A parallel control system for multiple fuel generator sets

Abstract

The present invention discloses a parallel operation control system for multiple fuel generator sets, which relates to the technical field of parallel operation control. The system collects the operation data of the generator sets in real time through sensors, and obtains the operation steady-state data group after data preprocessing; calculates the operation steady-state function index ywt based on the operation steady-state data group, and conducts a parallel operation compliance assessment with the preset operation steady-state safety threshold Z; calculates the load distribution index fhf and the actual output power sjg of the generator sets when meeting the parallel operation requirements; conducts a load assessment of the generator sets according to the load execution rate zxl, and determines whether the load status is abnormal; the offline decision-making module, when the load of the generator sets is abnormal, combines the total demand load to of the industrial zone and the remaining load capacity sf of the generator sets to decide whether to execute the offline and parallel operation instructions. Through the collaborative optimization of multiple modules, this system improves the stability of parallel operation and the efficiency of load management, ensuring the reliability and economy of the power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel operation control, and particularly provides a parallel operation control system for multiple fuel generator sets. Background Art

[0002] In modern industrial production, the stability of power supply is crucial. Especially when the power grid supply is unstable or the industrial area is located in a remote area, enterprises usually rely on fuel generators as the main or backup power source. However, the power supply capacity of a single fuel generator is limited and it is not easy to meet the complex load demands of the industrial area. Therefore, the combined operation of multiple fuel generators has become an important means to improve power supply stability and operation efficiency. In order to achieve the coordinated operation of multiple units, improve power supply reliability, and reduce operating costs, it is particularly important to develop and apply a parallel operation control system for multiple fuel generator sets.

[0003] In the Chinese invention application with the publication number of CN117713250A, it includes diesel generator sets and a control system. The control system includes a controller, a UPS power supply, a dual PLC, an industrial optical-electric converter, and an HMI human-machine interface; the control mode is divided into an automatic mode and a semi-automatic mode. When any one of the mains voltages fails, the dual PLC will output a remote control start-stop signal to each diesel generator set. After the voltage and frequency first reach the rated values, the outlet circuit breaker switch will be automatically closed, and the remaining ones will automatically adjust the generated voltage, frequency to be synchronized with the busbar, and close the corresponding outlet circuit breaker switch.

[0004] Combined with the existing technology, the above application still has the following deficiencies:

[0005] The above application mainly relies on the PLC to monitor basic parameters such as voltage and frequency, lacks in-depth analysis of the steady state of generator operation, fails to introduce sensor data or historical operation data to evaluate the health status of the unit, and it is difficult to accurately predict the operation stability and potential failure risks of the unit; the load distribution depends on the preset logic for adjustment, fails to optimize and adjust in combination with the actual load fluctuations, may lead to uneven load distribution among the units, reduce the overall operation efficiency of the system, especially in the case of large load changes, it is not easy to achieve dynamic adaptive optimization; the abnormal handling and off-line decision mainly perform shutdown processing after the unit has an abnormality and a fault, and fail to perform intelligent analysis and early warning based on real-time data, lacking an evaluation mechanism for the load capacity of the unit and the overall power supply demand, resulting in a lag in off-line and parallel operation decisions, affecting power supply stability and reliability. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a parallel operation control system for multiple fuel generator sets, which solves the problems in the above background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A parallel operation control system for multiple fuel generator sets, including an operating data acquisition module, an operating steady-state analysis module, a parallel operation management module, an operating load assessment module, and an offline decision-making module;

[0008] The operating data acquisition module is used to collect the operating data of the fuel generator in real time according to the sensor group installed in the fuel generator, construct a data processing platform for preprocessing, obtain an operating steady-state data group, and then store it in the data repository;

[0009] The operating steady-state analysis module is used to perform an operating steady-state analysis on the fuel generator according to the operating steady-state data group, and conduct a preliminary parallel operation compliance assessment with the preset operating steady-state safety threshold Z;

[0010] When the parallel operation compliance assessment meets the parallel operation requirements, the parallel operation management module is used to collect the load data of the fuel generator sets in real time according to the SCADA system of the fuel generator sets, obtain a load fluctuation data group after preprocessing, and calculate the load distribution index fhf and the actual output power sjg of the unit;

[0011] The operating load assessment module calculates and obtains the load execution rate zxl according to the load distribution index fhf and the actual output power sjg of the unit, and conducts a unit load assessment with the preset first load execution adaptive threshold A and the second load execution adaptive threshold B;

[0012] When the unit load is abnormal, the offline decision-making module is used to analyze the total load capacity sf of the remaining units and the total demand load to of the industrial area, and execute the corresponding offline and parallel operation instructions.

