Air conditioner cluster fault recovery method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of power system control, and particularly provides an air conditioner cluster fault recovery method and device, computer equipment and a storage medium. The method comprises the steps that an air conditioner cluster on a feeder line with the maximum recovery weight is selected as a to-be-recovered air conditioner cluster; solving a fault recovery model corresponding to the to-be-recovered air conditioner cluster by adopting an improved particle swarm algorithm to obtain an operation power control instruction of each air conditioner in the to-be-recovered air conditioner cluster; and the to-be-recovered air conditioner cluster is regulated and controlled through the operation power control instructions of all the air conditioners. Wherein a deterministic boundary strategy is adopted in the particle swarm initialization process of the improved particle swarm algorithm, and an asynchronous updating mechanism is adopted in the particle swarm position updating process. According to the technical scheme provided by the invention, the risk of frequency oscillation caused by load impact can be effectively inhibited while the air conditioner cluster is subjected to autonomous voltage stabilization.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and particularly relates to an air-conditioning cluster fault recovery method, device, computer device and storage medium. Background Art

[0002] Power system control technology is a core area in the operation of modern power grids. Among them, the post-disaster recovery ability is directly related to power supply reliability. Especially in the power disaster scenarios caused by natural disasters or equipment failures, the safe and orderly recovery of air-conditioning loads has become a key topic in the research of smart grids.

[0003] After a distribution network fault, the load aggregator needs to control and dispatch the air-conditioning cluster to recover orderly and fairly. The traditional centralized scheduling method relying on a central control node exposes inherent defects: one is that the global data collection and centralized processing lead to limited response speed, making it difficult to meet the timeliness requirements for rapid post-disaster recovery; the second is the difficult communication after the disaster, and it is difficult for the central control node to control all nodes simultaneously; the third is that the concentrated access of loads may cause potential safety hazards such as power grid frequency oscillation and voltage drop. Summary of the Invention

[0004] To overcome the above defects, the present invention proposes an air-conditioning cluster fault recovery method, device, computer device and storage medium.

[0005] In a first aspect, an air-conditioning cluster fault recovery method is provided. The air-conditioning cluster fault recovery method includes:

[0006] Select the air-conditioning cluster on the feeder with the largest recovery weight as the air-conditioning cluster to be recovered;

[0007] Solve the fault recovery model corresponding to the air-conditioning cluster to be recovered by using an improved particle swarm algorithm, and obtain the operation power control instructions for each air conditioner in the air-conditioning cluster to be recovered;

[0008] Regulate the air-conditioning cluster to be recovered by using the operation power control instructions for each air conditioner;

[0009] Among them, the particle swarm initialization process of the improved particle swarm algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism.

[0010] Preferably, after regulating the air-conditioning cluster to be recovered by using the operation power control instructions for each air conditioner, it includes:

[0011] Judge whether there is an unrecovered air-conditioning cluster. If so, execute the selection of the air-conditioning cluster to be recovered based on the recovery weight of each feeder. Otherwise, end the operation.

[0012] Preferably, the recovery weight is as follows:

[0013]

[0014] In the above formula, E i is the restoration weight of the i-th feeder, K is the look-back window, and μ i,t is the state of other load switches except air conditioners on the i-th feeder at time t, and ω i is the load factor on the i-th feeder, and P di is the total power of other loads except air conditioners on the i-th feeder, and A i,n is the total number of air conditioners on the i-th feeder, and μ i,j,t is the switch state of the j-th air conditioner on the i-th feeder at time t, and ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, and P ai,j is the power of the j-th air conditioner on the i-th feeder.

[0015] Preferably, the fault recovery model is as follows:

[0016]

[0017] In the above formula, f is the objective function value, and P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, and P max is the upper limit of the power that the air conditioner cluster to be restored is allowed to output under the current power grid state. is the user weight of air conditioner d at time t-1. is the indoor temperature where air conditioner d is located at time t.

[0018] Furthermore, the upper limit of the power that the air conditioner cluster to be restored is allowed to output under the current power grid state is as follows:

[0019] P max = P norm (1 - (V means - V ref ) / K deoop )

[0020] In the above formula, P norm is the total design power of the air conditioner cluster under rated conditions, V means is the effective value of the voltage collected in real time, V ref is the power grid voltage set value, and K deoop is the droop coefficient.

[0021] Furthermore, the user weight of air conditioner d at time t-1 is as follows:

[0022]

[0023] In the above formula, is the ranking of the indoor temperature of air conditioner d at time t-1 among the indoor temperatures of all air conditioners.

[0024] Furthermore, the indoor temperature of air conditioner d at time t is as follows:

[0025]

[0026] In the above formula, T out,d (t) is the outdoor temperature of air conditioner d at time t, is the indoor temperature of air conditioner d at time t-1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air conditioner, η is the efficiency factor of the air conditioning system, and τ is the time step.

