CN-122022855-A - Energy storage data diagnosis method and device for expressway zero-carbon service area

CN122022855ACN 122022855 ACN122022855 ACN 122022855ACN-122022855-A

Abstract

The invention belongs to the technical field of operation and maintenance of an energy storage system and data diagnosis, and particularly relates to an energy storage data diagnosis method and device for a highway zero-carbon service area, which are used for identifying abnormality and triggering self-checking by monitoring an SN set, channel-asset binding and channel physical characteristic data of an energy storage system in real time; and when binding is matched, an optimal re-matching scheme is solved through two-part graph matching according to physical topological constraint, and then a station-level power closed audit model is used for independent verification, a diagnosis baseline is reconstructed in a layering way after verification is passed, and a hierarchical gating strategy is used for outputting an alarm and a diagnosis conclusion in combination with multiple states, so that the stable support of alarm accuracy improvement, dispatch error work order reduction, traceable and auditable diagnosis conclusion and zero-carbon operation accounting is realized.

Inventors

FAN CHAOWEN
CHEN ZHIYIN
ZENG XUEWEI
CHEN GE
WU YONGFENG
SONG WEI
TAN CHANGMING
FAN ZHONGWEI
Fan Tingxing
ZHAO RUNCHEN
LI KUNJIAN
Duan Shinan

