A remote control system for generator parameters

Abstract

The application belongs to the field of remote control, and particularly discloses a generator parameter remote control system, which comprises a generator data acquisition module, a remote communication module, a generator modeling module and a generator parameter decision module. The application uses a cyclic group multi-layer encryption method, constructs a security foundation by using a bilinear mapping, divides parameters into data blocks, encrypts the data blocks one by one, allocates a private key to each data block and calculates a public key, generates a unique signature in combination with a hash function, constructs a verification set by randomly selecting a block index, realizes batch verification by using a bilinear mapping, quickly completes parameter verification, and guarantees the real-time performance and accuracy of remote control instructions. A multi-objective optimization function is constructed to ensure the balance of the three major objectives, and an artificial rabbit optimization algorithm is used to realize global optimization, avoid single-target deviation, stabilize the frequency deviation in a safe range, and realize efficient and stable operation of the power generation system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of remote control, in particular to a generator parameter remote control system. BACKGROUND

[0002] The generator parameter remote control refers to a technology of monitoring, adjusting and controlling various operation parameters of the generator at a location far away from the generator site by means of modern communication technology and automatic control means, which can obtain the operation parameters and state information of the generator in real time and improve the operation management efficiency of the generator. The generator operation parameters and the energy storage battery operation parameters are the core basis of the remote control, which directly determines the stable operation of the power generation system. If the data is maliciously tampered with or leaked during transmission, the remote control instruction may be incorrect, and there is an operation safety risk. The traditional encryption method mainly adopts a single-layer encryption algorithm, and when verifying multiple data blocks, each block needs to be verified one by one. In the large-scale parameter transmission scene, the verification time is too long, and it is difficult to meet the real-time control demand. The traditional optimization method has a subjective multi-objective weight distribution, and is often biased towards a single target, has a slow convergence speed and is easy to fall into a local optimum, which leads to a long adjustment time of the generator during fault recovery, and cannot meet the scene demand of high power supply stability requirement in medical treatment, industry and the like. SUMMARY

[0003] In view of the above problems, in order to overcome the defects of the prior art, the present application provides a generator parameter remote control system. In order to solve the technical problem that the traditional encryption method mainly adopts a single-layer encryption algorithm, and when verifying multiple data blocks, each block needs to be verified one by one, and in the large-scale parameter transmission scene, the verification time is too long, and it is difficult to meet the real-time control demand, the present application uses a cyclic group multi-layer encryption method, uses a bilinear mapping to construct a security foundation, divides the parameters into data blocks, and encrypts each block, and at the same time, allocates a private key to each data block and calculates a public key, generates a unique signature in combination with a hash function, constructs a verification set by randomly selecting a block index, realizes batch verification through a bilinear mapping, quickly completes parameter verification, and ensures the real-time performance and accuracy of the remote control instruction. In order to solve the technical problem that the traditional optimization method has a subjective multi-objective weight distribution, and is often biased towards a single target, has a slow convergence speed and is easy to fall into a local optimum, which leads to a long adjustment time of the generator during fault recovery, and cannot meet the scene demand of high power supply stability requirement in medical treatment, industry and the like, the present application constructs a multi-objective optimization function to ensure the balance of the three objectives, and realizes global optimization through an artificial rabbit optimization algorithm to avoid single target bias, stabilizes the frequency deviation in a safe range, and realizes efficient and stable operation of the power generation system.

[0004] The technical scheme adopted by the present application is as follows: the present application provides a generator parameter remote control system, which mainly comprises a generator data acquisition module, a remote communication module, a generator modeling module and a generator parameter decision module.

[0005] The generator data acquisition module collects the operating parameters of the generator and the operating parameters of the energy storage battery and sends them to the remote communication module;

[0006] The remote communication module includes a generator end, a cloud server and a data verification end, uses a cyclic group multi-layer encryption method for data encryption and verification, the generator end encrypts and signs the operating parameters of the generator and the operating parameters of the energy storage battery and sends them to the cloud server, the data verification end reads the operating parameters of the generator and the operating parameters of the energy storage battery in the cloud server and verifies them, and sends them to the generator parameter decision module after verification is successful;

[0007] The generator modeling module uses a third-order function to construct an output power deviation model for the generator and the energy storage battery in the generator system, and the formula used is as follows:

[0008] ;

[0009] ;

[0010] In the formula, is the output power deviation model of the generator, is the output power deviation model of the energy storage battery, , and are the inertia delay constant, the governor constant and the generator constant of the generator respectively, is the speed regulation coefficient of the generator, is the frequency deviation of the generator, is the control signal of the generator, , and are the measurement delay constant, the instruction delay constant and the inverter delay constant of the energy storage battery respectively, is the control signal of the energy storage battery;

[0011] The generator parameter decision module uses a rabbit group optimization method to output the optimal control parameters according to the output power deviation model.

