nl-2The output data of the jth node of the ith layer isThe output data of the kth node of the l-1 layer isThe output data of the s-th node of the l-2 th layer isThen according to
And obtaining the output of the power grid frequency disturbance recovery process, wherein O is the power grid output data.
Further, the error function is E, when the grid has only one output O, then:
O＝f(netL)，
wherein d is the desired output value.
Further, the adjustment amount of the weight isAnd:
wherein eta is a set learning rate and is a constant,the weight from the ith node of the l < th > layer to the jth node of the l +1 < th > layer.
Further, if the node j is an output unit, thenAnd:
if node j is not an output unit, then
Counting back from the L +1 layer,
further, the weight value obtained by the m +1 th iteration isAnd:
wherein,as the amount of adjustment of the weight value,for the weight obtained for the mth iteration, when m is 1,is the initial weight.
Further, the power grid input data comprise total load before fault, loss power, direct current input power before fault, reserve capacity, a difference adjustment coefficient, support amplitude limiting and frequency before fault, and the power grid output data comprise minimum frequency after fault, recovery frequency, recovery time and time recovered to 49.95 Hz.
The invention provides a power grid frequency disturbance recovery process output method, which is based on real-time data of a power grid, establishes a nonlinear real-time simulation model by comprehensively analyzing index information such as total load before fault, loss power, direct current input power before fault, reserve capacity, comprehensive frequency effect coefficient, recovery frequency estimation coefficient, frequency before fault and the like in real time, and provides estimation results of the frequency recovery whole process (the lowest frequency after fault, recovery frequency, recovery time and recovery time to 49.95HZ time) after power loss fault. By applying the model, the frequency change condition after a certain set fault can be predicted, the problems of off-line errors and reduced accuracy of an estimation result under the condition that direct-current high power is input in the traditional frequency estimation method are particularly solved, the matching check of the real-time reserve capacity of the power grid and the ultra-high-voltage high-power transmission power is realized, an auxiliary tool for predicting the reserve capacity is provided for a typical operation mode which may occur in the future of the power grid, and a reliable reserve reservation auxiliary decision is provided for the adjustment of the operation mode of the power grid, the risk prevention and the control.
Drawings
Fig. 1 is a hierarchical diagram of a power grid according to an embodiment of the present invention;
FIG. 2 is a flowchart of an algorithm of a steepest descent gradient method according to an embodiment of the present invention;
fig. 3 is a flowchart of the calculation provided in the embodiment of the present invention.
Wherein, 101: output layer, 102: hidden layer, 103: and inputting the layer.
Detailed Description
The following describes in more detail embodiments of the present invention with reference to the schematic drawings. Advantages and features of the present invention will become apparent from the following description and claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
The factors determining the frequency disturbance recovery process are many, the relationship between the factors is complicated, and the input and the output show a very obvious external nonlinear relationship. The traditional estimation method is difficult to process the non-linear relation, so that the calculation precision is not high. As shown in fig. 1, considering the extraction rule by the black box method, the problem to be solved is mainly divided into three levels: input layer 103, output layer 101, hidden layer 102. The input layer 103 is input information formed by prepared data, the output layer 101 is an output result, and the hidden layer 102 represents a complex nonlinear relation in a black box and is unknown outside the system. It has been demonstrated that any non-linear continuous function can be approximated by a three-layer network with any degree of precision, provided there are a sufficient number of hidden layer nodes. The strong nonlinear approximation capability is the theoretical basis of frequency disturbance recovery process estimation.
