Parallel operation control system for multiple fuel generator units

Abstract

The invention discloses a parallel operation control system for multiple fuel generator units, and relates to the technical field of parallel operation control, the system collects unit operation data in real time through a sensor, and an operation steady state data set is obtained after data preprocessing; calculating an operation steady-state function index ywt based on the operation steady-state data set, and performing parallel operation conformity evaluation on the operation steady-state function index ywt and a preset operation steady-state safety threshold Z; calculating a load distribution index fhf and the actual output power sjg of the unit when the parallel operation requirement is met; performing unit load evaluation according to the load execution rate zxl, and judging whether the load state is abnormal or not; and when the unit load is abnormal, the off-line decision module determines whether to execute off-line and parallel operation instructions by combining the total demand load to of the industrial area and the residual unit load capacity sf. According to the system, through multi-module collaborative optimization, the parallel operation stability and the load management efficiency are improved, and the reliability and the economical efficiency of the power generation system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel control, and in particular to a parallel control system for multiple fuel-fired generator sets. Background Art

[0002] In modern industrial production, the stability of power supply is crucial, especially when the power supply of the power grid is unstable or the industrial zone 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 requirements of the industrial zone. Therefore, the joint operation of multiple fuel generators has become an important means to improve power supply stability and operating 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 control system for multiple fuel generator sets.

[0003] In the Chinese invention application with application publication number CN117713250A, a diesel generator set and a control system are included. The control system includes a controller, a UPS power supply, a dual PLC, an industrial photoelectric converter, and an HMI human-machine interface. The control mode is divided into automatic mode and semi-automatic mode. When any mains voltage is lost, the dual PLC will output a remote control start and stop signal to each diesel generator set. The output circuit breaker switch will be automatically closed after the voltage and frequency reach the rated value first. The rest will automatically adjust the power generation voltage and frequency to synchronize with the busbar and close the corresponding output circuit breaker switch.

[0004] In combination with the prior art, the above application still has the following deficiencies: The above applications mainly rely on PLC monitoring of basic parameters such as voltage and frequency, lack in-depth analysis of the steady state of generator operation, fail to introduce sensor data or historical operation data to evaluate the health of the unit, and find it difficult to accurately predict the unit's operating stability and potential failure risks; load distribution relies on preset logic for adjustment, and fails to optimize and adjust based on actual load fluctuations, which may lead to unbalanced load distribution between units and reduce the overall operating efficiency of the system, especially when the load changes are large, and it is not easy to achieve dynamic adaptive optimization; abnormal handling and offline decisions mainly execute shutdown processing after abnormalities and failures occur in the unit, but fail to perform intelligent analysis and early warning based on real-time data, and lack an evaluation mechanism for the unit's load capacity and overall power supply demand, resulting in delayed offline and parallel decision-making, affecting power supply stability and reliability. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a parallel control system for multiple fuel-fired generator sets, which solves the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a parallel control system for multiple fuel-fired generator sets, including an operation data acquisition module, an operation steady-state analysis module, a parallel management module, an operation load evaluation module and an offline decision module; 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, and to build a data processing platform for preprocessing, to obtain the operation steady-state data group, and then store it in the data storage library; The operation steady-state analysis module is used to perform an operation steady-state analysis on the fuel generator according to the operation steady-state data group, and to perform a preliminary parallel operation compliance assessment with a preset operation steady-state safety threshold Z; The parallel management module is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the parallel conformity is evaluated to meet the parallel requirements, obtain the load fluctuation data group after pre-processing, and calculate the load distribution index fhf and the actual output power sjg of the unit; The operation load evaluation module calculates the load execution rate zxl according to the load distribution index fhf and the actual output power sjg of the unit, and performs unit load evaluation with the preset first load execution adaptive threshold A and second load execution adaptive threshold B; The offline decision module is used to analyze the total load capacity sf of the remaining units and the total required load to of the industrial zone when the unit load is abnormal, and execute corresponding offline and parallel instructions.

