Energy storage converter system applied to smart power grid area

Abstract

The invention discloses an energy storage converter system applied to an intelligent power grid area, and relates to the technical field of power grid analysis. The converter control center is in communication connection with a battery management system, an energy storage converter module, a data acquisition monitoring module, a state evaluation module, a predictive maintenance module and a control system module, and all the modules are in electric signal connection; and the battery management system is used for monitoring and managing the state of the battery pack. The stability and reliability of a power grid are remarkably improved through real-time monitoring and optimal control, relevant parameters are collected in real time through the battery management system and the energy storage converter module, it is ensured that a battery pack and a converter operate in a safe and efficient state, data analysis is conducted through the state evaluation module, and a state evaluation index is calculated; the system abnormity is found in time, the fault risk is reduced, and the scheduling and control strategy of the energy storage converter is further optimized in combination with the actual demand of the intelligent power grid area.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid analysis, and in particular to an energy storage converter system applied to a smart grid station area. Background Art

[0002] With the acceleration of global energy transformation, renewable energy such as solar energy and wind energy have been widely used. The current focus of electricity complaints is on the direct impact of the power quality and reliability of low-voltage distribution stations on the normal production and life of residents. Due to the volatility and intermittent nature of power generation, energy storage systems are needed to balance power supply and demand. In improving the power quality of low-voltage distribution stations, the distribution of active power and reactive power can have a relatively significant impact on the node voltage. In severe cases, it can cause voltage limit violations and increase line losses. Unreasonable single-phase load distribution can cause three-phase imbalance in the station area, which can seriously affect electricity safety and reduce power factors. In addition, in improving the reliability of power supply in low-voltage distribution stations, low-voltage users have obvious intermittent and seasonal power consumption characteristics, which can easily cause short-term heavy overloads of distribution transformers, posing hidden dangers to the safe operation of distribution transformers.

[0003] For example, Chinese patent publication number: CN118232385A discloses an energy storage converter system for use in smart grid stations, the system comprising: a control loop circuit and a main circuit; the main circuit comprises a DC side protection circuit, a power conversion circuit, an AC side protection circuit and an AC side isolation circuit connected in sequence.

[0004] In the prior art, the charging and discharging of batteries in the smart grid of the substation is intelligently managed to adapt to substations of different sizes and types, so as to solve the problem that it is impossible to carry out targeted design and optimization according to the actual working conditions of the substation. However, since the power grid station is susceptible to long-term troubles of low voltage and periodic heavy overload in the distribution network, it will interfere with the power supply quality of the low-voltage distribution substation, resulting in deviations in the status evaluation of the energy storage inverter system, affecting the safety of the system. Therefore, there is an urgent need for an energy storage inverter system applied to the smart grid substation to accurately analyze the system status and maintenance requirements to reduce the degree of deviation in the evaluation process, and optimize the scheduling and control strategy of the energy storage inverter according to the actual needs of the smart grid substation to improve the response speed and control accuracy of the system. Summary of the invention

[0005] The purpose of the present invention is to provide an energy storage converter system applied to a smart grid station area to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] An energy storage converter system applied to a smart grid station area includes a converter control center, wherein the converter control center is communicatively connected to a battery management system, an energy storage converter module, a data acquisition monitoring module, a status assessment module, a predictive maintenance module, and a control system module, wherein electrical signals are connected between the modules;

[0008] The battery management system is used to monitor and manage the status of the battery pack, including the voltage, current, and temperature parameters of the battery, to ensure safe and reliable operation of the battery. Through real-time monitoring and status evaluation, it can prevent battery overcharging, over-discharging, and thermal runaway problems, extend battery life, and improve system safety. In the smart grid area, it reports the health status and remaining capacity of the battery pack to the area in real time to help the area optimize energy distribution and scheduling;

[0009] The energy storage converter module realizes the conversion and charging control of electric energy by controlling the switching state of current and voltage, converts the direct current of the battery pack into alternating current for use in the power grid, and can also operate in reverse to convert alternating current into direct current for charging, thereby realizing the efficient conversion of electric energy between different forms, supporting the two-way energy flow of the power grid, improving the flexibility and reliability of the power grid, and performing charging and discharging operations in combination with the energy demand of the substation and the state of the power grid to balance the supply and demand relationship of the power grid;

[0010] The data acquisition and monitoring module monitors the energy storage converter system in real time, collects the operating data of the battery management system and the energy storage converter module, and obtains an energy storage data sequence;

[0011] The state evaluation module evaluates the operating state of the energy storage converter system based on the data of the energy storage data sequence, analyzes the abnormal cost and failure risk trend of the energy storage converter system during operation, reduces the deviation of the state evaluation, and improves the accuracy of the evaluation;

[0012] The predictive maintenance module analyzes the energy loss rate of the system based on the status evaluation results of the energy storage converter system, calculates the energy storage optimization coefficient, comprehensively analyzes the status optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, and assists the substation to arrange maintenance work in advance to reduce energy interruptions and losses caused by system failures;

[0013] The control system module monitors the operating status in real time, and optimizes the scheduling and control of the energy storage inverter system in combination with the prediction results of the current maintenance needs of the system, thereby ensuring the efficient and stable operation of the energy storage inverter system, and at the same time working in coordination with the smart grid substation to realize distributed energy storage in the substation.

