An energy consumption monitoring, analyzing and operation and maintenance platform for typical indoor substation in cold region

Abstract

This invention relates to the field of power grid management technology, specifically to a platform for energy consumption monitoring, analysis, and operation and maintenance of typical indoor substations in cold regions. The platform includes: an energy consumption change analysis module, an energy consumption cycle identification module, a power adaptability analysis module, a low-temperature impact assessment module, a fault risk identification module, and an equipment lifespan management module. This invention improves the accuracy of predicting power consumption trends and the speed of operational response by analyzing environmental data and energy consumption records of typical indoor substations in cold regions. By combining analysis of the relationship between temperature, wind speed, and energy consumption, it accurately predicts energy consumption changes and adjusts the power grid, matching seasonal and weather changes. Combined with real-time environmental data analysis, it improves the accuracy of assessing the impact of low temperatures in winter, optimizes the stability of the power system under extreme conditions, reduces losses caused by faults and unplanned outages, extends equipment lifespan, and improves operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of power grid management technology, and in particular to a typical indoor substation energy consumption monitoring, analysis and operation and maintenance platform in cold regions. Background Technology

[0002] The field of power grid management technology includes monitoring, controlling, and optimizing the transmission and distribution processes in the power platform to ensure the stability, reliability, and efficiency of power supply. Through load scheduling and fault handling, combined with information and communication technologies, it enables real-time data monitoring, analysis, and dynamic management of the power grid. Through data analysis technology and automated control platforms, it optimizes resource allocation, improves energy utilization efficiency, reduces operation and maintenance costs, and enhances the power grid's response capability to various emergencies.

[0003] The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions aims to monitor and analyze the energy consumption of substations and conduct operation and maintenance management. It uses sensor technology to collect energy consumption data of substations, analyzes the collected information through data analysis software, identifies energy waste and equipment failures, improves the operating efficiency and reliability of substations, reduces equipment failures and downtime through predictive maintenance, optimizes energy consumption structure, reduces operating costs, and maintains equipment reliability and efficiency in extremely cold environments.

[0004] Traditional energy consumption monitoring technologies are insufficient in rapidly responding to extreme climate change and seasonal impacts. They rely on pre-set maintenance plans and periodic data analysis, which limits their ability to respond to emergencies in a timely manner. When faced with sudden extreme low temperature events, they cannot adjust operating strategies in time, leading to equipment overload and failure, increased maintenance costs, and prolonged power outages. They are also ineffective in predictive maintenance and risk management, and cannot make full use of the collected data to optimize grid operation and reduce operational risks. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a typical indoor substation energy consumption monitoring, analysis, and operation and maintenance platform for cold regions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a typical indoor substation energy consumption monitoring, analysis, and operation and maintenance platform in cold regions includes:

[0007] The energy consumption change analysis module analyzes the relationship between temperature, wind speed and energy consumption based on the power plant's historical energy consumption records, calculates the power consumption trend in cold regions, and generates energy consumption trend prediction information.

[0008] Based on the energy consumption trend prediction information, the energy consumption cycle identification module analyzes the seasonal factors affecting energy consumption, identifies the periodic patterns of electricity consumption, and obtains seasonal energy consumption pattern information.

[0009] The power adaptability analysis module uses the seasonal energy consumption pattern information and combines it with ambient temperature data to analyze the impact of low winter temperatures on the stability of the power system and generate low-temperature responsiveness assessment results.

[0010] Based on the low-temperature responsiveness assessment results, the low-temperature impact assessment module analyzes the power load capacity and substation response speed, identifies key factors affecting power stability, and generates power grid risk analysis results.

[0011] The fault risk identification module analyzes the correlation between temperature fluctuations and current anomalies based on the power grid risk analysis results, identifies abnormal patterns and the probability of fault occurrence, and generates equipment fault prediction information.

[0012] Based on the equipment failure prediction information, the equipment life management module analyzes the life cycle cost of the equipment, updates the equipment life curve, and adjusts the maintenance plan to obtain a power maintenance schedule.

[0013] As a further aspect of the present invention, the energy consumption trend prediction information includes monthly energy consumption change curves in cold regions, energy consumption response data at key temperature points, and quantitative analysis results of the impact of wind speed on energy consumption. The seasonal energy consumption pattern information specifically includes summer and winter energy consumption statistics, energy consumption fluctuation patterns during the spring and autumn transition period, and energy consumption response speed during seasonal transitions. The low-temperature responsiveness assessment results include power system stability indicators under extreme low temperatures in winter, sensitivity analysis results of the impact of temperature drop on equipment operation, and prediction models of increased energy consumption during low temperatures. The power grid risk analysis results include power load limit test results, a system response time versus temperature graph, and low-temperature risk levels of key equipment. The equipment fault prediction information includes fault probability calculation based on temperature fluctuations, sensitivity parameters for current anomaly detection, expected fault types, and occurrence time intervals. The power maintenance schedule specifically includes equipment replacement priority ranking, expected maintenance time points and durations, and an assessment of the impact of maintenance activities on power grid operation.