[0013] Preferably, the operating data acquisition module includes a unit data acquisition unit, a data processing unit, and a data storage unit;

[0014] The unit data acquisition unit is used to collect the operating data of the fuel generator in real time according to the sensor group installed in the fuel generator;

[0015] The sensor group includes a Hall effect sensor, a thermocouple, and a piezoelectric vibration sensor;

[0016] The data processing unit is used to construct a data processing platform, establish a communication connection between the data processing platform and the sensor group through the Internet, and transmit the operating data to the data processing platform in real time for noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and fault detection and analysis to obtain an operating steady-state data group;

[0017] The fault detection and analysis is calculated based on the number of maintenance times and the number of faults in the unit's historical records to obtain the fault probability gl;

[0018] The operating steady-state data group includes the unit speed zs, the unit temperature T, the vibration amplitude zf, and the failure probability gl;

[0019] The data storage unit constructs a data storage repository based on the data processing platform and transmits the operating steady-state data group to the data storage repository for storage in real time.

[0020] Preferably, the operating steady-state analysis module includes an operating steady-state analysis unit and a parallel operation compliance evaluation unit;

[0021] The operating steady-state analysis unit is used to perform summary calculations based on the operating steady-state data group to obtain the operating steady-state function index ywt;

[0022] The operating steady-state function index ywt is obtained through the following formula;

[0023] ;

[0024] In the formula, ywt i represents the operating steady-state function index of the i-th unit, zs min represents the minimum unit speed set by the personnel, T opt represents the optimal temperature of the unit during normal operation, zf max represents the peak vibration amplitude when the unit operates safely as set by the manufacturer, and respectively represent the standard deviation of the unit temperature and the standard deviation of the failure probability, zs i , T i and zf i respectively represent the unit speed of the i-th unit, the unit temperature of the i-th unit, and the vibration amplitude of the i-th unit.

[0025] Preferably, the parallel operation compliance evaluation unit is used to count all historical normal and abnormal operating steady-state function indexes ywt based on all historical operating steady-state data groups in the data storage repository, calculate the mean value of the historical operating steady-state function index ywt using the statistical method , set a preset operating steady-state safety threshold Z based on the mean value, and perform a preliminary parallel operation compliance evaluation with the real-time obtained operating steady-state function index ywt. The specific evaluation scheme is as follows;

[0026] When the operating steady-state function index ywt > the operating steady-state safety threshold Z, the state of this unit meets the parallel operation requirements;

[0027] When the operating steady-state function index ywt ≤ the operating steady-state safety threshold Z, the state of this unit does not meet the parallel operation requirements.

[0028] Preferably, the parallel operation management module includes a load data acquisition unit and a load analysis unit;

[0029] The load data acquisition unit is used to, when the parallel operation compliance assessment meets the parallel operation requirements, collect the load data of the fuel generator set in real time according to the SCADA system of the fuel generator set, perform preprocessing, obtain a load fluctuation data set, and transmit the load fluctuation data set to the data repository for storage in real time through the Internet;

[0030] The preprocessing includes noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and load fluctuation analysis;

[0031] The load fluctuation analysis is used to calculate the load standard deviation of the generator set based on the acquired load data , and then calculate the load fluctuation fb, specifically as follows: , , where N represents the total number of sampling points, L(t) represents the load at time t, represents the average load within the sampling interval;

[0032] The load fluctuation data set includes the load fluctuation fb.

[0033] Preferably, the load analysis unit includes a load pre-distribution unit and a power analysis unit;

[0034] The load pre-distribution unit is used to, after the generator sets are paralleled, perform load distribution on all paralleled generator sets, perform summary calculation according to the load fluctuation data set and the operation steady-state function index ywt, obtain the load distribution index fhf, and distribute the load to each generator set according to the current total load demand and the status of each generator set;

[0035] The load distribution index fhf is calculated through the following formula;

[0036] ;

[0037] In the formula, fhf i represents the load distribution index of the i-th generator set, fh j，max represents the peak load capacity of the j-th generator set under normal operation, ywt j represents the operation steady-state function index of the j-th generator set, to represents the total demand load of the industrial area, n represents all paralleled generator sets, j represents the traversal variable, fh i，max represents the peak load capacity of the i-th generator set under normal operation, fb max represents the peak load fluctuation that the system can withstand under normal operation.

[0038] Preferably, after the load distribution is completed, the power analysis unit calculates the actual power output that each unit can provide, constructs a power analysis model through the data processing platform, and introduces a power decay factor w ywt and the load response time into the power analysis model to calculate and obtain the actual output power sjg of the unit, and analyze based on the result of the load distribution to determine the actual output power of the unit;

[0039] The actual output power sjg of the unit is calculated and obtained through the following formula;

[0040] ;

[0041] In the formula, w ywt represents the power decay factor of the operation steady-state function index, t i represents the load response time of the i-th unit, t max represents the peak value of the time required for the specified unit load adjustment, sjg i represents the actual output power of the i-th unit.

[0042] Preferably, the operation load evaluation module includes an execution rate analysis unit and a load evaluation unit;

[0043] The execution rate analysis unit is used to perform summary calculations based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl, specifically: , in the formula, zxl i represents the load execution rate of the i-th unit.

