Long-term evaluation system for steady-state electric energy quality treatment project effect

Abstract

The invention discloses a long-term evaluation system for a steady-state power quality management project effect. The long-term evaluation system comprises a front-end data acquisition layer, a middle-end data preprocessing layer, a rear-end core calculation layer and a terminal early warning output layer which are in communication connection in sequence, the front-end data acquisition layer is configured to synchronously acquire data by adopting a unified time reference; the middle-end data preprocessing layer is configured to perform timestamp calibration and abnormal data elimination on the acquired data and output standardized data; the back-end core calculation layer is configured to generate an annual technology-economy coupling index; calculating the comprehensive weight of the evaluation index; a discount weighting algorithm giving higher weight to recent scores is adopted to obtain a full-life-cycle long-term comprehensive evaluation result; performing trend analysis on the change sequence of the long-term comprehensive evaluation result through a trend diagnosis module, and identifying a treatment effect degradation trend; and the terminal early warning output layer is configured to output a long-term comprehensive evaluation result and trigger graded early warning based on the degradation trend.

Description

A Long-Term Evaluation System for the Effectiveness of Steady-State Power Quality Improvement Projects Technical Field

[0001] This invention relates to the field of power quality assessment technology for power distribution networks, and specifically to a long-term assessment system for the effectiveness of steady-state power quality improvement projects. Background Technology

[0002] Currently, various precision instruments have increasingly stringent requirements for power quality. Common power quality disturbances can be categorized into steady-state disturbances and transient disturbances. Steady-state power quality disturbances mainly include voltage deviations, voltage fluctuations, harmonics, and three-phase imbalances. Relatively mature mitigation solutions already exist for these disturbances. However, with the continuous optimization of these solutions, power users are increasingly concerned about the comprehensive effectiveness of mitigation projects after implementation. Therefore, it is necessary to comprehensively consider both technical and economic indicators to achieve a comprehensive post-evaluation of steady-state power quality disturbance mitigation projects.

[0003] For power quality assessment, researchers both domestically and internationally have provided several relevant techniques and methods. Well-known techniques mainly include using mathematical tools such as the Analytic Hierarchy Process (AHP), entropy weighting, subjective-objective combined weighting methods (e.g., AHP-entropy weighting combination), and fuzzy comprehensive evaluation to construct comprehensive power quality assessment models. These methods are widely used in power quality status assessment, quantifying power quality levels by establishing an indicator system, calculating indicator weights, and conducting comprehensive scoring.

[0004] Chinese patent CN104143052A, entitled "Method for Steady-State Power Quality Assessment of Grid-Connected Photovoltaic Power Generation System," discloses a comprehensive steady-state power quality assessment method for the point of common coupling (PCC) of a grid-connected photovoltaic system. Its technical solution mainly includes: (1) using a combined weighting method (deviation maximization method) to integrate subjective and objective weights; (2) using principal component projection to reduce the dimensionality of multi-dimensional assessment data to one dimension; and (3) determining the power quality status through grade interval division. This solution primarily focuses on assessing the power quality level before or during system operation. Its assessment object is the power quality of the system itself, rather than evaluating the long-term effects of a specific implemented remediation project.

[0005] Chinese patent CN111861248A, entitled "A Comprehensive Evaluation Method and Device for Power Quality Management Effect of Distribution Network," discloses a method for evaluating the management effect of active power quality management equipment after it is connected to the distribution network. Its technical solution considers four evaluation indicators: compensation effect, grid benefits, grid connection point voltage, and regional power quality, and employs blind number theory, AHP method, and inverse entropy weight method for comprehensive evaluation. While this solution's evaluation perspective is closer to that of management effect evaluation, its evaluation indicators focus on the immediate or short-term technical performance (such as compensation effect and grid connection point voltage) and grid benefits after equipment connection. It does not explicitly propose long-term tracking evaluation of the management effect, nor does it include long-term consideration of the economic cost indicators of the management project.

[0006] Chinese patent CN115081951A, entitled "Power Quality Assessment Method for Wind Power Grid-Connected Systems Based on Fuzzy Comprehensive Evaluation," discloses a power quality assessment method for wind power grid-connected systems. Its technical solution employs the analytic hierarchy process (AHP) and entropy weight method to determine and integrate subjective and objective weights, and utilizes Gaussian membership functions to construct a fuzzy comprehensive evaluation matrix for quantitative assessment. This scheme is primarily applied to power quality assessment caused by grid connection of specific power generation systems like wind power. Its core function is to assess the system's operational status, not to evaluate the long-term effectiveness of a steady-state power quality improvement project after implementation.

[0007] In addition, some scholars have proposed optimization-driven strategies and power quality assessment methods based on improved CRITIC-TOPSIS. Most of these methods focus on the comprehensive assessment of power quality itself, aiming to evaluate the quality of power quality at a certain moment or time period, or to provide a preliminary assessment for the design of governance schemes. They generally lack a long-term, post-evaluation tracking and comprehensive evaluation mechanism that takes into account both technical performance and economic benefits after the implementation of governance projects.

[0008] Although various comprehensive power quality assessment technologies exist, analysis of the closest existing technologies reveals the following technical drawbacks:

[0009] Limitations of the assessment purpose and object: Existing technologies mainly focus on evaluating the power quality status of power systems or specific locations before or during the operation of power improvement projects. Their purpose is to determine the power quality level or provide a basis for designing improvement schemes. However, there is a lack of technical solutions specifically designed for long-term, comprehensive post-assessment of the effectiveness of "steady-state power quality improvement projects" after implementation. This application aims to address the issue of how to scientifically and long-term evaluate the actual effectiveness of improvement projects.

[0010] The assessment dimensions are too narrow and lack long-term economic considerations: Although existing technologies involve the assessment of governance effectiveness, their assessment indicators are mostly focused on the technical performance level. They fail to systematically and long-term incorporate economic indicators such as the cost of governance equipment and annual operation and maintenance costs into the assessment system, resulting in the assessment results failing to fully reflect the comprehensive benefits of the governance project throughout its entire life cycle.

[0011] Lack of a long-term evaluation perspective: Existing evaluation methods are mostly aimed at a specific point in time or a short period, failing to reflect the long-term stability and sustainability of governance effects over time. For governance projects, the long-term effectiveness and cost-effectiveness of their operation are more important.

[0012] The universality of the evaluation methods needs to be strengthened: some existing technologies are designed for specific application scenarios such as wind power grid connection, and their evaluation models and indicator selection may not be applicable to the effect evaluation of a wide range of steady-state power quality improvement projects. Summary of the Invention

[0013] To address the shortcomings and deficiencies of existing technologies, this invention provides a long-term evaluation system for the effectiveness of steady-state power quality improvement projects. This system forms an evaluation closed loop by constructing a front-end data acquisition layer, a mid-end data preprocessing layer, a back-end core computing layer, and a terminal early warning output layer. Its core innovation lies in two aspects: First, the system constructs a technical evaluation index system for power quality, including voltage deviation, voltage fluctuation, total harmonic distortion (THD) of voltage / current, and three-phase voltage imbalance. This system quantifies the technical level of power quality after improvement and provides fundamental data support for long-term comprehensive evaluation and trend diagnosis. Specifically, for key technical indicators with significant quantitative relationships to economic losses, the system further establishes quantitative mapping models between voltage deviation and production downtime losses, THD (preferably THD of current) and additional losses, and three-phase voltage imbalance and equipment capacity reduction. This converts the technical improvement effect into annualized technical-economic coupled indicators, achieving economic quantification of the technical effect. Second, the system integrates expert experience with the objective distribution characteristics of long-term monitoring data to dynamically determine the comprehensive weight of evaluation indicators and introduces a hard constraint penalty mechanism for key technical indicators to ensure the engineering rigor of the evaluation results. Building upon this foundation, the system employs a time-series weighted algorithm that assigns higher weight to recent data to aggregate data across the entire lifecycle, yielding a long-term comprehensive evaluation result. It then performs trend analysis on the temporal changes of this result to identify potential degradation of the governance effect. Finally, based on the long-term evaluation results and trend diagnostic conclusions, the system jointly triggers tiered early warnings and pushes targeted equipment inspection or parameter adjustment suggestions, thereby achieving continuous and accurate evaluation and operation and maintenance guidance for the long-term effects and economic benefits of the governance project.