[0027] Preferably, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the initialization of the particle swarm includes:

[0028] Randomly initialize the i-th particle as x i =[P i 1 , P i 2 ,..., P i D in the preset search space, where P i D is the operating power of air conditioner D, and D is the number of air conditioners to be optimized;

[0029] Select two particles among the particles, set one particle as Set the other particle as where is the lower limit of air conditioner D in the preset search space, is the upper limit of air conditioner D in the preset search space.

[0030] Furthermore, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the position update of the particle includes:

[0031] Step a. Initialize i = 1, and initialize the position of particle i at time t+1 as the position of particle i at time t;

[0032] Step b. For particle i, sort the indoor temperatures of each air conditioner at time t-1 in descending order to obtain a descending sequence;

[0033] Step c. Initialize h = 1;

[0034] Step d. Determine whether i is greater than N. If so, output the positions of each particle at the moment t + 1; otherwise, execute Step e;

[0035] Step e. Determine whether h is greater than D. If so, let i = i + 1 and then return to Step b; otherwise, update the position of the air conditioner h in the particle i at the moment t + 1;

[0036] Step f. Replace the position of the air conditioner h in the position of the particle i at the moment t + 1 with the updated position of the air conditioner h at the moment t + 1;

[0037] Step g. Calculate the objective function based on the position of the particle i at the moment t + 1. If the value of this objective function is smaller than the value of the objective function corresponding to the position of the particle i at the moment t, then let h = h + 1 and return to Step d; otherwise, cancel the update operation of the position of the air conditioner h at the moment t + 1, and let h = h + 1 and return to Step d;

[0038] Wherein, N is the total number of particles.

[0039] Further, in the process of using the improved particle swarm optimization algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the update of the search space dimension of the particles after each iterative calculation includes:

[0040] If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, end the operation; otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, wherein, D add is the number of newly added air conditioners to be optimized, D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners allowed to be increased in a single expansion, and D2 = max(10, 0.2D).

[0041] In a second aspect, an air conditioner cluster fault recovery device is provided. The air conditioner cluster fault recovery device includes:

[0042] A selection module, configured to select the air conditioner cluster on the feeder with the largest recovery weight as the air conditioner cluster to be restored;

[0043] An analysis module, configured to use the improved particle swarm optimization algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, and obtain the operation power control instructions of each air conditioner in the air conditioner cluster to be restored;

[0044] A regulation module, configured to regulate the air conditioner cluster to be restored by using the operation power control instructions of each air conditioner;

[0045] Among them, the particle swarm initialization process of the improved particle swarm algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism.

[0046] Preferably, the device includes:

[0047] A judgment module, configured to judge whether there is an unrecovered air-conditioning cluster. If so, execute the selection of the air-conditioning cluster to be recovered based on the recovery weight of each feeder; otherwise, end the operation.

[0048] Preferably, the recovery weight is as follows:

[0049]

[0050] In the above formula, E i is the recovery weight of the i-th feeder, K is the look-back window, μ i,t is the state of other load switches except air conditioners on the i-th feeder at time t, ω i is the load factor on the i-th feeder, P di is the total power of other loads except air conditioners on the i-th feeder, A i,n is the total number of air conditioners on the i-th feeder, μ i,j,t is the switch state of the j-th air conditioner on the i-th feeder at time t, ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, P ai,j is the power of the j-th air conditioner on the i-th feeder.

[0051] Preferably, the fault recovery model is as follows:

[0052]

[0053] In the above formula, f is the objective function value, P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, P max is the upper limit of the power that the air-conditioning cluster to be recovered is allowed to output under the current power grid state, is the user weight of air conditioner d at time t-1, is the indoor temperature where air conditioner d is located at time t.

[0054] Furthermore, the upper limit of the power that the air-conditioning cluster to be recovered is allowed to output under the current power grid state is as follows:

[0055] P max = P norm (1 - (V means - V ref ) / K deoop )

[0056] In the above formula, Pnorm is the total design power of the air - conditioner cluster under rated conditions, V means is the effective voltage value collected in real - time, V ref is the set value of the grid voltage, K deoop is the droop coefficient.

[0057] Furthermore, the user weight of air - conditioner d at time t - 1 is as follows:

[0058]

[0059] In the above formula, is the ranking of the indoor temperature of air - conditioner d at time t - 1 among the indoor temperatures of all air - conditioners.

[0060] Furthermore, the indoor temperature of air - conditioner d at time t is as follows:

[0061]

[0062] In the above formula, T out,d (t) is the outdoor temperature of air - conditioner d at time t, is the indoor temperature of air - conditioner d at time t - 1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air - conditioner, η is the efficiency factor of the air - conditioner system, and τ is the time step.