[0014] A further improvement of the technical solution of the present invention is that: in the energy storage converter module, the process of balancing the supply and demand relationship of the power grid is:

[0015] The energy storage converter receives DC power from the energy storage system (battery pack). After receiving the DC power, the energy storage converter converts the DC power into high-frequency AC power through the internal DC / AC bidirectional converter, so that the power can be more conveniently transmitted and used in the power grid. The high-frequency AC power converted by the DC / AC bidirectional converter is filtered through the output filter to remove the high-frequency harmonic components and obtain AC power that meets the requirements, thereby preventing harmonic pollution to the power grid.

[0016] During the charging process, the energy storage converter converts the AC power of the power grid into DC power through a rectification process, and supplies the battery pack to charge, ensuring that the battery pack can receive electrical energy safely and efficiently;

[0017] When there is excess electricity in the grid, the energy storage inverter stores the excess electricity in the battery pack for use as a backup power source. When the grid is short of electricity, the energy storage inverter releases electricity from the battery pack to supplement the energy shortage in the grid. The energy storage inverter performs intelligent control and management through the control system module based on the real-time needs of the grid and the status information of the battery pack to achieve efficient use of electricity, balance the supply and demand of the grid, ensure that the grid can maintain stable operation under any circumstances, and avoid grid failures caused by imbalance in electricity supply and demand.

[0018] A further improvement of the technical solution of the present invention is that in the data acquisition and monitoring module, the process of acquiring the energy storage data sequence is as follows:

[0019] The energy storage converter system starts, and the data acquisition monitoring module starts synchronously and enters the initialization phase, and then loads the preset configuration parameters, including the frequency, data type and storage location of data acquisition;

[0020] Collect relevant operating data from the battery management system and the energy storage converter module. The battery management system collects the status data of the battery pack in real time, including voltage, current, temperature and state of charge parameters. The energy storage converter module obtains the output power, efficiency and fault status of the energy storage converter. The collected data is preliminarily verified to ensure the accuracy and completeness of the data. If the data is abnormal or missing, it is marked.

[0021] The collected raw data is preprocessed, including filtering, denoising, data cleaning and format conversion, and the preprocessed data is integrated to generate an energy storage data sequence, which is stored in the database of the converter control center.

[0022] A further improvement of the technical solution of the present invention is that: in the state evaluation module, the process of evaluating the operating state of the energy storage converter system is:

[0023] Obtain energy storage data sequence from the database of the converter control center, extract the voltage, current, temperature, state of charge data of the battery pack, and the output power, efficiency and fault status data of the energy storage converter;

[0024] According to the operation requirements of the energy storage converter system, the characteristics of the battery pack and the energy storage converter are selected, which are the voltage, current, temperature, and state of charge characteristics of the battery pack, and the output power, efficiency, and fault state characteristics of the energy storage converter. The normal threshold of each characteristic is determined using historical fault data.

[0025] Based on the normal thresholds of each feature of the battery pack, combined with the data of each feature of the battery pack, the battery pack evaluation index is comprehensively analyzed and calculated to analyze the operating status of the battery pack. Based on the normal thresholds of each feature of the energy storage converter, combined with the data of each feature of the energy storage converter, the converter evaluation index is comprehensively analyzed and calculated to analyze the performance of the energy storage converter.

[0026] By combining the battery pack evaluation indicators and the converter evaluation indicators, the status evaluation index of the entire energy storage system is calculated, and the abnormal cost and failure risk trend of the energy storage converter system operation process are analyzed.

[0027] A further improvement of the technical solution of the present invention is that the expression of the battery pack evaluation index is:

[0028]

[0029] Among them, BI is the battery pack evaluation index, V d is the actual measured battery pack voltage, V t is the normal threshold of the battery pack voltage, I d is the actual measured battery pack current, I t is the normal threshold of battery pack current, T d is the actual measured battery pack temperature, T t is the normal threshold of battery pack temperature, S d is the actual measured battery pack state of charge, S t It is the normal threshold of the battery pack state of charge. The value range of BI is between 0 and 1. As the actual measured value of each feature approaches its normal threshold, the value of BI increases, indicating that the battery pack state is better.

[0030] The expression of the converter evaluation index is:

[0031]

[0032] Among them, IAI is the converter evaluation index, P d is the actual measured output power of the energy storage converter, P t is the normal threshold of the energy storage converter output power, ηd is the actual measured energy storage converter efficiency, η t is the normal threshold of energy storage converter efficiency, F d is the fault state of the energy storage converter, where F d =0 means no fault, F d = 0 indicates that a fault exists, and the value range of IAI is between 0 and 1. As the actual measured value of each feature approaches its normal threshold and no fault occurs, the value of IAI increases, indicating that the converter status is better.

