Information control method for cost allocation of auxiliary service

Abstract

The invention relates to an information control method for assisting service cost allocation, which aims at a unit average load rate queue of a thermal power station unit, because an extreme unit average load rate is mainly a unit average load rate which does not fluctuate slightly, the unit average load rate queue needs to be analyzed better, and the unit average load rate queue is analyzed more accurately. According to the method, section expansion is performed on the unit average load rate queue, a plurality of expansion spaces and deviation spaces are obtained after section expansion, the unit average load rate in the expansion spaces is more stable and tends to a reasonable unit average load rate, and the unit average load rate in the deviation spaces is more unstable and tends to extreme data. The unit average load rates in all deviation spaces are not the extreme unit average load rate, so that the extreme unit average load rate detected by the deviation spaces on the extreme unit average load rate is reduced, and the deviation spaces and the adjacent expansion spaces are aggregated to obtain a plurality of target unit average load rate spaces; by analyzing the average load rate space of the target unit, the accurate average load rate of the extreme unit is obtained, and a manager can determine whether the operation of the thermal power station unit is in a reasonable state or not.

Description

Technical Field

[0001] The present invention belongs to the technical field of auxiliary service cost sharing, and in particular relates to an information control method for auxiliary service cost sharing. Background Art

[0002] In the new power system, as the penetration rate of new energy sources gradually increases, the role of auxiliary services to ensure real-time balance between supply and demand and system safety and stability becomes increasingly prominent. The provision of auxiliary services by market players will inevitably bring additional costs, which need to be compensated, and this part of the cost needs to be shared.

[0003] During the period of cost sharing of auxiliary services, it is often the case that the following procedures are followed as mentioned in the prior art solution with patent publication number "CN112510709B": the internal data of the unit and the external data of the unit with the average load rate of the unit during the peak-shaving period of the thermal power unit are collected, and the average cost per kilowatt-hour of the thermal power unit during the peak-shaving period with the shared cost is calculated respectively.

[0004] On the other hand, the average load rate of the unit can reflect whether the operation of the thermal power plant unit is in a reasonable state. By detecting and measuring the average load rate of the unit, the controller can identify unreasonable extreme conditions.

[0005] At present, the extreme detection of unit average load rate is often carried out by selecting the extreme unit average load rate by means of segment expansion. However, this method can only obtain the segment range where the extreme unit average load rate is located, and cannot accurately obtain the extreme unit average load rate. As a result, the extreme unit average load rate detected under certain conditions is inaccurate or the extreme unit average load rate is misidentified, which is ultimately not suitable for determining whether the operation of the thermal power plant unit is in a reasonable state. Summary of the invention

[0006] In order to solve the defects in the prior art, the present invention proposes an information control method for auxiliary service sharing costs. For a unit average load rate queue of a thermal power plant unit, since the extreme unit average load rate is mainly the average load rate of some units with high fluctuations, it is necessary to better perform analysis on the unit average load rate queue. The present invention performs segment expansion on the unit average load rate queue, and obtains multiple expansion spaces and deviation spaces after segment expansion. The unit average load rate in the expansion space is more stable and more inclined to a reasonable unit average load rate, while the unit average load rate in the deviation space is more unstable and more inclined to extreme data. Because not all unit average load rates in the deviation space are extreme unit average load rates, it is necessary to reduce the extreme unit average load rate detected by the deviation space for the extreme unit average load rate, and obtain multiple target unit average load rate spaces by aggregating the deviation space and the adjacent expansion space. By performing analysis on the target unit average load rate space, an accurate extreme unit average load rate is obtained, which is suitable for the controller to perform the determination of whether the operation of the thermal power plant unit is in a reasonable state.

[0007] The present invention uses the following technical solutions.

[0008] An information control method for auxiliary service cost sharing, comprising:

[0009] Collect the internal data and external data of the thermal power unit with the average load rate of the unit during the peak-shaving period, calculate the average cost of electricity during the peak-shaving period of the thermal power unit with the allocated cost, and process the average load rate of the unit;

[0010] The method for processing the average load factor of the unit includes:

[0011] Step 1: Get the average load rate queue of a thermal power plant unit;

[0012] Step 2: Obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue;

[0013] Step 3: Obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue;

[0014] Step 4: Based on the fluctuation state of the unit average load rate within a target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, obtain the extreme amplitude of any target unit average load rate space; based on the extreme amplitude difference and continuous duration difference between a target unit average load rate space and an adjacent target unit average load rate space, obtain the probability of a target unit average load rate space being an extreme unit average load rate space; based on the probability of a target unit average load rate space being an extreme unit average load rate space, obtain the extreme unit average load rate in the unit average load rate queue.