[0014] As a further aspect of the present invention, the energy consumption change analysis module includes:

[0015] The temperature correlation submodule analyzes the correlation between temperature changes and power consumption in cold regions based on the power plant’s historical energy consumption records, assesses the impact of temperature on energy consumption in cold regions, and generates temperature-energy consumption correlation information.

[0016] The wind speed correlation submodule analyzes wind speed data in cold regions based on the temperature energy consumption correlation information, analyzes and calculates the degree of influence of wind speed changes on energy consumption, and generates wind speed energy consumption impact information.

[0017] Based on the wind speed energy consumption impact information, the trend calculation submodule performs time series analysis on historical energy consumption data and meteorological data to predict energy consumption trends in cold regions and generate energy consumption trend prediction information.

[0018] As a further aspect of the present invention, the energy consumption cycle identification module includes:

[0019] Based on the energy consumption trend prediction information, the seasonal factor analysis submodule analyzes the impact of seasonal changes on temperature and sunshine in cold regions, assesses the impact of temperature fluctuations and sunshine duration changes on electricity consumption, and generates multi-seasonal energy consumption analysis data.

[0020] The periodic pattern recognition submodule identifies periodic patterns in the energy consumption data based on the multi-seasonal energy consumption analysis data, including annual peaks and seasonal variations, and generates energy consumption pattern analysis information.

[0021] Based on the energy consumption pattern analysis information, the periodic information output submodule uses the Fourier transform algorithm to analyze the impact of seasonal changes in cold regions on the periodic pattern of electricity consumption, including peak and off-peak periods, and generates seasonal energy consumption pattern information.

[0022] As a further aspect of the present invention, the Fourier transform algorithm is defined by the formula:

[0023]

[0024] Calculate the periodic pattern of energy consumption, where X k Let x be the complex form of the k-th frequency component, ∑ be the summation symbol, n be the position of the current data point, N be the total number of data points in the entire time series, and x be the summation symbol. n Let be the nth energy consumption data point in the time series, where 'a' is the weighting coefficient for the corresponding energy consumption data, 'b' is the weighting coefficient for temperature, 'c' is the weighting coefficient for sunshine duration, 'd' is the weighting coefficient for load variation, and T is the weighting coefficient for load variation. n Let D be the temperature at time point n. n Let L be the duration of sunshine at time point n. n Let e ​​be the load change at time n, e be the base of the natural logarithm, i be the imaginary unit, 2π be twice pi, and k be the frequency index.

[0025] As a further aspect of the present invention, the power adaptability analysis module includes:

[0026] The environmental impact analysis submodule, based on the seasonal energy consumption pattern information and combined with current and historical ambient temperature data, analyzes the impact of temperature changes on electricity consumption, identifies the correlation between temperature fluctuations and energy consumption, and generates temperature-energy consumption relationship information.

[0027] The low-temperature sensitivity analysis submodule evaluates the sensitivity and stability of the power system response during low-temperature periods based on the temperature-energy consumption relationship information, and generates stability impact assessment results by simulating load response under different temperatures.

[0028] Based on the stability impact assessment results, the substation low temperature analysis submodule analyzes the substation's operational capability under predicted extreme low temperature conditions, assesses the substation's response capability under actual low temperature conditions, and generates low temperature responsiveness assessment results.

[0029] As a further aspect of the present invention, the low-temperature impact assessment module includes:

[0030] Based on the low-temperature responsiveness assessment results, the load analysis submodule identifies the load-bearing capacity of the power system under low-temperature conditions by comparing historical data and current load changes, and generates load capacity calculation results.

[0031] Based on the load capacity calculation results, the system response evaluation submodule tests the power system response time by simulating temperature changes under various conditions, evaluates the power system's response speed and efficiency to temperature changes, and generates power response performance analysis results.

[0032] Based on the power response performance analysis results, the risk identification submodule identifies key risk factors affecting the stability of the power system under low temperature conditions, assesses the risk level of various influencing factors, and generates power grid risk analysis results.

[0033] As a further aspect of the present invention, the fault risk identification module includes:

[0034] Based on the power grid risk analysis results, the temperature fluctuation analysis submodule analyzes the fluctuation patterns of temperature data in cold regions, identifies the relationship between temperature changes and equipment efficiency, assesses the impact of temperature fluctuations on equipment performance, and generates temperature sensitivity assessment data.

[0035] The current anomaly analysis submodule analyzes the real-time current data of the substation based on the temperature sensitivity assessment data, identifies abnormal current fluctuations, assesses the correlation pattern between temperature changes and current anomalies, and generates anomaly correlation analysis results.

[0036] Based on the abnormal correlation analysis results, the fault probability calculation submodule combines historical equipment fault data and real-time monitoring data, and uses a logistic regression algorithm to predict the probability of faults occurring in multiple substations in cold regions, generating equipment fault prediction information.