[0033] A further improvement of the technical solution of the present invention is that the state evaluation index is obtained based on the battery pack evaluation index and the converter evaluation index, and its expression is:

[0034]

[0035] Among them, S is the status evaluation index, BI is the battery pack evaluation index, and IAI is the converter evaluation index. It should be noted that the value range of S is between 0 and 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in good condition. On the contrary, if the value of BI or IAI decreases, the value of S will decrease significantly, indicating that the energy storage system is in poor condition.

[0036] A further improvement of the technical solution of the present invention is that in the predictive maintenance module, the analysis process of the energy storage converter system state optimization trend is:

[0037] Based on the status evaluation results of the energy storage converter system, obtain the battery pack evaluation index, converter evaluation index and status evaluation index to analyze the current status and performance of the system;

[0038] Monitor the energy input and output of the system within a fixed period, analyze the loss factors of energy transmission, including battery pack internal resistance loss, line loss, and energy storage inverter loss, etc., to determine the energy loss ratio, and then calculate the energy loss rate of the energy storage inverter system during operation, analyze the change trend of the energy loss rate, and identify potential energy consumption problems;

[0039] Combine the state assessment index and energy loss rate to calculate and analyze the energy storage optimization coefficient, and conduct a comprehensive assessment of the system's energy storage efficiency and performance optimization potential;

[0040] Based on the value of the energy storage optimization coefficient, the operating state of the energy storage converter system is divided into different state levels, namely, excellent state level, good state level and poor state level, and the corresponding optimization evaluation threshold is matched for each state level;

[0041] According to the energy storage optimization coefficient and the set status level, the future status of the energy storage inverter system is analyzed, and the maintenance requirements of the energy storage inverter system are determined, the maintenance priority is clarified, and the auxiliary station area arranges maintenance work in advance.

[0042] A further improvement of the technical solution of the present invention is that the expression of the energy storage optimization coefficient is:

[0043]

[0044] Among them, SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the state assessment index, E in is the total energy input in a fixed period, E out It is the total energy output in a fixed period, n is the number of loss factors, which is the sum of battery internal resistance loss, cable loss, converter loss, etc. The SOC value range is between 0 and 1. With the increase of state assessment index and the decrease of energy loss rate, the value of energy storage optimization coefficient increases, indicating that the optimization potential of energy storage system is greater.

[0045] A further improvement of the technical solution of the present invention is that: a plurality of the state levels correspond to a plurality of the optimization evaluation thresholds, wherein the state levels correspond to the optimization evaluation thresholds one by one, specifically:

[0046] Excellent status level: SOC H ≤SOC<1;

[0047] Good condition level: SOC L ≤SOC <SOC H ;

[0048] Bad status level: 0 <SOC<SOC L ;

[0049] Among them, SOC is the energy storage optimization coefficient, SOC H The lower threshold of the excellent state level and the upper threshold of the good state level, SOC L is the lower threshold of the good state level and the upper threshold of the bad state level, SOC H =0.8, SOC L =0.6.

[0050] A further improvement of the technical solution of the present invention is that in the control system module, the process of optimizing the scheduling and control of the energy storage converter system is:

[0051] Through the data acquisition and monitoring module, various operating data of the energy storage inverter system are collected in real time, including but not limited to the voltage, current, temperature, state of charge of the battery pack, and the output power, efficiency and fault status of the energy storage inverter, and the problems of the power grid area are identified, among which the problems of the power grid area include the long-term three-phase load imbalance and low voltage problems at the end of the power grid area and the long-term heavy overload conditions of the transformer in the power grid area;

[0052] Based on the real-time monitoring data and the status assessment results of the predictive maintenance module, as well as the predicted maintenance needs, the control system module optimizes the scheduling and control strategy of the energy storage inverter system, including formulating appropriate charging and discharging plans and adjusting the control parameters of the energy storage inverter to ensure that the system can operate in an efficient and stable state;

[0053] The control system module intelligently controls the output of the energy storage inverter system according to the energy demand and grid status of the smart grid substation to achieve a balance between grid supply and demand. For the long-term three-phase load imbalance and low voltage problems at the end of the grid substation, the substation energy storage system is connected to the substation distribution end, and the low voltage and three-phase imbalance control mode is turned on. The control effect is achieved by adjusting the reactive / active output of the three phases of the energy storage inverter respectively. For the long-term heavy overload condition of the grid substation transformer, which leads to the problems of excessive transformer temperature and burning, the substation energy storage system is connected to the front end of the substation transformer, and the overload control mode is turned on. The output of the energy storage system is adjusted by detecting the real-time load power, discharging when the load power is too large and charging when the load power is small, effectively reducing the transformer load rate.

[0054] Based on the characteristics of distributed energy storage, a small energy storage system is connected to the low-voltage side of the distribution transformer, and power compensation is performed in combination with the operating load of the power grid station area to alleviate problems such as temporary overload of the distribution transformer and low voltage at the power grid station area outlet. The substation energy storage system is connected to the substation distribution terminal, and the low voltage and three-phase imbalance control mode is turned on. The control effect is achieved by adjusting the reactive / active output of the three phases of the energy storage converter separately, completing the distributed energy storage in the substation area.