[0015] Preferably, Step 1 specifically includes: collecting the average unit load rate of a thermal power plant unit to be detected via sampling from a three-axis acceleration sensor, where the three-axis acceleration sensor samples an average unit load rate every 2 seconds and transmits it to a processing unit, and defining a queue formed by the latest sampled and transmitted UI1-minute average unit load rate in the order of the sampling time points as the average unit load rate queue of the thermal power plant unit.

[0016] Preferably, UI1 is a preset value of one, which is 40.

[0017] Preferably, Step 1 specifically also includes: removing the unit average load rates in the unit average load rate queue whose unit average load rates are not in the extreme unit average load rate range, and obtaining the unit average load rate queue after removing the unit average load rates that are not in the extreme unit average load rate range.

[0018] Preferably, Step 2 specifically includes: selecting UI2 unit average load rates equidistantly in the unit average load rate queue, defining them as a benchmark unit average load rate cluster, defining the average load rate of a unit in the benchmark unit average load rate cluster as the target unit average load rate, taking the target unit average load rate as the midpoint, and taking the range with a near domain span of S as the starting expansion space of the target unit average load rate, S is the value to be set, and the value of S here is incremented by one each time, and the standard deviation of the average load rates of all units in the starting expansion space of the target unit average load rate is obtained each time, Defined as E, set the critical value one in advance, defined as ui1, obtain the corresponding value S to be set when it is lower than ui1 for the first time, defined as S1, then take the average load rate of the target unit as the midpoint, and the range with a near-domain span of S1 as the expansion space of the average load rate of the target unit, obtain the expansion space of the average load rate of each unit in the benchmark unit average load rate cluster, and then obtain multiple expansion spaces of the unit average load rate queue, take the remaining space outside the expansion space in the unit average load rate queue as the deviation space, and then obtain multiple deviation spaces of the unit average load rate queue.

[0019] Preferably, UI2 is a preset value of two, which is twenty.

[0020] Preferably, the value of ui1 is five.

[0021] Preferably, in Step 3, the method of obtaining an aggregate benchmark of a deviation space according to the difference between the average load factor of the unit in an expansion space and a deviation space specifically includes:

[0022] Define any deviation space among multiple deviation spaces as the target deviation space, define the most adjacent expansion space after the target deviation space as the target expansion space, and calculate the following equation:

[0023]

[0024] In the equation, nm is the number of the average load rate of the unit in the target deviation space, nt is the number of the average load rate of the unit in the target expansion space, Inm is the maximum value of the average load rate of the unit in the target deviation space, Mnm is the minimum value of the average load rate of the unit in the target deviation space, Int is the maximum value of the average load rate of the unit in the target expansion space, Mnt is the minimum value of the average load rate of the unit in the target expansion space, Hnm is the mean of the average load rate of the unit in the target deviation space, Hnt is the mean of the average load rate of the unit in the target expansion space, e is the Euler number, Representatives Normalization was performed using the Z-score method, and Rnm was the aggregated benchmark in the target deviation space.

[0025] Preferably, in Step 3, a method for aggregating and obtaining a plurality of target unit average load rate spaces of the unit average load rate queue is performed according to the aggregation benchmark of each deviation space, which includes:

[0026] Define critical value 2 in advance, defined as ui2, the value of ui2 is four-fifths. If Rnm≥ui2, aggregate the target deviation space and the target expansion space according to the order of their sampling time points to obtain a target unit average load rate space. If Rnm<ui2, treat the target deviation space as a target unit average load rate space to obtain all target unit average load rate spaces of the unit average load rate queue.

[0027] Preferably, in Step 4, according to the fluctuation state of the unit average load rate in any target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, the operation equation for obtaining the extreme amplitude of any target unit average load rate space is:

[0028]

[0029] For any unit average load rate space, in the equation, y l+1 is the value of the average load rate of the l+1th unit in the average load rate space of the target unit, y l is the value of the lth unit average load rate in the target unit average load rate space, L is the number of unit average load rates in the target unit average load rate space, HY is the mean of the unit average load rates in the target unit average load rate space, HM is the mean of the unit average load rates in the unit average load rate queue, and SM is the extreme amplitude of the target unit average load rate space.