[0014] The present invention specifically adopts the following technical solution:

[0015] A long-term evaluation system for the effect of steady-state power quality management projects includes a front-end data acquisition layer, a middle-end data preprocessing layer, a back-end core computing layer, and a terminal early warning output layer that are connected in sequence via communication.

[0016] The front-end data acquisition layer is configured to synchronously acquire steady-state power quality technical data, equipment operation and maintenance data, and economic correlation data between the power grid and the user side after the implementation of the governance project using a unified time reference, and to record steady-state power quality baseline data before the governance. The steady-state power quality technical data includes at least voltage deviation, voltage fluctuation, total harmonic distortion rate of voltage / current, and three-phase voltage imbalance data.

[0017] The mid-range data preprocessing layer is configured to perform timestamp calibration and outlier removal on the collected data, and output standardized data.

[0018] The backend core computing layer is configured as follows:

[0019] Through the techno-economic coupling calculation module, a quantitative mapping relationship is established based on the standardized data between voltage deviation and production downtime loss, total harmonic distortion rate of current and additional loss, and three-phase voltage imbalance and equipment capacity reduction, generating annualized techno-economic coupling indicators.

[0020] The comprehensive weight of the evaluation indicators is calculated by combining subjective and objective weighting modules with expert experience and long-term time series data.

[0021] Through the long-term comprehensive evaluation module, based on the comprehensive weight and the technology-economic coupling index, a discounted weighted algorithm that assigns higher weight to recent scores is used to obtain the long-term comprehensive evaluation result of the entire life cycle.

[0022] The trend diagnosis module performs trend analysis on the change sequence of the long-term comprehensive assessment results to identify the trend of degradation of governance effectiveness;

[0023] The terminal early warning output layer is configured to output the long-term comprehensive evaluation results and trigger graded early warnings based on the degradation trend.

[0024] Furthermore, the front-end data acquisition layer establishes a unified time reference through synchronous timing technology, which includes satellite timing or power system-specific timing technology. The front-end data acquisition layer presets the acquisition frequency according to the steady-state power quality disturbance characteristics and automatically completes data acquisition through a hardware triggering mechanism, reducing manual input errors and ensuring data accuracy and timeliness.

[0025] Furthermore, the economic correlation data between the power grid and the user side includes data on costs and losses related to the entire life cycle of the governance equipment, specifically covering necessary expenditures during the equipment purchase, installation and commissioning phases, annual maintenance-related costs during the equipment operation phase, electricity-related unit prices, equipment capacity-related investment costs, and the amount of single losses caused by production interruptions.

[0026] Furthermore, the timestamp calibration of the intermediate data preprocessing layer uses a unified time benchmark as an anchor point, removes data whose timestamp deviation exceeds a preset threshold, and completes data with missing timestamps using interpolation. The outlier removal is based on statistical criteria to identify and remove outliers of each indicator, while performing dimensional unification processing on technical and economic indicators respectively, mapping different types of indicator values ​​to preset numerical ranges to meet subsequent calculation needs.

[0027] The mid-level data preprocessing layer uses batch processing optimization logic to process long-term, multi-dimensional data in parallel. The back-end core computing layer accelerates the calculation process of the technology-economic coupling model through parallel computing technology to improve the processing efficiency of long-term data and shorten the evaluation response latency, thereby meeting the real-time requirements of long-term evaluation of governance projects.

[0028] Furthermore, the quantitative mapping relationship established by the techno-economic coupling calculation module specifically includes:

[0029] Mapping of Total Harmonic Distortion (THD) of Current to Additional Losses: Based on the THD of current and the system reference loss, the power of additional harmonic losses is determined. Then, combined with the annual equivalent operating time and the unit price of electricity, the additional harmonic losses are converted into annualized costs.

[0030] Mapping between three-phase voltage imbalance and equipment capacity reduction: Construct a capacity reduction coefficient based on three-phase voltage imbalance, calculate capacity loss by combining the rated capacity of the equipment with the capacity reduction coefficient, and then convert the capacity loss into annualized economic loss by combining the annualized conversion coefficient and the unit capacity investment cost.

[0031] Mapping of voltage deviation and production downtime losses: Statistically count the number of times voltage exceeds the limit and the total time exceeding the limit, calculate the number of downtimes based on the equipment sensitivity coefficient, and then combine the average production loss per downtime to convert the downtime losses into annualized costs.

[0032] Furthermore, the calculation process of the subjective-objective combination weighting module includes:

[0033] Subjective weight calculation: A hierarchical structure of target layer, criterion layer and sub-indicator layer is constructed. The target layer is the long-term comprehensive evaluation result of steady-state power quality management project. The criterion layer includes technical indicator group and economic and technical-economic coupling indicator group. The sub-indicator layer consists of each specific evaluation indicator. A judgment matrix is ​​formed by pairwise comparison of the relative importance of indicators by domain experts. The 1-9 scale method is used to determine the relative importance of indicators. The global subjective weight of each indicator is calculated based on the judgment matrix.

[0034] Objective weight calculation: The original values ​​of each indicator in the long-term time series data are standardized. For benefit-type indicators, the weight is calculated as the ratio of the difference between the original value and the extreme value of the indicator to the range of the indicator. For cost-type indicators, the weight is calculated as the ratio of the difference between the extreme value and the original value of the indicator to the range of the indicator. Based on the standardized data, the information entropy of each indicator is calculated, and then the objective weight is obtained by normalizing the information utility value.

[0035] Comprehensive weight fusion: Subjective and objective weights are combined by balancing coefficients. The balancing coefficients range from 0 to 1 and can be flexibly adjusted according to the richness of engineering experience and the sufficiency of long-term operating data. The combined weights are normalized to obtain the final comprehensive weights used for evaluation.

[0036] Furthermore, the calculation process of the long-term comprehensive evaluation module includes:

[0037] Single-period unconstrained comprehensive score: Based on the comprehensive weight and standardized indicator values, the unconstrained comprehensive score for each evaluation period is obtained by weighted summation;

[0038] Comprehensive scoring with technical constraints: Set standard limits for key technical indicators. If the original value of an indicator exceeds the standard limit, calculate the relative degree of exceeding the limit and determine the overall degree of exceeding the limit. Introduce a penalty coefficient based on the overall degree of exceeding the limit. The penalty coefficient decreases as the degree of exceeding the limit increases. Multiply the unconstrained comprehensive score by the penalty coefficient to obtain the comprehensive score with technical constraints.

[0039] Discounted weighted calculation: The discount weight of each assessment period is determined based on the discount factor and the total number of assessment periods, so that the comprehensive score with technical constraints closer to the current moment has a higher proportion in the long-term comprehensive assessment result; the long-term comprehensive assessment result of the whole life cycle is obtained by summing the products of the comprehensive score with technical constraints of each period and the corresponding discount weight.

[0040] Furthermore, the diagnostic process of the trend diagnosis module includes:

[0041] Trend fitting: Linear regression fitting is performed on the comprehensive scores with technical constraints for each evaluation period to obtain the trend slope of the score change over time;

[0042] Degradation determination: A preset degradation threshold is set. When the trend slope is lower than the reverse value of the degradation threshold, it is determined that the treatment effect has a significant degradation trend; when the trend slope is not lower than the reverse value of the degradation threshold, it is determined that the treatment effect is generally stable.

[0043] The improvement rate of governance effectiveness of key technical indicators is diagnosed separately. If the slope of the improvement rate trend of a certain key technical indicator is lower than the corresponding threshold, the specific degradation type of the technical indicator is marked to provide direction for operation and maintenance investigation.

[0044] Furthermore, the backend core computing layer also includes an improvement rate calculation module. Based on the baseline data before governance and the statistical values ​​of indicators during the operation period, the improvement rate calculation module calculates the improvement rate of governance effect for each key technical indicator. The improvement rate is determined by the ratio of the difference between the baseline value before governance and the statistical value of indicators during the operation period to the baseline value before governance. The improvement rate calculation module also substitutes the improvement rate of technical indicators into the technology-economic coupling model to obtain the corresponding economic benefit improvement rate, so as to quantify the long-term benefit improvement of the governance project.