[0037] As a further aspect of the present invention, the logistic regression algorithm is based on the formula:

[0038]

[0039] Calculate the probability of equipment failure, where p is the probability of equipment failure, T is the temperature fluctuation value, I is the current anomaly value, H is historical failure data, E is the environmental impact factor, D is the equipment operating time, β0 is the intercept term of the regression model, β1 is the regression coefficient of temperature fluctuation, β2 is the regression coefficient of current anomaly, β3 is the regression coefficient of historical failure data, β4 is the regression coefficient of environmental impact factor, β5 is the regression coefficient of equipment operating time, and e is the base of the natural logarithm.

[0040] As a further aspect of the present invention, the equipment lifespan management module includes:

[0041] Based on the equipment failure prediction information, the cost analysis submodule analyzes the substation operation and maintenance costs affected by the failure and generates cost-benefit assessment data.

[0042] The life curve update submodule updates the equipment life curve based on the cost-benefit assessment data, assesses the expected service life and replacement cycle of the equipment, and generates equipment life calculation information.

[0043] The maintenance plan adjustment submodule adjusts and updates the maintenance plans and maintenance cycles of multiple substations in cold regions based on the equipment life calculation information, and generates a power maintenance schedule.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] This invention improves the accuracy of predicting power consumption trends and the speed of operational response by analyzing environmental data and energy consumption records of typical indoor substations in cold regions. By combining the analysis of the relationship between temperature, wind speed, and energy consumption, it accurately predicts energy consumption changes, helping maintenance personnel to adjust the power grid to match seasonal and weather changes in a timely manner. Combined with real-time environmental data analysis, it improves the accuracy of assessing the impact of low temperatures in winter, ensuring the stable operation of the power system under extreme conditions, reducing losses caused by faults and unplanned outages, and extending the service life of equipment and improving operational efficiency by dynamically adjusting maintenance plans. Attached Figure Description

[0046] Figure 1 This is a platform flowchart of the present invention;

[0047] Figure 2 This is a schematic diagram of the platform framework of the present invention;

[0048] Figure 3 This is a flowchart of the energy consumption change analysis module of the present invention;

[0049] Figure 4 This is a flowchart of the energy consumption cycle identification module of the present invention;

[0050] Figure 5 This is a flowchart of the power adaptability analysis module of the present invention;

[0051] Figure 6 This is a flowchart of the low-temperature impact assessment module of the present invention;

[0052] Figure 7 This is a flowchart of the fault risk identification module of the present invention;

[0053] Figure 8 This is a flowchart of the equipment life management module of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0056] Example 1

[0057] Please see Figures 1 to 2 A typical indoor substation energy consumption monitoring, analysis, and operation and maintenance platform in cold regions includes:

[0058] The energy consumption change analysis module analyzes the relationship between temperature, wind speed and energy consumption based on the power plant's historical energy consumption records, calculates the power consumption trend in cold regions, and generates energy consumption trend prediction information.

[0059] The energy consumption cycle identification module analyzes the seasonal factors affecting energy consumption based on energy consumption trend prediction information, identifies the periodic patterns of electricity consumption, and obtains seasonal energy consumption pattern information.

[0060] The power adaptability analysis module uses seasonal energy consumption pattern information, combined with ambient temperature data, to analyze the impact of low winter temperatures on power system stability and generate low-temperature responsiveness assessment results.

[0061] The low-temperature impact assessment module analyzes the power load capacity and substation response speed based on the low-temperature responsiveness assessment results, identifies key factors affecting power stability, and generates power grid risk analysis results.

[0062] The fault risk identification module analyzes the correlation between temperature fluctuations and current anomalies based on the power grid risk analysis results, identifies abnormal patterns and the probability of fault occurrence, and generates equipment fault prediction information.

[0063] The equipment life management module analyzes the life cycle cost of equipment based on equipment failure prediction information, updates the equipment life curve, and adjusts the maintenance plan to obtain a power maintenance schedule.

[0064] Energy consumption trend prediction information includes monthly energy consumption change curves in cold regions, energy consumption response data at key temperature points, and quantitative analysis results of the impact of wind speed on energy consumption. Seasonal energy consumption pattern information specifically includes summer and winter energy consumption statistics, energy consumption fluctuation patterns during the spring and autumn transition period, and energy consumption response speed during seasonal transitions. Low temperature responsiveness assessment results include power system stability indicators under extreme low temperatures in winter, sensitivity analysis results of the impact of temperature drop on equipment operation, and prediction models of increased energy consumption during low temperatures. Power grid risk analysis results include power load limit test results, system response time versus temperature graphs, and low temperature risk levels of key equipment. Equipment failure prediction information includes failure probability calculations based on temperature fluctuations, sensitivity parameters for current anomaly detection, expected failure types and occurrence time intervals. Power maintenance schedules specifically include equipment replacement priority ranking, expected maintenance time points and durations, and assessments of the impact of maintenance activities on power grid operation.