[0030] Preferably, in Step 4, according to the extreme amplitude difference and continuous duration difference between an arbitrary target unit average load rate space and an adjacent target unit average load rate space, a method for obtaining the probability of an arbitrary target unit average load rate space being used as an extreme unit average load rate space specifically includes:

[0031] Define any target unit average load rate space in the unit average load rate queue as space one, define the most adjacent target unit average load rate space before space one as space two, define the most adjacent target unit average load rate space after space one as space three, define the space cluster formed by space one, space two and space three as cluster one, and then calculate the following equation:

[0032]

[0033] In the equation, C1 is the extreme amplitude of space 1, e is the Euler number, J is the number of target unit average load rate spaces in cluster 1, and u j+1 is the continuous duration of the average load rate space of the j+1th target unit in cluster 1, u j is the continuous duration of the average load rate space of the jth target unit in cluster 1, b is a set non-zero quantity, and its value is 1. Yes Normalization is performed using the Z-score method, and U1 is the probability that space 1 is the extreme unit average load rate space.

[0034] Preferably, in Step 4, the method for obtaining the extreme unit average load rate in the unit average load rate queue according to the probability of using a target unit average load rate space as an extreme unit average load rate space specifically includes:

[0035] Define critical value three in advance, defined as ui3, and its value is one-fifth. If U1≤ui3, space one is regarded as an extreme unit average load rate space of the unit average load rate queue, and all extreme unit average load rate spaces of the unit average load rate queue are obtained. The unit average load rate in all extreme unit average load rate spaces is the extreme unit average load rate in the unit average load rate queue, and then the extreme unit average load rate in the unit average load rate queue is sent to the display unit for display and the message of the thermal power plant unit failure is annotated on the display unit.

[0036] An information control device for auxiliary service cost sharing, comprising:

[0037] A queue module, which is used to obtain a queue of average load rates of units of a thermal power station;

[0038] A space module, which is used to obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue;

[0039] An aggregation module, which is used to obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and to perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue;

[0040] An identification module is used to obtain the extreme amplitude of any target unit average load rate space based on the ratio of the fluctuation state of the unit average load rate within a target unit average load rate space and the average of the target unit average load rate space in the unit average load rate queue, obtain the probability of a target unit average load rate space being regarded as an extreme unit average load rate space based on the difference in extreme amplitude and continuous duration between a target unit average load rate space and an adjacent target unit average load rate space, and obtain the extreme unit average load rate in the unit average load rate queue based on the probability of a target unit average load rate space being regarded as an extreme unit average load rate space.

[0041] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0042] For the unit average load rate queue of a thermal power plant unit, since the extreme unit average load rate is mainly the average load rate of some units with significant fluctuations, in order to better perform analysis on the unit average load rate queue, the present invention performs segment expansion on the unit average load rate queue, and obtains multiple expansion spaces and deviation spaces after segment expansion. The unit average load rate in the expansion space is more stable and more inclined to a reasonable unit average load rate, while the unit average load rate in the deviation space is more unstable and more inclined to extreme data, because not all unit average load rates in the deviation space are extreme unit average load rates, so it is necessary to reduce the extreme unit average load rate detected by the deviation space for the extreme unit average load rate, and obtain multiple target unit average load rate spaces by aggregating the deviation space and the adjacent expansion space, and perform analysis on the target unit average load rate space to obtain an accurate extreme unit average load rate, which is convenient for the controller to determine whether the operation of the thermal power plant unit is in a reasonable state. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the information control method for auxiliary service cost sharing described in the present invention;

[0044] Figure 2 It is a partial structural diagram of the information control device for auxiliary service cost sharing described in the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of them. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0046] like Figure 1 As shown, the information control method of an auxiliary service cost sharing method of the present invention includes:

[0047] Collect the internal data and external data of the thermal power unit with the average load rate of the unit during the peak-shaving period, calculate the average cost of electricity during the peak-shaving period of the thermal power unit with the allocated cost, and process the average load rate of the unit;

[0048] The method for processing the average load factor of the unit includes:

[0049] Step 1: Get the average load rate queue of a thermal power plant unit;

[0050] The present invention obtains the deviation space by expanding the execution section of the unit average load rate, and obtains the entire overall target unit average load rate space by identifying the affiliation of the deviation space through spatial approximation. By analyzing the fluctuation attribute of the target unit average load rate space, the extreme unit average load rate space is selected to obtain an accurate extreme unit average load rate, which is convenient for the controller to measure whether the operation of the thermal power plant unit is reasonable. Before analysis, the unit average load rate must be obtained and pre-processing must be performed.

[0051] In a preferred but non-restrictive implementation of the present invention, Step 1 specifically includes: collecting the average unit load rate of a thermal power plant unit to be detected via sampling from a three-axis acceleration sensor, where the three-axis acceleration sensor samples an average unit load rate every 2 seconds and transmits it to a processing unit, and defines a queue formed by the latest sampled and transmitted UI1-minute average unit load rate in the order of the sampling time points as the average unit load rate queue of the thermal power plant unit.