[0055] The optimized scheduling and control instructions are sent by the control system module to the converter control center, and the execution of the instructions is tracked in real time. The control system module continuously monitors the operating status and control effect of the system, collects feedback data, fine-tunes and further optimizes the control strategy, and formulates a detailed maintenance plan based on the predicted results and actual operating conditions, and then executes the maintenance plan, repairs and maintains the system to ensure that the system can always maintain the best operating state.

[0056] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0057] 1. The present invention provides an energy storage inverter system applied to smart grid stations, which significantly improves the stability and reliability of the power grid through real-time monitoring and optimized control. The battery management system and the energy storage inverter module collect relevant parameters in real time to ensure that the battery pack and the inverter operate in a safe and efficient state. The state evaluation module is used to perform data analysis and calculate the state evaluation index to promptly detect system anomalies and reduce the risk of failure. Combined with the actual needs of the smart grid station, the scheduling and control strategy of the energy storage inverter is further optimized.

[0058] 2. The present invention provides an energy storage inverter system applied to smart grid substations. Based on the status assessment results and energy loss rate, the energy storage optimization coefficient is calculated, the energy storage efficiency and performance optimization potential of the system are evaluated, and then the maintenance needs are predicted, guiding the system to operate in the best state and reducing unexpected downtime. The real-time monitoring and predictive maintenance are combined to enable the power grid to supply electricity more stably, which not only reduces energy loss, but also reduces costs and improves economy by reducing unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0060] Figure 1 It is a module diagram of the present invention;

[0061] Figure 2 A flow chart for evaluating the operating status of an energy storage converter system according to the present invention;

[0062] Figure 3 The flowchart of the analysis of the state optimization trend of the energy storage converter system of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Embodiment 1, as Figure 1 , Figure 2As shown, the present invention provides an energy storage converter system applied to a smart grid area, including a converter control center, the converter control center is communicatively connected with a battery management system, an energy storage converter module, a data acquisition monitoring module, a state evaluation module, a predictive maintenance module and a control system module, wherein electrical signals are connected between the modules;

[0065] The battery management system is used to monitor and manage the status of the battery pack, including the battery voltage, current, and temperature parameters, to ensure safe and reliable operation of the battery. Through real-time monitoring and status evaluation, it can prevent battery overcharging, over-discharging, and thermal runaway, extend battery life, and improve system safety. In the smart grid area, it reports the health status and remaining capacity of the battery pack to the area in real time, helping the area optimize energy distribution and scheduling;

[0066] The energy storage inverter module realizes the conversion and charging control of electric energy by controlling the switching state of current and voltage, converting the DC power of the battery pack into AC power for use in the power grid. It can also operate in reverse to convert AC power into DC power for charging, realizing the efficient conversion of electric energy between different forms, supporting the two-way energy flow of the power grid, improving the flexibility and reliability of the power grid, and performing charging and discharging operations based on the energy demand of the substation and the state of the power grid to balance the supply and demand relationship of the power grid. The energy storage inverter receives DC power from the energy storage system (battery pack). After receiving the DC power, the energy storage inverter converts the DC power into high-frequency AC power through the internal DC / AC bidirectional inverter, so that the electric energy can be more conveniently transmitted and used in the power grid. The high-frequency AC power converted by the DC / AC bidirectional inverter is then passed through the output filter for The energy storage inverter performs filtering to remove high-frequency harmonic components and obtains AC power that meets the requirements, thereby preventing harmonic pollution to the power grid. During the charging process, the energy storage inverter converts the AC power of the power grid into DC power through a rectification process, and supplies the battery pack for charging, thereby ensuring that the battery pack can safely and efficiently receive power. When there is excess power in the power grid, the energy storage inverter stores the excess power in the battery pack for use as a backup power source. When the power grid is insufficient, the energy storage inverter releases power from the battery pack to supplement the energy shortage in the power grid. The energy storage inverter performs intelligent control and management through the control system module according to the real-time needs of the power grid and the status information of the battery pack, thereby realizing efficient use of power, balancing the supply and demand relationship of the power grid, ensuring that the power grid can maintain stable operation under any circumstances, and avoiding power grid failures caused by imbalanced power supply and demand.

[0067] The data acquisition and monitoring module monitors the energy storage inverter system in real time, collects the operating data of the battery management system and the energy storage inverter module, obtains the energy storage data sequence, starts the energy storage inverter system, and the data acquisition and monitoring module starts synchronously and enters the initialization stage, and then loads the preset configuration parameters, including the frequency, data type and storage location of data acquisition, and collects relevant operating data from the battery management system and the energy storage inverter module, wherein the battery management system collects the status data of the battery pack in real time, including voltage, current, temperature and state of charge parameters, and obtains the output power, efficiency and fault status of the energy storage inverter through the energy storage inverter module, and performs preliminary data verification on the collected data to ensure the accuracy and completeness of the data. If the data is abnormal or missing, it is marked, and the collected raw data is preprocessed, including filtering, denoising, data cleaning and format conversion, and the preprocessed data is integrated to generate an energy storage data sequence, and stored in the database of the inverter control center;