[0065] Please see Figure 2 and Figure 3 The energy consumption change analysis module includes:

[0066] The temperature correlation submodule analyzes the correlation between temperature changes and power consumption in cold regions based on the power plant’s historical energy consumption records, assesses the impact of temperature on energy consumption in cold regions, and generates temperature-energy consumption correlation information. The specific process is as follows:

[0067] In the temperature correlation submodule, based on the power plant's historical energy consumption records, temperature and energy consumption data from different years are collected and integrated to calculate the average energy consumption under different temperatures. A temperature-energy consumption relationship model is used to analyze the correlation between temperature changes and electricity consumption in cold regions. The formula is E = a + b·T, where E represents electricity consumption, T represents average temperature, a is the basic energy consumption offset, and b is the temperature sensitivity coefficient. The impact of temperature on energy consumption in cold regions is assessed, and temperature-energy consumption correlation information is generated.

[0068] The wind speed correlation submodule analyzes wind speed data in cold regions based on temperature energy consumption correlation information, analyzes and calculates the degree of impact of wind speed changes on energy consumption, and generates wind speed energy consumption impact information. The specific process is as follows:

[0069] In the wind speed correlation submodule, based on temperature energy consumption correlation information, wind speed data in cold regions is collected, and wind speed data is paired with energy consumption data. By calculating the energy consumption change ratio corresponding to multi-level wind speed changes, the wind speed energy consumption impact model is applied, with the formula E = c + d·W, where E represents energy consumption, W represents wind speed, c is the energy consumption baseline value, and d is the wind speed sensitivity coefficient. The impact of wind speed changes on energy consumption is analyzed and calculated, generating wind speed energy consumption impact information.

[0070] The trend calculation submodule, based on wind speed energy consumption impact information, performs time series analysis on historical energy consumption data and meteorological data to predict energy consumption trends in cold regions. The specific process for generating energy consumption trend prediction information is as follows:

[0071] In the trend calculation submodule, based on wind speed energy consumption impact information, energy consumption data and meteorological data are collected and integrated. Using time series analysis methods, an energy consumption trend prediction model is established, with the formula E. t =α·E t-1 +β·X t +t, where E t To predict energy consumption, E t-1 For historical energy consumption, X t Given a meteorological dataset, α and β are the autoregressive coefficient and moving average coefficient, respectively, and t is the error term. This dataset is used to predict energy consumption trends in cold regions and generate energy consumption trend prediction information.

[0072] Please see Figure 2 and Figure 4 The energy consumption cycle identification module includes:

[0073] The seasonal factor analysis submodule analyzes the impact of seasonal changes on temperature and sunshine duration in cold regions based on energy consumption trend prediction information, assesses the impact of temperature fluctuations and sunshine duration changes on electricity consumption, and generates multi-seasonal energy consumption analysis data. The specific process is as follows:

[0074] In the seasonal factor analysis submodule, based on energy consumption trend prediction information, temperature and sunshine data from cold regions are collected, including extreme temperatures and average sunshine duration for each season. A seasonal energy consumption impact model is then applied, with the formula E. s =e+f·T s +g·S s E s T represents seasonal energy consumption. s S represents the seasonal average temperature. s Let e ​​be the seasonal average sunshine duration, f be the initial energy consumption offset, and g be the influence coefficients of temperature and sunshine duration, respectively. This study assesses the impact of temperature fluctuations and changes in sunshine duration on electricity consumption and generates multi-seasonal energy consumption analysis data.

[0075] The periodic pattern recognition submodule identifies periodic patterns in energy consumption data based on multi-seasonal energy consumption analysis data, including annual peaks and seasonal variations, and generates energy consumption pattern analysis information. The specific process is as follows:

[0076] In the periodic pattern recognition submodule, based on multi-seasonal energy consumption analysis data, the annual and seasonal changes in energy consumption data are analyzed, and a periodic pattern recognition model is applied, with the formula E. p = h·cos(ωt+), where E p It represents the periodic energy consumption pattern, where h is the amplitude, ω is the angular frequency, and ω is the phase shift. It describes the annual peak and seasonal variations, identifies periodic patterns in energy consumption data, and generates energy consumption pattern analysis information.

[0077] The periodic information output submodule analyzes energy consumption pattern information and uses the Fourier transform algorithm to analyze the impact of seasonal changes in cold regions on the periodic pattern of electricity consumption, including peak and valley periods. The specific process for generating seasonal energy consumption pattern information is as follows.

[0078] In the periodic information output submodule, based on energy consumption pattern analysis information, historical data of electricity consumption is analyzed, including hourly, daily, monthly and yearly energy consumption records. The overall trend and seasonal variation of electricity consumption are identified, the impact of seasonal variation is analyzed using the Fourier transform algorithm, and the impact on the periodic pattern of electricity consumption is calculated. The seasonal peaks and troughs of electricity consumption are identified, and seasonal energy consumption pattern information is generated.