Assignees

四川省公路规划勘察设计研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The energy storage data diagnosis method for the expressway zero-carbon service area is characterized by comprising the following steps of: Monitoring asset SN set data, channel-to-asset binding data and channel physical characteristic data of an energy storage system in real time, and triggering a self-checking flow when the change of the asset SN set, the jump of the channel-to-asset binding relationship or the mutation of the channel physical characteristic are identified; After triggering self-verification, based on charging session logs and station-level metering data of the charging pile, under the constraint condition that no ongoing charging session is met and grid-connected power is in a stable range, identifying a natural anchor point or applying controllable verification pulse by an energy storage converter as an artificial anchor point, and determining an anchor point time window; Extracting fingerprint characteristic indexes of each sampling channel in an anchor point time window, and classifying and acquiring a historical characteristic template of each asset SN according to the current operation mode label; Calculating the normalized difference between the fingerprint characteristic index of the sampling channel and the historical characteristic template of the asset SN, and checking the validity of the current sampling channel-SN binding relation based on the normalized difference; constructing a station-level power closed audit model based on an energy conservation principle, and performing independent consistency verification on an optimal re-matching scheme by using the station-level power closed audit model; And according to the binding trusted state, the closing audit result and the steady state of the diagnosis baseline, outputting alarm information and diagnosis conclusion of different grades by adopting a grading gating strategy.
2. The method for diagnosing energy storage data of a expressway zero-carbon service area according to claim 1, wherein the method for judging mutation of physical characteristics of a channel is as follows: Calculating steady state statistics of channel physical characteristic indexes according to a fixed time window, and calculating a difference value of the steady state statistics of the current time window and historical steady state segment statistics; Combining the measurement uncertainty U c by adopting a mode of combining the class A evaluation and the class B evaluation, and determining a tolerance range based on a confidence coefficient k conf , wherein the tolerance range=k conf ×U c ; and if the difference value exceeds the tolerance range and the duration exceeds a plurality of continuous sampling periods, judging that the physical characteristics of the channel are abrupt.
3. The method for diagnosing energy storage data of a expressway zero-carbon service area according to claim 1, wherein the method for identifying anchor points is as follows: identifying a session start, a session end or a current limit switching moment as a natural anchor event based on the charging session log; When the charging session log is missing or does not meet the constraint condition that no ongoing charging session exists and the grid-connected power is in a stable range, identifying a power step moment as a natural anchor point event based on station-level metering data; If the natural anchor point event is not identified in the anchor point time window, controlling the energy storage converter to output square wave pulse as an artificial anchor point event; the amplitude of the square wave pulse is required to be smaller than the minimum value of the maximum disturbance power allowed by the device and the grid-connected disturbance allowed value, the pulse duration is an integer multiple of the sampling period, and the constraint condition that the grid-connected power is in a stable range is that the absolute value of the change rate of the grid-connected metering point power is smaller than the tolerance range determined by the measurement uncertainty and the confidence coefficient in a stable discrimination window, and the length of the stable discrimination window is an integer multiple of the sampling period.
4. The method for diagnosing energy storage data of a highway zero-carbon service area according to claim 1, wherein the fingerprint characteristic index comprises an equivalent transient resistance estimated value Time constant for voltage recovery And the mean value of the temperature response ; The method for calculating the fingerprint characteristic index comprises the following steps: Calculating steady state voltage difference DeltaV, deltaV=V post -V pre of the front window and the rear window based on steady state voltage mean V pre of the front window and steady state voltage mean V post of the rear window of the anchor point time window, calculating steady state current difference DeltaI, deltaI=I post -I pre of the front window and the rear window based on steady state current mean I pre of the front window and steady state current mean I post of the rear window of the anchor point time window, and calculating equivalent transient resistance estimated value according to the steady state voltage difference DeltaV and the steady state current difference DeltaI , =|ΔV/ΔI|; Based on a battery first-order RC equivalent circuit model, fitting voltage data in a rear window by using a least square method, and solving a voltage recovery time constant ; And calculating the variation of the temperature sampling average value in a time window covering the thermal inertia response period of the battery relative to the temperature value at the starting moment of the window.
5. The method for diagnosing the energy storage data of the expressway zero-carbon service area according to claim 1, wherein the evidence chain associated with the new version mapping table at least comprises an event type triggering change, an anchor point event ID, a fingerprint feature vector participating in calculation, a difference matrix, a physical topology constraint check result and a digest value, and the digest value is obtained by carrying out hash calculation on the event type triggering change, the anchor point event ID, the fingerprint feature vector participating in calculation, the difference matrix and the physical topology constraint check result.
6. The method for diagnosing energy storage data of a expressway zero-carbon service area according to claim 4, wherein the method for calculating the normalized difference is as follows: Calculating the deviation of the current characteristic value of the channel and the average value of the historical characteristic template of the SN of the asset respectively aiming at each fingerprint characteristic index, and dividing the deviation by the measurement uncertainty of the fingerprint characteristic index to obtain characteristic deviation; based on the historical stable segment data, calculating the variation coefficient of each fingerprint characteristic index, and carrying out normalization processing according to the square of the reciprocal of the variation coefficient of each fingerprint characteristic index to obtain the characteristic weight of each characteristic; and carrying out weighted summation on the absolute values of the characteristic deviations to obtain the normalized difference between the sampling channel and the asset SN.
7. The method for diagnosing energy storage data of a expressway zero-carbon service area according to claim 1, wherein the method for independent consistency verification is as follows: Unifying the time axis and the symbol direction of grid-connected metering point power P grid (t), photovoltaic power P pv (t), energy storage power P ess (t), charging pile power P charge (t) and in-station load power P load (t); Calculating a power closure residual r (t) =p grid (t)+P pv (t)+P ess (t)-P charge (t)-P load (t); Calculating a root mean square value R rms of R (t) in an anchor point time window, synthesizing measurement uncertainty of each metering link by adopting a square and root method to obtain residual error tolerance U r , if R rms ≤k conf ×U r , judging that a re-matching result passes independent consistency verification, otherwise, judging that the re-matching result does not pass independent consistency verification, wherein k conf is a confidence coefficient.
8. The method for diagnosing energy storage data of a highway zero-carbon service area according to claim 1, wherein, The operation mode label at least comprises a charging session state, a time-of-use electricity price period and a carbon accounting period; The decision condition for diagnosing the steady state of the baseline is that the number of newly added samples reaches a minimum statistical number determined by the measurement uncertainty and the variation of the baseline parameter converges to within a tolerance range determined by the measurement uncertainty and the confidence coefficient of the modeled sample.
9. An energy storage data diagnosis device facing a highway zero-carbon service area is characterized by comprising: The data interface unit is used for acquiring the operation data and the asset configuration data of the energy storage system, acquiring a charging session log from the charging pile management platform and acquiring station-level metering data from the multi-source metering equipment; A logic calculation unit connected with the data interface unit for executing an energy storage data diagnosis method facing the expressway zero-carbon service area according to any one of claims 1 to 8; The alarm output unit is used for outputting diagnosis conclusion and alarm information according to the hierarchical gating strategy; And the storage unit is used for storing the diagnosis baseline, the versioning mapping table and the audit evidence chain which are layered according to the operation mode label.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a method for diagnosing energy storage data for a highway zero-carbon-oriented service area according to any one of claims 1 to 8.