[0007] Preferably, the operation 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 on 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, 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 analysis, so as to obtain a steady-state operation data group; The fault detection analysis is calculated based on the number of maintenance records and the number of failures during maintenance to obtain the fault probability gl; The steady-state operation data group includes the unit speed zs, the unit temperature T, the vibration amplitude zf and the failure probability gl; The data storage unit constructs a data storage library based on the data processing platform, and transmits the operation steady-state data group to the data storage library for storage in real time.

[0008] Preferably, the operation steady-state analysis module includes an operation steady-state analysis unit and a parallel operation compliance evaluation unit; The operation steady-state analysis unit is used to perform summary calculation based on the operation steady-state data group to obtain the operation steady-state function index ywt; The running steady-state function index ywt is calculated and obtained by the following formula: ; In the formula, ywt i represents the steady-state function index of the i-th unit, zs min Indicates the minimum speed of the unit set by personnel, T opt Indicates the optimal temperature of the unit during normal operation, zf max It indicates the peak value of vibration amplitude during safe operation of the unit set by the manufacturer. and represent the standard deviation of unit temperature and failure probability respectively.

[0009] Preferably, the paralleling compliance evaluation unit is used to count all historical normal and abnormal operation steady-state function indexes ywt based on all historical operation steady-state data groups in the data storage library, and calculate the mean value of the historical operation steady-state function index ywt using a statistical method. , based on the mean value, the preset operation steady-state safety threshold Z is used, and the operation steady-state function index ywt obtained in real time is used to conduct a preliminary parallel operation compliance assessment. The specific assessment scheme is as follows; When the operation steady-state function index ywt> the operation steady-state safety threshold Z, the unit status meets the paralleling requirements; When the operation steady-state function index ywt ≤ the operation steady-state safety threshold Z, the unit status does not meet the parallel requirements.

[0010] Preferably, the parallel management module includes a load data acquisition unit and a load analysis unit; The load data acquisition unit is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the paralleling conformity is evaluated to meet the paralleling requirements, and perform preprocessing to obtain the load fluctuation data group, and transmit the load fluctuation data group to the data storage library in real time through the Internet for storage; 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 analyze the load standard deviation of the computer group based on 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, It represents the average load in the sampling interval; The load fluctuation data set includes load fluctuation fb.

[0011] Preferably, the load analysis unit includes a load pre-distribution unit and a power analysis unit; The load pre-distribution unit is used to distribute the load to all the generator sets in parallel after the units are connected in parallel, and to obtain the load distribution index fhf by summarizing and calculating the load fluctuation data group and the operation steady-state function index ywt, and to distribute the load to each unit according to the current total load demand and the status of each unit; The load distribution index fhf is calculated and obtained by the following formula: ; In the formula, to represents the total load demand of the industrial area, n represents all parallel units, j represents the ergodic variable, and fh i，max represents the peak load capacity of the i-th unit under normal operation, fb max Indicates the peak load fluctuation that the system can withstand for normal operation.

[0012] Preferably, the power analysis unit is used to calculate the actual power output that can be provided by each unit after the load distribution is completed, and to construct a power analysis model through the data processing platform, by introducing the power attenuation factor w ywt and load response time to the power analysis model to calculate and obtain the actual output power sjg of the unit, and analyze based on the results of load distribution to determine the actual output power of the unit; The actual output power sjg of the unit is calculated and obtained by the following formula: ; In the formula, w ywt The power reduction factor representing the operating steady-state function index, t i represents the load response time of the i-th unit, t max Indicates the peak time required to adjust the load of the specified unit.

[0013] Preferably, the operation load evaluation module includes an execution rate analysis unit and a load evaluation unit; The execution rate analysis unit is used to perform summary calculation based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl, which is specifically: , where zxl i Represents the load execution rate of the i-th unit.