[0063] Preferably, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air - conditioner cluster to be restored, the initialization of the particle swarm includes:

[0064] Randomly initialize the i - th particle as x i =[P i 1 ,P i 2 ,...,P i D , where P i D is the operating power of air - conditioner D, and D is the number of air - conditioners to be optimized;

[0065] Select two particles among all particles, set one particle as Set the other particle as where, is the lower limit of air - conditioner D in the preset search space, is the upper limit of air - conditioner D in the preset search space.

[0066] Furthermore, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air - conditioner cluster to be restored, the position update of the particle includes:

[0067] Step a. Initialize i = 1, and initialize the position of particle i at time t + 1 to be the position of particle i at time t;

[0068] Step b. For particle i, sort the indoor temperatures of each air conditioner at time t - 1 in descending order to obtain a descending sequence;

[0069] Step c. Initialize h = 1;

[0070] Step d. Determine whether i is greater than N. If so, output the positions of each particle at time t + 1. Otherwise, execute Step e;

[0071] Step e. Determine whether h is greater than D. If so, set i = i + 1 and then return to Step b. Otherwise, update the position of air conditioner h in particle i at time t + 1;

[0072] Step f. Replace the position of air conditioner h at time t + 1 in the position of particle i at time t + 1 with the updated position of air conditioner h at time t + 1;

[0073] Step g. Calculate the objective function based on the position of particle i at time t + 1. If the value of this objective function is smaller than the value of the objective function corresponding to the position of particle i at time t, then set h = h + 1 and return to Step d. Otherwise, cancel the update operation of the position of air conditioner h at time t + 1, and set h = h + 1 and return to Step d;

[0074] Among them, N is the total number of particles.

[0075] Furthermore, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the update of the search space dimension of the particles after each iterative calculation includes:

[0076] If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, end the operation. Otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, where D add is the number of newly added air conditioners to be optimized, D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners allowed to be increased in a single expansion, and D2 = max(10, 0.2D).

[0077] In a third aspect, a computer device is provided, including: one or more processors;

[0078] The processor is used to execute one or more programs;

[0079] When the one or more programs are executed by the one or more processors, the air-conditioning cluster fault recovery method described above is implemented.

[0080] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the air-conditioning cluster fault recovery method described above is implemented.

[0081] One or more of the above technical solutions of the present invention have at least one of the following beneficial effects:

[0082] The present invention provides an air-conditioning cluster fault recovery method, device, computer device and storage medium. The method includes: selecting the air-conditioning cluster on the feeder with the largest recovery weight as the air-conditioning cluster to be recovered; using an improved particle swarm optimization algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be recovered, and obtaining the operation power control instructions of each air conditioner in the air-conditioning cluster to be recovered; using the operation power control instructions of each air conditioner to regulate the air-conditioning cluster to be recovered. Among them, the particle swarm initialization process of the improved particle swarm optimization algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism. The technical solution provided by the present invention aims at the dual bottlenecks of centralized control response latency and slow convergence of the particle swarm optimization algorithm, and proposes an orderly recovery strategy for feeders. By selecting the air-conditioning cluster on the feeder with the recovery weight as the air-conditioning cluster to be recovered, and sequentially using the air-conditioning cluster on the feeder as the unit control object, the disadvantages brought by traditional centralized processing are solved. On this basis, an improved particle swarm optimization algorithm is proposed, which uses a deterministic boundary strategy and an asynchronous update mechanism, and speeds up the air-conditioning cluster recovery speed by controlling the smallest air-conditioning cluster. Based on the user weight in the control process, the air-conditioning power is dynamically adjusted to ensure fairness during recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the main step flow of the air-conditioning cluster fault recovery method according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] The following further describes in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0085] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] As disclosed in the background art, power system control technology is a core area in the operation of modern power grids. Among them, the post-disaster recovery ability is directly related to power supply reliability. Especially in the power disaster scenarios caused by natural disasters or equipment failures, the safe and orderly recovery of air-conditioning loads has become a key topic in the research of smart grids.

[0087] After a distribution network fault, the load aggregator needs to control and dispatch the air-conditioning clusters for orderly and fair recovery. The traditional centralized scheduling method relying on a central control node exposes inherent defects: First, the global data collection and centralized processing result in limited response speed, making it difficult to meet the timeliness requirements for rapid post-disaster recovery; Second, communication is difficult after a disaster, and it is difficult for the central control node to control all nodes simultaneously; Third, the concentrated access of loads may cause potential safety hazards such as power grid frequency oscillation and voltage drop.