[0044] Preferably, the load evaluation unit statistically calculates the load execution rate zxl of all historical normal load and abnormal load units based on the historical operation steady-state data group and the load fluctuation data group in the data repository, and calculates the mean value of the historical load execution rate zxl of the normal units and the abnormal units by the statistical method , based on the mean value as the standard, preset the first load execution adaptive threshold A and the second load execution adaptive threshold B according to the operation safety fluctuation range of the unit, where , c represents the operation safety fluctuation range of the unit, and performs unit load evaluation with the load execution rate zxl obtained in real time. The specific evaluation scheme is as follows;

[0045] When the load execution rate zxl < the first load execution adaptive threshold A, the unit load is abnormal, and at this time the unit load is insufficient;

[0046] When the first load execution adaptive threshold A ≤ the load execution rate zxl ≤ the second load execution adaptive threshold B, the load distribution is reasonable;

[0047] When the load execution rate zxl > the second load execution adaptive threshold B, the unit load is abnormal, and at this time the unit load is overloaded.

[0048] Preferably, the offline decision module is used to analyze the total load capacity sf of the remaining units and the total demand load to of the industrial area when the unit load is overloaded and the secondary iterative operation analysis shows that the state of this unit does not meet the parallel operation requirements. Specifically: , where M represents the total number of remaining units;

[0049] When the total load capacity sf of the remaining units ≥ the total demand load to of the industrial area and the load volatility fb ≤ the peak load volatility fb max , the offline requirement is met and the offline instruction is executed;

[0050] When the total load capacity sf of the remaining units ≥ the total demand load to of the industrial area and the load volatility fb > the peak load volatility fb max , wait for the load volatility fb to stabilize and then execute the offline instruction;

[0051] When the total load capacity sf of the remaining units < the total demand load to of the industrial area, the offline requirement is not met.

[0052] The present invention provides a parallel operation control system for multiple fuel generator units. It has the following beneficial effects:

[0053] (1) The operation data acquisition module of this system relies on a variety of high-precision sensor groups to collect the operation data of the fuel generator in real time and perform efficient preprocessing through a data processing platform, including noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and fault detection and analysis, so as to form a stable and reliable operation steady-state data group, ensuring the accuracy and stability of the data. The processed operation steady-state data group is stored in the data repository, providing data support for subsequent operation state analysis and decision-making. The role of this module is to ensure the real-time update of the unit state information and lay a solid foundation for the intelligent control of the system.

[0054] (2) The steady-state operation analysis module of the system evaluates the operating stability of the unit by calculating the steady-state operation function index ywt, and sets a preset steady-state operation safety threshold Z through historical statistical data for preliminary parallel operation compliance evaluation. The units that meet the parallel operation requirements enter the parallel operation management module. This module collects the load data of the fuel generators in real time through the SCADA system, calculates the load fluctuation index fb, and then determines the load distribution index fhf and the actual output power sjg. The operating load evaluation module performs a summary calculation based on the load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl, and sets a preset first load execution adaptive threshold A and a second load execution adaptive threshold B for unit load evaluation to evaluate the rationality of the load distribution. If the load execution rate exceeds the set range, the system automatically adjusts the unit load, optimizes the load distribution strategy, and ensures that each unit operates in the optimal efficiency range. This process guarantees the balanced operation of the generating units, improves the fuel utilization rate, and reduces the power loss and equipment wear caused by uneven load distribution.

[0055] (3) The offline decision-making module of the system further improves the stability and flexibility of the system. When the load of a certain unit is abnormal and the secondary iterative operation analysis determines that its state does not meet the parallel operation requirements, the system calculates the total load capacity sf of the remaining units and the total demand load to of the industrial area. If the load capacity of the remaining units is sufficient and the load fluctuation fb is within the safe range, the offline instruction is executed; if the load fluctuation is too large, it waits for stabilization before executing the offline; and when the load capacity of the remaining units is insufficient, the system preferentially reduces the operating load of this unit and waits for the standby unit to start before executing the offline. This strategy ensures the continuous stability of power supply and avoids power supply shortages caused by blind offline. At the same time, the intelligent control ability of the system enables it to automatically adjust the operating state of the units according to the dynamic changes in load demand, improving the overall stability and power supply safety of the system. Description of the Drawings

[0056] Figure 1 It is a schematic flowchart of a parallel operation control system for multiple fuel generator units of the present invention;

[0057] Figure 2 It is a schematic operation principle diagram of a parallel operation control system for multiple fuel generator units of the present invention. Detailed Embodiments

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0059] Embodiment 1

[0060] Please refer to Figure 1 , the present invention provides a parallel operation control system for multiple fuel generator sets. To achieve the above objectives, the present invention is realized through the following technical solutions: including an operation data acquisition module, an operation steady-state analysis module, a parallel operation management module, an operation load evaluation module, and an offline decision-making module;

[0061] The operation data acquisition module is used to collect the operation data of the fuel generator in real time according to the sensor group installed on the fuel generator, construct a data processing platform for preprocessing, obtain the operation steady-state data group, and then store it in the data storage library;