As shown in fig. 2, the present invention provides a method for outputting a grid frequency disturbance recovery process, which is applied to a grid having multiple layers of data units and at least two data nodes on each layer, and includes the following steps:
measuring to obtain power grid input data related to power grid faults and frequency changes;
inputting the power grid input data into a power grid, connecting any two data nodes through a weight, receiving the output information of the previous layer by the data node of each layer and outputting the output information to each node of the next layer, and acquiring the power grid output data according to the power grid input data and the weight by taking the weight and the power grid output data as variables;
obtaining the output of the power grid frequency disturbance recovery process according to the power grid input data, the weight and the power grid output data;
obtaining an error function of the power grid according to the power grid output data and a set expected output value;
inputting the power grid input data and the set initial weight value into the power grid to obtain initial power grid output data, combining the expected output value to obtain an initial error function value, setting a target range of an error function, and comparing the initial error function value with the target range of the error function: if the initial error function value falls outside the target range, adjusting the initial weight by adopting a gradient steepest descent method, wherein the gradient steepest descent method is to adjust the initial weight along the negative gradient direction of the weight so that the adjustment amount of the weight is in direct proportion to the gradient descent of the error function, and performing iterative operation by taking the sum of the initial weight and the adjustment amount of the weight as a new weight so as to minimize the adjusted error function value until the error function value falls within the target range, and the weight obtained by the last adjustment is the target weight; and if the initial error function value falls within the target range, the initial weight value is the target weight value.
The present invention adopts the gradient steepest descent method, and the basic principle thereof is to make the error function of the whole network (in this embodiment, namely, the power grid) minimum by continuously adjusting the weight, that is, to adopt the gradient search technology, so as to make the mean square error between the actual output value (in this embodiment, namely, the power grid output data) and the expected output value of the network minimum. The learning process of the network is a process of adjusting the weight along the direction of the negative gradient.
The two data nodes are mutually connected through the weight value and carry out information transmission, the weight value can represent the connection strength between the data nodes, the activation is represented when the weight value is positive, and the inhibition is represented when the weight value is negative. The existence of the weight values better characterizes that different input data have different enhancing, weakening or inhibiting effects on the transmitted output data.
The power grid input data is xnAnd N is 1,2 … N, the power grid is provided with an input layer, a hidden layer and an output layer, the input data of the power grid is input into the input layer, the output data of the power grid is obtained through the output of the hidden layer and the output layer, the hidden layer is provided with L-layer data units, wherein the number of the nodes on the L-th layer is NlL, setting a weight w for the kth node of the L-1 layer, and obtaining a weighted summation value u of the kth node according to the power grid input data and the weight wkThe output data of the kth node is ykAnd is and
yk＝f(uk)，
wherein k is more than 0 and less than Nl-1A is a set first coefficient, wknInputting data x for the kth node relative to the gridnThe activation function f (x) is used for realizing nonlinear mapping and limiting the output amplitude of the data node within a certain range.
The weighted summation value of the jth node of the ith layer isAnd is
Wherein j is more than 0 and less than Nl，0＜s＜Nl-2The output data of the jth node of the ith layer isThe output data of the kth node of the l-1 layer isThe output data of the s-th node of the l-2 th layer isThen according to
And obtaining the output of the power grid frequency disturbance recovery process, wherein O is the power grid output data.
The error function is E, in this embodiment, the grid has only one output, i.e. the grid output data O has only one quantity (in fig. 1, three outputs are schematically shown for clarity of describing the relationship of three levels, but here, only one grid output data is taken for explaining the calculation of the error function, and the number of the grid output data is not limited to one, nor to three as shown in fig. 1), then:
O＝f(netL)，
wherein d is the desired output value.
It can be seen that the output error function is a function of the weights w of each layer, and therefore adjusting w can change the magnitude of the error function E.
The adjustment amount of the weight isAnd:
wherein eta is a set learning rate and is a constant,the weight from the ith node of the l < th > layer to the jth node of the l +1 < th > layer.
If node j is an output unit, thenAnd:
if node j is not an output unit, then
Counting back from the L +1 layer,
the first layer can be seenThe value of (1) is determined by the value of the (l + 1) th layer, and the calculation of the weight value is a reverse layer-by-layer process. Summarizing the result, iteratively updating the connection weight and the closed value change increment of each layer for the next round of network according to the obtained connection weight and the closed value change increment of each layer, and setting the weight obtained by the (m + 1) th iteration asThen:
wherein,as the amount of adjustment of the weight value,for the weight obtained for the mth iteration, when m is 1,is the initial weight.