[0088] To address the above problems, the present invention provides a method, device, computer device, and storage medium for the fault recovery of air-conditioning clusters. The method includes: selecting the air-conditioning cluster on the feeder with the largest recovery weight as the air-conditioning cluster to be recovered; using an improved particle swarm optimization algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be recovered, and obtaining the operation power control instructions for each air conditioner in the air-conditioning cluster to be recovered; using the operation power control instructions of each air conditioner to regulate the air-conditioning cluster to be recovered. Among them, the particle swarm initialization process of the improved particle swarm optimization algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism. The technical solution provided by the present invention, aiming at the dual bottlenecks of slow response of centralized control and slow convergence of the particle swarm optimization algorithm, proposes an orderly recovery strategy for feeders. By selecting the air-conditioning cluster on the feeder with the largest recovery weight as the air-conditioning cluster to be recovered, and sequentially using the air-conditioning clusters on the feeder as the unit control objects, the disadvantages brought by traditional centralized processing are solved. On this basis, an improved particle swarm optimization algorithm is proposed, which uses a deterministic boundary strategy and an asynchronous update mechanism, and speeds up the recovery speed of the air-conditioning cluster by controlling the smallest air-conditioning cluster. During the control process, based on the user weight, the air-conditioning power is dynamically adjusted to ensure fairness during recovery.

[0089] The above solution will be elaborated in detail below.

[0090] Embodiment 1

[0091] Refer to the appendix Figure 1 , Figure 1 which is a schematic diagram of the main step flow of the air-conditioning cluster fault recovery method according to an embodiment of the present invention. As Figure 1 shown, the air-conditioning cluster fault recovery method in the embodiment of the present invention mainly includes the following steps:

[0092] Step S101: Select the air-conditioning cluster on the feeder with the largest recovery weight as the air-conditioning cluster to be recovered;

[0093] Step S102: Solve the fault recovery model corresponding to the air conditioner cluster to be restored by using an improved particle swarm optimization algorithm, and obtain the operation power control instructions for each air conditioner in the air conditioner cluster to be restored;

[0094] Step S103: Regulate the air conditioner cluster to be restored by using the operation power control instructions for each air conditioner.

[0095] In this embodiment, after regulating the air conditioner cluster to be restored by using the operation power control instructions for each air conditioner, it includes:

[0096] Judge whether there is an unrecovered air conditioner cluster. If so, execute the step of selecting the air conditioner cluster to be restored based on the restoration weight of each feeder. Otherwise, end the operation.

[0097] In this embodiment, the restoration weight is as follows:

[0098]

[0099] In the above formula, E i is the restoration weight of the i-th feeder, K is the look-back window, μ i,t is the state of other load switches except air conditioners on the i-th feeder at time t, ω i is the load factor on the i-th feeder, P di is the total power of other loads except air conditioners on the i-th feeder, A i,n is the total number of air conditioners on the i-th feeder, μ i,j,t is the switch state of the j-th air conditioner on the i-th feeder at time t, ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, P ai,j is the power of the j-th air conditioner on the i-th feeder.

[0100] In this embodiment, K = 30. Data is obtained through real-time acquisition, and the restoration benefits of each feeder at different times are calculated by rolling and sorted. After a distribution network fault occurs, the restoration priority of the feeder is determined according to the last sorting.

[0101] In this embodiment, the fault recovery model is as follows:

[0102]

[0103] In the above formula, f is the objective function value, P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, which is a small positive constant (for example, the initial value is 0.01), P max is the power upper limit allowed for the air conditioner cluster to be restored under the current power grid state, is the user weight of air conditioner d at time t - 1, is the indoor temperature of air conditioner d at time t.

[0104] The fault recovery model further includes a constraint condition:

[0105] In one embodiment, the upper limit of the power that the air conditioner cluster to be recovered is allowed to output under the current power grid state is as follows:

[0106] P max = P norm (1 - (V means - V ref ) / K deoop )

[0107] In the above formula, P norm is the total designed power of the air conditioner cluster under rated conditions, V means is the effective voltage value collected in real time, V ref is the set value of the power grid voltage, and K deoop is the droop coefficient.

[0108] In one embodiment, the user weight of air conditioner d at time t - 1 is as follows:

[0109]

[0110] In the above formula, is the ranking of the indoor temperature of air conditioner d at time t - 1 among the indoor temperatures of all air conditioners.

[0111] In one embodiment, the indoor temperature of air conditioner d at time t is as follows:

[0112]

[0113] In the above formula, T out,d (t) is the outdoor temperature of air conditioner d at time t, is the indoor temperature of air conditioner d at time t - 1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air conditioner, η is the efficiency factor of the air conditioner system, and τ is the time step.