[0062] The operation steady-state analysis module is used to perform operation steady-state analysis on the fuel generator according to the operation steady-state data group, and conduct a preliminary parallel operation compliance evaluation with the preset operation steady-state safety threshold Z;

[0063] The parallel operation management module is used to, when the parallel operation compliance evaluation meets the parallel operation requirements, collect the load data of the fuel generator sets in real time according to the SCADA system of the fuel generator sets, obtain the load fluctuation data group after preprocessing, and calculate the load distribution index fhf and the actual output power sjg of the unit;

[0064] The operation load evaluation module calculates and obtains the load execution rate zxl according to the load distribution index fhf and the actual output power sjg of the unit, and conducts unit load evaluation with the preset first load execution adaptive threshold A and the second load execution adaptive threshold B;

[0065] The offline decision-making module is used to, when the unit load is abnormal, analyze the total load capacity sf of the remaining units and the total demand load to of the industrial area, and execute the corresponding offline and parallel operation instructions.

[0066] In this embodiment, the operating data acquisition module realizes the precise monitoring of the real-time operating state of the fuel generator. By using a variety of sensors installed on the unit, the system can continuously collect key operating data, preprocess it through a data processing platform, obtain a set of operating steady-state data, and store it in a data repository. This process ensures the accuracy and integrity of the data, providing a reliable basis for subsequent steady-state analysis and parallel operation decision-making. Compared with the traditional method that relies on manual inspection or simple SCADA data monitoring, it improves the accuracy and real-time performance of data acquisition, and reduces the false alarm rate of abnormal equipment states. The operating steady-state analysis module performs summary calculations based on the set of operating steady-state data to obtain the operating steady-state function index ywt, conducts operating steady-state analysis on the fuel generator, and conducts a preliminary parallel operation compliance assessment with the preset operating steady-state safety threshold Z, enabling accurate assessment of whether the unit's operating state meets the requirements for parallel operation. When the parallel operation compliance assessment indicates that the requirements for parallel operation are met, the parallel operation management module collects load data through the SCADA system, preprocesses it to obtain a set of load fluctuation data, and calculates the load distribution index fhf and the actual output power sjg of the unit. The system can dynamically adjust the power output of the unit to ensure the balance and efficiency of load distribution. Compared with the existing static load distribution strategy, this method can more flexibly adapt to the dynamic load changes in industrial areas, avoid energy waste and equipment loss caused by single-unit overload or low-load operation, and improve the operating efficiency of the overall unit group. The operating load assessment module calculates the load execution rate zxl based on the load distribution index fhf and the actual output power sjg of the unit, and uses historical data to preset the first load execution adaptive threshold A and the second load execution adaptive threshold B. The system can accurately determine whether the unit is in a state of insufficient load and overload, and adjust the load distribution plan in a timely manner. When a serious abnormality occurs, the offline decision-making module can analyze the total load capacity of the remaining units and automatically decide whether to perform an offline or parallel operation. Compared with the traditional method that relies on manual intervention, this system improves the automation level of parallel operation management, reduces the delay caused by human decision-making, and enables the unit group to more stably and efficiently adapt to the load requirements of complex industrial scenarios.

[0067] Embodiment 2

[0068] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 and Figure 2 , specifically: The operating data acquisition module includes a unit data acquisition unit, a data processing unit, and a data storage unit;

[0069] The unit data acquisition unit is used to collect the operating data of the fuel generator in real time according to the sensor group installed on the fuel generator;

[0070] The sensor group includes a Hall effect sensor, a thermocouple, and a piezoelectric vibration sensor;

[0071] The data processing unit is used to construct a data processing platform, and establish a communication connection between the data processing platform and the sensor group through the Internet, and transmit the operation data to the data processing platform in real time for noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing and fault detection and analysis, so as to obtain an operation steady-state data group;

[0072] The fault detection and analysis is calculated based on the number of repairs and the number of faults repaired in the unit's historical records to obtain the fault probability gl;

[0073] The operation steady-state data group includes the unit speed zs, the unit temperature T, the vibration amplitude zf and the fault probability gl;

[0074] The data storage unit constructs a data storage repository according to the data processing platform, and transmits the operation steady-state data group to the data storage repository for storage in real time.

[0075] In this embodiment, with the help of Hall effect sensors, thermocouples and piezoelectric vibration sensors, the system can comprehensively obtain the operation data of the unit. The data processing unit enables the system to not only achieve efficient data transmission, but also obtain the operation steady-state data group through noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing and fault detection and analysis, improving the accuracy and stability of the data, and thus enhancing the accurate assessment of the unit's health status. In addition, the data storage unit constructs a complete data storage repository, enabling the system to perform trend analysis and fault prediction based on long-term accumulated data, thereby warning potential problems in advance, optimizing maintenance strategies, and avoiding the occurrence of sudden failures. Compared with the traditional intermittent detection and manual judgment methods, this solution significantly improves the integrity, timeliness and decision-making intelligence level of the data, providing a solid guarantee for the efficient and stable operation of the fuel generator.