[0052] In a preferred but non-limiting embodiment of the present invention, UI1 is a preset value of one, which is 40.

[0053] However, since the reasonable peak unit average load rate range of thermal power plant units is often 55g to 75g, due to the location of the thermal power plant units during detection or the influence of certain unexpected conditions, some unreasonable unit average load rates will often be formed, that is, not within the peak unit average load rate range, so the unit average load rate queue must be processed before execution.

[0054] In a preferred but non-restrictive implementation of the present invention, Step 1 specifically also includes: removing the unit average load rates in the unit average load rate queue that are not in the extreme unit average load rate range, and obtaining the unit average load rate queue after removing the unit average load rates that are not in the extreme unit average load rate range, and the present invention still defines the unit average load rate queue after removing the unit average load rates that are not in the extreme unit average load rate range as the unit average load rate queue, and the following does not specifically specify that the unit average load rate queue is obtained by removing the unit average load rate queue that is not in the extreme unit average load rate range, where the unit average load rate queue contains multiple unit average load rates, and each unit average load rate corresponds to its sampling time point.

[0055] Thus, the average load rate queue of the units in a thermal power plant is obtained.

[0056] Step 2: Obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue;

[0057] The above has obtained a unit average load rate queue of a thermal power plant unit. Since the extreme unit average load rate is generally the average load rate of some units that fluctuates greatly, it is necessary to better analyze the unit average load rate queue, and perform segment expansion on the unit average load rate queue. After segment expansion, multiple expansion spaces and deviation spaces are obtained. The unit average load rate in the expansion space is more stable, and is more inclined to a reasonable unit average load rate, while the unit average load rate in the deviation space is more unstable, and is more inclined to extreme data. However, not all unit average load rates in the deviation space are extreme unit average load rates, but only tend to extreme data. Therefore, in order to reduce the extreme unit average load rate detected by the deviation space for the extreme unit average load rate, it is necessary to analyze the deviation space, and obtain multiple target unit average load rate spaces by aggregating the deviation space and the adjacent expansion space. By analyzing the target unit average load rate space, an accurate extreme unit average load rate is obtained, which is convenient for the controller to determine whether the operation of the thermal power plant unit is in a reasonable state.

[0058] In a preferred but non-restrictive implementation of the present invention, Step 2 specifically includes: selecting UI2 unit average load rates equidistantly in the unit average load rate queue, defining them as a benchmark unit average load rate cluster, defining a unit average load rate in the benchmark unit average load rate cluster as the target unit average load rate, taking the target unit average load rate as the midpoint, and taking the near domain span (the near domain span is the number of elements adjacent to the midpoint on both sides of the unit average load rate queue, and the near domain is the number of elements adjacent to the midpoint on both sides) as the range of S as the starting expansion space of the target unit average load rate, S is the value to be set, and the value of S here is increased by one each time until it meets a set requirement. Calculate the standard deviation of the average load rates of all units in the starting expansion space of the target unit average load rate each time, defined as E, set the critical value one in advance, defined as ui1, obtain the corresponding value S to be set when it is lower than ui1 for the first time, defined as S1, and take the target unit average load rate as the midpoint, and the range with a near-domain span of S1 as the expansion space of the target unit average load rate, obtain the expansion space of the average load rate of each unit in the benchmark unit average load rate cluster, and then obtain multiple expansion spaces of the unit average load rate queue, take the remaining space outside the expansion space within the unit average load rate queue as the deviation space, and then obtain multiple deviation spaces of the unit average load rate queue.

[0059] In a preferred but non-limiting embodiment of the present invention, UI2 is a preset value of two, which is twenty.

[0060] In a preferred but non-limiting embodiment of the present invention, the value of ui1 is five.

[0061] The average load rate of the units in the expansion space is more stable, with lower fluctuations, while the average load rate of the units in the deviation space is unstable, with significant fluctuations.

[0062] Thus, multiple expansion spaces and multiple deviation spaces of the unit average load rate queue are obtained.

[0063] Step 3: Obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue;

[0064] However, since the expanded space contains some small deviation spaces, and such deviation spaces cannot be identified as belonging to the reasonable unit average load rate space or the extreme unit average load rate space, it is necessary to identify such deviation sections, and use the approximate amplitude to aggregate the adjacent spaces, or to treat the spaces that are not aggregated separately as a target unit average load rate space, so as to obtain the entire target unit average load rate space.

