CN-122022590-A - Multi-dimensional index energy efficiency diagnosis and operation and maintenance auxiliary system for new energy power plant

CN122022590ACN 122022590 ACN122022590 ACN 122022590ACN-122022590-A

Abstract

The invention relates to the technical field of intelligent operation and maintenance of new energy power plants, in particular to an energy efficiency diagnosis and operation and maintenance auxiliary system of a new energy power plant with multidimensional indexes. And establishing a hierarchical linkage diagnosis model from the group string to the whole field, realizing hierarchical loss tracing through association analysis, and positioning specific loss equipment and root. Based on the diagnostic results, the system generates dynamic operation and maintenance priorities and suggested schemes, and continuously optimizes the plan by evaluating the operation and maintenance results. According to the scheme, energy efficiency management from data fusion, cross-level accurate diagnosis to strategy closed loop is achieved, and the accuracy and efficiency of operation and maintenance of the new energy power plant are effectively improved.

Inventors

ZHANG JINNING
ZHAO XINKAI
DONG LULU
ZOU XUEFENG
HUANG HAONAN

Assignees

大连大鱼科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (10)

1. The utility model provides a new energy power plant energy efficiency diagnosis and operation maintenance auxiliary system of multidimensional index, its characterized in that, the system includes: The data acquisition module acquires a full-scene operation data set of a new energy power plant, wherein the full-scene operation data set comprises equipment operation data, energy efficiency loss data, equipment health data, environment adaptation data and basic ledger data; The index calculation module is used for calculating an energy efficiency index set covering a device level, a square matrix level and a full-field level based on the full-field operation data set, wherein the energy efficiency index set comprises a power generation energy efficiency type index, a loss energy efficiency type index, a health energy efficiency type index and a distribution characteristic type index; The diagnosis analysis module establishes a hierarchical linkage diagnosis model from a group string to a full field, performs hierarchical loss tracing on the energy efficiency index set through association analysis processing, and generates a diagnosis result comprising a loss root level and specific loss equipment; the strategy generation module is used for executing dynamic operation and maintenance strategy generation processing according to the diagnosis result to generate operation and maintenance priority ordering and operation and maintenance suggestion schemes aiming at different loss types; and the evaluation optimization module is used for dynamically adjusting the operation and maintenance plan and generating an operation and maintenance effect evaluation report based on comparison and analysis of the real-time energy efficiency data and the operation and maintenance execution result.
2. The multi-dimensional energy plant energy efficiency diagnostic and operation and maintenance support system according to claim 1, wherein said computing the energy efficiency index set covering the equipment level, the matrix level and the full-field level based on the full-field operation data set comprises: Extracting real power generation amount and active power sequences from the equipment operation data, and generating theoretical power generation amount, theoretical power of each level, effective utilization hours and theoretical power generation amount deviation rate through a theoretical power generation amount calculation model; extracting the internet loss rate, the line loss rate, the transformer loss rate, the ash loss rate, the conversion loss rate and the electricity limiting loss rate from the energy efficiency loss data, and calculating an environment-corrected loss energy efficiency index by combining irradiance parameters in environment adaptation data; Extracting overtemperature times, abnormal attenuation loss rates and equipment failure frequencies from the equipment health data, and generating equipment health energy efficiency scores through a health scoring algorithm; carrying out distribution characteristic analysis on the power generation energy efficiency indexes, and calculating index mean value, standard deviation and skewness characteristic values of each level; And carrying out weighted fusion on the theoretical generating capacity deviation rate, the loss energy efficiency index, the equipment health energy efficiency score and the distribution characteristic value by adopting a nonlinear punishment algorithm to generate a multidimensional energy efficiency index set comprising a comprehensive energy efficiency score and a grade.
3. The system for energy efficiency diagnosis and operation and maintenance assistance of a new energy power plant according to claim 2, wherein the weighted fusion of the theoretical power generation deviation rate, the loss energy efficiency index, the equipment health energy efficiency score and the distribution characteristic value by using a nonlinear penalty algorithm is performed to generate a multidimensional energy efficiency index set including a comprehensive energy efficiency score and a level, and the method comprises: constructing a standard deviation and deflection dual-characteristic penalty function, and performing score penalty calculation on the energy efficiency index with the exceeding dispersion; setting differentiated weight coefficients according to the equipment hierarchy relation, wherein the weight coefficients are determined based on equipment capacity and installation positions in basic ledger data; performing association compensation calculation on the theoretical power generation deviation rate and the loss energy efficiency index, and eliminating the influence of environmental factors on the energy efficiency score; Correcting the power generation energy efficiency score through the health energy efficiency score to generate a comprehensive energy efficiency score reflecting the health state of the equipment; And dividing four energy efficiency grades with good, vigilance and critical energy efficiency according to the numerical intervals of the comprehensive energy efficiency scores to form a complete energy efficiency index set.
4. The system for energy efficiency diagnosis and operation and maintenance assistance of a new energy power plant according to claim 1, wherein the establishing a hierarchical linkage diagnosis model from group string to full-scale, performing hierarchical loss tracing on the energy efficiency index set through association analysis processing, generating a diagnosis result comprising a loss root level and specific loss equipment, comprises: Performing single equipment diagnosis analysis at the group string level, and judging the health state of equipment based on the abnormal attenuation loss rate and the ash loss rate; performing group string energy efficiency association analysis at an inverter level, and identifying the aggregation phenomenon of low-yield group strings through skewness characteristics; executing loss type distinguishing processing at the box transformer level, and distinguishing loss caused by the environment and loss of equipment by combining the environment adaptation data; Performing multi-period trend comparison analysis on a square matrix level, and early warning potential loss risks through energy efficiency score change trend; and carrying out association mapping on the diagnosis results of each level to generate complete diagnosis results of the position information and loss reasons pointing to the specific equipment.
