CN113705973A - Neural network learning-based power grid security risk online evaluation method - Google Patents
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Abstract
The invention discloses a neural network learning-based power grid safety risk online evaluation method, which comprises the steps of classifying different faults according to the safety and stability principle of a power system, and marking the faults as different fault type factors and different equipment type factors after normalization processing; judging to obtain historical risk factors of equipment faults based on historical fault data of the power system, substituting the historical risk factors into the different fault type factors and the different equipment type factors, and calculating the risk level probability value of each equipment; and establishing an evaluation model by using a neural network learning strategy, importing the risk level probability value to carry out safety risk evaluation analysis on the power grid equipment, and outputting an evaluation analysis result. The method provided by the invention provides a reliable calculation result, greatly improves the accuracy and efficiency of risk evaluation of the power grid equipment, and reduces the maintenance cost of the power grid equipment.
Description
Technical Field
The invention relates to the technical field of online evaluation of power grid security risks, in particular to a neural network learning-based online evaluation method of power grid security risks.
Background
Whether the power distribution network is good or not depends on whether the planning and construction of the power distribution network are scientific or not and whether the economy is reasonable or not, for a power supply enterprise with huge fixed asset amount, the planning work of the power distribution network plays a decisive role in the survival and development of the power supply enterprise all the time, the power distribution network is an important support for the development of the power grid, and the level and the quality of the power distribution network directly influence the safety, the reliability and the economical level of the power supply of the power grid.
However, most of the existing power distribution network risk assessment is carried out according to various index classifications, such as line and transformer risk assessment, comprehensive analysis and consideration cannot be carried out, and assessment and analysis on the operation risk of the power distribution network equipment cannot be accurately carried out.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a neural network learning-based power grid security risk online evaluation method, which can solve the problem that risks existing in the operation of power grid equipment cannot be evaluated and analyzed accurately and in real time.
In order to solve the technical problems, the invention provides the following technical scheme: classifying different faults according to the safety and stability principle of a power system, and marking the faults as different fault type factors and different equipment type factors after normalization processing; judging to obtain historical risk factors of equipment faults based on historical fault data of the power system, substituting the historical risk factors into the different fault type factors and the different equipment type factors, and calculating the risk level probability value of each equipment; and establishing an evaluation model by using a neural network learning strategy, importing the risk level probability value to carry out safety risk evaluation analysis on the power grid equipment, and outputting an evaluation analysis result.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the classification includes dividing the fault types into three classes, namely a first class of fault, a second class of fault and a third class of fault; the first type of fault corresponds to a low risk level, the second type of fault corresponds to a medium risk level, and the third type of fault corresponds to a high risk level; a first type of fault type factor of 1, a second type of fault type factor of 0.6 and a third type of fault type factor of 0.2 are defined.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the normalization processing includes performing linear processing on the divided fault types to eliminate differences and form normalized data, as follows,
y＝(x-min)/(max-min)
wherein min is the minimum value of x, max is the maximum value of x, the input vector is x, and the normalized output vector is y.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the different equipment type factors are respectively obtained corresponding to three fault types, including that the equipment factor value of the first type fault comprises that the main line is 0.9, the bus is 0.7, the cable which is more than 50km is 0.8, the cable which is less than or equal to 50km is 0.7, and the generator is 1; the equipment factor values of the second type of fault comprise that the main change is 0.6, the bus is 0.4, the cable which is more than 50km is 0.6, the cable which is less than or equal to 50km is 0.4, and the generator is 0.8; the equipment factor values for the third type of fault include a principal variation of 0.4, a bus of 0.3, cables greater than 50km of 0.4, cables less than or equal to 50km of 0.3, and generators of 0.4.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the historical risk factors of the equipment faults comprise the influence caused by the occurrence of the power grid faults, the influence caused by external environment factors and the influence caused by potential safety hazards existing in the equipment; the effects of the grid faults include extra losses, heavy losses, large losses and general losses.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the risk level probability value comprises the risk level probability value, namely main transformer risk probability value + bus risk probability value + various line risk probability values + generator risk probability value.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: the evaluation model may include a set of one or more of,
wij(t+1)＝wij(t)+α(di-yi)xj(t)
where Wij represents the weight of connection of neuron j to neuron i, di is the desired output of neuron i, yi is the actual output of neuron i, xj represents the state of neuron j, xj is 1 if neuron j is in the activated state, xj is 0 or-1 if neuron j is in the inhibited state, and a is a constant representing the learning rate.
As an optimal scheme of the neural network learning-based online evaluation method for the grid security risk, the method comprises the following steps: defining xi as 1, if di is larger than yi, Wij will be increased, and the risk level probability value of each device is increased, which is unsafe; if di is smaller than yi, Wij will be smaller, and the risk level probability value of each device will be smaller, so that the security is achieved.