[0065] In a preferred but non-limiting embodiment of the present invention, in Step 3, a method for obtaining an aggregate benchmark of a deviation space according to a difference between an expansion space and an average load rate of a unit in a deviation space specifically includes:

[0066] Define any deviation space among multiple deviation spaces as the target deviation space, define the most adjacent expansion space after the target deviation space as the target expansion space, and calculate the following equation:

[0067]

[0068] In the equation, nm is the number of the average load rate of the unit in the target deviation space, nt is the number of the average load rate of the unit in the target expansion space, Inm is the maximum value of the average load rate of the unit in the target deviation space, Mnm is the minimum value of the average load rate of the unit in the target deviation space, Int is the maximum value of the average load rate of the unit in the target expansion space, Mnt is the minimum value of the average load rate of the unit in the target expansion space, Hnm is the mean of the average load rate of the unit in the target deviation space, Hnt is the mean of the average load rate of the unit in the target expansion space, e is the Euler number, Representatives Normalization was performed using the Z-score method, and Rnm was the aggregated benchmark in the target deviation space.

[0069] If there is only one unit average load factor in the deviation space, the average difference between the unit average load factor and the nearest expansion space after it is calculated as the aggregation benchmark of the deviation space.

[0070] The |nm-nt| represents the information difference of the average load rate of the unit in a pair of spaces. The information content of the deviation space is often not high, so the aggregation amplitude is estimated by analyzing the information difference of the average load rate of the unit in the adjacent space. The higher the difference, the higher the aggregation amplitude. Represents the fluctuation range of the average load rate of the units in a pair of spaces. If the fluctuation range is similar, that is, the closer it is to one, the higher the aggregate amplitude will be. It represents the mean quotient of the average load factors of the units in a pair of spaces, and infers the overall difference in the average load factors of the units in a pair of spaces. Similarly, the closer they are to one, the higher the aggregation amplitude.

[0071] In a preferred but non-limiting embodiment of the present invention, in Step 3, a method for aggregating and obtaining a plurality of target unit average load rate spaces of the unit average load rate queue is performed according to the aggregation benchmark of each deviation space, which includes:

[0072] Define critical value 2 in advance, defined as ui2, the value of ui2 is four-fifths. If Rnm≥ui2, aggregate the target deviation space and the target expansion space according to the order of their sampling time points to obtain a target unit average load rate space. If Rnm<ui2, treat the target deviation space directly as a target unit average load rate space to obtain all target unit average load rate spaces of the unit average load rate queue.

[0073] Therefore, multiple destination unit average load rate spaces of the unit average load rate queue are obtained.

[0074] Step 4: Based on the fluctuation state of the unit average load rate within a target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, obtain the extreme amplitude of any target unit average load rate space; based on the extreme amplitude difference and continuous duration difference between a target unit average load rate space and an adjacent target unit average load rate space, obtain the probability of a target unit average load rate space being an extreme unit average load rate space; based on the probability of a target unit average load rate space being an extreme unit average load rate space, obtain the extreme unit average load rate in the unit average load rate queue.

[0075] For the target unit average load rate space obtained, it is necessary to select the section where the unit average load rate is extreme in the entire space to obtain the extreme unit average load rate. For the extreme unit average load rate, the extreme unit average load rate changes caused by the failure and operation status of the thermal power plant unit are often higher than the reasonable unit average load rate of the thermal power plant unit, and the fluctuation frequency of the unit average load rate is often larger. For the reasonable unit average load rate, its fluctuation is very stable and the frequency is gentle. Therefore, the extreme unit average load rate space can be estimated through the fluctuation frequency distinction in different spaces and the overall level of the unit average load rate.

[0076] In a preferred but non-limiting embodiment of the present invention, in Step 4, based on the fluctuation state of the unit average load rate in any target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, the operation equation for obtaining the extreme amplitude of any target unit average load rate space is:

[0077]

[0078] For any unit average load rate space, in the equation, y l+1 is the value of the average load rate of the l+1th unit in the average load rate space of the target unit, y l is the value of the lth unit average load rate in the target unit average load rate space, L is the number of unit average load rates in the target unit average load rate space, HY is the mean of the unit average load rates in the target unit average load rate space, HM is the mean of the unit average load rates in the unit average load rate queue, SM is the extreme amplitude of the target unit average load rate space, Represents the fluctuation state of the average load rate of the unit within the average load rate space of the target unit. Represents the average ratio of the average load rate space of the destination unit in the average load rate queue of the unit.