[0045] Furthermore, the tiered early warning system of the terminal early warning output layer is jointly triggered based on the interval division of long-term comprehensive evaluation results and trend diagnosis conclusions, specifically including:

[0046] Potential degradation warning: Triggered when the long-term comprehensive evaluation results are in a stable range and the trend diagnosis determines that there is a degradation trend, and at the same time, a suggestion to increase the frequency of equipment inspection is pushed.

[0047] Strong degradation warning: Triggered when the long-term comprehensive assessment results are within the attention range and the trend diagnosis determines that there is a degradation trend, and suggestions for on-site investigation and treatment equipment parameters are pushed at the same time;

[0048] Severe warning: Triggered when the long-term comprehensive assessment result is lower than the warning interval threshold. Regardless of the trend diagnosis conclusion, a suggestion to immediately review the treatment plan and adjust the equipment operating parameters will be pushed.

[0049] When pushing out warning information, the terminal warning output layer also associates it with historical equipment operation and maintenance data to provide operation and maintenance personnel with a reference for handling similar problems in the past.

[0050] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0051] Firstly, at the system architecture level, by constructing a hardware and software collaborative hierarchy encompassing front-end data acquisition, mid-end preprocessing, back-end computation, and terminal output, the system achieves automated acquisition and integrated processing of data throughout the entire lifecycle of steady-state power quality management projects. This system effectively solves technical challenges in traditional solutions, such as asynchronous timing of multi-source monitoring data and low efficiency in processing long-cycle data, significantly improving the overall efficiency and reliability of the assessment work.

[0052] Secondly, at the evaluation model level, an innovative quantitative coupling relationship between technical and economic indicators was established. This design breaks through the limitations of existing technologies, which are mostly confined to pure technical performance evaluation or short-term economic analysis. It directly maps power quality parameters such as voltage deviation, total harmonic distortion rate of current, and three-phase voltage imbalance into quantifiable economic losses such as production downtime losses, additional losses, and capacity reduction. This allows the evaluation results to simultaneously reflect the technical effectiveness and economic impact of the governance project, providing a more comprehensive basis for investment decisions and operation and maintenance management.

[0053] Furthermore, at the level of evaluation methodology, a combined weighting method that takes into account both subjective and objective information, as well as a long-term evaluation mechanism that emphasizes discounted weighting based on recent data, have been introduced. This not only enhances the scientific rigor and adaptability of weight determination, but also enables the keen capture of the evolutionary characteristics of governance effects over time, playing a crucial role, especially in the early identification of trends of effect degradation, thus overcoming the lagging shortcomings of traditional single-point or short-term evaluations.

[0054] Finally, at the engineering application level, a closed-loop technical system integrating comprehensive assessment, trend diagnosis, and tiered early warning has been formed. This mechanism can automatically trigger differentiated early warning information and push targeted rectification suggestions based on assessment results and trend analysis conclusions, thereby transforming post-event assessment into pre-event predictive maintenance. This greatly improves the long-term stability and economy of governance projects and provides core technical support for the precision and intelligence of operation and maintenance work. Attached Figure Description

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0056] Figure 1 is a hierarchical architecture diagram of the long-term evaluation system for the effect of steady-state power quality management engineering according to an embodiment of the present invention;

[0057] Figure 2 is a schematic diagram of the hierarchical structure of the indicator system for long-term evaluation of the effect of steady-state power quality management project according to an embodiment of the present invention. Detailed Implementation

[0058] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0059] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings:

[0060]

[0061] To address the technical shortcomings of existing steady-state power quality governance effectiveness assessment scenarios, such as asynchronous time-series multi-source monitoring data, lack of standardized technical mapping models between technical indicators and economic losses, low efficiency in processing long-cycle time-series data, and lack of real-time technical early warning mechanisms for governance effect degradation, this invention provides a long-term evaluation system for the effectiveness of steady-state power quality governance projects. This system, through a hardware and software collaborative technical architecture, solidifies functions such as multi-source data acquisition, coupled technical and economic modeling, subjective and objective weighted calculation, long-term trend diagnosis, and multi-level early warning into executable technical modules, achieving the technicalization, automation, and verifiability of governance effect assessment.

[0062] The core design of this system includes: relying on the front-end multi-source synchronous acquisition module to acquire steady-state power quality monitoring data, equipment operation data, and economic correlation data between the power grid and the user side after the implementation of the governance project; establishing a quantitative technical mapping model between technical indicators such as voltage deviation, total harmonic distortion rate of current, and three-phase voltage imbalance and downtime losses, additional losses, and capacity reduction through the back-end coupled calculation module, and converting these technical effects into annualized technical and economic coupled indicators; completing the fusion and subjective and objective combination weighting of long-term multi-dimensional data based on a customized time-series data processing engine; constructing a long-term comprehensive evaluation model through a discount weighting algorithm, and realizing technical-level early warning of governance effect degradation by combining it with a trend diagnosis module; and finally realizing the visualization and linkage push of evaluation results and early warning information through the terminal output module.

[0063] This system addresses corresponding technical issues through targeted technical means: for the asynchronous timing of multi-source data, timestamp synchronization calibration technology is used to achieve time-series unification of collected data; for the lack of a unified technical mapping model between technical and economic indicators, a standardized coupled algorithm model is constructed and embedded into the calculation module; for the low efficiency of long-cycle data processing, the batch processing logic of time-series data is optimized to improve computational throughput; and for the lag in early warning response, a graded technical trigger threshold is set to achieve real-time push of early warning signals.

[0064] Ultimately, it can achieve the technical effects of effectively controlling the time-series deviation of collected data within a low range, keeping the technical verification error of evaluation results within a reasonable range, significantly shortening the early warning response delay, and significantly improving the efficiency of long-term data processing. At the same time, it solves the methodological defects of traditional schemes, such as incomplete evaluation dimensions and lack of long-term perspective, and provides quantifiable and verifiable technical basis for the technical rectification of governance projects.

[0065] As shown in Figure 1, this system is a hardware and software collaborative technology system, consisting of a four-level architecture: front-end data acquisition layer, mid-end data preprocessing layer, back-end core computing layer, and terminal early warning output layer. Each level achieves data interaction through industrial Ethernet or power-specific communication protocols.

[0066] Front-end data acquisition layer: Composed of voltage and current sensors, equipment operation and maintenance data acquisition terminals, and economic data entry terminals, it is responsible for collecting various basic data;

[0067] Mid-range data preprocessing layer: Deployed on edge computing nodes, it includes a time-series calibration module and a data cleaning module to preprocess the collected data;

[0068] Backend core computing layer: Deployed in the cloud or local server, it integrates core modules such as coupled computing, combined weighting, long-term evaluation, and trend diagnosis to complete data computing and analysis;

[0069] Terminal early warning output layer: including operation and maintenance personnel terminals, governance equipment linkage terminals, and visual monitoring screens, to realize the output of assessment results and early warning.

[0070] The core functions and technologies implemented at each level specifically include:

[0071] 1. Front-end data acquisition layer

[0072] This layer integrates hardware devices such as voltage deviation sensors, voltage / current total harmonic distortion monitoring terminals, and three-phase unbalance acquisition instruments, and is also equipped with equipment operation and maintenance data acquisition terminals and economic data interface terminals.

[0073] Synchronous time synchronization technology is adopted to provide a unified time reference for all data acquisition devices, ensuring the time consistency between technical data and economic data.

[0074] The system presets the data collection frequency and uses a hardware triggering mechanism to automatically collect data, reducing manual input errors and ensuring the accuracy and timeliness of data collection.

[0075] 2. Mid-range data preprocessing layer

[0076] This layer primarily performs preprocessing of the collected data, providing high-quality data support for backend computation.

[0077] Timing calibration module: Using a unified time base as the anchor point, it corrects the timestamps of the collected data for deviations, removes misaligned data, and completes the data when necessary;

[0078] Data cleaning module: Based on preset rules, it performs dimensional unification processing on different types of indicators, while removing abnormal outliers to improve the accuracy of the input backend data.