[0114] In this embodiment, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be recovered, the initialization of the particle swarm includes:

[0115] Randomly initialize the i - th particle as x i = [P i 1 , P i 2 ,..., P iD , where P i D is the operating power of air conditioner D, and D is the number of air conditioners to be optimized;

[0116] Select two particles from among the particles, and set one of the particles as Set the other particle as where is the lower limit of air conditioner D in the preset search space, is the upper limit of air conditioner D in the preset search space.

[0117] This method includes particles representing two extreme operating states (all minimum power and all maximum power) of the air conditioner cluster during initialization, covering the boundaries of the search space, ensuring that the particle swarm can evaluate the boundary conditions and sense the possible positions of the optimal solution after the first round of iteration, helping to quickly determine the feasible range of the total cluster power, especially when compared with the upper limit of the total power given by droop control, and accelerating the convergence of the algorithm to an effective air conditioner power allocation scheme that satisfies the constraints under the guidance of the global optimal value. Compared with the random initialization of the standard particle swarm algorithm, this method is more efficient in continuous power control problems.

[0118] It is different from the synchronous overall update method of traditional particle swarm algorithms. The present invention proposes an asynchronous update strategy. Each particle performs a tentative search independently according to the dimension, and the search order of the dimension is dynamically adjusted according to the descending order of the indoor temperature of the user at the previous moment. Finally, only the dimensions that can optimize the objective function are retained for update, and the final position of the particle is determined by combining the per-dimensional updates.

[0119] In one embodiment, during the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the position update of the particles includes:

[0120] Step a. Initialize i = 1, and initialize the position of particle i at time t + 1 as the position of particle i at time t;

[0121] Step b. For particle i, sort the indoor temperatures of each air conditioner at time t - 1 in descending order to obtain a descending sequence;

[0122] Step c. Initialize h = 1;

[0123] Step d. Determine whether i is greater than N. If so, output the positions of the particles at time t + 1. Otherwise, execute step e;

[0124] Step e. Determine whether h is greater than D. If so, set i = i + 1 and then return to step b. Otherwise, update the position of air conditioner h in particle i at time t + 1;

[0125] Step f. Replace the position of the updated air conditioner h at time t+1 in the position of particle i at time t+1 with the position of air conditioner h at time t+1.

[0126] Step g. Calculate the objective function based on the position of particle i at time t+1. If the value of this objective function is smaller than the objective function value corresponding to the position of particle i at time t, then let h = h + 1 and return to step d. Otherwise, cancel the update operation of the position of air conditioner h at time t+1, and let h = h + 1 and return to step d.

[0127] Where N is the total number of particles.

[0128] Finally, a dynamic cluster expansion strategy is proposed, which is a mechanism for gradually expanding the number of air conditioners directly controlled by the PSO algorithm. Its purpose is to quickly start the optimization control process with a smaller-scale "minimum cluster" during the orderly recovery of the air conditioner cluster, and then, in subsequent iterations, smoothly and controllably incorporate other recoverable air conditioners in the area into the optimization scope of the particle swarm algorithm in batches until all the air conditioners that need to be recovered are included. This can avoid the problem of excessive initial computational burden and potential algorithm stability issues caused by incorporating a large number of air conditioners into the optimization calculation at one time.

[0129] In one embodiment, during the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be recovered, the update of the search space dimension of the particle after each iterative calculation includes:

[0130] If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be recovered, then end the operation. Otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be recovered, where D add is the number of newly added air conditioners to be optimized, D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners allowed to be increased in a single expansion, D2 = max(10, 0.2D), and the initial value of D is 10.

[0131] Embodiment 2

[0132] Based on the same inventive concept, the present invention also provides an air conditioner cluster fault recovery device, and the air conditioner cluster fault recovery device includes:

[0133] A selection module, configured to select the air conditioner cluster on the feeder with the largest recovery weight as the air conditioner cluster to be recovered.

[0134] An analysis module, which is used to solve the fault recovery model corresponding to the air conditioner cluster to be restored by using an improved particle swarm algorithm, and obtain the operation power control instructions of each air conditioner in the air conditioner cluster to be restored;

[0135] A regulation module, which is used to regulate the air conditioner cluster to be restored by using the operation power control instructions of each air conditioner.

[0136] Preferably, the device includes:

[0137] A judgment module, which is used to judge whether there is an unrecovered air conditioner cluster. If so, the air conditioner cluster to be restored is selected based on the recovery weight of each feeder, otherwise, the operation is ended.

[0138] Preferably, the recovery weight is as follows:

[0139]

[0140] In the above formula, E i is the recovery weight of the i-th feeder, K is the look-back window, μ i,t is the state of other load switches except air conditioners on the i-th feeder at time t, ω i is the load factor on the i-th feeder, P di is the total power of other loads except air conditioners on the i-th feeder, A i,n is the total number of air conditioners on the i-th feeder, μ i,j,t is the switch state of the j-th air conditioner on the i-th feeder at time t, ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, P ai,j is the power of the j-th air conditioner on the i-th feeder.