[0079] and Represents the fluctuation state of the unit average load rate within the target unit average load rate space. The higher its value is, the higher the probability that the target unit average load rate space belongs to the extreme unit average load rate space. It represents the trend of the target unit average load rate space in the target unit average load rate space in the unit average load rate queue. The higher the value, the higher the probability that the target unit average load rate space belongs to the extreme unit average load rate space.

[0080] The above obtains an extreme inference benchmark for a target unit average load rate space. Since the target extreme to be detected is caused by a drastic change in the unit average load rate due to some faults in the thermal power plant unit, it is necessary to eliminate other states, such as the continuous extreme of the unit average load rate caused by internal circuit failure of the thermal power plant unit or interference of the thermal power plant unit. Therefore, by analyzing the unit average load rate queue, the unit average load rate queue contains multiple target unit average load rate spaces, and each target unit average load rate space corresponds to an extreme amplitude. Therefore, by comparing the change state of the extreme amplitude of each target unit average load rate space with its adjacent target space, it is determined whether this target unit average load rate space is an extreme unit average load rate space.

[0081] However, due to some faults in the thermal power plant units, the average load rate of the units changes drastically and the duration of the fluctuations in the extreme space is compensated accordingly, which is significantly different from the fluctuation levels of the adjacent sections before and after. The extreme average load rate of the units caused by operation will continue during the entire operation period, and the duration of the fluctuations in the section where it is located is relatively continuous, and the difference in fluctuation levels with the adjacent sections before and after is not high. Therefore, based on this attribute and the overall speculation combined with the extreme amplitude, it is determined that it has the probability of belonging to the extreme unit average load rate space.

[0082] In a preferred but non-limiting embodiment of the present invention, in Step 4, based on the extreme amplitude difference and continuous duration difference between an arbitrary target unit average load rate space and an adjacent target unit average load rate space, a method for obtaining the probability of an arbitrary target unit average load rate space being used as an extreme unit average load rate space specifically includes:

[0083] Define any target unit average load rate space in the unit average load rate queue as space one, define the most adjacent target unit average load rate space before space one as space two, define the most adjacent target unit average load rate space after space one as space three, define the space cluster formed by space one, space two and space three as cluster one, and then calculate the following equation:

[0084]

[0085] In the equation, C1 is the extreme amplitude of space 1, e is the Euler number, J is the number of target unit average load rate spaces in cluster 1, and u j+1 is the continuous duration of the average load rate space of the j+1th target unit in cluster 1 (that is, the time interval obtained by subtracting the earliest sampling time point from the latest sampling time point of the elements in the average load rate space of the unit), u jis the continuous duration of the average load rate space of the jth target unit in cluster 1 (that is, the time interval obtained by subtracting the earliest sampling time point from the latest sampling time point of the elements in the average load rate space of the unit), b is a set non-zero value, its value is 1, the purpose is to avoid is zero, Yes Normalization is performed using the Z-score method, and U1 is the probability that space 1 is the extreme unit average load rate space.

[0086] In a preferred but non-limiting embodiment of the present invention, in Step 4, the method for obtaining the extreme unit average load rate in the unit average load rate queue according to the probability of a target unit average load rate space being used as the extreme unit average load rate space specifically includes:

[0087] Define critical value three in advance, defined as ui3, and its value is one-fifth. If U1≤ui3, space one is regarded as an extreme unit average load rate space in the unit average load rate queue, and all extreme unit average load rate spaces in the unit average load rate queue are obtained. The unit average load rate in all extreme unit average load rate spaces is the extreme unit average load rate in the unit average load rate queue, and then the extreme unit average load rate in the unit average load rate queue is sent to the display unit for display and the message of the failure of the thermal power plant unit is annotated on the display unit. The extreme unit average load rate is an extreme value caused by some failures of the thermal power plant unit, which is convenient for the controller to determine whether the operation of the thermal power plant unit is in a reasonable state.

[0088] like Figure 2 As shown, the information control device for auxiliary service cost sharing of the present invention includes:

[0089] A queue module, which is used to obtain a queue of average load rates of units of a thermal power station;

[0090] A space module, which is used to obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue;

[0091] An aggregation module, which is used to obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and to perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue;

[0092] An identification module is used to obtain the extreme amplitude of any target unit average load rate space based on the ratio of the fluctuation state of the unit average load rate within a target unit average load rate space and the average of the target unit average load rate space in the unit average load rate queue, obtain the probability of a target unit average load rate space being regarded as an extreme unit average load rate space based on the difference in extreme amplitude and continuous duration between a target unit average load rate space and an adjacent target unit average load rate space, and obtain the extreme unit average load rate in the unit average load rate queue based on the probability of a target unit average load rate space being regarded as an extreme unit average load rate space.