When the error function E satisfies a sufficiently small requirement (i.e., the error function value after the weight value is adjusted falls within the set target range), the calculation may be stopped. And the nonlinear relation network constructed by the weight obtained by the last calculation is a relation model capable of representing the power grid input data and the power grid output data.
The power grid input data comprise total load before fault, loss power, direct current input power before fault, reserve capacity, difference adjustment coefficient, support amplitude limiting and frequency before fault, and the power grid output data comprise lowest frequency after fault, recovery frequency, recovery time and time recovered to 49.95 Hz.
The model obtained by calculation of a large amount of historical data has higher accuracy and can be used for calculating the spare capacity.
In this embodiment, as shown in fig. 3, based on the method, other real-time data of the power grid state without spare capacity is used as input data, and safe frequency recovery data (the minimum frequency after the fault, the recovery frequency, the recovery time, and the time recovered to 49.95 hz) which needs to be met after the fault is generated is used as output data, and an iterative solution method for setting a spare capacity initial value is used to finally obtain the spare capacity which can enable the frequency to be recovered to meet the safety requirement after the fault is generated under the condition of the known power grid state.
According to the method, frequency curves of Binjin direct current bipolar latch-up (740 ten thousand KW) at peak and off-peak are obtained. The known guest-gold dc bipolar latch input information is shown in table 1:
TABLE 1
In table 1, the occurrence time includes two cases, i.e., the highest load and the lowest load, and the unit of the total load before the fault, the loss power, the dc input power before the fault and the reserve capacity is Megawatts (MW), the integrated frequency effect coefficient and the recovery frequency estimation coefficient are dimensionless numbers, and the unit of the frequency before the fault is Hertz (HZ).
Firstly, a frequency curve after the binjin direct current bipolar locking under the condition of the highest load is calculated, the initial value of the maximum calculation spare capacity (namely the initial spare) under the highest load is 5000MW, and the initial value of the calculation spare capacity under the lowest load is 8000 MW. The initial value of the calculation result of the frequency disturbance overall process estimation model is as follows:
TABLE 2
In table 2, the units of the lowest frequency and recovery frequency (first smoother transition point) after the fault are both hertz, and the recovery time (time to first smoother transition point) and recovery time to 49.95 hertz are seconds.
By utilizing the frequency disturbance estimation model, the calculated reserve capacity value can be continuously adjusted, the input reserve capacity value is continuously corrected according to the frequency result, and the frequency disturbance condition meets different operation requirements through feedback iteration. In the calculation example, the lowest frequency after the fault is higher than 49.7Hz (the low-valley load is 49.85Hz) when the peak load is set, the recovery frequency is higher than 49.8Hz (the low-valley load is 49.9Hz), the recovery time is lower than 10 seconds, and the recovery time to 49.95Hz is lower than 60 seconds. The calculation is iterated once every 10MW of spare capacity, and after several iterations, the calculation results meeting the frequency requirement are shown in table 3:
TABLE 3
In summary, the invention provides a method for outputting a power grid frequency disturbance recovery process, which is based on real-time data of a power grid, establishes a nonlinear real-time simulation model by comprehensively analyzing index information such as total load before failure, loss power, direct current input power before failure, spare capacity, comprehensive frequency effect coefficient, recovery frequency estimation coefficient, frequency before failure and the like in real time, and provides estimation results of the frequency recovery whole process (the lowest frequency after failure, the recovery frequency, the recovery time and the time for recovering to 49.95 HZ) after power loss and failure. By applying the model, the frequency change condition after a certain set fault can be predicted, the problems of off-line errors and reduced accuracy of an estimation result under the condition that direct-current high power is input in the traditional frequency estimation method are particularly solved, the matching check of the real-time reserve capacity of the power grid and the ultra-high-voltage high-power transmission power is realized, an auxiliary tool for predicting the reserve capacity is provided for a typical operation mode which may occur in the future of the power grid, and a reliable reserve reservation auxiliary decision is provided for the adjustment of the operation mode of the power grid, the risk prevention and the control.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A power grid frequency disturbance recovery process output method is applied to a power grid with multiple layers of data units and at least two data nodes on each layer, and is characterized by comprising the following steps:
measuring to obtain power grid input data related to power grid faults and frequency changes;
inputting the power grid input data into a power grid, connecting any two data nodes through a weight, receiving the output information of the previous layer by the data node of each layer and outputting the output information to each node of the next layer, and acquiring the power grid output data according to the power grid input data and the weight by taking the weight and the power grid output data as variables;
obtaining the output of the power grid frequency disturbance recovery process according to the power grid input data, the weight and the power grid output data;
obtaining an error function of the power grid according to the power grid output data and a set expected output value;
inputting the power grid input data and the set initial weight value into the power grid to obtain initial power grid output data, combining the expected output value to obtain an initial error function value, setting a target range of an error function, and comparing the initial error function value with the target range of the error function: if the initial error function value falls outside the target range, adjusting the initial weight by adopting a gradient steepest descent method, wherein the gradient steepest descent method is to adjust the initial weight along the negative gradient direction of the weight so that the adjustment quantity of the weight is in direct proportion to the gradient descent of the error function, and performing iterative operation by taking the sum of the initial weight and the adjustment quantity of the weight as a new weight so as to minimize the adjusted error function value until the error function value falls within the target range, and finally adjusting the obtained weight as the target weight; and if the initial error function value falls within the target range, the initial weight value is the target weight value.
2. The grid frequency disturbance recovery process output method of claim 1, wherein the grid input data is xnAnd N is 1,2 … N, the power grid is provided with an input layer, a hidden layer and an output layer, the input data of the power grid is input into the input layer, the output data of the power grid is obtained through the output of the hidden layer and the output layer, the hidden layer is provided with L-layer data units, wherein the number of the nodes on the L-th layer is NlAnd L, for the kth node of the L-1 layer, obtaining a weighted summation value u of the kth node according to the power grid input data and the weight valuekThe output data of the kth node is ykAnd is and
yk＝f(uk)，
wherein, 0<k<Nl-1A is a set first coefficient, wknInputting data x for the kth node relative to the gridnThe weight of (2).
3. The grid frequency disturbance recovery process output method according to claim 2, wherein the weighted summation value of the jth node of the ith layer isAnd is
Wherein, 0<j<Nl，0<s<Nl-2Output data of jth node of the ith layerIs composed ofThe output data of the kth node of the l-1 layer isThe output data of the s-th node of the l-2 th layer isThen according to
And obtaining the output of the power grid frequency disturbance recovery process, wherein O is the power grid output data.
4. A method as claimed in claim 3 wherein the error function is E, and when the grid has only one output O:
O＝f(netL)，
wherein d is the desired output value.
5. The output method of the grid frequency disturbance recovery process according to claim 4, wherein the adjustment amount of the weight isAnd:
wherein eta is a set learning rate and is a constant,the weight from the ith node of the l < th > layer to the jth node of the l +1 < th > layer.
6. The grid frequency disturbance recovery process output method according to claim 5, wherein if the node j is an output unit, thenAnd:
if node j is not an output unit, then
Counting back from the L +1 layer,
7. the output method of the grid frequency disturbance recovery process according to claim 6, wherein the weight obtained in the (m + 1) th iteration isAnd:
wherein,as the amount of adjustment of the weight value,for the weight obtained for the mth iteration, when m is 1,is the initial weight.
8. A method as claimed in any one of claims 1 to 7 wherein the grid input data is total load before fault, lost power, DC input power before fault, reserve capacity, adjustment factor, support clipping and frequency before fault, and the grid output data is minimum frequency after fault, recovery frequency, recovery time and recovery to 49.95 Hz.
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| CN104331551B (en) | 2017-09-19 |
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