[0141] Preferably, the fault recovery model is as follows:

[0142]

[0143] In the above formula, f is the objective function value, P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, P max is the upper limit of the power allowed to be output by the air conditioner cluster to be restored under the current power grid state, is the user weight of air conditioner d at time t-1, is the indoor temperature of air conditioner d at time t.

[0144] Furthermore, the upper limit of the power allowed to be output by the air conditioner cluster to be restored under the current power grid state is as follows:

[0145] P max = P norm (1-(Vmeans -V ref ) / K deoop )

[0146] In the above formula, P norm is the total design power of the air-conditioning cluster under rated conditions, V means is the effective value of the voltage collected in real time, V ref is the set value of the grid voltage, and K deoop is the droop coefficient.

[0147] Furthermore, the user weight of air conditioner d at time t-1 is as follows:

[0148]

[0149] In the above formula, is the ranking of the indoor temperature of air conditioner d at time t-1 among the indoor temperatures of all air conditioners.

[0150] Furthermore, the indoor temperature of air conditioner d at time t is as follows:

[0151]

[0152] In the above formula, T out,d (t) is the outdoor temperature of air conditioner d at time t, is the indoor temperature of air conditioner d at time t-1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air conditioner, η is the efficiency factor of the air-conditioning system, and τ is the time step.

[0153] Preferably, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be restored, the initialization of the particle swarm includes:

[0154] Randomly initialize the i-th particle as x i =[P i 1 , P i 2 ,..., P i D in the preset search space, where P i D is the operating power of air conditioner D, and D is the number of air conditioners to be optimized;

[0155] Select two particles among the particles, and set one of the particles as Set the other particle as where, is the lower limit of air conditioner D in the preset search space, is the upper limit of air conditioner D in the preset search space.

[0156] Further, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the position update of the particles includes:

[0157] Step a. Initialize i = 1, and initialize the position of particle i at time t + 1 as the position of particle i at time t;

[0158] Step b. For particle i, sort the indoor temperatures of each air conditioner at time t - 1 in descending order to obtain a descending sequence;

[0159] Step c. Initialize h = 1;

[0160] Step d. Determine whether i is greater than N. If so, output the positions of each particle at time t + 1. Otherwise, execute step e;

[0161] Step e. Determine whether h is greater than D. If so, set i = i + 1 and then return to step b. Otherwise, update the position of air conditioner h in particle i at time t + 1;

[0162] Step f. Replace the position of air conditioner h at time t + 1 in the position of particle i at time t + 1 with the updated position of air conditioner h at time t + 1;

[0163] Step g. Calculate the objective function based on the position of particle i at time t + 1. If the value of this objective function is smaller than the objective function value corresponding to the position of particle i at time t, then set h = h + 1 and return to step d. Otherwise, cancel the update operation of the position of air conditioner h at time t + 1, and set h = h + 1 and return to step d;

[0164] Where N is the total number of particles.

[0165] Further, in the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air conditioner cluster to be restored, the update of the search space dimension of the particles after each iterative calculation includes:

[0166] If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, end the operation. Otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, where D add is the number of newly added air conditioners to be optimized, D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners allowed to be increased in a single expansion, and D2 = max(10, 0.2D).

[0167] Example 3

[0168] Based on the same inventive concept, the present invention further provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for fault recovery of an air-conditioning cluster in the above embodiments.

[0169] Embodiment 4

[0170] Based on the same inventive concept, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of a method for fault recovery of an air-conditioning cluster in the above embodiments.

[0171] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0172] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0173] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not deviate from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An air conditioner cluster fault recovery method, characterized in that The method includes: Selecting the air conditioner cluster on the feeder with the largest restoration weight as the air conditioner cluster to be restored; Using an improved particle swarm optimization algorithm to solve the fault restoration model corresponding to the air conditioner cluster to be restored, and obtaining the operation power control instructions for each air conditioner in the air conditioner cluster to be restored; Using the operation power control instructions of each air conditioner to regulate the air conditioner cluster to be restored; Among them, the particle swarm initialization process of the improved particle swarm optimization algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism.

2. The method according to claim 1, characterized in that, After using the operation power control instructions of each air conditioner to regulate the air conditioner cluster to be restored, it includes: Judging whether there is an air conditioner cluster that has not been restored. If so, execute the selection of the air conditioner cluster to be restored based on the restoration weight of each feeder. Otherwise, end the operation.