[0014] Preferably, the load evaluation unit counts the load execution rates zxl of all historical normal load and abnormal load units based on the historical steady-state operation data group and load fluctuation data group in the data storage library, and calculates the mean of the historical load execution rates zxl of the normal units and the abnormal units by using a statistical method. , based on the mean value as the standard, the first load execution adaptive threshold A and the second load execution adaptive threshold B are preset according to the unit operation safety fluctuation range, where, , c represents the unit operation safety fluctuation range, and the unit load evaluation is performed with the real-time load execution rate zxl. The specific evaluation scheme is as follows; When the load execution rate zxl is less than the first load execution adaptive threshold A, the unit load is abnormal, and the unit load is insufficient at this time; When the first load execution adaptive threshold A ≤ load execution rate zxl ≤ the second load execution adaptive threshold B, the load distribution is reasonable; When the load execution rate zxl> the second load execution adaptive threshold B, the unit load is abnormal, and the unit load is overloaded.

[0015] 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 zone when the unit is overloaded and the secondary iterative operation analysis shows that the state of the unit does not meet the paralleling requirements, specifically: , where n represents the total number of remaining units; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb ≤ the load fluctuation peak fb max When the offline requirement is met, the offline instruction is executed; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb > the load fluctuation peak fb max When the load fluctuation rate fb is stable, the offline command is executed; When the total load capacity of the remaining units sf is less than the total demand load to of the industrial zone, the offline requirements are not met.

[0016] The present invention provides a parallel control system for multiple fuel-fired generator sets. It has the following beneficial effects: (1) The system's operation data acquisition module relies on a variety of high-precision sensor groups to collect the operating data of the fuel generator in real time, and performs efficient preprocessing through the data processing platform, including noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing and fault detection analysis, thereby forming a stable and reliable operation steady-state data group to ensure 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 status analysis and decision-making. The role of this module is to ensure the real-time update of the unit status information and lay a solid foundation for the intelligent control of the system.

[0017] (2) The system's operation steady-state analysis module evaluates the unit's operation stability by calculating the operation steady-state function index ywt, and performs a preliminary paralleling compliance assessment by presetting the operation steady-state safety threshold Z through historical statistical data. The units that meet the paralleling requirements enter the paralleling management module, which collects the load data of the fuel generator 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 operation load evaluation module summarizes and calculates the load distribution index fhf and the actual output power sjg of the unit, obtains the load execution rate zxl, presets the first load execution adaptive threshold A and the second load execution adaptive threshold B to evaluate the unit load, and evaluates the rationality of the load distribution. If the load execution rate exceeds the set range, the system automatically adjusts the unit load and optimizes the load distribution strategy to ensure that each unit operates in the optimal efficiency range. This process ensures the balanced operation of the generator set, improves the fuel utilization rate, and reduces the power loss and equipment loss caused by uneven load distribution.

[0018] (3) The offline decision module of the system further improves the stability and flexibility of the system. When the load of a unit is abnormal and the secondary iterative operation analysis determines that its status does not meet the parallel requirements, the system will calculate the total load capacity sf of the remaining units and the total demand load to of the industrial zone. If the load capacity of the remaining units is sufficient and the load fluctuation fb is within the safe range, the offline command will be executed; if the load fluctuation is too large, wait for it to stabilize before executing the offline; and when the load capacity of the remaining units is insufficient, the system will give priority to reducing the operating load of the unit and wait 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 system's intelligent control capability enables it to automatically adjust the operating status of the unit according to the dynamic changes in load demand, improving the stability of the overall system and power supply safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of a parallel control system for multiple fuel-fired generator sets according to the present invention; Figure 2 The present invention is a schematic diagram of the operation principle of a parallel control system for multiple fuel-fired generator sets. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0021] Example 1 See also Figure 1 The present invention provides a parallel control system for multiple fuel-fired generator sets. To achieve the above purpose, the present invention is implemented through the following technical solutions: including an operation data acquisition module, an operation steady-state analysis module, a parallel management module, an operation load evaluation module and an offline decision module; The operation data acquisition module is used to collect the operation data of the fuel generator in real time based on the sensor group installed on the fuel generator, and build a data processing platform for preprocessing, obtain the operation steady-state data group, and then store it in the data storage library; 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 to conduct a preliminary parallel operation compliance assessment with the preset operation steady-state safety threshold Z; The parallel management module is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the parallel conformity assessment is in compliance with the parallel requirements, obtain the load fluctuation data group after pre-processing, and calculate the load distribution index fhf and the actual output power sjg of the unit; The operation load evaluation module calculates the load execution rate zxl according to the load distribution index fhf and the actual output power sjg of the unit, and performs unit load evaluation with the preset first load execution adaptive threshold A and second load execution adaptive threshold B; The offline decision module is used to analyze the total load capacity sf of the remaining units and the total required load to of the industrial zone when the unit load is abnormal, and execute the corresponding offline and parallel instructions.