[0093] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0094] For the unit average load rate queue of a thermal power plant unit, since the extreme unit average load rate is mainly the average load rate of some units with significant fluctuations, in order to better perform analysis on the unit average load rate queue, the present invention performs segment expansion on the unit average load rate queue, and obtains multiple expansion spaces and deviation spaces after segment expansion. The unit average load rate in the expansion space is more stable and more inclined to a reasonable unit average load rate, while the unit average load rate in the deviation space is more unstable and more inclined to extreme data, because not all unit average load rates in the deviation space are extreme unit average load rates, so it is necessary to reduce the extreme unit average load rate detected by the deviation space for the extreme unit average load rate, and obtain multiple target unit average load rate spaces by aggregating the deviation space and the adjacent expansion space, and perform analysis on the target unit average load rate space to obtain an accurate extreme unit average load rate, which is convenient for the controller to determine whether the operation of the thermal power plant unit is in a reasonable state.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. An information control method for allocating costs of auxiliary services, characterized in that: include: Collect the internal data and external data of the thermal power unit with the average load rate of the unit during the peak-shaving period, calculate the average cost of electricity during the peak-shaving period of the thermal power unit with the allocated cost, and process the average load rate of the unit; The method for processing the average load factor of the unit includes: Step 1: Get the average load rate queue of a thermal power plant unit; Step 2: Obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue; Step 3: Obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue; Step 4: Based on the fluctuation state of the unit average load rate within a target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, obtain the extreme amplitude of any target unit average load rate space; based on the extreme amplitude difference and continuous duration difference between a target unit average load rate space and an adjacent target unit average load rate space, obtain the probability of a target unit average load rate space being an extreme unit average load rate space; based on the probability of a target unit average load rate space being an extreme unit average load rate space, obtain the extreme unit average load rate in the unit average load rate queue.

2. The information control method for auxiliary service cost sharing according to claim 1, characterized in that: Step 1 specifically includes: receiving an average load rate of a thermal power plant unit to be detected by sampling from a triaxial acceleration sensor, wherein the triaxial acceleration sensor samples an average load rate of a unit every 2 seconds and transmits it to a processing unit, and defining a queue formed by the latest sampled and transmitted average load rates of UI1 minutes in the order of their sampling time points as the average load rate queue of the thermal power plant unit; UI1 is a preset value of one, and its value is 40.

3. The information control method for auxiliary service cost sharing according to claim 2, characterized in that: Step 1 specifically includes: removing the unit average load rates in the unit average load rate queue whose unit average load rates are not in the extreme unit average load rate range, and obtaining the unit average load rate queue after removing the unit average load rates that are not in the extreme unit average load rate range.

4. The information control method for auxiliary service cost sharing according to claim 3, characterized in that: Step 2 specifically includes: selecting UI2 unit average load rates equidistantly in the unit average load rate queue, defining it as a benchmark unit average load rate cluster, defining the average load rate of a unit in the benchmark unit average load rate cluster as the target unit average load rate, taking the target unit average load rate as the midpoint, and the range of the near domain span S as the starting expansion space of the target unit average load rate, S is the value to be set, and the value of S here is incremented by one each time, and the standard deviation of the average load rates of all units in the starting expansion space of the target unit average load rate is obtained each time, and defined E, pre-set a critical value of one, defined as ui1, obtain the corresponding value S to be set when it is lower than ui1 for the first time, defined as S1, then take the target unit average load rate as the midpoint, and the range of the near domain span S1 as the expansion space of the target unit average load rate, obtain the expansion space of the average load rate of each unit in the benchmark unit average load rate cluster, and then obtain multiple expansion spaces of the unit average load rate queue, take the space remaining outside the expansion space in the unit average load rate queue as the deviation space, and then obtain multiple deviation spaces of the unit average load rate queue; UI2 is a pre-set value of two, and its value is twenty; The value of ui1 is five.

5. The information control method for auxiliary service cost sharing according to claim 4, characterized in that: In Step 3, a method for obtaining an aggregate benchmark of a deviation space according to a difference between an expansion space and an average load factor of a unit in a deviation space specifically includes: Define any deviation space among multiple deviation spaces as the target deviation space, define the most adjacent expansion space after the target deviation space as the target expansion space, and calculate the following equation: In the equation, nm is the number of the average load rate of the unit in the target deviation space, nt is the number of the average load rate of the unit in the target expansion space, Inm is the maximum value of the average load rate of the unit in the target deviation space, Mnm is the minimum value of the average load rate of the unit in the target deviation space, Int is the maximum value of the average load rate of the unit in the target expansion space, Mnt is the minimum value of the average load rate of the unit in the target expansion space, Hnm is the mean of the average load rate of the unit in the target deviation space, Hnt is the mean of the average load rate of the unit in the target expansion space, e is the Euler number, Representatives Normalization was performed using the Z-score method, and Rnm was the aggregated benchmark in the target deviation space.