3. The method according to claim 1, characterized in that, The restoration weight is as follows: In the above formula, E i is the restoration weight of the i-th feeder, K is the look-back window, μ i,t is the status of other load switches except air conditioners on the i-th feeder at time t, ω i is the load factor on the i-th feeder, P di is the total power of other loads except air conditioners on the i-th feeder, A i,n is the total number of air conditioners on the i-th feeder, μ i,j,t is the switch status of the j-th air conditioner on the i-th feeder at time t, ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, P ai,j is the power of the j-th air conditioner on the i-th feeder.

4. The method according to claim 1, characterized in that, The fault restoration model is as follows: In the above formula, f is the objective function value, P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, P max is the upper limit of the power that the air conditioner cluster to be restored is allowed to output under the current power grid state, is the user weight of air conditioner d at time t-1, is the indoor temperature where air conditioner d is located at time t.

5. The method according to claim 4, characterized in that, The upper limit of the power that the air conditioner cluster to be restored is allowed to output under the current power grid state is as follows: P max = P norm (1 - (V means - V ref ) / K deoop ) In the above formula, P norm is the total design power of the air-conditioning cluster under rated conditions, V means is the effective value of the voltage collected in real time, V ref is the set value of the grid voltage, K deoop is the droop coefficient.

6. The method according to claim 4, wherein The user weight of air conditioner d at time t-1 is as follows: In the above formula, is the ranking of the indoor temperature of air conditioner d at time t-1 among the indoor temperatures of all air conditioners.

7. The method according to claim 4, wherein The indoor temperature of air conditioner d at time t is as follows: In the above formula, T out,d (t) is the outdoor temperature where the air conditioner d is located at time t, is the indoor temperature where the air conditioner d is located at time t-1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air conditioner, η is the efficiency factor of the air conditioning system, and τ is the time step.

8. The method according to claim 1, characterized in that In the process of using the improved particle swarm optimization algorithm to solve the fault restoration model corresponding to the air conditioner cluster to be restored, the initialization of the particle swarm includes: Randomly initialize the i-th particle as x according to the preset search space i =[P i 1 , P i 2 ,..., P i D , where P i D is the operating power of air conditioner D, and D is the number of air conditioners to be optimized; Select two particles from all the particles, and set one of the particles as and set the other particle as where is the lower limit of air conditioner D in the preset search space, is the upper limit of air conditioner D in the preset search space.

9. The method according to claim 8, wherein In the process of using the improved particle swarm optimization algorithm to solve the fault restoration model corresponding to the air conditioner cluster to be restored, the position update of the particle includes: Step a. Initialize i = 1, and initialize the position of particle i at time t+1 as the position of particle i at time t; Step b. For particle i, sort the indoor temperatures of each air conditioner at time t-1 in descending order to obtain a descending sequence; Step c. Initialize h = 1; Step d. Judge whether i is greater than N. If so, output the positions of each particle at time t+1. Otherwise, execute step e; Step e. Judge whether h is greater than D. If so, let i = i+1 and then return to step b. Otherwise, update the position of air conditioner h in particle i at time t+1; Step f. Replace the position of air conditioner h at time t+1 in the position of particle i at time t+1 with the updated position of air conditioner h at time t+1; Step g. Calculate the objective function based on the position of particle i at time t+1. If the value of this objective function is smaller than the objective function value corresponding to the position of particle i at time t, then let h = h+1 and then return to step d. Otherwise, cancel the update operation of the position of air conditioner h at time t+1, and let h = h+1 and then return to step d; Among them, N is the total number of particles.

10. The method according to claim 9, wherein In the process of using the improved particle swarm optimization algorithm to solve the fault restoration model corresponding to the air conditioner cluster to be restored, the update of the search space dimension of the particle after each iterative calculation includes: If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, the operation ends; otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, where D add is the number of newly added air conditioners to be optimized, and D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners allowed to be increased in a single expansion, and D2 = max(10, 0.2D).

11. An air conditioner cluster fault recovery device, characterized in that, The device includes: A selection module, configured to select the air conditioner cluster on the feeder with the largest restoration weight as the air conditioner cluster to be restored; An analysis module, configured to use an improved particle swarm optimization algorithm to solve the fault restoration model corresponding to the air conditioner cluster to be restored, and obtain the operation power control instructions for each air conditioner in the air conditioner cluster to be restored; A regulation module, configured to use the operation power control instructions of each air conditioner to regulate the air conditioner cluster to be restored; Among them, the particle swarm initialization process of the improved particle swarm algorithm adopts a deterministic boundary strategy, and the particle swarm position update process adopts an asynchronous update mechanism.

12. The device according to claim 11, wherein The device includes: A judgment module, configured to judge whether there is an unrecovered air-conditioning cluster. If so, execute the selection of the air-conditioning cluster to be recovered based on the recovery weight of each feeder; otherwise, end the operation.