5. The multi-dimensional index new energy power plant energy efficiency diagnosis and operation and maintenance auxiliary system according to claim 4, wherein the performing the loss type distinguishing process at the box-section level, distinguishing the loss caused by the environment and the loss of the equipment itself by combining the environment adaptation data, comprises: Extracting irradiance mutation sequences in environment adaptation data, and establishing a corresponding relation between environment disturbance and energy efficiency fluctuation; calculating a reference loss rate under normal environmental conditions, and comparing the real-time loss rate with the reference loss rate in a difference manner; When the real-time loss rate exceeds a reference loss rate threshold, marking as the loss type of the equipment; marking as an environment-induced loss type when the real-time loss rate is positively correlated with the environment disturbance intensity; A loss analysis report is generated that includes the loss type classification and the degree of influence as part of the diagnostic result.
6. The multi-dimensional index new energy plant energy efficiency diagnosis and operation and maintenance auxiliary system according to claim 1, wherein the executing the dynamic operation and maintenance strategy generation process according to the diagnosis result generates operation and maintenance priority ordering and operation and maintenance proposal schemes for different loss types, comprising: Extracting energy efficiency influence degree parameters and loss emergency degree parameters in the diagnosis result, and generating an operation and maintenance priority grade through weighted calculation; according to the loss type, matching a preset operation and maintenance knowledge base, and generating specific operation and maintenance suggestions for ash loss cleaning, over-temperature maintenance and parameter optimization; Combining the historical operation and maintenance record and the real-time operation state of the equipment to determine an operation and maintenance time window and a resource allocation scheme; generating a dynamic operation and maintenance strategy document containing priority ordering, operation and maintenance content, time arrangement and resource requirements; The method for extracting the energy efficiency influence degree parameter and the loss emergency degree parameter in the diagnosis result, generating the operation and maintenance priority score through weighted calculation comprises the following steps: acquiring the discrete rate data of the power generation energy efficiency class indexes of the equipment level from the energy efficiency index set, and calculating the energy efficiency influence weight of the high discrete rate equipment on the square matrix level to which the high discrete rate equipment belongs; Extracting theoretical power generation amount deviation rate abnormality degree data of equipment hierarchy from the diagnosis result, and determining a loss emergency degree coefficient of equipment by combining energy efficiency levels divided in the energy efficiency index set; Adopting a multi-factor weighting algorithm, carrying out fusion calculation on the energy efficiency influence weight, the loss emergency degree coefficient and the capacity weight determined based on the equipment capacity in the basic ledger data, and generating a numeric operation and maintenance priority score; dividing the equipment into three priority classes of immediate processing, planning processing and observation monitoring according to the numerical interval of the operation and maintenance priority scores; and forming an operation and maintenance priority list which is arranged according to the operation and maintenance priority scores in descending order.
7. The multi-dimensional energy plant energy efficiency diagnosis and operation and maintenance auxiliary system according to claim 6, wherein the energy efficiency influence degree parameter and loss emergency degree parameter in the diagnosis result are extracted, and the operation and maintenance priority score is generated by weighting calculation, comprising: Acquiring discrete rate data from the energy efficiency index set, and calculating the influence weight of high discrete rate equipment on the hierarchical energy efficiency; Extracting deviation rate abnormality degree data from the diagnosis result, and determining an emergency degree coefficient by combining the energy efficiency score grade; adopting a multi-factor weighting algorithm to fuse the influence weight and the emergency degree coefficient to generate a numeric priority grade; And dividing the equipment into three priority categories of immediate processing, planning processing and observation monitoring according to the priority scores, and forming an operation and maintenance priority list which is arranged according to the descending scores.
8. The multi-dimensional index new energy plant energy efficiency diagnosis and operation and maintenance auxiliary system according to claim 1, wherein the comparison analysis based on the real-time energy efficiency data and the operation and maintenance execution result dynamically adjusts the operation and maintenance plan and generates an operation and maintenance effect evaluation report, comprising: Collecting the latest energy efficiency data set after the operation and maintenance operation is finished, and calculating the change amplitude of key energy efficiency indexes; Comparing and analyzing the index change amplitude with an expected improvement target to generate a single operation and maintenance effect score; Adjusting subsequent operation and maintenance plans of the similar equipment according to the effect scores, and optimizing operation and maintenance strategy parameters; summarizing effect data of multiple operation and maintenance operations to generate an evaluation report containing energy efficiency improvement values, loss reduction rates and return on investment analysis; And feeding back the evaluation result to the hierarchical linkage diagnosis model, and updating the energy efficiency reference parameters.
9. The multi-dimensional index new energy plant energy efficiency diagnosis and operation and maintenance auxiliary system according to claim 8, wherein the comparing the index change amplitude with the expected improvement target to generate a single operation and maintenance effect score comprises: Setting a gray loss rate reduction amplitude and an energy efficiency score lifting value quantization improvement target; Calculating the achievement proportion of the actual index change value and the improvement target; calculating a weighted effect score by combining the index importance weights; dividing the operation and maintenance effect into three grades of obvious improvement, general improvement and poor effect according to the grading result; And generating a single operation and maintenance effect record containing the effect level and the specific improvement value.
10. The multi-dimensional pointing new energy plant energy efficiency diagnostic and operation and maintenance support system according to claim 1, wherein the system further comprises: Establishing a closed-loop management flow of energy efficiency diagnosis and operation and maintenance assistance, and realizing continuous optimization from data acquisition to effect evaluation; setting a multi-period assessment mechanism, and periodically carrying out calibration and update on the energy efficiency index calculation method and the diagnosis model parameters; an energy efficiency prediction model is built based on historical data, so that early warning and preventive maintenance planning of potential problems are realized; And forming a complete energy efficiency management system comprising a data stream, a service stream and a control stream, and ensuring continuous and stable operation of the system.