[0022] In this embodiment, the operation data acquisition module realizes accurate monitoring of the real-time operation status of the fuel generator. Using a variety of sensors installed on the unit, the system can continuously collect key operation data, and pre-process through the data processing platform to obtain the operation steady-state data group, and store it in the data storage library; this process ensures the accuracy and integrity of the data, and provides a reliable basis for subsequent steady-state analysis and parallel decision-making. Compared with the traditional method of relying on manual inspection or simple SCADA data monitoring, it improves the accuracy and real-time performance of data acquisition and reduces the underreporting rate of abnormal equipment status. The operation steady-state analysis module summarizes and calculates based on the operation steady-state data group, obtains the operation steady-state function index ywt, performs operation steady-state analysis on the fuel generator, and performs a preliminary parallel conformity assessment with the preset operation steady-state safety threshold Z, which can accurately assess whether the unit operation state meets the parallel requirements. When the parallel conformity assessment is in compliance with the parallel requirements, the parallel management module collects load data through the SCADA system, obtains the load fluctuation data group after pre-processing, 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 the industrial zone, avoid energy waste and equipment loss caused by overload or low load operation of a single unit, and improve the operation efficiency of the overall unit group. The operation load evaluation 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 an underloaded and overloaded state, and adjust the load distribution plan in time. When a serious abnormality occurs in the offline decision module, the system can analyze the total load capacity of the remaining units and automatically decide whether to perform offline or parallel operation. Compared with the traditional method that relies on manual intervention, the system improves the automation level of parallel management and reduces the delay caused by human decision-making, so that the unit group can adapt to the load requirements of complex industrial scenarios more stably and efficiently.

[0023] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 and Figure 2 ,Specifically: the operation 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 on the fuel generator; The sensor set includes Hall effect sensors, thermocouples, and piezoelectric vibration sensors; The data processing unit is used to build 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 analysis to obtain a steady-state operation data group; Fault detection analysis is performed based on the number of maintenance records and the number of failures during maintenance to obtain the fault probability gl; The steady-state operation data set includes unit speed zs, unit temperature T, vibration amplitude zf and failure probability gl; The data storage unit constructs a data repository based on the data processing platform, and transmits the running steady-state data group to the data repository for storage in real time.

[0024] In this embodiment, with the help of Hall effect sensors, thermocouples and piezoelectric vibration sensors, the system can fully obtain the operating data of the unit. The data processing unit enables the system to not only achieve efficient data transmission, but also obtain the operating steady-state data group through noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing and fault detection analysis, thereby improving the accuracy and stability of the data, and thus enhancing the accurate assessment of the health status of the unit. In addition, the data storage unit has built a complete data repository, enabling the system to perform trend analysis and fault prediction based on long-term accumulated data, thereby providing early warning of potential problems, optimizing maintenance strategies, and avoiding the occurrence of sudden failures. Compared with traditional intermittent detection and manual judgment methods, this solution significantly improves the integrity, timeliness and intelligent decision-making level of data, providing a solid guarantee for the efficient and stable operation of fuel generators.

[0025] Example 3 This embodiment is explained in Example 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 ,conformity evaluation unit; The operation steady-state analysis unit is used to perform summary calculation based on the operation steady-state data group to obtain the operation steady-state function index ywt; The running steady-state function index ywt is calculated by the following formula; ; In the formula, ywt i represents the steady-state function index of the i-th unit, zs min Indicates the minimum speed of the unit set by personnel, T opt Indicates the optimal temperature of the unit during normal operation, zf max It indicates the peak value of vibration amplitude during safe operation of the unit set by the manufacturer. and represent the standard deviation of unit temperature and failure probability respectively.