[0068] The state assessment module evaluates the operating state of the energy storage inverter system based on the data of the energy storage data sequence, analyzes the abnormal cost and failure risk trend of the energy storage inverter system during operation, reduces the deviation of the state assessment, and improves the accuracy of the assessment. The energy storage data sequence is obtained from the database of the inverter control center, and the voltage, current, temperature, and state of charge data of the battery pack, as well as the output power, efficiency, and fault status data of the energy storage inverter are extracted. According to the operating requirements of the energy storage inverter system, the characteristics of the battery pack and the energy storage inverter are selected, which are the voltage, current, temperature, and state of charge characteristics of the battery pack, and the output power, efficiency, and fault status data of the energy storage inverter. The output power, efficiency and fault status characteristics of the converter, use historical fault data to determine the normal threshold of each characteristic, based on the normal threshold of each characteristic of the battery pack, combined with the data of each characteristic of the battery pack, comprehensively analyze and calculate the battery pack evaluation index, analyze the operating status of the battery pack, based on the normal threshold of each characteristic of the energy storage converter, combined with the data of each characteristic of the energy storage converter, comprehensively analyze and calculate the converter evaluation index, analyze the performance of the energy storage converter, combine the battery pack evaluation index and the converter evaluation index, calculate the status evaluation index of the entire energy storage system, and analyze the abnormal cost and fault risk trend of the energy storage converter system operation process;

[0069] Furthermore, the expression of the battery pack evaluation index is:

[0070]

[0071] Among them, BI is the battery pack evaluation index, V d is the actual measured battery pack voltage, V t is the normal threshold of the battery pack voltage, I d is the actual measured battery pack current, I tis the normal threshold of battery pack current, T d is the actual measured battery pack temperature, T t is the normal threshold of battery pack temperature, S d is the actual measured battery pack state of charge, S t It is the normal threshold of the battery pack state of charge. The value range of BI is between 0 and 1. As the actual measured value of each feature approaches its normal threshold, the value of BI increases, indicating that the battery pack state is better. On the contrary, if the actual measured value of any feature deviates seriously from its normal threshold, the value of BI will decrease significantly, indicating that the battery pack state is poor.

[0072] The expression of converter evaluation index is:

[0073]

[0074] Among them, IAI is the converter evaluation index, P d is the actual measured output power of the energy storage converter, P t is the normal threshold of the energy storage converter output power, η d is the actual measured energy storage converter efficiency, η t is the normal threshold of energy storage converter efficiency, F d is the fault state of the energy storage converter, where F d =0 means no fault, F d =0 indicates a fault exists. The value range of IAI is between 0 and 1. As the actual measured value of each feature approaches its normal threshold and no fault occurs, the value of IAI increases, indicating that the converter state is better. On the contrary, if the actual measured value of any feature deviates seriously from its normal threshold or a fault occurs, the value of IAI will decrease significantly, indicating that the converter state is poor.

[0075] Furthermore, the state evaluation index is obtained based on the battery pack evaluation index and the converter evaluation index, and its expression is:

[0076]

[0077] Among them, S is the state evaluation index, BI is the battery pack evaluation index, IAI is the converter evaluation index, The product of BI and IAI is adjusted using an exponential function. When both BI and IAI are close to 1, the value of this part is close to 1, and when either evaluation is low, the value of this part decreases. The logarithmic function and radical expression are used to further adjust the evaluation index, taking into account the combined impact of BI and IAI. When both are high, the value inside the logarithmic function is close to 0, and the value of the entire expression is close to 1. When any evaluation index is low, the value inside the logarithmic function increases, and the value of the entire expression decreases. It should be noted that the value range of S is between 0 and 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in a good state. On the contrary, if the value of BI or IAI decreases, the value of S will decrease significantly, indicating that the energy storage system is in a poor state.

[0078] The predictive maintenance module analyzes the energy loss rate of the system and calculates the energy storage optimization coefficient based on the status evaluation results of the energy storage converter system. It comprehensively analyzes the status optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, and assists the substation to arrange maintenance work in advance to reduce energy interruptions and losses caused by system failures.

[0079] The control system module monitors the operating status in real time, and optimizes the scheduling and control of the energy storage inverter system based on the prediction results of the system's current maintenance needs to ensure efficient and stable operation of the energy storage inverter system. At the same time, it works in coordination with the smart grid substation to realize distributed energy storage in the substation.

[0080] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the predictive maintenance module, the analysis process of the state optimization trend of the energy storage converter system is:

[0081] Based on the status evaluation results of the energy storage inverter system, the battery group evaluation index, inverter evaluation index and status evaluation index are obtained to analyze the current status and performance of the system, monitor the energy input and output of the system within a fixed period, and analyze the loss factors of energy transmission, including battery group internal resistance loss, line loss and energy storage inverter loss, etc., to determine the energy loss ratio, and then calculate the energy loss rate of the energy storage inverter system during operation, analyze the changing trend of the energy loss rate, identify potential energy consumption problems, and calculate the energy storage optimization coefficient in combination with the status evaluation index and the energy loss rate. The energy storage optimization coefficient is analyzed to comprehensively evaluate the energy storage efficiency and performance optimization potential of the system. Based on the value of the energy storage optimization coefficient, the operating status of the energy storage inverter system is divided into different status levels, namely, excellent status level, good status level and poor status level, and the corresponding optimization evaluation threshold is matched for each status level. According to the energy storage optimization coefficient and the set status level, the future status of the energy storage inverter system is analyzed, and the maintenance requirements of the energy storage inverter system are determined, the maintenance priority is clarified, and the auxiliary station area arranges maintenance work;