[0012] Further, in the remote communication module, the cyclic group multi-layer encryption method specifically includes the following steps:

[0013] Step A1: define three cyclic groups of the same order, the cyclic groups include the multiplicative group G2, the multiplicative group GT and the additive group G1 with the generator P, and the additive group G1 and the multiplicative group G2 can obtain the multiplicative group GT through the bilinear mapping;

[0014] Step A2: the generator end divides the operating parameters of the generator and the operating parameters of the energy storage battery into data blocks, each data block is encrypted using an AES-256 algorithm to obtain an encrypted data block, and all encrypted data blocks constitute an encrypted file;

[0015] Step A3: the generator end randomly selects a positive integer as a private key for each encrypted data block, calculates a public key for each encrypted data block through the private key and a generator of an additive group, and obtains a public key set and a private key set;

[0016] Step A4: the generator end generates a signature for each encrypted data block, and the formula used is as follows:

[0017] ;

[0018] In the formula, is the signature, is the index of the data block, is a hash function SHA-1, is a unique identifier of the encrypted data block, is a randomly selected positive integer, is a generator of an additive group G1;

[0019] Step A5: the data verification end randomly selects a block index to form a verification set, and sends the verification set to the cloud server. After receiving the verification set, the cloud server calculates an aggregated signature and an aggregated public key, and sends the aggregated signature and the aggregated public key to the data verification end. The formula used is as follows:

[0020] ;

[0021] ;

[0022] In the formula, is the aggregated signature, is the aggregated public key, is the block index, is the number of block indexes, is the public key;

[0023] Step A6: the data verification end verifies the aggregated signature and the aggregated public key through a bilinear mapping. If the mappings are equal, the verification is passed, otherwise the verification fails, and the data verification end issues a warning message. The formula used is as follows:

[0024] ;

[0025] In the formula, denotes a bilinear mapping.

[0026] Further, the rabbit swarm optimization method in the generator parameter decision module specifically includes the following steps:

[0027] Step B1: Construct a multi-objective optimization function including a frequency deviation suppression function, a control signal suppression function and a time adjustment optimization function, and the specific definition formula is as follows:

[0028] ;

[0029] ;

[0030] ;

[0031] In the formula, , and are the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively, is the simulation time, is the target adjustment time for the generator frequency to converge to the frequency deviation of 0;

[0032] Step B2: After linear normalization of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function, the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function are weighted and fused by using the equal weight method to obtain the final optimization function, and the formula is as follows:

[0033] ;

[0034] In the formula, is the final optimization function, , and are the normalized values of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively, , and are the weight coefficients of , and respectively, and , and are equal, , and are the scaling factors of the normalized values of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively;

[0035] Step B3: The artificial rabbit optimization algorithm is used to globally optimize the control parameters of the generator, which specifically includes the following steps:

[0036] Step B31: population initialization, 50 rabbit population individuals are randomly generated, each individual corresponds to a set of generator control parameters, the initial fitness of each individual, i.e. the value of the final optimization function, is calculated, and the optimal individual is marked;

[0037] Step B32: calculate the energy factor, preset the maximum number of iterations, control the switching of rabbit population detour foraging and random hiding, calculate the energy factor, the formula used is as follows:

[0038] ;

[0039] In the formula, is the energy factor, is the current iteration number, is the maximum iteration number, is a random number belonging to (0, 1);

[0040] Step B33: rabbit population detour foraging, individuals in the rabbit population randomly select another individual, realize global search through position update, the formula used is as follows:

[0041] ;

[0042] In the formula, is the candidate position of the individual in global search, is the rounding function, is the Gaussian noise;

[0043] Step B34: rabbit population random hiding, individuals in the rabbit population generate a cave around themselves, randomly select a cave to update the position, realize local search, the formula used is as follows:

[0044] ;

[0045] In the formula, is the candidate position of the individual in local search;

[0046] Step B35: if the fitness of the candidate position of the individual in global search is better than the current position, update the rabbit population position, otherwise keep the current rabbit population position, update the optimal individual, until the maximum iteration number is reached or the final optimization function converges, at this time the optimal individual is the optimal control parameter, and the optimal control parameter is output.