[0076] Embodiment 3

[0077] This embodiment is an explanatory description based on Embodiment 2, please refer to Figure 1 and Figure 2 , specifically: The operation steady-state analysis module includes an operation steady-state analysis unit and a parallel operation compliance evaluation unit;

[0078] The operation steady-state analysis unit is used to perform summary calculations based on the operation steady-state data group to obtain the operation steady-state function index ywt;

[0079] The operation steady-state function index ywt is obtained through the following formula;

[0080] ;

[0081] In the formula, ywt i represents the operation steady-state function index of the i-th unit, zsmin Denote the minimum unit speed set by personnel, T opt Denote the optimal temperature of the unit during normal operation, zf max Denote the peak vibration amplitude when the unit operates safely as set by the manufacturer and Denote the standard deviation of the unit temperature and the standard deviation of the failure probability respectively, zs i 、T i and zf i Denote the unit speed of the i-th unit, the unit temperature of the i-th unit and the vibration amplitude of the i-th unit respectively

[0082] The parallel operation compliance evaluation unit is used to count all historical normal and abnormal operation steady-state function indices ywt based on all historical operation steady-state data groups in the data repository, and calculate the mean value of the historical operation steady-state function index ywt using the statistical method , preset the operation steady-state safety threshold Z based on the mean value, and conduct a preliminary parallel operation compliance evaluation with the operation steady-state function index ywt obtained in real time. The specific evaluation scheme is as follows;

[0083] When the operation steady-state function index ywt > the operation steady-state safety threshold Z, the state of this unit meets the parallel operation requirements, maintain normal monitoring, and continue to connect to the grid;

[0084] When the operation steady-state function index ywt ≤ the operation steady-state safety threshold Z, the state of this unit does not meet the parallel operation requirements, the unit enters the load reduction monitoring mode, there are potential fault hazards, and generate fault information to be transmitted to relevant personnel for equipment maintenance

[0085] In this embodiment, the operation steady-state analysis module realizes the comprehensive health assessment of the fuel generator by calculating the operation steady-state function index ywt, and dynamically sets the operation steady-state safety threshold Z through the parallel operation compliance evaluation unit combined with historical data and real-time monitoring data, thereby ensuring the parallel operation reliability of the unit. The core advantage of this mechanism is that it not only provides a method for quantitatively evaluating the steady-state performance of the unit, but also improves the accurate determination ability of the unit operation state through the combination of historical statistics and real-time monitoring. Compared with the traditional method that relies on experience or simple threshold setting, this scheme can dynamically adjust the determination criteria based on data, making the parallel operation evaluation more adaptable and robust. In addition, when detecting that the operation state of the unit is abnormal, the system can automatically enter the load reduction monitoring mode and generate fault information in time to notify relevant personnel for maintenance, which not only reduces the probability of faults, but also effectively avoids serious faults or unplanned shutdowns caused by the accumulation of potential hazards of the unit, greatly improving the operation safety, stability and maintenance efficiency of the fuel generator

[0086] Embodiment 4

[0087] This embodiment is explained in Embodiment 3. Please refer to Figure 1 and Figure 2 , specifically: The parallel operation management module includes a load data acquisition unit and a load analysis unit;

[0088] The load data acquisition unit is used to, when the parallel operation compliance assessment meets the parallel operation requirements, collect the load data of the fuel generator set in real time according to the SCADA system of the fuel generator set, perform preprocessing, obtain the load fluctuation data group, and transmit the load fluctuation data group to the data repository for storage in real time through the Internet;

[0089] The preprocessing includes noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and load fluctuation analysis;

[0090] The load fluctuation analysis is used to calculate the load standard deviation of the generator set according to the acquired load data , and then calculate the load fluctuation fb, specifically: , , where N represents the total number of sampling points, L(t) represents the load at time t, represents the average load within the sampling interval;

[0091] The load fluctuation data group includes the load fluctuation fb.

[0092] The load analysis unit includes a load pre-distribution unit and a power analysis unit;

[0093] The load pre-distribution unit is used to, after the generator sets are paralleled, perform load distribution on all paralleled generator sets, optimize the operation efficiency, perform summary calculation according to the load fluctuation data group and the operation steady-state function index ywt, obtain the load distribution index fhf, and distribute the load to each generator set according to the current total load demand and in combination with the status of each generator set;

[0094] The load distribution index fhf is obtained by calculating through the following formula;

[0095] ;

[0096] In the formula, fhf i represents the load distribution index of the i-th generator set, fh j，max represents the peak load capacity under normal operation of the j-th generator set, ywt j represents the operation steady-state function index of the j-th generator set, to represents the total demand load of the industrial area, n represents all paralleled generator sets, j represents the traversal variable, fh i，max represents the peak load capacity under normal operation of the i-th generator set, fb max represents the peak load fluctuation that the system can withstand under normal operation.