6. The information control method for auxiliary service cost sharing according to claim 5, characterized in that: In Step 3, a method for aggregating and obtaining a plurality of target unit average load rate spaces of the unit average load rate queue is performed according to the aggregation benchmark of each deviation space, which includes: Define critical value 2 in advance, defined as ui2, the value of ui2 is four-fifths. If Rnm≥ui2, aggregate the target deviation space and the target expansion space according to the order of their sampling time points to obtain a target unit average load rate space. If Rnm<ui2, treat the target deviation space as a target unit average load rate space to obtain all target unit average load rate spaces of the unit average load rate queue.

7. The information control method for auxiliary service cost sharing according to claim 6, characterized in that: In Step 4, according to the fluctuation state of the unit average load rate in any target unit average load rate space and the average ratio of the target unit average load rate space in the unit average load rate queue, the operation equation for obtaining the extreme amplitude of any target unit average load rate space is: For any unit average load rate space, in the equation, y l+1 is the value of the average load rate of the l+1th unit in the average load rate space of the target unit, y l is the value of the lth unit average load rate in the target unit average load rate space, L is the number of unit average load rates in the target unit average load rate space, HY is the mean of the unit average load rates in the target unit average load rate space, HM is the mean of the unit average load rates in the unit average load rate queue, and SM is the extreme amplitude of the target unit average load rate space.

8. The information control method for auxiliary service cost sharing according to claim 7, characterized in that: In Step 4, based on the extreme amplitude difference and continuous duration difference between the average load rate space of an arbitrary target unit and the average load rate space of an adjacent target unit, a method for obtaining the probability that the average load rate space of an arbitrary target unit is used as the average load rate space of an extreme unit is specifically including: Define any target unit average load rate space in the unit average load rate queue as space one, define the most adjacent target unit average load rate space before space one as space two, define the most adjacent target unit average load rate space after space one as space three, define the space cluster formed by space one, space two and space three as cluster one, and then calculate the following equation: In the equation, C1 is the extreme amplitude of space 1, e is the Euler number, J is the number of target unit average load rate spaces in cluster 1, and u j+1 is the continuous duration of the average load rate space of the j+1th target unit in cluster 1, u j is the continuous duration of the average load rate space of the jth target unit in cluster 1, b is a set non-zero quantity, and its value is 1. Yes Normalization is performed using the Z-score method, and U1 is the probability that space 1 is the extreme unit average load rate space.

9. The information control method for auxiliary service cost sharing according to claim 8, characterized in that: In Step 4, a method for obtaining the extreme unit average load rate in the unit average load rate queue according to the probability of a target unit average load rate space being used as an extreme unit average load rate space specifically includes: Define critical value three in advance, defined as ui3, and its value is one-fifth. If U1≤ui3, space one is regarded as an extreme unit average load rate space of the unit average load rate queue, and all extreme unit average load rate spaces of the unit average load rate queue are obtained. The unit average load rate in all extreme unit average load rate spaces is the extreme unit average load rate in the unit average load rate queue, and then the extreme unit average load rate in the unit average load rate queue is sent to the display unit for display and the message of the thermal power plant unit failure is annotated on the display unit.

10. An information control device for auxiliary service cost sharing, characterized in that: include: A queue module, which is used to obtain a queue of average load rates of units of a thermal power station; A space module, which is used to obtain multiple expansion spaces and multiple deviation spaces of the unit average load rate queue according to the fluctuation of the unit average load rate in the unit average load rate queue; An aggregation module, which is used to obtain an aggregation benchmark of a deviation space according to the difference between the average load rates of the units in an expansion space and the deviation space, and to perform aggregation according to the aggregation benchmarks of each deviation space to obtain multiple target average load rate spaces of the unit average load rate queue; An identification module is used to obtain the extreme amplitude of any target unit average load rate space based on the ratio of the fluctuation state of the unit average load rate within a target unit average load rate space and the average of the target unit average load rate space in the unit average load rate queue, obtain the probability of a target unit average load rate space being regarded as an extreme unit average load rate space based on the difference in extreme amplitude and continuous duration between a target unit average load rate space and an adjacent target unit average load rate space, and obtain the extreme unit average load rate in the unit average load rate queue based on the probability of a target unit average load rate space being regarded as an extreme unit average load rate space.

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