[0047] The beneficial effects obtained by the application using the above scheme are as follows:

[0048] (1) In view of the technical problem that the traditional encryption method adopts a single-layer encryption algorithm, and when verifying multiple data blocks, it needs to check block by block, and in the large-scale parameter transmission scene, the verification time is too long, and it is difficult to meet the real-time control requirement, the scheme uses a cyclic group multi-layer encryption method, uses a bilinear mapping to build a security foundation, divides the parameters into data blocks, and encrypts them block by block, at the same time, allocates a private key to each data block and calculates a public key, combines a hash function to generate a unique signature, randomly selects a block index to build a verification set, and realizes batch verification through a bilinear mapping, quickly completes parameter verification, and guarantees the real-time performance and accuracy of the remote control instruction;

[0049] (2) In view of the technical problem that the traditional optimization method has a subjective multi-objective weight distribution, is often biased towards a single target, has a slow convergence speed and is easy to fall into a local optimum, resulting in that the generator takes too long to adjust when recovering from a fault, and cannot meet the scene demand of high power supply stability requirement of medical treatment, industry and the like, the scheme builds a multi-objective optimization function to ensure the balance of the three targets, and realizes global optimization through an artificial rabbit optimization algorithm to avoid single target bias, and stabilizes the frequency deviation in a safe range to realize efficient and stable operation of the power generation system. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A module connection diagram of the generator parameter remote control system provided by the application is provided.

[0051] The accompanying drawings are used to provide a further understanding of the application, and constitute a part of the specification, and are used to explain the application together with embodiments of the application, and do not constitute a limitation on the application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application, obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments; based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the application.

[0053] Embodiment one, refer to Figure 1 The application provides a generator parameter remote control system, the generator parameter remote control system mainly comprises a generator data acquisition module, a remote communication module, a generator modeling module and a generator parameter decision module.

[0054] The generator data acquisition module collects the operating parameters of the generator and the operating parameters of the energy storage battery, and sends them to the remote communication module.

[0055] The remote communication module comprises a generator end, a cloud server and a data verification end, uses a cyclic group multi-layer encryption method for data encryption and verification, the generator end sends the running parameters of the generator and the running parameters of the energy storage battery to the cloud server after encryption and signature, the data verification end reads and verifies the running parameters of the generator and the running parameters of the energy storage battery in the cloud server, and sends them to the generator parameter decision module after verification is successful;

[0056] The generator modeling module adopts a third-order function to construct an output power deviation model of the generator and the energy storage battery in the generator system, and the formula used is as follows:

[0057] ;

[0058] ;

[0059] In the formula, is the output power deviation model of the generator, is the output power deviation model of the energy storage battery, , and are the inertia delay constant, the governor constant and the generator constant of the generator respectively, is the speed regulation coefficient of the generator, is the frequency deviation of the generator, is the control signal of the generator, , and are the measurement delay constant, the instruction delay constant and the converter delay constant of the energy storage battery respectively, is the control signal of the energy storage battery;

[0060] The generator parameter decision module adopts a rabbit group optimization method to output the optimal control parameters according to the output power deviation model.

[0061] Embodiment two, refer to Figure 1 , which is based on the above embodiment, in the remote communication module, the cyclic group multi-layer encryption method specifically comprises the following steps:

[0062] Step A1: define three cyclic groups of the same order, the cyclic groups comprise a multiplication group G2, a multiplication group GT and an additive group G1 with a generator P, and the additive group G1 and the multiplication group G2 can obtain the multiplication group GT through a bilinear mapping;

[0063] Step A2: the generator end divides the running parameters of the generator and the running parameters of the energy storage battery into data blocks, each data block is encrypted using an AES-256 algorithm to obtain an encrypted data block, and all the encrypted data blocks constitute an encrypted file;

[0064] Step A3: The generator end randomly selects a positive integer as a private key for each encrypted data block, calculates the public key of each encrypted data block through the private key and the generator of the additive group, obtains a public key set and a private key set;

[0065] Step A4: The generator end generates a signature for each encrypted data block, and the formula used is as follows:

[0066] ;

[0067] In the formula, is the signature, is the index of the data block, is the hash function SHA-1, is the unique identifier of the encrypted data block, is a randomly selected positive integer, is the generator of the additive group G1;