Description

Multi-dimensional index energy efficiency diagnosis and operation and maintenance auxiliary system for new energy power plant Technical Field The invention relates to the technical field of intelligent operation and maintenance of new energy power plants, in particular to an energy efficiency diagnosis and operation and maintenance auxiliary system of a new energy power plant with multidimensional indexes. Background The operation and maintenance of current new energy power plants generally depend on monitoring and data acquisition systems. The system mainly realizes monitoring of the running state of equipment and overrun warning, and energy efficiency analysis is concentrated on macroscopic statistics of station level or independent performance evaluation of key equipment. A core drawback of the prior art solution is the hysteresis and isolation of its analysis pattern. The system alarm depends on a single-point threshold value, is triggered only after the equipment performance is obviously deteriorated, and cannot actively discover and quantify the continuous implicit energy efficiency loss of the alarm. Because of the lack of effective tracing means, operators have difficulty in rapidly locating the root cause of the fluctuation of the whole-field power generation amount from mass data, and generally, only a large-scale inspection with low efficiency can be adopted. This limitation stems mainly from two aspects. The data base is single, the conventionally collected data is mainly composed of electrical parameters and basic weather information, systematic fusion of multidimensional information such as direct characterization of deep health state and energy efficiency loss of equipment, association of refined environment and the like is lacking, energy efficiency assessment stays on an output surface and cannot be associated with equipment degradation mechanism and environmental microscopic influence, an analysis model is flattened, the existing assessment is multi-pair to independent levels, association coupling among level indexes is lacking, and a loss transmission analysis path from specific equipment to a full-field system cannot be constructed. Therefore, a technical scheme capable of fusing multidimensional data and realizing cross-level index linkage and loss tracing on the basis of the multidimensional data is needed to support accurate operation and maintenance decision. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides an energy efficiency diagnosis and operation and maintenance auxiliary system for a new energy power plant with multidimensional indexes. In order to achieve the purpose, the invention adopts the following technical scheme that the new energy power plant energy efficiency diagnosis and operation and maintenance auxiliary system with multidimensional indexes comprises: The data acquisition module acquires a full-scene operation data set of a new energy power plant, wherein the full-scene operation data set comprises equipment operation data, energy efficiency loss data, equipment health data, environment adaptation data and basic ledger data; The index calculation module is used for calculating an energy efficiency index set covering a device level, a square matrix level and a full-field level based on the full-field operation data set, wherein the energy efficiency index set comprises a power generation energy efficiency type index, a loss energy efficiency type index, a health energy efficiency type index and a distribution characteristic type index; The diagnosis analysis module establishes a hierarchical linkage diagnosis model from a group string to a full field, performs hierarchical loss tracing on the energy efficiency index set through association analysis processing, and generates a diagnosis result comprising a loss root level and specific loss equipment; the strategy generation module is used for executing dynamic operation and maintenance strategy generation processing according to the diagnosis result to generate operation and maintenance priority ordering and operation and maintenance suggestion schemes aiming at different loss types; and the evaluation optimization module is used for dynamically adjusting the operation and maintenance plan and generating an operation and maintenance effect evaluation report based on comparison and analysis of the real-time energy efficiency data and the operation and maintenance execution result. As a further aspect of the present invention, the computing, based on the full scene operation data set, an energy efficiency index set covering a device level, a square matrix level, and a full scene level includes: Extracting real power generation amount and active power sequences from the equipment operation data, and generating theoretical power generation amount, theoretical power of each level, effective utilization hours and theoretical power generation amount deviation rate through a the