[0079] The Fourier transform algorithm, through the formula:

[0080]

[0081] Calculate the periodic pattern of energy consumption, where X kLet be the complex form of the k-th frequency component, representing the spectral analysis result of the energy consumption data at frequency k. ∑ is the summation symbol, used to accumulate the contribution of each data point from n = 0 to N - 1. n is the index, indicating the position of the current data point, and N is the total number of data points in the entire time series. x n Let be the nth energy consumption data point in the time series, where 'a' is the weighting coefficient for the corresponding energy consumption data, 'b' is the weighting coefficient for temperature, 'c' is the weighting coefficient for sunshine duration, 'd' is the weighting coefficient for load variation, and T is the weighting coefficient for load variation. n Let D be the temperature at time point n. n Let L be the duration of sunshine at time point n. n Let represent the load change at time point n, e be the base of the natural logarithm used for the complex exponential function in the Fourier transform, i be the imaginary unit used to generate complex numbers, 2π be a constant, twice the value of pi, used for the conversion between frequency and time point in the Fourier transform, and k be the frequency index, representing the frequency component currently being analyzed.

[0082] The specific execution process of the formula is as follows:

[0083] Based on historical data analysis, correlation analysis with environmental factors is typically used to estimate the contribution of energy consumption data, temperature, sunshine duration, and load factors to energy consumption using regression models. Optimization algorithms are then applied to adjust weights and minimize prediction errors. Environmental parameters are combined with energy consumption data for weighted averaging, and Fourier transform is used to convert time series data from the time domain to the frequency domain to identify periodic patterns in energy consumption data, including annual peaks and seasonal variations.

[0084] Please see Figure 2 and Figure 5 The power adaptability analysis module includes:

[0085] The environmental impact analysis submodule analyzes the impact of temperature changes on electricity consumption based on seasonal energy consumption pattern information and combined with current and historical ambient temperature data, identifies the correlation between temperature fluctuations and energy consumption, and generates temperature-energy consumption relationship information. The specific process is as follows:

[0086] In the environmental impact analysis submodule, based on seasonal energy consumption pattern information, by integrating current and historical environmental temperature data, the temperature and energy consumption data at each time point are matched and analyzed, and a temperature-energy consumption correlation model is applied, with the formula being E. v =i+j·ΔT, where, E v The equation represents the amount of electricity consumption, ΔT represents the amount of temperature change, i is the baseline energy consumption, and j is the temperature change sensitivity coefficient. The analysis focuses on the impact of temperature changes on electricity consumption, identifies the correlation between temperature fluctuations and energy consumption, and generates temperature-energy consumption relationship information.

[0087] The low-temperature sensitivity analysis submodule evaluates the sensitivity and stability of the power system response during low-temperature periods based on temperature-energy consumption relationship information. It generates stability impact assessment results by simulating load response under different temperatures. The specific process is as follows:

[0088] In the low-temperature sensitivity analysis submodule, the response sensitivity and stability of the power system during low-temperature periods are evaluated based on the temperature-energy consumption relationship information. The low-temperature sensitivity model is adopted, with the formula S = k·log(T+1) + l·E, where S represents the stability of the system, T represents the temperature, E represents the power load, and k and l are adjustment coefficients. By simulating the load response under different temperatures, the stability impact assessment results are generated.

[0089] The substation low-temperature analysis submodule analyzes the substation's operational capability under predicted extreme low-temperature conditions based on the stability impact assessment results, evaluates the substation's response capability under actual low-temperature conditions, and generates low-temperature responsiveness assessment results. The specific process is as follows:

[0090] In the substation low-temperature analysis submodule, based on the stability impact assessment results, the substation's operational capability under predicted extreme low-temperature conditions is analyzed. The substation low-temperature response model is applied, with the formula R = m·Sn·T. low Where R represents response capability, S is the substation stability score, and T... low The predicted minimum temperature is used, and m and n are weighting coefficients. The response capability of the substation under actual low temperature conditions is evaluated, and low temperature responsiveness assessment results are generated.

[0091] Please see Figure 2 and Figure 6 The low-temperature impact assessment module includes:

[0092] The load analysis submodule, based on the low-temperature responsiveness assessment results, identifies the load-bearing capacity of the power system under low-temperature conditions by comparing historical data and current load changes, and generates load capacity calculation results. The specific process is as follows:

[0093] In the load analysis submodule, based on the low-temperature responsiveness assessment results, historical and current load data are collected and compared, and a load capacity model is applied, with the formula L. c =p·L hist +q·L cur , where L c L represents the calculated load capacity. hist For historical average load, L cur The load is the current load, and p and q are the weighting coefficients for historical and current data. The load capacity calculation results are generated by identifying the load capacity of the power system under low temperature conditions.