[0082] Furthermore, the expression of energy storage optimization coefficient is:

[0083]

[0084] Among them, SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the state assessment index, E in is the total energy input in a fixed period, E out It is the total energy output in a fixed period, n is the number of loss factors, which is the sum of battery internal resistance loss, cable loss, converter loss, etc. The smaller the ELR value, the higher the energy utilization efficiency of the energy storage converter system and the lower the loss; the larger the ELR value, the higher the energy loss of the system and the lower the efficiency. The exponential function is used to adjust the impact of the energy loss rate. When the state evaluation index is high, the value of the exponential function increases, thereby improving the energy storage optimization coefficient. The logarithmic function is used to further adjust the energy storage optimization coefficient. This section analyzes the combined impact of the state assessment index and the energy loss rate. When S is higher and ELR is lower, the value inside the logarithmic function increases and the value of the entire expression decreases, thereby increasing the energy storage optimization coefficient. The value range of SOC is between 0 and 1. As the state assessment index increases and the energy loss rate decreases, the value of the energy storage optimization coefficient increases, indicating that the optimization potential of the energy storage system is greater. On the contrary, if S decreases or ELR increases, the value of SOC will decrease, indicating that the optimization potential of the system is reduced.

[0085] Furthermore, multiple status levels correspond to multiple optimization evaluation thresholds, wherein the status levels correspond to the optimization evaluation thresholds one by one, specifically:

[0086] Excellent status level: SOC H ≤SOC<1, the system operating efficiency and performance are at their best, the energy loss rate is low, and the state assessment index is high, indicating that the system is in an efficient operating state, with low maintenance requirements and low maintenance priority. Regular inspections and preventive maintenance are recommended;

[0087] Good condition level: SOC L ≤SOC <SOC H , the system operates with good efficiency and performance, the energy loss rate is low, and the condition assessment index is high, indicating that the system operates in good condition, but may need some preventive maintenance, medium maintenance priority, and regular diagnostic tests and maintenance of some components are recommended;

[0088] Bad status level: 0 <SOC<SOC L ,The system operation efficiency and performance are poor, the energy loss rate is high, and the status assessment index is low, indicating that there may be problems with the system and timely maintenance and optimization are required. It has a high maintenance priority and it is recommended to conduct a detailed system inspection and necessary repairs immediately;

[0089] Among them, SOC is the energy storage optimization coefficient, SOC H The lower threshold of the excellent state level and the upper threshold of the good state level, SOC L is the lower threshold of the good state level and the upper threshold of the bad state level, SOC H =0.8, SOC L =0.6;

[0090] In the control system module, the process of optimizing the scheduling and control of the energy storage converter system is as follows:

[0091] Through the data acquisition and monitoring module, various operating data of the energy storage inverter system are collected in real time, including but not limited to the voltage, current, temperature, state of charge of the battery pack, and the output power, efficiency and fault status of the energy storage inverter, and the problems of the power grid area are clarified. Among them, the power grid area problems include the long-term three-phase load imbalance and low voltage problems at the end of the power grid area and the long-term heavy overload conditions of the transformer in the power grid area. According to the real-time monitoring data and the status evaluation results of the predictive maintenance module, as well as the predicted maintenance needs, the control system module optimizes the scheduling and control strategy of the energy storage inverter system, including formulating appropriate charging and discharging plans and adjusting the control parameters of the energy storage inverter to ensure that the system can operate in an efficient and stable state. The control system module intelligently controls the output of the energy storage inverter system according to the energy demand and power grid status of the smart grid area to achieve a balance between the supply and demand of the power grid. Among them, for the long-term three-phase load imbalance and low voltage problems at the end of the power grid area, the area energy storage system is connected to the area distribution end, and the low voltage and three-phase imbalance management mode is turned on. By adjusting the three-phase unbalanced Active / active output achieves governance effects; for the long-term heavy overload conditions of the power grid transformer, which leads to the high temperature and burning of the transformer, the energy storage system in the transformer area is connected to the front end of the transformer area, and the overload governance mode is turned on. The output of the energy storage system is adjusted by detecting the real-time load power. When the load power is too large, it is discharged, and when the load power is small, it is charged, which effectively reduces the load rate of the transformer. Based on the flexible and responsive characteristics of distributed energy storage, the small energy storage system is connected to the low-voltage side of the distribution transformer, and power compensation is carried out in combination with the operating load conditions of the power grid area to alleviate the temporary overload of the distribution transformer and the low voltage at the outlet of the power grid area. Distributed energy storage in the area is realized. The optimized dispatching and control instructions are sent to the converter control center by the control system module, and the execution of the instructions is tracked in real time. The control system module continuously monitors the operating status and control effect of the system, collects feedback data, fine-tunes and further optimizes the control strategy, and formulates a detailed maintenance plan based on the prediction results and actual operation conditions, and then executes the maintenance plan to repair and maintain the system to ensure that the system can always maintain the best operating state.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An energy storage converter system applied to a smart grid station area, comprising a converter control center, characterized in that: The converter control center is communicatively connected with a battery management system, an energy storage converter module, a data acquisition monitoring module, a status assessment module, a predictive maintenance module, and a control system module, wherein electrical signals are connected between the modules; The battery management system is used to monitor and manage the status of the battery pack, and in the smart grid area, report the health status and remaining capacity of the battery pack to the area in real time to optimize the energy distribution and scheduling of the area; The energy storage converter module controls the switching state of current and voltage, performs charging and discharging operations in combination with the energy demand of the power grid area and the state of the power grid, and balances the supply and demand relationship of the power grid; The data acquisition and monitoring module monitors the energy storage converter system in real time, collects the operating data of the battery management system and the energy storage converter module, and obtains an energy storage data sequence; The state evaluation module evaluates the operating state of the energy storage converter system based on the data of the energy storage data sequence, and analyzes the abnormal cost and failure risk trend of the operation process of the energy storage converter system; The predictive maintenance module analyzes the energy loss rate of the system based on the status evaluation results of the energy storage converter system, calculates the energy storage optimization coefficient, comprehensively analyzes the status optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, and assists the power grid station area in arranging maintenance work; The control system module monitors the operating status in real time, optimizes the scheduling and control of the system in combination with the maintenance demand prediction results, and works in coordination with the smart grid substation to realize distributed energy storage in the substation.