[0079] 3. Backend Core Computing Layer

[0080] This layer is the core of the system's assessment and diagnostic functions. Through the collaborative work of multiple functional modules, it completes in-depth data processing and analysis.

[0081] Technological-economic coupling calculation module: Establishes a quantitative mapping relationship between technical indicators and economic losses, converts technical effects into annualized economic indicators, and forms technological-economic coupling indicators;

[0082] Subjective and objective combined weighting module: Combining expert experience and objective data information, subjective weights and objective weights are calculated separately, and then a comprehensive weight is obtained through a combination algorithm, providing a weight basis for evaluation;

[0083] Long-term comprehensive evaluation module: Based on comprehensive weights and preprocessed data, calculate the evaluation score for a single period, introduce the discounting concept to construct a long-term comprehensive evaluation model, and obtain the full-cycle evaluation results;

[0084] Trend Diagnosis Module: Performs trend analysis on the assessment score sequence to identify whether there is a trend of degradation in the governance effect. It can also conduct trend diagnosis for key technical indicators separately to achieve precise positioning.

[0085] 4. Terminal Early Warning Output Layer

[0086] This layer is responsible for presenting the evaluation results and pushing early warning information, providing direct guidance for operation and maintenance work.

[0087] Visualization: The long-term evolution curve of the governance effect is plotted on the monitoring screen, and different states are displayed differently based on the assessment results, which intuitively reflects the changes in the governance effect;

[0088] Tiered early warning: Based on the assessment results and trend diagnosis conclusions, the corresponding level of early warning is triggered, and attention is drawn through sound and light prompts, while early warning information is pushed to the terminals of operation and maintenance personnel;

[0089] Linkage suggestion: When pushing out early warning information, link the historical operation and maintenance data of the governance equipment to provide operation and maintenance personnel with targeted technical troubleshooting suggestions and assist them in quickly carrying out rectification work.

[0090] Based on the above systematic design, the system working mechanism provided by this invention is as follows:

[0091] 1. The front-end acquisition layer automatically collects technical data, operation and maintenance data, and economic data related to the governance project through synchronously timed sensors and terminals;

[0092] 2. The mid-level preprocessing layer performs time-series calibration and standardization cleaning on the collected data, removes noisy data, and then uploads it to the back-end core computing layer;

[0093] 3. In the backend core computing layer, the coupled computing module generates technical and economic coupled indicators, the combined weighting module outputs comprehensive weights, the long-term evaluation module obtains single-period and long-term comprehensive scores, and the trend diagnosis module identifies degradation trends and triggers early warnings.

[0094] 4. The terminal early warning output layer visualizes the evaluation results and pushes early warning information and technical rectification suggestions to the operation and maintenance terminal, forming a complete evaluation-early warning-guidance closed loop.

[0095] The following provides a more detailed demonstration and description of the technical content of the system according to embodiments of the present invention:

[0096] 1. Construction of the indicator system

[0097] To support the quantitative calculation and trend diagnosis functions of the long-term evaluation system for the effectiveness of steady-state power quality governance projects, it is necessary to first construct a multi-level indicator system covering technical indicators, economic and technical-economic coupling indicators, and governance effectiveness improvement rate indicators. This system serves as the standard for data collection objects in the front-end data acquisition layer, the basis for cleaning and calibration in the mid-end data preprocessing layer, and the core dimension for quantitative analysis in the back-end core calculation layer, ensuring the consistency of data processing and evaluation logic throughout the entire system process.

[0098] Based on the typical disturbance characteristics of steady-state power quality (such as voltage deviation, harmonics, etc.) and the full life cycle characteristics of the mitigation project (such as equipment operation and maintenance, cost recovery, etc.), the indicator system in this embodiment is constructed from the following three levels, as shown in Figure 2. Each level corresponds to the core requirements of different modules of the system:

[0099] (1) Technical evaluation index layer: As the core collection object of the front-end data acquisition layer (such as the collection target of voltage sensors and harmonic monitoring terminals), it directly quantifies the technical level of power quality after treatment and provides basic technical data support for the back-end technology-economic coupling calculation module;

[0100] (2) Economic and technical-economic coupling index layer: It is generated by the back-end technical-economic coupling calculation module based on technical indicators and power grid / user side economic data. It reflects the technical and economic impact of the governance project on additional losses, capacity utilization, production losses, etc., and is the key link connecting system technical data and economic results.

[0101] (3) Improvement rate of governance effect index layer: Based on the baseline data before governance (historical benchmark value entered in the early stage of system deployment) collected by the front end and the technical data during operation, it is the core dimension of the back-end trend diagnosis module to analyze the evolution of governance effect over time, and directly supports the system to accurately judge the trend of governance effect degradation.

[0102] 1.1 Technical Evaluation Indicators

[0103] Technical assessment indicators are used to directly quantify the technical status of steady-state power quality after the implementation of the governance project. These indicators mainly include voltage deviation, voltage fluctuation, voltage harmonic distortion rate, and voltage three-phase imbalance.

[0104] 1.1.1 Voltage deviation ( )

[0105] Voltage deviation index Used to characterize the actual voltage at the grid connection point in a power system. With rated voltage The difference between the two values ​​represents the proportion of the rated voltage. The formula for calculating the voltage deviation index is as follows.

[0106]

[0107] 1.1.2 Voltage fluctuations ( )

[0108] Voltage fluctuation index It is used to reflect the fluctuation range of the effective voltage value over a certain period of time, and is usually expressed as the percentage of the difference between two adjacent extreme values ​​on the effective voltage value curve relative to the nominal voltage. The formula for calculating the voltage fluctuation index is as follows.

[0109]

[0110] In the formula, and These represent the maximum and minimum values ​​of the effective voltage at the grid connection point, respectively.

[0111] 1.1.3 Total Harmonic Distortion of Voltage (THD) )

[0112] Voltage Total Harmonic Distortion Index Used to characterize the content of each harmonic in a voltage waveform, it is defined as the percentage of the root-mean-square (RMS) value of each harmonic voltage to the RMS value of the fundamental voltage. Among these, the total harmonic distortion (THD) index is... The calculation formula is as follows.

[0113]

[0114] In the formula, This represents the root mean square value of the fundamental voltage. This represents the root mean square value of the nth harmonic voltage.

[0115] 1.1.4 Three-phase voltage imbalance ( )

[0116] Three-phase voltage imbalance index The three-phase voltage unbalance index is used to reflect the degree to which the root mean square (RMS) value of the three-phase voltage deviates from its average value. It is defined as the ratio of the maximum difference between the RMS value of the three-phase voltage and its average value to the average value of the three-phase voltage. The formula for calculating the three-phase voltage unbalance index is as follows.

[0117]

[0118] in,

[0119]

[0120] In the formula, , , These are the root mean square values ​​of the three-phase phase voltages. It represents the average value of the root mean square values ​​of the three-phase phase voltages.

[0121] 1.2 Economic Efficiency and Techno-Economic Coupling Evaluation Indicators

[0122] The economic indicators used in this invention are techno-economic coupling indicators derived from steady-state power quality technical indicators, including:

[0123] 1.2.1 Equipment cost indicators ( )

[0124] Equipment cost indicators Used to quantify the one-time investment scale of a pollution control project. The total investment cost of the equipment can be obtained by summing up the purchase price of the equipment, taxes, and necessary expenses before it reaches its intended usable state, such as transportation, loading and unloading, installation, and commissioning. This cost can be further converted and dimensionless as needed. Equipment Cost Index The calculation formula is as follows.

[0125]

[0126] In the formula, For the purchase price; For related taxes and fees; Transportation, loading and unloading, installation and professional service fees attributable to the fixed asset incurred before it reaches its intended usable state.

[0127] 1.2.2 Annual Operation and Maintenance Cost Indicators ( )

[0128] Annual operation and maintenance cost indicators The annual operating and maintenance cost is used to quantify the annual maintenance costs of the treatment equipment during normal operation. This includes fuel and power costs, routine maintenance costs, management fees, and other related expenses. The annual operating and maintenance cost index is used to calculate this cost. The calculation formula is as follows.