[0094] The system response assessment submodule, based on the load capacity calculation results, tests the power system response time by simulating temperature changes under various conditions, evaluates the power system's response speed and efficiency to temperature changes, and generates power response performance analysis results. The specific process is as follows:

[0095] In the system response assessment submodule, based on the load capacity calculation results, the power system response time model is applied by simulating load changes under various temperature conditions. The formula is as follows: Among them, R t The response time is represented by ΔT, the temperature change is represented by r and s, and the response speed and efficiency of the power system to temperature changes are evaluated to generate power response performance analysis results.

[0096] Based on the power response performance analysis results, the risk identification submodule identifies key risk factors affecting the stability of the power system under low temperature conditions, assesses the risk level of various influencing factors, and generates power grid risk analysis results. The specific process is as follows:

[0097] In the risk identification submodule, based on the power response performance analysis results, a power grid risk analysis model is adopted, with the formula R. f =u·E eff +v·F, where R f E represents the risk assessment results. eff The response performance score is F, the identified risk factors are u and v, and the key risk factors affecting the stability of the power system under low temperature conditions are identified. The risk levels of multiple influencing factors are assessed, and the power grid risk analysis results are generated.

[0098] Please see Figure 2 and Figure 7 The fault risk identification module includes:

[0099] The temperature fluctuation analysis submodule analyzes the fluctuation patterns of temperature data in cold regions based on the results of power grid risk analysis, identifies the relationship between temperature changes and equipment efficiency, assesses the impact of temperature fluctuations on equipment performance, and generates temperature sensitivity assessment data. The specific process is as follows:

[0100] In the temperature fluctuation analysis submodule, based on the results of power grid risk analysis, temperature data from cold regions is collected, and a temperature fluctuation model is applied, with the formula V. t =w·σ(T)+x·μ(T), where, V t The value represents the impact of temperature fluctuations on equipment performance. σ(T) is the standard deviation of temperature data, μ(T) is the average temperature, and w and x are weighting coefficients. The system analyzes temperature fluctuation patterns, identifies the relationship between temperature changes and equipment efficiency, assesses the impact of temperature fluctuations on equipment performance, and generates temperature sensitivity assessment data.

[0101] The current anomaly analysis submodule analyzes real-time current data of the substation based on temperature sensitivity assessment data, identifies abnormal current fluctuations, evaluates the correlation pattern between temperature changes and current anomalies, and generates anomaly correlation analysis results. The specific process is as follows:

[0102] In the current anomaly analysis submodule, based on temperature sensitivity assessment data, the real-time current data of the substation is analyzed, and a current anomaly correlation model is applied, with the formula being A. c =y·δ(I)+z·ΔT, where A c The result represents the current anomaly correlation analysis, where δ(I) is the current anomaly fluctuation, ΔT is the temperature change, and y and z are adjustment coefficients. The correlation pattern between current anomaly fluctuation and temperature change is identified, and the anomaly correlation analysis result is generated.

[0103] The fault probability calculation submodule, based on the results of anomaly correlation analysis and combined with historical equipment fault data and real-time monitoring data, uses a logistic regression algorithm to predict the probability of faults occurring in multiple substations in cold regions and generates equipment fault prediction information. The specific process is as follows:

[0104] In the fault probability calculation submodule, based on the results of anomaly correlation analysis, historical fault data is organized and analyzed, including fault frequency, fault type, and environmental conditions in which the fault occurs. Real-time monitoring data provides the current operating status of the equipment, including temperature, current, and voltage. The probability of faults occurring in multiple substations in cold regions is predicted, key factors leading to faults are identified, and equipment fault prediction information is generated.

[0105] Logistic regression algorithm, according to the formula:

[0106]

[0107] Calculate the probability of equipment failure, where p is the probability of equipment failure, T is the temperature fluctuation value, I is the current anomaly value, H is historical failure data, E is the environmental impact factor, D is the equipment operating time, β0 is the intercept term of the regression model, β1 is the regression coefficient of temperature fluctuation, β2 is the regression coefficient of current anomaly, β3 is the regression coefficient of historical failure data, β4 is the regression coefficient of environmental impact factor, β5 is the regression coefficient of equipment operating time, and e is the base of the natural logarithm.

[0108] The specific execution process of the formula is as follows:

[0109] By collecting environmental parameters E and equipment operating time D, combined with temperature fluctuations T, current anomalies I, and historical fault data H, the regression coefficients are optimized using the maximum likelihood estimation method, regression analysis is performed, and the weight coefficients β0, β1, β2, β3, β4, and β5 of the variables are determined. Using the logistic regression formula, the probability of equipment failure is calculated, the probability of equipment failure under different environmental and operating conditions is predicted, and equipment failure prediction information is generated.