2. The energy storage converter system applied to a smart grid area according to claim 1, characterized in that: In the energy storage converter module, the process of balancing the supply and demand relationship of the power grid is as follows: The energy storage converter receives DC power from the battery pack. After receiving the DC power, the energy storage converter converts the DC power into high-frequency AC power through the internal DC / AC bidirectional converter. The high-frequency AC power converted by the DC / AC bidirectional converter is filtered through the output filter to remove the high-frequency harmonic components and obtain AC power that meets the requirements. During the charging process, the energy storage converter converts the AC power of the power grid into DC power through the rectification process, which is then supplied to the battery pack for charging; When there is excess electricity in the grid, the energy storage inverter stores the excess electricity in the battery pack for use as a backup power source. When the grid is short of electricity, the energy storage inverter releases electricity from the battery pack to supplement the energy shortage in the grid. The energy storage inverter performs intelligent control and management through the control system module based on the real-time needs of the grid and the status information of the battery pack to balance the supply and demand of the grid.

3. The energy storage converter system applied to a smart grid area according to claim 2 is characterized in that: In the data acquisition and monitoring module, the process of acquiring the energy storage data sequence is as follows: The energy storage converter system starts, and the data acquisition monitoring module starts synchronously and enters the initialization phase, and then loads the preset configuration parameters, including the frequency, data type and storage location of data acquisition; Collect relevant operating data from the battery management system and the energy storage converter module, wherein the battery management system collects the status data of the battery pack in real time, including voltage, current, temperature and state of charge parameters, and the energy storage converter module obtains the output power, efficiency and fault status of the energy storage converter, and performs preliminary data verification on the collected data; The collected raw data is preprocessed, including filtering, denoising, data cleaning and format conversion, and the preprocessed data is integrated to generate an energy storage data sequence, which is stored in the database of the converter control center.

4. The energy storage converter system applied to a smart grid area according to claim 3 is characterized in that: In the state evaluation module, the process of evaluating the operating state of the energy storage converter system is as follows: Obtain energy storage data sequence from the database of the converter control center, extract the voltage, current, temperature, state of charge data of the battery pack, and the output power, efficiency and fault status data of the energy storage converter; According to the operation requirements of the energy storage converter system, the characteristics of the battery pack and the energy storage converter are selected, which are the voltage, current, temperature, and state of charge characteristics of the battery pack, and the output power, efficiency, and fault state characteristics of the energy storage converter. The normal threshold of each characteristic is determined using historical fault data. Based on the normal thresholds of each feature of the battery pack, combined with the data of each feature of the battery pack, the battery pack evaluation index is comprehensively analyzed and calculated to analyze the operating status of the battery pack. Based on the normal thresholds of each feature of the energy storage converter, combined with the data of each feature of the energy storage converter, the converter evaluation index is comprehensively analyzed and calculated to analyze the performance of the energy storage converter. By combining the battery pack evaluation indicators and the converter evaluation indicators, the status evaluation index of the entire energy storage system is calculated, and the abnormal cost and failure risk trend of the energy storage converter system operation process are analyzed.

5. The energy storage converter system applied to a smart grid area according to claim 4 is characterized in that: The expression of the battery pack evaluation index is: Among them, BI is the battery pack evaluation index, V d is the actual measured battery pack voltage, V t is the normal threshold of the battery pack voltage, I d is the actual measured battery pack current, I t is the normal threshold of battery pack current, T d is the actual measured battery pack temperature, T t is the normal threshold of battery pack temperature, S d is the actual measured battery pack state of charge, S t is the normal threshold value of the battery pack state of charge; The expression of the converter evaluation index is: Among them, IAI is the converter evaluation index, P d is the actual measured output power of the energy storage converter, P t is the normal threshold of the energy storage converter output power, η d is the actual measured efficiency of the energy storage converter, η t is the normal threshold of energy storage converter efficiency, F d is the fault state of the energy storage converter, where F d =0 means no fault, F d =0 indicates a fault exists.