[0129]

[0130] In the formula, For fuel and power costs; For repair costs; For administrative management expenses; Other expenses.

[0131] 1.2.3 Economic indicators of harmonic-induced additional losses ( )

[0132] Harmonic currents and voltages can cause additional copper and iron losses in equipment such as transformers, lines, and capacitors. The total harmonic distortion (THD) of current, obtained through long-term monitoring, is... i Based on system parameters, this invention establishes a quantitative relationship between harmonic technical indicators and additional active power loss, obtaining the additional harmonic loss power ΔP. h :

[0133]

[0134] in, The harmonic additional loss coefficient is determined based on the equipment structure and operating conditions. The selected reference loss (such as transformer rated loss or line rated loss).

[0135] Total Harmonic Distortion Index The calculation formula is as follows.

[0136]

[0137] In the formula, This represents the root mean square value of the fundamental current. This represents the root mean square value of the nth harmonic current.

[0138] The annual equivalent operating hours are The unit price of electricity is Under these conditions, the annual energy loss cost calculated based on harmonic additional losses is:

[0139] .

[0140] 1.2.4 Economic indicators for reducing three-phase unbalanced capacity ( )

[0141] Three-phase voltage imbalance generates negative sequence current, leading to increased equipment temperature rise. To ensure lifespan, the allowable operating capacity is usually reduced. This invention addresses this issue based on voltage imbalance indices obtained through long-term monitoring. Construction capacity reduction factor :

[0142]

[0143] in, , To characterize the capacity reduction sensitivity coefficient of the equipment under three-phase imbalance conditions, it is preferred to satisfy the following: , . , Calibration can be obtained through equipment temperature rise testing or capacity reduction curves provided by the manufacturer: obtain multiple sets Data points (of which) Using least squares fitting to make ,get , .

[0144] in, To assign the data point sequence number; Indicates the first The voltage three-phase unbalance index value under the specified operating condition (i.e., the index on the [number]th [year]) (Values ​​for each evaluation period). Indicates and The corresponding capacity reduction factor is defined as the available capacity under this operating condition. With rated capacity The ratio, that is This is reflected in the three-phase voltage imbalance. The percentage of the rated capacity that the equipment is allowed to operate for an extended period of time.

[0145] When only single-point calibration data is available When, the preferred option can be selected. ,make Or take ,make .

[0146] K uThe safety factor for equipment operation is determined based on data from the equipment manufacturer or operating procedures. The capacity reduction factor satisfy Rated capacity is At that time, the available capacity is:

[0147]

[0148] The capacity loss is:

[0149]

[0150] The unit capacity investment cost is recorded as follows: (RMB / kVA), annualized conversion factor is (Approximately the reciprocal of the lifespan in years), then the annualized economic loss due to the reduction in three-phase unbalanced capacity is:

[0151] .

[0152] 1.2.5 Voltage Deviation Production Loss Index ( )

[0153] Voltage deviations leading to over-limit operations can cause sensitive loads to malfunction or shut down, resulting in production losses. This invention statistically analyzes data such as the number of voltage over-limit occurrences and total over-limit time over a long-term evaluation period, and uses a simplified linear model to estimate the number of shutdowns. :

[0154]

[0155] in, To evaluate the number of times the voltage deviation exceeded the limit during the evaluation period. The total duration of voltage deviation exceeding the limit during the evaluation period; The baseline over-limit duration is used to convert the over-limit duration into an equivalent number of times, and can be obtained by calibration from protection action characteristics, process sensitivity thresholds, or historical data. , This is a coefficient related to the user's process and equipment disturbance sensitivity. The annual production loss cost caused by voltage deviation is:

[0156]

[0157] in, The average production loss per downtime (RMB / downtime) can be obtained from historical downtime records, and the preferred value has taken into account factors such as downtime duration, restart loss, scrap and opportunity cost.

[0158] By introducing , , Based on indicators such as power quality technical indicators, this invention constructs a set of technical-economic coupling models with power quality technical indicators as independent variables and annual power loss costs, capacity reduction costs, and production loss costs as dependent variables.

[0159] 1.3 Improvement rate of governance effectiveness

[0160] For each key technical indicator Obtain the baseline values ​​before treatment. And the statistical value of the t-th evaluation period during the operation period. The improvement rate of governance effectiveness is defined as:

[0161]

[0162] A higher improvement rate indicates a better governance effect. When the improvement rate is incorporated into the techno-economic coupling model, the economic benefit improvement rate over time can be further calculated, providing a unified dimensional basis for long-term evaluation.

[0163] 2. Calculation of indicator weights

[0164] To comprehensively reflect the impact of expert experience and long-term operational data on the importance of each indicator, this invention adopts a combination of subjective weighting analytic hierarchy process (AHP) and entropy weighting method based on information entropy to determine the indicator weights.

[0165] 2.1 Subjective Weight Calculation Based on AHP Method

[0166] First, based on the indicator system described in Part 1, a hierarchical structure is constructed: target layer - criterion layer - sub-indicator layer. The target layer is the long-term comprehensive evaluation result of the steady-state power quality management project. The indicator system adopted in the criterion layer includes: technical indicator group and economic and technical-economic coupling indicator group. The sub-indicator layer consists of specific technical indicators and technical-economic coupling indicators.

[0167] Suppose the underlying layer contains n metrics to be weighted, denoted as . The subjective weight determination process based on AHP includes the following steps:

[0168] (1) Construct the judgment matrix

[0169] For n indicators in the same layer, experts in power quality, system planning, operation and maintenance, etc., are organized to use the 1–9 scaling method to evaluate any two indicators. , The relative importance is compared pairwise to obtain the judgment matrix:

[0170]

[0171] in, Indicators relative to indicators The importance of satisfying , , .

[0172] (2) Calculate the index weight vector

[0173] To obtain the subjective weight vector of this layer's indicators The weights can be solved using the eigenvalue method or the geometric mean method. Preferably, this invention uses the geometric mean method to approximate the weights:

[0174]

[0175]

[0176] If the eigenvalue method is used, then the characteristic equation of the matrix needs to be solved.

[0177]

[0178] in To determine the largest eigenvalue of a matrix and to analyze the eigenvectors... Normalization, making .

[0179] (3) Consistency check

[0180] To ensure the rationality of expert judgments, a consistency check needs to be performed on the judgment matrix.

[0181] Calculate the consistency ratio (CR):

[0182]

[0183] Where CI is the consistency index, and its calculation formula is:

[0184]

[0185] RI stands for Random Consistency Index, which depends on the dimension n of the judgment matrix. Generally, RI should be less than 0.1, indicating that the judgment has good rationality.

[0186] (4) Hierarchical integration and global subjective weighting

[0187] The criteria layer of the system of this invention includes a group of technical indicators. and economic and techno-economic coupling index group Let the local weights of the criterion layer be respectively , ,and Within a group of technical indicators, there are K technical indicators, and the local weight of the k-th technical indicator within the group is... Within the economic and techno-economic coupling indicator group, there are M indicators of this type, and the local weight of the m-th indicator within the group is... The global subjective weights of the underlying indicators can then be expressed as:

[0188]

[0189]

[0190] Among them, the local subjective weights satisfy the within-group normalization constraint:

[0191]

[0192]

[0193] Arrange the global subjective weights of all underlying metrics into a vector to obtain the subjective weight vector used in this invention:

[0194]

[0195] The underlying global subjective weights can be satisfied as follows:

[0196]

[0197] This weighting emphasizes the expert's engineering experience in judging different types of disturbances and the impact of different economic losses.

[0198] 2.2 Objective Weight Calculation Based on Entropy Weight Method

[0199] Considering that the system of this invention is based on long-term monitoring data, the degree of fluctuation of each indicator on the time axis contains a lot of objective information. Therefore, the entropy weight method is introduced to automatically extract the objective weight of each indicator based on long-term time series data.

[0200] Suppose the evaluation period is divided into m evaluation periods, and there are n indicators at the bottom level. Let the original statistical value of the i-th period and the j-th indicator be... .