13. The device according to claim 11, wherein The recovery weight is as follows: In the above formula, E i is the restoration weight of the i-th feeder, K is the look-back window, μ i,t is the status of other load switches except air conditioners on the i-th feeder at time t, ω i is the load factor on the i-th feeder, P di is the total power of other loads except air conditioners on the i-th feeder, A i,n is the total number of air conditioners on the i-th feeder, μ i,j,t is the switch status of the j-th air conditioner on the i-th feeder at time t, ω ai,j is the load factor of the j-th air conditioner on the i-th feeder, P ai,j is the power of the j-th air conditioner on the i-th feeder.

14. The device according to claim 11, characterized in that, The fault recovery model is as follows: In the above formula, f is the objective function value, and P d,t is the operating power of air conditioner d at time t, D is the number of air conditioners to be optimized, M is a preset positive penalty term, α is the boundary condition offset, and P max is the upper limit of the power that the air conditioner cluster to be restored is allowed to output under the current power grid state, is the user weight of air conditioner d at time t - 1, is the indoor temperature of air conditioner d at time t.

15. The device according to claim 14, characterized in that, The upper limit of the power that the air-conditioning cluster to be recovered is allowed to output under the current power grid state is as follows: P max = P norm (1 - (V means - V ref ) / K deoop ) In the above formula, P norm is the total design power of the air-conditioning cluster under rated conditions, V means is the effective value of the voltage collected in real time, V ref is the set value of the grid voltage, K deoop is the droop coefficient.

16. The device according to claim 14, characterized in that, The user weight of air conditioner d at the (t - 1)th moment is as follows: In the above formula, is the ranking of the indoor temperature of air conditioner d at time t-1 among the indoor temperatures of all air conditioners.

17. The device according to claim 14, characterized in that, The indoor temperature of air conditioner d at the tth moment is as follows: In the above formula, T out,d (t) is the outdoor temperature where the air conditioner d is located at time t, is the indoor temperature where the air conditioner d is located at time t - 1, e is the natural constant, R and C are the equivalent thermal resistance and equivalent heat capacity respectively, P is the current power of the air conditioner, η is the efficiency factor of the air conditioning system, and τ is the time step.

18. The device according to claim 11, wherein, In the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be recovered, the initialization of the particle swarm includes: Randomly initialize the i-th particle as x according to the preset search space i =[P i 1 , P i 2 ,..., P i D , where P i D is the operating power of air conditioner D, and D is the number of air conditioners to be optimized; Select two particles from each particle, and set one of the particles as Set the other particle as Among them, is the lower limit of air conditioner D in the preset search space, is the upper limit of air conditioner D in the preset search space.

19. The device according to claim 18, wherein In the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be recovered, the position update of the particle includes: Step a. Initialize i = 1, and initialize the position of particle i at the (t + 1)th moment as the position of particle i at the tth moment; Step b. For particle i, sort the indoor temperatures of each air conditioner at the (t - 1)th moment in descending order to obtain a descending sequence; Step c. Initialize h = 1; Step d. Judge whether i is greater than N. If so, output the positions of each particle at the (t + 1)th moment; otherwise, execute step e; Step e. Judge whether h is greater than D. If so, let i = i + 1 and then return to step b; otherwise, update the position of air conditioner h in particle i at the (t + 1)th moment; Step f. Replace the position of air conditioner h at the (t + 1)th moment in the position of particle i at the (t + 1)th moment with the updated position of air conditioner h at the (t + 1)th moment; Step g. Calculate the objective function based on the position of particle i at the (t + 1)th moment. If the value of this objective function is smaller than the objective function value corresponding to the position of particle i at the tth moment, let h = h + 1 and then return to step d; otherwise, cancel the update operation of the position of air conditioner h at the (t + 1)th moment, and let h = h + 1 and then return to step d; Among them, N is the total number of particles.

20. The device according to claim 19, wherein In the process of using the improved particle swarm algorithm to solve the fault recovery model corresponding to the air-conditioning cluster to be recovered, the update of the search space dimension of the particle after each iterative calculation includes: If the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, the operation ends; otherwise, update the search space dimension of the particle D = D + D add , until the search space dimension D of the particle is equal to the total number of air conditioners in the air conditioner cluster to be restored, where D add is the number of newly added air conditioners to be optimized, and D add = min(D1, D2), D1 is the number of remaining unoptimized air conditioners, D2 is the upper threshold of the number of air conditioners that can be increased allowed for a single expansion, and D2 = max(10, 0.2D).

21. A computer device, characterized in that, Includes: One or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the air-conditioning cluster fault recovery method described in any one of claims 1 to 10 is implemented.

22. A computer-readable storage medium, characterized in that, There is a computer program stored thereon, and when the computer program is executed, the air-conditioning cluster fault recovery method described in any one of claims 1 to 10 is implemented.

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