The invention has the beneficial effects that: the method eliminates the difference of fault type data through normalization processing, uniformly marks the fault type data to form a certain standard factor value, facilitates later-stage calculation, and provides a reliable calculation result in a quick, accurate and layered calculation process through an evaluation model established by a neural network algorithm, thereby greatly improving the accuracy and efficiency of risk evaluation of power grid equipment and reducing the maintenance cost of the power grid equipment.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a method for online evaluation of grid security risk based on neural network learning according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of an output curve of an experiment comparison test of the neural network learning-based online evaluation method for grid security risk according to the second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a neural network learning-based online risk assessment method for grid security, which specifically includes:
s1: classifying different faults according to the safety and stability principle of the power system, and marking the faults as different fault type factors and different equipment type factors after normalization processing. It should be noted that the classification includes:
dividing the fault types into three classes, namely a first class fault, a second class fault and a third class fault;
the first type of fault corresponds to a low risk level, the second type of fault corresponds to a medium risk level, and the third type of fault corresponds to a high risk level;
a first type of fault type factor of 1, a second type of fault type factor of 0.6 and a third type of fault type factor of 0.2 are defined.
Specifically, the normalization process includes:
the divided fault types are subjected to linear processing to eliminate differences and form normalized data, as follows,
y＝(x-min)/(max-min)
wherein min is the minimum value of x, max is the maximum value of x, the input vector is x, and the normalized output vector is y.
Further, different device type factors are obtained corresponding to three fault types respectively, including:
the equipment factor values of the first type of faults comprise 0.9 of main change, 0.7 of bus, 0.8 of cable more than 50km, 0.7 of cable less than or equal to 50km and 1 of generator;
the equipment factor values of the second type of fault comprise that the main change is 0.6, the bus is 0.4, the cable which is more than 50km is 0.6, the cable which is less than or equal to 50km is 0.4, and the generator is 0.8;
the equipment factor values for the third type of fault include a principal variation of 0.4, a bus of 0.3, cables greater than 50km of 0.4, cables less than or equal to 50km of 0.3, and generators of 0.4.
S2: and judging to obtain historical risk factors of equipment faults based on historical fault data of the power system, and substituting different fault type factors and different equipment type factors to calculate the risk level probability value of each equipment. It should be noted in this step that the historical risk factors of the equipment failure include:
the influence caused by power grid faults, the influence caused by external environmental factors and the influence caused by potential safety hazards existing in equipment per se occur;
the effects of grid faults include large losses, heavy losses, large losses and general losses.
Further, the risk level probability values include:
and the risk level probability value is the main transformer risk probability value + the bus risk probability value + the various line risk probability values + the generator risk probability value.
S3: and establishing an evaluation model by using a neural network learning strategy, importing the risk level probability value to carry out safety risk evaluation analysis on the power grid equipment, and outputting an evaluation analysis result. It should be further noted that the evaluation model includes:
wij(t+1)＝wij(t)+α(di-yi)xj(t)
where Wij represents the weight of connection of neuron j to neuron i, di is the desired output of neuron i, yi is the actual output of neuron i, xj represents the state of neuron j, xj is 1 if neuron j is in the activated state, xj is 0 or-1 if neuron j is in the inhibited state, and a is a constant representing the learning rate.
Defining xi as 1, if di is larger than yi, Wij will be increased, and the risk level probability value of each device is increased, which is unsafe;
if di is smaller than yi, Wij will be smaller, and the risk level probability value of each device will be smaller, so that the security is achieved.
The method eliminates the difference of fault type data through normalization processing, uniformly marks the fault type data to form a certain standard factor value, facilitates later-stage calculation, and provides a reliable calculation result in a quick, accurate and layered calculation process through an evaluation model established by a neural network algorithm, thereby greatly improving the accuracy and efficiency of risk evaluation of power grid equipment and reducing the maintenance cost of the power grid equipment.
Example 2
Referring to fig. 2, a second embodiment of the present invention is different from the first embodiment in that the present embodiment provides an experimental test of a neural network learning-based online risk assessment method for grid security, which specifically includes:
in order to better verify and explain the technical effects adopted in the method of the invention, the embodiment selects the traditional machine learning-based power grid risk evaluation method to perform a comparison test with the method of the invention, and compares the test results by means of scientific demonstration to verify the real effect of the method of the invention.
In order to verify that the method has higher accuracy and calculation efficiency compared with the traditional method, the traditional power grid risk evaluation method based on machine learning and the method of the invention are adopted to respectively evaluate, test and compare the power grid safety risk of the simulation platform.