[0097] The power analysis unit is used to calculate the actual power output that each unit can provide after the load distribution is completed. A power analysis model is constructed through the data processing platform, and by introducing the power decay factor w ywt and the load response time into the power analysis model, the actual output power sjg of the unit is calculated through calculation. Based on the result of the load distribution, an analysis is carried out to determine the actual output power of the unit;

[0098] The actual output power sjg of the unit is calculated through the following formula;

[0099] ;

[0100] In the formula, w ywt represents the power decay factor of the operation steady-state function index, t i represents the load response time of the i-th unit, t max represents the peak value of the time required for the specified unit load adjustment, sjg i represents the actual output power of the i-th unit.

[0101] In this embodiment, the parallel operation management module realizes the accurate monitoring and optimal distribution of the load status of the fuel generator set through load data acquisition and analysis. Its load data acquisition unit collects load data in real time through the SCADA system, and combines preprocessing technology to obtain a load fluctuation data group to ensure the accuracy and stability of the data. At the same time, the load fluctuation is quantified through load fluctuation analysis, providing a reliable reference basis for subsequent optimization. The load analysis unit further combines the load fluctuation data group and the operation steady-state function index ywt to perform summary calculation to obtain the load distribution index fhf, realizing adaptive load distribution, not only ensuring the rationality of the load distribution of each unit, but also optimizing the energy utilization efficiency and reducing unnecessary fuel consumption. In addition, the introduction of the power analysis unit enables the system to dynamically adjust the output power of the unit. Through the modeling of the power decay factor and the load response time, the actual output power sjg of the unit is accurately calculated, avoiding problems of overload or underload. The special advantage of this solution is that it not only pays attention to the balance of the current load distribution, but also improves the stability and response ability of the unit operation through real-time load fluctuation analysis and power dynamic adjustment, enabling the parallel operation system to achieve intelligent and accurate power scheduling when the load demand fluctuates, ensuring the long-term efficient and stable operation of the system.

[0102] Embodiment 5

[0103] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 and Figure 2 , specifically: The operation load assessment module includes an execution rate analysis unit and a load assessment unit;

[0104] The execution rate analysis unit is used to perform summary calculations based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl. Specifically: , where zxl i represents the load execution rate of the i-th unit.

[0105] The load evaluation unit statistically calculates the load execution rate zxl of all historical normal load and abnormal load units based on the historical operation steady-state data group and the load fluctuation data group in the data repository, and calculates the mean values of the historical load execution rates zxl of normal units and abnormal units using the statistical method . Based on the mean value as the standard, preset the first load execution adaptive threshold A and the second load execution adaptive threshold B according to the safe operation fluctuation range of the unit. Among them, , c represents the safe operation fluctuation range of the unit, and performs unit load evaluation with the load execution rate zxl obtained in real time. The specific evaluation scheme is as follows;

[0106] When the load execution rate zxl < the first load execution adaptive threshold A, the unit load is abnormal. At this time, the unit load is insufficient, and a control command is generated to increase the load of this unit;

[0107] When the first load execution adaptive threshold A ≤ the load execution rate zxl ≤ the second load execution adaptive threshold B, the load distribution is reasonable, and monitoring is maintained;

[0108] When the load execution rate zxl > the second load execution adaptive threshold B, the unit load is abnormal. At this time, the unit load is overloaded, a control command is generated to reduce the load of this unit, and the secondary iterative operation analysis of this unit is performed through the operation steady-state analysis module.

[0109] In this embodiment, the introduction of the operating load assessment module enables the system to dynamically monitor the load execution of each unit after load distribution and perform intelligent load adjustment through the parallel operation assessment unit. Based on the load execution rate zxl calculated by the execution rate analysis unit, the system can accurately identify the load status of the unit and adaptively preset the first load execution adaptive threshold A and the second load execution adaptive threshold B in combination with historical operation data to achieve rapid identification and adjustment of abnormal unit loads. When the unit load is insufficient or overloaded, the system can automatically generate control commands to increase or decrease the load of the corresponding unit to ensure that the load distribution of the entire unit group always remains within a reasonable range. Compared with the traditional static threshold judgment method, this solution establishes a more scientific and reasonable load assessment system through statistical analysis of historical data, making the load distribution of the unit more accurate and stable. In addition, the system can also perform secondary iterative operation analysis on overloaded units to further ensure their operation stability and avoid equipment damage or power supply fluctuations caused by overloaded operation. This adaptive load adjustment mechanism not only improves the overall operation efficiency of the unit, but also effectively reduces energy waste, extends the equipment life, and improves the safety and stability of power supply.

[0110] Embodiment 6

[0111] This embodiment is an explanatory description carried out in Embodiment 5. Please refer to Figure 1 and Figure 2 , specifically: The offline decision module is used to analyze the total load capacity sf of the remaining units and the total demand load to of the industrial area when the unit load is overloaded and the secondary iterative operation analysis shows that the status of this unit does not meet the parallel operation requirements. Specifically: , where M represents the total number of remaining units;

[0112] When the total load capacity sf of the remaining units ≥ the total demand load to of the industrial area and the load volatility fb ≤ the load fluctuation peak fb max , the offline requirement is met and the offline instruction is executed;

[0113] When the total load capacity sf of the remaining units ≥ the total demand load to of the industrial area and the load volatility fb > the load fluctuation peak fb max , wait for the load volatility fb to stabilize and then execute the offline instruction;

[0114] When the total load capacity sf of the remaining units < the total demand load to of the industrial area, the offline requirement is not met. At this time, reduce the operation load of this unit and wait for the standby unit to start before executing the offline instruction.