[0201] (1) Standardization process

[0202] Different indicators may have different dimensions and positive / negative attributes, so they need to be standardized to the [0,1] range first. For benefit-type indicators (the larger the value, the better), a standardized formula is used:

[0203]

[0204] in, This represents the set of original statistical values ​​of the j-th indicator over all m evaluation periods, i.e. ; , These represent the maximum and minimum values ​​in the set, respectively.

[0205] For cost-related indicators (the smaller the value, the better), the following approach is adopted:

[0206]

[0207] After standardization .

[0208] (2) Calculate the proportionality coefficient

[0209] For each indicator j, calculate its relative proportion in each evaluation sample. :

[0210]

[0211] (3) Calculate information entropy and information utility value

[0212] Define constants:

[0213]

[0214] The information entropy of the j-th indicator is:

[0215]

[0216] If a certain indicator remains almost unchanged across all samples, then Nearly uniform distribution A value close to 1 indicates low discrimination; conversely, a value close to 1 indicates high discrimination. Too small.

[0217] The information utility value of an indicator is defined as follows:

[0218]

[0219] The larger the value, the more information the indicator carries and the stronger its ability to distinguish between different evaluation periods.

[0220] (4) Calculate the objective weights

[0221] Normalize each indicator based on its information utility value to obtain an objective weight vector. .

[0222]

[0223] The greater the volatility and the higher the information content of an indicator, the higher its entropy weight. The larger the value, the higher its weight in the long-term governance effectiveness assessment.

[0224] 2.3 Calculation of the combined subjective and objective weights

[0225] To balance expert experience with objective information from long-term operational data, the system of this invention employs subjective weighting. With objective weight The combined weighting method yields the final comprehensive weight vector. .

[0226] Let the balance coefficient be α, satisfying Then the overall weight of the j-th indicator is:

[0227]

[0228] To ensure the normalization of the comprehensive weight vector, for Perform a normalization process:

[0229]

[0230] income This is the final weight vector used for long-term comprehensive evaluation in this invention.

[0231] When engineering experience is abundant and expert judgment is reliable, the value of α can be appropriately increased to give greater weight to subjective information; when long-term operational data is sufficient and indicator fluctuations are significant, α can be decreased to increase the influence of objective weights. By adjusting α, the relative weights of expert experience and data-driven approaches can be flexibly adjusted in different engineering scenarios.

[0232] 3 Comprehensive evaluation indicators

[0233] After obtaining the standardized values ​​of each indicator and the comprehensive weight vector Then, the system of the present invention first calculates the comprehensive score for each evaluation period. Furthermore, based on this, constraints and penalties are introduced for key technical indicators to obtain a comprehensive score with technical constraints. Subsequently, a long-term comprehensive evaluation index was constructed using discounted weighting. It is used to characterize the overall level of governance effectiveness and its temporal evolution characteristics throughout the entire assessment period.

[0234] 3.1 Single-period comprehensive score

[0235] Let the evaluation period be divided into T evaluation periods, and let the standardized index vector for the t-th evaluation period be denoted as:

[0236]

[0237] in, Let be the standardized value of the k-th indicator in time period t. The comprehensive weight vector is denoted as:

[0238]

[0239] (1) Unconstrained comprehensive score

[0240] Without considering technical constraints, the comprehensive score for the t-th evaluation period. Using a weighted summation method:

[0241]

[0242] The higher the value, the better the steady-state power quality management effect during that period.

[0243] (2) Constraints and penalty coefficients of key technical indicators

[0244] To ensure that the assessment results primarily reflect the compliance of power quality standards, this invention sets hard constraints on several key technical indicators. When a key technical indicator exceeds the standard limit, a penalty factor is introduced into the overall score.

[0245] Suppose there are Q key technical indicators that require constraints, and let the original value of the q-th key indicator in time period t be... The standard limit is The relative degree of exceeding the limit is defined according to the different types of indicators. :

[0246] For upper limit constraints

[0247]

[0248] For lower bound constraints

[0249]

[0250] when This indicates that the indicator meets the limit requirements; This indicates a certain degree of exceeding or failing to meet the standards. To comprehensively reflect the worst-case scenario for multiple key indicators, this invention defines the overall exceedance level for the t-th evaluation period:

[0251]

[0252] Based on this, construct the penalty coefficient. Based on the overall attenuation score:

[0253]

[0254] in, The penalty sensitivity coefficient, The larger the value, the more severe the impact of exceeding the limit on the overall score.

[0255] When all key technical indicators meet the limit requirements ,have No penalty will be imposed on the overall score; when the limit is exceeded, The overall score decreases proportionally to the degree of exceeding the limit.

[0256] (3) Comprehensive scoring with technical constraints

[0257] Taking into account both weighting and penalty factors, the final comprehensive score for the t-th evaluation period is defined as follows:

[0258]

[0259] This rating reflects the comprehensive performance of various technical indicators and techno-economic coupling indicators, and also uses penalty factors to indicate the priority of whether key technical indicators are met.

[0260] 3.2 Comprehensive Evaluation Indicators for Long-Term Discounted Realization

[0261] To better reflect the comprehensive performance and temporal evolution of the governance effect throughout the entire operation period, the system of this invention provides a single-time period scoring sequence. Based on this, a long-term comprehensive evaluation index is constructed by introducing the concept of discounted weighting. .

[0262] (1) Definition of discount weight

[0263] Let the evaluation period be divided into T time periods, and let the discount factor be... ,satisfy The discount weight for the t-th time period is defined as:

[0264]

[0265] Obviously there are:

[0266]

[0267] when hour, The weight of the score increases with increasing t, meaning the closer to the current time (the closer t is to T), the higher the score weight. hour, It degenerates into an equal-weighted average.

[0268] (2) Calculation formula for discounted weighted long-term composite index

[0269] Long-term comprehensive evaluation indicators Defined as a weighted average with discounted weights:

[0270]

[0271] when Recent ratings , exist The higher proportion reflects the impact of recent operational status on long-term assessment results; when hour, It places greater emphasis on overall balanced performance throughout the entire evaluation period.

[0272] The long-term comprehensive evaluation index constructed by the system of this invention It can take into account both historical performance and recent status within a unified framework, and can adjust the discount factor according to different engineering scenarios. Enables flexible configuration.

[0273] 4. Long-term status tracking and early warning mechanism

[0274] To achieve continuous tracking and technical early warning of the long-term operation status of steady-state power quality management projects, this invention constructs a joint early warning mechanism based on state classification and trend diagnosis on the basis of comprehensive scoring.

[0275] 4.1 Long-term evolution curve of governance effectiveness and state classification

[0276] Let the evaluation period be divided into T evaluation periods, and let the comprehensive score with technical constraints for the t-th period be denoted as . , .

[0277] Set three thresholds on the scoring axis:

[0278] Excellent – ​​Stable Threshold: (For example, 85 can be taken);

[0279] Stability – Focus on the boundary threshold: (For example, 70 can be taken);

[0280] Attention – Warning Threshold: (For example, 60 can be taken).

[0281] Then the static state level of the t-th time period It can be divided according to the following rules:

[0282]

[0283] Each time period Connecting them in chronological order forms a long-term evolution curve of governance effectiveness, and the above division constitutes the basis for static state discrimination based on the absolute level of the score.

[0284] 4.2 Diagnosis of the trend of degradation in treatment effectiveness

[0285] To determine whether the governance effect shows a deteriorating trend, this invention uses a scoring sequence. Perform trend analysis. Use linear regression to fit the following equation:

[0286]

[0287] in, The slope of the trend in the score over time. Preset trend threshold. :

[0288] when At that time, it was determined that the treatment effect showed a clear trend of degradation;

[0289] when At that time, it was determined that the overall effect of the treatment was stable.

[0290] Improvement rate of governance effectiveness for certain key technical indicators The trend slope can be obtained using the same method. ,when When the value is below the corresponding threshold, it can be determined that the technical indicator has a degradation trend, which can be used to help determine the warning type.