[0110] Please see Figure 2 and Figure 8 The equipment lifespan management module includes:

[0111] The cost analysis submodule analyzes the substation operation and maintenance costs affected by equipment failures based on equipment failure prediction information, and generates cost-benefit assessment data. The specific process is as follows:

[0112] In the cost analysis submodule, based on equipment failure prediction information, a cost-benefit analysis model is applied by evaluating the direct increase in operation and maintenance costs and downtime losses caused by failures. The formula is C. f =c·F p +d·M, where C f F represents the total cost affected by the failure. p Let M be the failure probability, c be the average maintenance cost, and d be the weighting factors. Analyze the operation and maintenance costs of substations to generate cost-benefit assessment data.

[0113] The life curve update submodule updates the equipment life curve based on cost-benefit assessment data, assesses the expected service life and replacement cycle of the equipment, and generates equipment life calculation information. The specific process is as follows:

[0114] In the life curve update submodule, based on cost-benefit assessment data, the relationship between equipment failure data and service life is analyzed, and an equipment life update model is applied, with the formula L = l·e -kt Where L represents the expected service life of the equipment, t is the time the equipment has been used, k is the decay constant, and l is the initial expected lifespan. The expected service life and replacement cycle of the equipment are evaluated and updated to generate equipment lifespan calculation information.

[0115] The maintenance plan adjustment submodule adjusts and updates the maintenance plans and maintenance cycles of multiple substations in cold regions based on equipment life calculation information, and generates a power maintenance schedule. The specific process is as follows:

[0116] In the maintenance plan adjustment submodule, based on equipment lifespan calculation information, and according to the updated expected lifespan of the equipment and the predicted failure probability, the substation maintenance plan is adjusted using a maintenance cycle adjustment model, the formula of which is: Among them, T m F represents the maintenance cycle. pLet ρ be the failure probability and ρ be the safety redundancy coefficient. Optimize the maintenance cycle to ensure that operational interruptions due to equipment failure are reduced while maintaining cost-effectiveness, and generate a power maintenance schedule.

[0117] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A typical indoor substation energy consumption monitoring, analysis, and operation and maintenance platform in cold regions, characterized in that, The platform includes: The energy consumption change analysis module analyzes the relationship between temperature, wind speed and energy consumption based on the power plant's historical energy consumption records, calculates the power consumption trend in cold regions, and generates energy consumption trend prediction information. Based on the energy consumption trend prediction information, the energy consumption cycle identification module analyzes the seasonal factors affecting energy consumption, identifies the periodic patterns of electricity consumption, and obtains seasonal energy consumption pattern information. The power adaptability analysis module uses the seasonal energy consumption pattern information and combines it with ambient temperature data to analyze the impact of low winter temperatures on the stability of the power system and generate low-temperature responsiveness assessment results. The power adaptability analysis module includes: The environmental impact analysis submodule, based on the seasonal energy consumption pattern information and combined with current and historical ambient temperature data, analyzes the impact of temperature changes on electricity consumption, identifies the correlation between temperature fluctuations and energy consumption, and generates temperature-energy consumption relationship information. The low-temperature sensitivity analysis submodule evaluates the sensitivity and stability of the power system response during low-temperature periods based on the temperature-energy consumption relationship information, and generates stability impact assessment results by simulating load response under different temperatures. Based on the stability impact assessment results, the substation low temperature analysis submodule analyzes the substation's operational capability under predicted extreme low temperature conditions, assesses the substation's response capability under actual low temperature conditions, and generates low temperature responsiveness assessment results. Based on the low-temperature responsiveness assessment results, the low-temperature impact assessment module analyzes the power load capacity and substation response speed, identifies key factors affecting power stability, and generates power grid risk analysis results. The low-temperature impact assessment module includes: Based on the low-temperature responsiveness assessment results, the load analysis submodule identifies the load-bearing capacity of the power system under low-temperature conditions by comparing historical data and current load changes, and generates load capacity calculation results. Based on the load capacity calculation results, the system response evaluation submodule tests the power system response time by simulating temperature changes under various conditions, evaluates the power system's response speed and efficiency to temperature changes, and generates power response performance analysis results. Based on the power response performance analysis results, the risk identification submodule identifies key risk factors affecting the stability of the power system under low temperature conditions, assesses the risk level of various influencing factors, and generates power grid risk analysis results. The fault risk identification module analyzes the correlation between temperature fluctuations and current anomalies based on the power grid risk analysis results, identifies abnormal patterns and the probability of fault occurrence, and generates equipment fault prediction information. Based on the equipment failure prediction information, the equipment life management module analyzes the life cycle cost of the equipment, updates the equipment life curve, and adjusts the maintenance plan to obtain a power maintenance schedule.

2. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 1, characterized in that, The energy consumption trend prediction information includes monthly energy consumption change curves in cold regions, energy consumption response data at key temperature points, and quantitative analysis results of the impact of wind speed on energy consumption. The seasonal energy consumption pattern information specifically includes summer and winter energy consumption statistics, energy consumption fluctuation patterns during the spring and autumn transition period, and energy consumption response speed during seasonal transitions. The low-temperature responsiveness assessment results include power system stability indicators under extreme low temperatures in winter, sensitivity analysis results of the impact of temperature drop on equipment operation, and prediction models of increased energy consumption during low temperatures. The power grid risk analysis results include power load limit test results, system response time versus temperature graphs, and low-temperature risk levels of key equipment. The equipment fault prediction information includes fault probability calculation based on temperature fluctuations, sensitivity parameters for current anomaly detection, expected fault types, and occurrence time intervals. The power maintenance schedule specifically includes equipment replacement priority ranking, expected maintenance time points and durations, and assessments of the impact of maintenance activities on power grid operation.

3. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 1, characterized in that, The energy consumption change analysis module includes: The temperature correlation submodule analyzes the correlation between temperature changes and power consumption in cold regions based on the power plant’s historical energy consumption records, assesses the impact of temperature on energy consumption in cold regions, and generates temperature-energy consumption correlation information. The wind speed correlation submodule analyzes wind speed data in cold regions based on the temperature energy consumption correlation information, analyzes and calculates the degree of influence of wind speed changes on energy consumption, and generates wind speed energy consumption impact information. Based on the wind speed energy consumption impact information, the trend calculation submodule performs time series analysis on historical energy consumption data and meteorological data to predict energy consumption trends in cold regions and generate energy consumption trend prediction information.

4. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 1, characterized in that, The energy consumption cycle identification module includes: Based on the energy consumption trend prediction information, the seasonal factor analysis submodule analyzes the impact of seasonal changes on temperature and sunshine in cold regions, assesses the impact of temperature fluctuations and sunshine duration changes on electricity consumption, and generates multi-seasonal energy consumption analysis data. The periodic pattern recognition submodule identifies periodic patterns in the energy consumption data based on the multi-seasonal energy consumption analysis data, including annual peaks and seasonal variations, and generates energy consumption pattern analysis information. Based on the energy consumption pattern analysis information, the periodic information output submodule uses the Fourier transform algorithm to analyze the impact of seasonal changes in cold regions on the periodic pattern of electricity consumption, including peak and off-peak periods, and generates seasonal energy consumption pattern information.

5. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 4, characterized in that, The Fourier transform algorithm is based on the formula: ； Calculate the periodicity pattern of energy consumption, where, For the first The complex form of the frequency component, For summation, This represents the current location of the data point. This represents the total number of data points in the entire time series. For the first in the time series One energy consumption data point, These are the weighting coefficients for the corresponding energy consumption data. Weighting coefficients corresponding to temperature, The weighting coefficient corresponding to sunshine duration, Weighting coefficients corresponding to load changes For at a certain point in time temperature, For at a certain point in time The length of sunshine, For at a certain point in time Load changes, is the base of the natural logarithm. The imaginary unit, It is twice the value of pi. For frequency indexing.

6. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 1, characterized in that, The fault risk identification module includes: Based on the power grid risk analysis results, the temperature fluctuation analysis submodule analyzes the fluctuation patterns of temperature data in cold regions, identifies the relationship between temperature changes and equipment efficiency, assesses the impact of temperature fluctuations on equipment performance, and generates temperature sensitivity assessment data. The current anomaly analysis submodule analyzes the real-time current data of the substation based on the temperature sensitivity assessment data, identifies abnormal current fluctuations, assesses the correlation pattern between temperature changes and current anomalies, and generates anomaly correlation analysis results. Based on the abnormal correlation analysis results, the fault probability calculation submodule combines historical equipment fault data and real-time monitoring data, and uses a logistic regression algorithm to predict the probability of faults occurring in multiple substations in cold regions, generating equipment fault prediction information.

7. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 6, characterized in that, The logistic regression algorithm is based on the formula: ； Calculate the probability of equipment failure, where, The probability of equipment failure. This represents the temperature fluctuation value. This is an abnormal current value. This is historical fault data. As environmental impact factors, For equipment runtime, The intercept term of the regression model, The regression coefficient for temperature fluctuations is... The regression coefficient for the current anomaly is... The regression coefficients are the regression coefficients for historical fault data. The regression coefficients of environmental impact factors are given. The regression coefficient for equipment runtime. is the base of the natural logarithm.

8. The energy consumption monitoring, analysis, and operation and maintenance platform for typical indoor substations in cold regions according to claim 1, characterized in that, The equipment lifespan management module includes: Based on the equipment failure prediction information, the cost analysis submodule analyzes the substation operation and maintenance costs affected by the failure and generates cost-benefit assessment data. The life curve update submodule updates the equipment life curve based on the cost-benefit assessment data, assesses the expected service life and replacement cycle of the equipment, and generates equipment life calculation information. The maintenance plan adjustment submodule adjusts and updates the maintenance plans and maintenance cycles of multiple substations in cold regions based on the equipment life calculation information, and generates a power maintenance schedule.

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