[0115] In this embodiment, the offline decision-making module analyzes the abnormal situation of the unit load intelligently and combines the secondary iterative operation analysis to ensure that the offline decision is only executed when the unit truly does not meet the parallel operation requirements, thus avoiding unnecessary shutdowns and improving the stability of the system. This module precisely matches the total load capacity sf of the remaining units with the total demand load to of the industrial area to ensure that the offline instruction is executed only when the total load capacity sf is greater than or equal to the total demand load to of the industrial area and the load volatility fb is within a stable range, which effectively reduces the risk of insufficient power supply caused by misjudgment. At the same time, when the total load capacity sf is greater than or equal to the total demand load to of the industrial area but fb exceeds the safe range, the system can intelligently wait for the load fluctuation to stabilize before executing the offline operation, avoiding system instability problems caused by excessive load fluctuations. When the total load capacity sf is less than the total demand load to of the industrial area, the system does not immediately execute the offline operation but instead reduces the operating load of the faulty unit and waits for the standby unit to start to ensure the continuity of the overall power supply. Compared with the traditional offline mode that relies on manual decision-making, the adaptive adjustment mechanism of this module improves the response ability of the power supply system to sudden load changes, reduces power outages caused by incorrect offline operations, optimizes the scheduling efficiency of standby units, and ultimately improves the overall energy utilization rate and the reliability of system operation.

[0116] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A parallel operation control system for multiple fuel generator sets, characterized in that: It includes an operating data acquisition module, an operating steady-state analysis module, a parallel operation management module, an operating load assessment module, and an offline decision-making module; The operating data acquisition module is used to collect the operating data of the fuel generator in real time based on the sensor group installed in the fuel generator, construct a data processing platform for preprocessing, obtain an operating steady-state data group, and then store it in the data storage repository; The operating steady-state data group includes the unit speed zs, the unit temperature T, the vibration amplitude zf, and the failure probability gl; The operating steady-state analysis module is used to perform an operating steady-state analysis on the fuel generator based on the operating steady-state data group, and conduct a preliminary parallel operation compliance assessment with the preset operating steady-state safety threshold Z; The operating steady-state analysis module includes an operating steady-state analysis unit and a parallel operation compliance assessment unit; The operating steady-state analysis unit is used to perform a summary calculation based on the operating steady-state data group to obtain the operating steady-state function index ywt; ； where ywt i represents the operating steady-state function index of the i-th unit, zs min represents the minimum speed of the unit set by the personnel, T opt represents the optimal temperature of the unit during normal operation, zf max represents the peak vibration amplitude when the unit operates safely as set by the manufacturer, and represent the standard deviation of the unit temperature and the standard deviation of the failure probability respectively, zs i 、T i and zf i represent the unit speed of the i-th unit, the unit temperature of the i-th unit, and the vibration amplitude of the i-th unit respectively; When the parallel operation compliance assessment meets the parallel operation requirements, the parallel operation management module is used to collect the load data of the fuel generator in real time based on the SCADA system of the fuel generator set, obtain a load fluctuation data group after preprocessing, and calculate the load distribution index fhf and the actual output power sjg of the unit; The load fluctuation data group includes the load fluctuation fb; ； where, fhf i represents the load distribution index of the i-th unit, fh j，max represents the peak load capacity of the j-th unit under normal operation, ywt j represents the steady-state operation function index of the j-th unit, to represents the total demand load of the industrial area, n represents all paralleled units, j represents the traversal variable, fh i，max represents the peak load capacity of the i-th unit under normal operation, fb max represents the peak load fluctuation that the system can withstand under normal operation; The operating load assessment module calculates and obtains the load execution rate zxl based on the load distribution index fhf and the actual output power sjg of the unit, and conducts a unit load assessment with the preset first load execution adaptive threshold A and the second load execution adaptive threshold B; When the unit load is abnormal, the offline decision-making module is used to analyze the total load capacity sf of the remaining units and the total demand load to of the industrial area, and execute the corresponding offline and parallel operation instructions.

2. The parallel control system for multiple fuel generator sets according to claim 1, characterized in that: The operating data acquisition module includes a unit data acquisition unit, a data processing unit, and a data storage unit; The unit data acquisition unit is used to collect the operating data of the fuel generator in real time based on the sensor group installed in the fuel generator; The sensor group includes a Hall effect sensor, a thermocouple, and a piezoelectric vibration sensor; The data processing unit is used to construct a data processing platform, establish a communication connection between the data processing platform and the sensor group through the Internet, and transmit the operating data to the data processing platform in real time for noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and fault detection and analysis to obtain an operating steady-state data group; The fault detection and analysis is calculated based on the number of maintenance times and the number of faults in the unit's historical records to obtain the failure probability gl; The data storage unit constructs a data storage repository based on the data processing platform and transmits the operating steady-state data group to the data storage repository for storage in real time.