[0291] 4.3 Multi-level joint early warning mechanism

[0292] Based on static state grading and trend diagnosis, this invention will assess the current evaluation period. Overall rating With trend slope Combined, a multi-level joint early warning mechanism can be constructed, for example:

[0293] (1) Normal operating status:

[0294] Located in the stable region and above, and The degradation threshold has not been exceeded ( ).

[0295] (2) Early warning of potential degradation:

[0296] Still in the stable zone or above, but This indicates that the scoring level is acceptable but shows a clear downward trend, and monitoring should be strengthened.

[0297] (3) Early warning of severe degradation:

[0298] Falling into the attention zone, and This indicates that the current level and development trend are both unfavorable, and on-site investigation and operation and maintenance optimization should be arranged.

[0299] (4) Severe warning status:

[0300] If a region falls into a warning zone, regardless of the trend, a technical diagnosis and review or adjustment of the remediation plan should be organized immediately.

[0301] If the trend slope of a certain key technical indicator is found at the same time If the degradation level is below the degradation threshold, the corresponding "harmonic degradation", "imbalance degradation" or "voltage quality degradation" can be marked on the basis of the above warning level to guide the on-site priority inspection of related devices.

[0302] Through the above method, the present invention can provide comprehensive evaluation results for each assessment period, while simultaneously achieving quantitative diagnosis of the long-term stability of the governance effect and the risk of degradation, thus providing continuous technical feedback to power grid operators, users, and equipment manufacturers.

[0303] Compared with existing power quality assessment schemes, the advantages of the system in this invention are as follows:

[0304] The long-term evaluation system for the effectiveness of steady-state power quality management projects constructed in this invention, through a hierarchical architecture that integrates software and hardware, solidifies traditional power quality assessment methods into implementable technical modules. Its core innovative advantages are deeply integrated with the system modules, as detailed below:

[0305] 1. A systematic data collection and tracking architecture for long-term post-evaluation

[0306] Unlike existing technologies that assess power quality at a single point before or during the treatment process, this system relies on a front-end multi-source synchronous acquisition layer to automate and time-series the collection of data throughout the entire lifecycle of the treatment project (including technical monitoring data, equipment operation and maintenance data, and economic correlation data). It also uses a back-end trend diagnosis module to continuously track long-term data, accurately anchoring the actual long-term effectiveness of the treatment project in the assessment, thus filling the gap in existing technologies that lack long-term post-evaluation technology carriers.

[0307] 2. Modular and quantitative modeling capabilities for technology-economic coupling

[0308] This system establishes a quantitative mapping model between technical indicators such as harmonics, three-phase imbalance, and voltage deviation and additional losses, capacity reduction, and production losses through a backend technical-economic coupling calculation module, and automatically converts them into annualized technical-economic coupling indicators. Compared with the shortcomings of existing technologies that only consider technical or economic indicators in isolation, this module realizes the standardized and automated transformation of technical data into economic benefits, enabling economic assessment to have verifiable technical attributes.

[0309] 3. Long-term evolution tracking dimension based on baseline comparison

[0310] This system uses pre-treatment baseline data as the initial input benchmark for the front-end data acquisition layer. Through the back-end trend diagnosis module, it continuously calculates and tracks the improvement rate of key technical indicators, achieving a unified characterization of "comparison before and after treatment + full-cycle evolution." Compared to the short-cycle evaluation perspective of existing technologies, this design can accurately identify the long-term decay trend of treatment effects, providing a quantitative basis for operation and maintenance optimization.

[0311] 4. A weight calculation engine that combines subjective and objective methods.

[0312] This system integrates the Analytic Hierarchy Process (AHP) and entropy weighting in its backend subjective and objective weighting module. The AHP submodule incorporates expert engineering experience regarding disturbance types and economic losses, while the entropy weighting submodule mines objective and sensitive information from long-term time-series data. Furthermore, it supports flexible configuration of the balancing coefficients. Compared to single weighting methods, this engine's evaluation results are both professional and objective, adaptable to the evaluation needs of different engineering scenarios.

[0313] 5. An integrated assessment and early warning mechanism combining discounted weighting and tiered early warning.

[0314] This system achieves discounted weighted full-cycle scoring calculation through a backend long-term comprehensive evaluation module. Simultaneously, it integrates a trend diagnosis module and a terminal early warning output layer to construct a multi-level early warning mechanism of "static grading + trend diagnosis," enabling quantitative assessment of governance effectiveness, identification of degradation trends, and real-time push of early warning information. Compared to existing technologies that lack early warning or rely on single-threshold early warnings, this mechanism forms a closed-loop technology of "assessment-diagnosis-early warning-guidance."

[0315] The aforementioned innovations are achieved through the collaborative implementation of modules at various levels of the system, constituting the core technical differences and protection points of this invention compared to existing technologies.

[0316] 1. Core Technology Effectiveness

[0317] This system achieves several quantifiable technological breakthroughs through a hierarchical hardware and software architecture, including:

[0318] At the data acquisition level: synchronous timing technology is used to control the time series deviation of multi-source data to a low level, ensuring the spatiotemporal consistency of the evaluation data;

[0319] At the data processing level: Through a batch processing-optimized time-series data engine, the processing efficiency of long-cycle, multi-dimensional data is increased several times to that of traditional methods, significantly shortening the evaluation response time;

[0320] In terms of assessment accuracy: through standardized modeling that couples technology and economics, the technical verification error of the assessment results is controlled within a reasonable range, ensuring the reliability of the assessment conclusions;

[0321] At the early warning and response level: By using a tiered early warning mechanism, the early warning and response time for degradation risks can be reduced to a short time frame, enabling early detection and early handling of governance issues.

[0322] 2. Engineering and Industry Application Value

[0323] For the design and construction teams of the remediation project: the quantitative assessment results and technical rectification suggestions output by the system can directly guide the optimization and iteration of the remediation plan, and improve the technical effectiveness and economic rationality of the project;

[0324] For power quality management equipment manufacturers: The long-term evaluation data accumulated by the system can provide feedback on the full life cycle operation performance of the equipment, providing data support for the technical upgrade and operation and maintenance service optimization of the equipment;

[0325] For industry development: With the large-scale accumulation of system application data, it can promote the formation of a standardized design, construction and operation and maintenance system for steady-state power quality governance projects, improve the overall governance effect while reducing the industry's comprehensive investment cost, and help the technology upgrade and industrial quality improvement in the field of power quality assessment of distribution networks.

[0326] Definitions of technical terms appearing in this embodiment:

[0327] AHP, or Analytic Hierarchy Process, is a quantitative and qualitative method for decision analysis. It helps decision-makers systematically evaluate and compare multiple options by breaking down complex decision problems into multiple levels.

[0328] Entropy weighting is a method used for multi-indicator decision analysis, primarily for determining the weights of various indicators. It is based on the concept of information entropy, objectively allocating weights by analyzing the information content of each indicator.

[0329] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0330] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

[0331] This invention is not limited to the preferred embodiment described above. Anyone inspired by this invention can derive other forms of long-term evaluation systems for steady-state power quality management projects. All equivalent variations and modifications made within the scope of the patent applications of this invention shall fall within the scope of this invention.