[0068] Step A5: The data verification end randomly selects a block index to form a verification set, and sends the verification set to the cloud server. After receiving the verification set, the cloud server calculates the aggregated signature and the aggregated public key, and sends the aggregated signature and the aggregated public key to the data verification end. The formula used is as follows:

[0069] ;

[0070] ;

[0071] In the formula, is the aggregated signature, is the aggregated public key, is the block index, is the number of block indexes, is the public key;

[0072] Step A6: The data verification end verifies the aggregated signature and the aggregated public key through a bilinear mapping. If the mappings are equal, the verification passes, otherwise the verification fails, and the data verification end issues a warning message. The formula used is as follows:

[0073] ;

[0074] In the formula, denotes the bilinear mapping.

[0075] Example Three, see Figure 1 This example is based on the above-mentioned example, and the rabbit swarm optimization method in the generator parameter decision module specifically includes the following steps:

[0076] Step B1: Construct a multi-objective optimization function including a frequency deviation suppression function, a control signal suppression function and a time adjustment optimization function, and the specific definition formula is as follows:

[0077]

[0078]

[0079]

[0080] wherein, and are the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively, is the simulation time, is the target adjustment time for the generator frequency to converge to the frequency deviation of 0;

[0081] Step B2: After linear normalization of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function, the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function are weighted and fused by using the equal weight method to obtain the final optimization function, and the formula used is as follows:

[0082]

[0083] wherein, is the final optimization function, and are the normalized values of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively, and are the weight coefficients of and respectively, and and are equal, and are the scaling factors of the normalized values of the frequency deviation suppression function, the control signal suppression function and the time adjustment optimization function respectively;

[0084] Step B3: The artificial rabbit optimization algorithm is used to globally optimize the control parameters of the generator, and the specific steps include the following steps:

[0085] ​​​​​​​​​​Step B31: population initialization, 50 rabbit population individuals are randomly generated, each individual corresponds to a set of generator control parameters, the initial fitness of each individual is calculated, that is, the value of the final optimization function, and the optimal individual is marked;

[0086] Step B32: calculate the energy factor, preset the maximum number of iterations, control the switching of rabbit population detour foraging and random hiding, calculate the energy factor, the formula used is as follows:

[0087] ;

[0088] In the formula, is the energy factor, is the current iteration number, is the maximum iteration number, is a random number belonging to (0, 1);

[0089] Step B33: rabbit population detour foraging, individuals in the rabbit population randomly select another individual, realize global search through position update, the formula used is as follows:

[0090] ;

[0091] In the formula, is the candidate position of the individual in global search, is the rounding function, is the Gaussian noise;

[0092] Step B34: rabbit population random hiding, individuals in the rabbit population generate a cave around themselves, randomly select a cave to update the position, realize local search, the formula used is as follows:

[0093] ;

[0094] In the formula, is the candidate position of the individual in local search;

[0095] Step B35: if the fitness of the candidate position of the individual in global search is better than the current position, update the rabbit population position, otherwise keep the current rabbit population position, update the optimal individual, until the maximum iteration number is reached or the final optimization function converges, at this time the optimal individual is the optimal control parameter, output the optimal control parameter.

[0096] Example four, this example is based on the above example, in example two, three cyclic groups of the same order are defined, the order is a large prime number, and the length is 2048 bits.

[0097] Example five, this example is based on the above example, in example three, , and are scaling factors for the normalized values of the frequency deviation suppression function, the control signal suppression function, and the time adjustment optimization function, respectively, and take values of 1000, 1, and 1000, respectively.

[0098] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other drivers from other disciplines can select and employ. For example, the term "first" and "second" are used herein only to differentiate one element or operation from another, and do not necessarily imply or require any actual relationship or order between the elements or operations. Moreover, the terms "include", "have", or any other variant thereof are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a series of elements are not required to comprise only those elements that are literally listed, but can include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses.

[0099] While the embodiments of the application have been shown and described, it is to be understood that the embodiments described are only by way of example and are not limiting of the scope of the application. The scope of the application is limited only by the claims and the full scope of equivalents thereof.

[0100] The above description of the application and its embodiments is not restrictive, and the embodiments shown in the drawings are only one of the embodiments of the application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired by it, without departing from the purpose of the application, without creative design, similar structure and embodiments of the technical solution should belong to the protection scope of the application.