Description

Energy storage data diagnosis method and device for expressway zero-carbon service area Technical Field The invention belongs to the technical field of operation and maintenance of an energy storage system and data diagnosis, and particularly relates to an energy storage data diagnosis method and device for a highway zero-carbon service area. Background The expressway zero-carbon service area is used as a key node for deep fusion of new energy traffic and green energy, and a station-level energy system of the expressway zero-carbon service area generally adopts a core framework of 'photovoltaic power generation unit-energy storage system-charging pile-grid-connected metering point', wherein the energy storage system is a core link for balancing photovoltaic output fluctuation, guaranteeing quick charging service continuity and realizing accurate accounting of time-of-use electricity price arbitrage and carbon emission. Because the core business of the service area is quick charging service, the load characteristic of the service area shows the remarkable characteristics of frequent power step and larger amplitude, and the multi-element operation strategies such as time-of-use electricity price charging, carbon emission accounting and the like are overlapped, so that extremely high requirements are put on the operation stability and the data diagnosis accuracy of the energy storage system. However, the operation and maintenance of the energy storage system in the expressway zero-carbon service area has two prominent pain points, so that the application effects of the existing diagnosis technology are severely restricted, firstly, the service area is in a scattered and mostly unattended mode, the on-site operation window of operation and maintenance personnel is extremely limited, operation and maintenance work can be carried out only in the night or in the valley period of traffic, the diagnosis system is required to have high automation and accuracy, dependence on the on-site operation and maintenance personnel can be reduced, secondly, various structural changes frequently occur in the operation and maintenance process, including but not limited to replacement of a Battery module or a Battery cluster, replacement of a Battery Management System (BMS), reconnection of a sampling wire harness, replacement of gateway equipment and the like, the changes can directly lead to irreversible changes in the binding relation between a sampling channel and an asset Serial Number (SN) so as to influence the accuracy of a diagnosis result, and the deep layer is due to the fact that the physical connection relation between the sampling channel and an asset is changed in time after the structural changes, and the existing system cannot sense and adapt to the changes. On one hand, the existing system excessively depends on a sampling channel-SN mapping table and a historical diagnosis base line which are originally configured to judge the health state of the energy storage asset, when the structure is changed to cause the change of the mapping relation, the system cannot timely sense the change, and still carry out diagnosis work along the old mapping table and the diagnosis base line, so that data such as voltage, resistance and the like caused by channel mismatch are abnormal, the false judgment is the true fault of the energy storage asset, and further, the false alarm and the false assignment operation and maintenance work sheet are triggered, thereby not only increasing the operation and maintenance cost, but also possibly interfering with the normal operation of the energy storage system, on the other hand, the existing diagnosis method lacks a special verification and constraint mechanism which is adaptive to the scene of the high-speed channel zero-carbon service area, the charging session, the photovoltaic output and the coupling characteristic of the load in the station lead to complex data fluctuation, but the existing method cannot carry out traceable and affirmation on the mapping change under the condition of remote unattended operation, and can not trigger the normal audit and verify the grid-connected power, and can trigger the normal audit and audit when the grid-connected power is in a grid-connected condition is abnormal, and the abnormal power is not normally connected. In addition, the feature extraction and matching logic of the existing method lacks enough theoretical support, and the matching precision is difficult to meet the requirement of high-precision operation and maintenance of a service area. Disclosure of Invention In order to solve the technical problems, the invention is realized by the following technical scheme: In a first aspect, an energy storage data diagnosis method for a expressway zero-carbon service area is provided, including the following steps: Monitoring asset SN set data, channel-to-asset binding data and channel physical characteristic data of an energy storage system in rea