Claims

1. A long-term evaluation system for the effectiveness of steady-state power quality improvement projects, characterized in that, The system comprises a front-end data acquisition layer, a mid-end data preprocessing layer, a back-end core computing layer, and a terminal early warning output layer, which are connected in sequence. The front-end data acquisition layer is configured to synchronously acquire steady-state power quality technical data, equipment operation and maintenance data, and economic correlation data between the power grid and the user side after the implementation of the governance project using a unified time reference, and to input steady-state power quality baseline data before the governance. The steady-state power quality technical data includes at least voltage deviation, voltage fluctuation, total harmonic distortion rate of voltage / current, and three-phase voltage imbalance data. The mid-end data preprocessing layer is configured to perform timestamp calibration and abnormal data removal on the acquired data, and output standardized data. The backend core computing layer is configured as follows: A technical evaluation index calculation module calculates steady-state power quality technical evaluation indicators based on the standardized data. These technical evaluation indicators include at least voltage deviation, voltage fluctuation, total harmonic distortion (THD) of voltage / current, and three-phase voltage imbalance data. An economic cost calculation module calculates the full life-cycle cost index of the governance equipment based on the economic correlation data between the power grid and the user side. This full life-cycle cost index includes at least equipment purchase and installation / commissioning costs and annual operation and maintenance costs. A technical-economic coupling calculation module establishes quantitative mapping relationships between voltage deviation and production downtime losses, THD and additional losses, and three-phase voltage imbalance and equipment capacity reduction based on the standardized data, generating annualized technical-economic coupling indicators. A subjective-objective combined weighting module calculates the comprehensive weight of the evaluation indicators by combining expert experience and long-term time-series data. A long-term comprehensive evaluation module discounts and weights each evaluation indicator, including the technical evaluation indicators and the technical-economic coupling evaluation indicators, based on the comprehensive weights to obtain the long-term comprehensive evaluation result of the steady-state power quality governance project. The trend diagnosis module performs trend analysis on the change sequence of the long-term comprehensive assessment results to identify the trend of degradation of governance effectiveness; The terminal early warning output layer is configured to output the long-term comprehensive evaluation results and trigger graded early warnings based on the degradation trend.

2. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The front-end data acquisition layer establishes a unified time reference through synchronous time synchronization technology, which includes satellite time synchronization or power system-specific time synchronization technology. The front-end data acquisition layer presets the acquisition frequency according to the steady-state power quality disturbance characteristics and automatically completes data acquisition through a hardware triggering mechanism, reducing manual input errors and ensuring data accuracy and timeliness.

3. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The economic correlation data between the power grid and the user side includes data on costs and losses related to the entire life cycle of the governance equipment. Specifically, it covers necessary expenditures during the equipment purchase, installation and commissioning phases, annual maintenance costs during the equipment operation phase, electricity unit prices, investment costs related to equipment capacity, and the amount of loss caused by a single production interruption.

4. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The timestamp calibration of the mid-range data preprocessing layer uses a unified time benchmark as an anchor point, removing data whose timestamp deviation exceeds a preset threshold, and completing data with missing timestamps using interpolation. The outlier removal is based on statistical criteria to identify and remove outliers from each indicator, while simultaneously unifying the dimensions of technical and economic indicators, mapping different types of indicator values ​​to preset numerical ranges to meet subsequent calculation needs. The mid-range data preprocessing layer uses batch processing optimization logic to process long-term, multi-dimensional data in parallel. The back-end core computing layer accelerates the calculation process of the technology-economic coupling model through parallel computing technology, improving the processing efficiency of long-term data and shortening the evaluation response latency to meet the real-time requirements of long-term governance project evaluation.

5. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The quantitative mapping relationships established by the technical-economic coupling calculation module specifically include: mapping between total harmonic distortion (THD) of current and additional losses: determining the additional harmonic loss power based on the THD and system reference losses, and then converting the additional harmonic losses into annualized costs by combining the annual equivalent operating time and the unit price of electricity; mapping between three-phase voltage imbalance and equipment capacity reduction: constructing a capacity reduction coefficient based on the three-phase voltage imbalance, calculating the capacity loss through the rated capacity of the equipment and the capacity reduction coefficient, and then converting the capacity loss into annualized economic losses by combining the annualized conversion coefficient and the unit capacity investment cost; mapping between voltage deviation and production downtime losses: statistically analyzing the number of voltage over-limits and the total over-limit time, calculating the number of downtimes based on the equipment sensitivity coefficient, and then converting the downtime losses into annualized costs by combining the average production loss per downtime.

6. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The calculation process of the subjective and objective weighting module includes: Subjective weight calculation: Constructing a hierarchical structure of target layer, criterion layer, and sub-indicator layer, where the target layer is the long-term comprehensive evaluation result of steady-state power quality governance project, the criterion layer includes technical indicator group and economic and technical-economic coupling indicator group, and the sub-indicator layer consists of each specific evaluation indicator; forming a judgment matrix by pairwise comparison of the relative importance of indicators by domain experts, determining the relative importance of indicators using the 1-9 scaling method, and calculating the global subjective weight of each indicator based on the judgment matrix; Objective weight calculation: Calculating the original values ​​of each indicator in the long-term time series data. Standardization is performed, with benefit-type indicators calculated as the ratio of the difference between the original value and the indicator's extreme value to the indicator's range, and cost-type indicators calculated as the ratio of the difference between the indicator's extreme value and the original value to the indicator's range. Information entropy is calculated for each indicator based on the standardized data, and then objective weights are obtained by normalizing the information utility value. Comprehensive weight fusion: subjective and objective weights are combined using a balance coefficient, which ranges from 0 to 1 and is flexibly adjusted based on the richness of engineering experience and the sufficiency of long-term operational data. The combined weights are then normalized to obtain the final comprehensive weights used for evaluation.

7. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The calculation process of the long-term comprehensive evaluation module includes: unconstrained comprehensive score for a single time period: based on the comprehensive weight and standardized indicator values, the unconstrained comprehensive score for each evaluation period is obtained by weighted summation; comprehensive score with technical constraints: standard limits are set for key technical indicators. If the original value of an indicator exceeds the standard limit, the relative degree of exceeding the limit is calculated and the overall degree of exceeding the limit is determined; a penalty coefficient is introduced based on the overall degree of exceeding the limit. The penalty coefficient decreases as the degree of exceeding the limit increases. The unconstrained comprehensive score is multiplied by the penalty coefficient to obtain the comprehensive score with technical constraints; discounted weighted calculation: the discount weight for each evaluation period is determined based on the discount factor and the total number of evaluation periods, so that the comprehensive score with technical constraints closer to the current time has a higher proportion in the long-term comprehensive evaluation result; the long-term comprehensive evaluation result for the entire life cycle is obtained by summing the products of the comprehensive scores with technical constraints for each time period and the corresponding discount weights.

8. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The diagnostic process of the trend diagnosis module includes: trend fitting: performing linear regression fitting on the comprehensive score with technical constraints for each evaluation period to obtain the trend slope of the score changing over time; degradation judgment: setting a degradation threshold, when the trend slope is lower than the reverse value of the degradation threshold, it is determined that there is a significant degradation trend in the governance effect; when the trend slope is not lower than the reverse value of the degradation threshold, it is determined that the overall governance effect is stable; performing trend diagnosis separately on the improvement rate of the governance effect of key technical indicators, if the trend slope of the improvement rate of a certain key technical indicator is lower than the corresponding threshold, marking the specific degradation type of the technical indicator to provide direction for operation and maintenance troubleshooting.

9. The long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The backend core computing layer also includes an improvement rate calculation module. Based on the baseline data before governance and the statistical values ​​of indicators during the operation period, the improvement rate calculation module calculates the improvement rate of governance effect of each key technical indicator. The improvement rate is determined by the ratio of the difference between the baseline value before governance and the statistical value of indicators during the operation period to the baseline value before governance. The improvement rate calculation module also substitutes the improvement rate of technical indicators into the technology-economic coupling model to obtain the corresponding economic benefit improvement rate, so as to quantify the long-term benefit improvement of the governance project.

10. A long-term evaluation system for the effect of steady-state power quality improvement projects according to claim 1, characterized in that: The tiered early warning system of the terminal early warning output layer is triggered jointly based on the interval division of long-term comprehensive evaluation results and trend diagnosis conclusions. Specifically, it includes: Potential degradation early warning: triggered when the long-term comprehensive evaluation results are in a stable range and the trend diagnosis determines that there is a degradation trend, and at the same time, a suggestion to increase the frequency of equipment inspection is pushed; Strong degradation early warning: triggered when the long-term comprehensive evaluation results are in a concern range and the trend diagnosis determines that there is a degradation trend, and at the same time, a suggestion to conduct on-site inspection and treatment of equipment parameters is pushed; Severe early warning: triggered when the long-term comprehensive evaluation results are below the early warning interval threshold, and regardless of the trend diagnosis conclusion, a suggestion to immediately review the treatment plan and adjust the equipment operating parameters is pushed; When pushing early warning information, the terminal early warning output layer also associates the equipment operation and maintenance historical data to provide operation and maintenance personnel with a reference for handling similar problems in the past.

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