3. The parallel control system for multiple fuel generator sets according to claim 1, wherein: The parallel operation compliance evaluation unit is used to count all historical normal and abnormal operation steady-state function indices ywt based on all historical operation steady-state data groups in the data repository, and calculate the mean value of the historical operation steady-state function index ywt using the statistical method. , preset an operation steady-state safety threshold Z based on the mean value, and conduct a preliminary parallel operation compliance evaluation with the operation steady-state function index ywt obtained in real time. The specific evaluation scheme is as follows; When the operating steady-state function index ywt > the operating steady-state safety threshold Z, the state of this unit meets the parallel operation requirements; When the operating steady-state function index ywt ≤ the operating steady-state safety threshold Z, the state of this unit does not meet the parallel operation requirements.

4. The parallel control system for multiple fuel generator sets according to claim 3, wherein: The parallel operation management module includes a load data acquisition unit and a load analysis unit; The load data acquisition unit is used to, when the parallel operation compliance assessment meets the parallel operation requirements, collect the load data of the fuel generator set in real time according to the SCADA system of the fuel generator set, perform preprocessing, obtain a load fluctuation data set, and transmit the load fluctuation data set to the data storage repository for storage in real time through the Internet; The preprocessing includes noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and load fluctuation analysis; The load fluctuation analysis is used to calculate the load standard deviation of the computer set based on the acquired load data , and then calculate the load fluctuation fb, specifically as follows: , , where N represents the total number of sampling points, L(t) represents the load at time t, represents the average load within the sampling interval.

5. The parallel control system for multiple fuel generator sets according to claim 4, characterized in that: The load analysis unit includes a load pre-allocation unit and a power analysis unit; The load pre-allocation unit is used to, after the units are paralleled, allocate the load to all paralleled generator sets, perform summary calculations based on the load fluctuation data set and the operation steady-state function index ywt, obtain the load allocation index fhf, and allocate the load to each unit according to the current total load demand and the status of each unit.

6. The parallel control system for multiple fuel generator sets according to claim 5, characterized in that: The power analysis unit is used to calculate the actual power output that each unit can provide after the load distribution is completed, construct a power analysis model through the data processing platform, and introduce a power attenuation factor w ywt and the load response time into the power analysis model to calculate and obtain the actual output power sjg of the unit, and analyze based on the result of the load distribution to determine the actual output power of the unit; The actual output power sjg of the unit is obtained by calculating according to the following formula; ； where, w ywt represents the power decay factor of the operating steady-state function index, t i represents the time of the load response of the i-th unit, t max represents the peak value of the time required for the specified unit load adjustment, sjg i represents the actual output power of the i-th unit.

7. A parallel control system for multiple fuel generator sets according to claim 5, characterized in that: The operation load assessment module includes an execution rate analysis unit and a load assessment unit; The execution rate analysis unit is used to perform summary calculations based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl. Specifically: , where zxl i represents the load execution rate of the i-th unit, and fhf i represents the load distribution index fhf of the i-th unit.

8. The parallel control system for multiple fuel generator sets according to claim 7, characterized in that: The load evaluation unit, based on the historical operation steady-state data group and the load fluctuation data group in the data repository, statistically calculates the load execution rate zxl of all historical normal load and abnormal load units, and calculates the mean values of the historical load execution rates zxl of normal units and abnormal units by using the statistical method. , based on the mean value as the standard, preset the first load execution adaptive threshold A and the second load execution adaptive threshold B according to the unit operation safety fluctuation range. Among them, , c represents the unit operation safety fluctuation range, and conducts unit load evaluation with the load execution rate zxl obtained in real time. The specific evaluation scheme is as follows; When the load execution rate zxl < the first load execution adaptive threshold A, the unit load is abnormal, and at this time the unit load is insufficient; When the first load execution adaptive threshold A ≤ the load execution rate zxl ≤ the second load execution adaptive threshold B, the load allocation is reasonable; When the load execution rate zxl > the second load execution adaptive threshold B, the unit load is abnormal, and at this time the unit load is overloaded.

9. The parallel operation control system for multiple fuel generator sets according to claim 8, characterized in that: The offline decision-making module is used to analyze the total load capacity sf of the remaining units and the total demand load to of the industrial zone when the unit load is overloaded and the secondary iterative operation analysis shows that the state of this unit does not meet the parallel operation requirements. Specifically: , where M represents the total number of remaining units; When the total remaining unit load capacity sf ≥ the total demand load to of the industrial zone and the load volatility fb ≤ the peak load volatility fb max the offline requirement is met and the offline instruction is executed; When the total remaining unit load capacity sf ≥ the total demand load to of the industrial park and the load volatility fb > the peak load volatility fb max Execute the offline command when the load volatility fb stabilizes; When the total load capacity sf of the remaining units < the total demand load to of the industrial zone, the offline requirements are not met.

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