Claims

1. A remote control system for generator parameters, characterized in that, The system mainly includes a generator data acquisition module, a remote communication module, a generator modeling module, and a generator parameter decision module; The generator data acquisition module collects the operating parameters of the generator and the energy storage battery, and sends them to the remote communication module. The remote communication module includes a generator end, a cloud server, and a data verification end. It uses a cyclic group multi-layer encryption method for data encryption and verification. The generator end encrypts and signs the operating parameters of the generator and the energy storage battery and then sends them to the cloud server. The data verification end reads the operating parameters of the generator and the energy storage battery in the cloud server and verifies them. After successful verification, the data is sent to the generator parameter decision module. The generator modeling module uses third-order functions to construct an output power deviation model for the generator and energy storage battery in the generator system. The generator parameter decision module outputs the optimal control parameters based on the output power deviation model and a rabbit swarm optimization method. In the remote communication module, the cyclic group multi-layer encryption method specifically includes the following steps: Step A1: Define three cyclic groups of the same order, including the multiplicative group G2, the multiplicative group GT, and the additive group G1 with generator P, and the additive group G1 and the multiplicative group G2 can be obtained by bilinear mapping to the multiplicative group GT; Step A2: The generator end divides the generator's operating parameters and the energy storage battery's operating parameters into data blocks. Each data block is encrypted using the AES-256 algorithm to obtain encrypted data blocks. All encrypted data blocks constitute an encrypted file. Step A3: The generator randomly selects a positive integer as the private key for each encrypted data block. The public key for each encrypted data block is calculated using the private key and the generator of the addition group, resulting in a public key set and a private key set. Step A4: The generator generates a signature for each encrypted data block; Step A5: The data verification end randomly selects block indices to form a verification set and sends the verification set to the cloud server. After receiving the verification set, the cloud server calculates the aggregate signature and aggregate public key and sends the aggregate signature and aggregate public key to the data verification end. Step A6: The data verification end verifies the aggregate signature and aggregate public key through a bilinear mapping. If the mappings are equal, the verification passes; otherwise, the verification fails and the data verification end issues a warning message. The rabbit swarm optimization method described in the generator parameter decision module specifically includes the following steps: Step B1: Construct a multi-objective optimization function, which includes a frequency deviation suppression function, a control signal suppression function, and a time adjustment optimization function, defined by the following formulas: ； ； ； In the formula, , and These are the frequency deviation suppression function, the control signal suppression function, and the time adjustment optimization function, respectively. It is the simulation time. It is the target settling time for the generator frequency to converge to zero frequency deviation. It is the frequency deviation of the generator. These are the control signals for the generator. These are control signals for the energy storage battery; Step B2: After linearly normalizing the frequency deviation suppression function, control signal suppression function, and time adjustment optimization function, an equal-weight method is used to weight and fuse them to obtain the final optimization function. The formula used is as follows: ； In the formula, It is the final optimization function. , and These are the normalized values ​​of the frequency deviation suppression function, the control signal suppression function, and the time adjustment optimization function, respectively. , and They are , and The weighting coefficients, and , and equal, , and These are the scaling factors for the normalized values ​​of the frequency deviation suppression function, the control signal suppression function, and the time adjustment optimization function, respectively. Step B3: Use the artificial rabbit optimization algorithm to globally optimize the control parameters of the generator.

2. The generator parameter remote control system according to claim 1, characterized in that, Step B3 involves using the artificial rabbit optimization algorithm to globally optimize the generator's control parameters. This is performed after obtaining the final optimization function in step B2, and specifically includes the following steps: Step B31: Population initialization. Randomly generate 50 rabbit individuals, each corresponding to a set of generator control parameters. Calculate the initial fitness of each individual, i.e. the value of the final optimization function, and mark the best individual. Step B32: Calculate the energy factor, preset the maximum number of iterations, control the switching between the rabbit group's roundabout foraging and random hiding, and calculate the energy factor using the following formula: ； In the formula, It is an energy factor. It is the current iteration number. It is the maximum number of iterations. It is a random number belonging to (0, 1); Step B33: The rabbit group forages by meandering around, and each individual in the group randomly selects another individual, achieving a global search through position updates; Step B34: The rabbits randomly hide, and each individual in the group generates a burrow around itself. They then randomly select a burrow to update their position, thus achieving a local search. Step B35: If the fitness of the candidate position of an individual in the global search is better than the current position, then update the rabbit group position; otherwise, retain the current rabbit group position and update the optimal individual until the maximum number of iterations is reached or the final optimization function converges. At this point, the optimal individual is the optimal control parameter, and the optimal control parameter is output.

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