6. The energy storage converter system applied to a smart grid area according to claim 5, characterized in that: The state evaluation index is obtained based on the battery pack evaluation index and the converter evaluation index, and its expression is: Among them, S is the status evaluation index, BI is the battery pack evaluation index, and IAI is the converter evaluation index. It should be noted that the value range of S is between 0 and 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in good condition. If the value of BI or IAI decreases, the value of S decreases significantly, indicating that the energy storage system is in poor condition.

7. The energy storage converter system applied to a smart grid area according to claim 6 is characterized in that: In the predictive maintenance module, the analysis process of the energy storage converter system state optimization trend is as follows: Based on the status evaluation results of the energy storage converter system, obtain the battery pack evaluation index, converter evaluation index and status evaluation index to analyze the current status and performance of the system; Monitor the energy input and output of the system within a fixed period, analyze the loss factors of energy transmission, including battery pack internal resistance loss, line loss and energy storage inverter loss, to determine the energy loss ratio, and then calculate the energy loss rate of the energy storage inverter system during operation, analyze the change trend of the energy loss rate, and identify potential energy consumption problems; Combine the state assessment index and energy loss rate to calculate and analyze the energy storage optimization coefficient, and conduct a comprehensive assessment of the system's energy storage efficiency and performance optimization potential; Based on the value of the energy storage optimization coefficient, the operating state of the energy storage converter system is divided into different state levels, namely, excellent state level, good state level and poor state level, and the corresponding optimization evaluation threshold is matched for each state level; According to the energy storage optimization coefficient and the set status level, the future status of the energy storage inverter system is analyzed, and the maintenance requirements of the energy storage inverter system are determined, the maintenance priority is clarified, and the substation is assisted in arranging maintenance work.

8. The energy storage converter system applied to a smart grid area according to claim 7 is characterized in that: The expression of the energy storage optimization coefficient is: Among them, SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the state assessment index, E in is the total energy input in a fixed period, E out is the total energy output in a fixed period, n is the number of loss factors, and the SOC value range is between 0 and 1.

9. The energy storage converter system applied to a smart grid area according to claim 8, characterized in that: A plurality of the state levels correspond to a plurality of the optimization evaluation thresholds, wherein the state levels correspond to the optimization evaluation thresholds one by one, specifically: Excellent status level: SOC H ≤SOC<1; Good condition level: SOC L ≤SOC <SOC H ; Bad status level: 0 <SOC<SOC L ; Among them, SOC is the energy storage optimization coefficient, SOC H The lower threshold of the excellent state level and the upper threshold of the good state level, SOC L is the lower threshold of the good state level and the upper threshold of the bad state level, SOC H =0.8, SOC L =0.

6.

10. The energy storage converter system applied to a smart grid area according to claim 9, characterized in that: In the control system module, the process of optimizing the scheduling and control of the energy storage converter system is as follows: Through the data acquisition and monitoring module, various operating data of the energy storage inverter system are collected in real time, including the voltage, current, temperature, charge state of the battery pack, and the output power, efficiency and fault status of the energy storage inverter, and the problems of the power grid area are identified. Among them, the problems of the power grid area include the long-term three-phase load imbalance and low voltage problems at the end of the power grid area and the long-term heavy overload conditions of the transformer in the power grid area; Based on the real-time monitoring data and the status assessment results of the predictive maintenance module, as well as the predicted maintenance needs, the control system module optimizes the scheduling and control strategy of the energy storage converter system and adjusts the control parameters of the energy storage converter; The control system module intelligently controls the output of the energy storage inverter system according to the energy demand and grid status of the smart grid substation to achieve a balance between grid supply and demand. For long-term three-phase load imbalance and low voltage problems at the end of the grid substation, the substation energy storage system is connected to the substation distribution end, and the low voltage and three-phase imbalance control mode is turned on. The control effect is achieved by adjusting the reactive / active output of the three phases of the energy storage inverter respectively. For long-term heavy overload conditions of the grid substation transformer, the substation energy storage system is connected to the front end of the substation transformer, and the overload control mode is turned on. The output of the energy storage system is adjusted by detecting the real-time load power, and the load power is discharged when the load power is too large and charged when the load power is small, so as to reduce the transformer load rate. Based on the characteristics of distributed energy storage, a small energy storage system is connected to the low-voltage side of the distribution transformer, and power compensation is performed in combination with the operating load conditions of the power grid station area to achieve distributed energy storage in the station area; The optimized scheduling and control instructions are sent by the control system module to the converter control center, and the execution of the instructions is tracked in real time. The control system module continuously monitors the operating status and control effect of the system, collects feedback data, fine-tunes and further optimizes the control strategy, and formulates a detailed maintenance plan based on the predicted results and actual operating conditions, and then executes the maintenance plan to repair and maintain the system.

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