And (3) testing environment: the method comprises the steps of importing the power grid system data into a simulation platform for simulation operation and simulating a random risk existence scene, respectively utilizing a machine learning algorithm of a traditional method for data calculation processing and obtaining test data.
10000 groups of data are tested in each method, the time and the error root mean square of each group of data are obtained through calculation, the error is compared and calculated with the actual predicted value input by simulation, and the result is shown in figure 2.
Referring to fig. 2, a solid line is a curve output by the method of the present invention, a dotted line is a curve output by a conventional method, and according to the schematic diagram of fig. 2, it can be seen intuitively that the solid line and the dotted line show different trends along with the increase of time, the solid line shows a stable rising trend in the former period compared with the dotted line, although the solid line slides down in the latter period, the fluctuation is not large and is always above the dotted line and keeps a certain distance, and the dotted line shows a large fluctuation trend and is unstable, so that the calculation efficiency of the solid line is always greater than that of the dotted line, i.e. the real effect of the method of the present invention is verified.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A power grid security risk online evaluation method based on neural network learning is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
classifying different faults according to the safety and stability principle of the power system, and marking the faults as different fault type factors and different equipment type factors after normalization processing;
judging to obtain historical risk factors of equipment faults based on historical fault data of the power system, substituting the historical risk factors into the different fault type factors and the different equipment type factors, and calculating the risk level probability value of each equipment;
and establishing an evaluation model by using a neural network learning strategy, importing the risk level probability value to carry out safety risk evaluation analysis on the power grid equipment, and outputting an evaluation analysis result.
2. The neural network learning-based power grid security risk online evaluation method according to claim 1, characterized in that: the classification includes the steps of,
dividing the fault types into three classes, namely a first class fault, a second class fault and a third class fault;
the first type of fault corresponds to a low risk level, the second type of fault corresponds to a medium risk level, and the third type of fault corresponds to a high risk level;
a first type of fault type factor of 1, a second type of fault type factor of 0.6 and a third type of fault type factor of 0.2 are defined.
3. The neural network learning-based grid security risk online evaluation method according to claim 1 or 2, characterized in that: the normalization process includes the steps of,
the divided fault types are subjected to linear processing to eliminate differences and form normalized data, and the normalized data is obtained by the following steps,
y＝(x-min)/(max-min)
wherein min is the minimum value of x, max is the maximum value of x, the input vector is x, and the normalized output vector is y.
4. The neural network learning-based power grid security risk online evaluation method according to claim 3, characterized in that: the different equipment type factors are obtained respectively corresponding to three fault types including,
the equipment factor values of the first type of faults comprise 0.9 of main change, 0.7 of bus, 0.8 of cable more than 50km, 0.7 of cable less than or equal to 50km and 1 of generator;
the equipment factor values of the second type of fault comprise that the main change is 0.6, the bus is 0.4, the cable which is more than 50km is 0.6, the cable which is less than or equal to 50km is 0.4, and the generator is 0.8;
the equipment factor values for the third type of fault include a principal variation of 0.4, a bus of 0.3, cables greater than 50km of 0.4, cables less than or equal to 50km of 0.3, and generators of 0.4.
5. The neural network learning-based power grid security risk online evaluation method according to claim 4, characterized in that: the historical risk factors of the equipment faults comprise the influence caused by the occurrence of the power grid faults, the influence caused by external environment factors and the influence caused by potential safety hazards existing in the equipment;
the effects of the grid faults include extra losses, heavy losses, large losses and general losses.
6. The neural network learning-based power grid security risk online evaluation method according to claim 5, characterized in that: the risk level probability values include, for example,
and the risk level probability value is the main transformer risk probability value + the bus risk probability value + the various line risk probability values + the generator risk probability value.
7. The neural network learning-based power grid security risk online evaluation method according to claim 6, characterized in that: the evaluation model may include a set of one or more of,
wij(t+1)＝wij(t)+α(di-yi)xj(t)
where Wij represents the weight of connection of neuron j to neuron i, di is the desired output of neuron i, yi is the actual output of neuron i, xj represents the state of neuron j, xj is 1 if neuron j is in the activated state, xj is 0 or-1 if neuron j is in the inhibited state, and a is a constant representing the learning rate.
8. The neural network learning-based power grid security risk online evaluation method according to claim 7, characterized in that: also comprises the following steps of (1) preparing,
defining xi as 1, if di is larger than yi, Wij will be increased, and the risk level probability value of each device is increased, which is unsafe;
if di is smaller than yi, Wij will be smaller, and the risk level probability value of each device will be smaller, so that the security is achieved.
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