[0026] The parallel conformity evaluation unit is used to count all historical normal and abnormal operation steady-state function indexes 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 statistical methods. , based on the mean value, the preset operation steady-state safety threshold Z is used, and the operation steady-state function index ywt obtained in real time is used to conduct a preliminary parallel operation compliance assessment. The specific assessment scheme is as follows; When the operation steady-state function index ywt> the operation steady-state safety threshold Z, the unit status meets the parallel connection requirements, maintains normal monitoring, and continues to be connected to the grid; When the operation steady-state function index ywt≤the operation steady-state safety threshold Z, the unit status does not meet the parallel requirements, the unit enters the load reduction monitoring mode, there is a potential fault, and fault information is generated and transmitted to relevant personnel for equipment maintenance.

[0027] In this embodiment, the operation steady-state analysis module realizes a 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 by combining historical data and real-time monitoring data through the parallel machine compliance evaluation unit, thereby ensuring the parallel machine 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 ability to accurately determine the operating status of the unit by combining 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 judgment criteria based on data-driven, making the parallel machine evaluation more adaptable and robust. In addition, when the abnormal operation status of the unit is detected, 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 failure, but also effectively avoids serious failures or unplanned shutdowns of the unit due to the accumulation of hidden dangers, greatly improving the operating safety, stability and maintenance efficiency of the fuel generator.

[0028] Example 4 This embodiment is explained in Example 3, please refer to Figure 1 and Figure 2 ,Specifically: the parallel management module includes a load data acquisition unit and a load analysis unit; The load data acquisition unit is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the paralleling conformity is evaluated to meet the paralleling requirements, and to perform preprocessing to obtain the load fluctuation data group, and to transmit the load fluctuation data group to the data storage library in real time through the Internet for storage; Preprocessing includes noise filtering, data correction, outlier detection, data time synchronization, dimensionless processing, and load fluctuation analysis; Load fluctuation analysis is used to analyze the load standard deviation of a computer group based on 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, It represents the average load in the sampling interval; The load fluctuation data group includes load fluctuation fb.

[0029] The load analysis unit includes a load pre-distribution unit and a power analysis unit; The load pre-distribution unit is used to distribute the load to all the generator sets in parallel after the units are connected in parallel, optimize the operation efficiency, and obtain the load distribution index fhf based on the load fluctuation data group and the operation steady-state function index ywt. According to the current total load demand and the status of each unit, the load is distributed to each unit; The load distribution index fhf is calculated by the following formula: ; In the formula, to represents the total load demand of the industrial area, n represents all parallel units, j represents the ergodic variable, and fh i，max represents the peak load capacity of the i-th unit under normal operation, fb max Indicates the peak load fluctuation that the system can withstand for normal operation.

[0030] The power analysis unit is used to calculate the actual power output that each unit can provide after the load distribution is completed. The power analysis model is constructed through the data processing platform. By introducing the power attenuation factor w ywt and load response time to the power analysis model to calculate and obtain the actual output power sjg of the unit, and analyze based on the results of load distribution to determine the actual output power of the unit; The actual output power sjg of the unit is calculated by the following formula: ; In the formula, w ywt The power reduction factor representing the operating steady-state function index, t i represents the load response time of the i-th unit, t max Indicates the peak time required to adjust the load of the specified unit.

[0031] In this embodiment, the parallel management module realizes accurate monitoring and optimized distribution of the load state of the fuel generator 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 situation is quantified through load fluctuation analysis to provide 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 calculations to obtain the load distribution index fhf to achieve adaptive load distribution, which not only ensures the rationality of the load distribution of each unit, but also optimizes the energy utilization efficiency and reduces unnecessary fuel consumption. In addition, the introduction of the power analysis unit enables the system to dynamically adjust the output power of the unit, and accurately calculates the actual output power sjg of the computer unit through modeling of the power attenuation factor and the load response time, avoiding the problem 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 capability of the unit operation through real-time load fluctuation analysis and dynamic power adjustment, so that the parallel system can realize intelligent and accurate power scheduling when the load demand fluctuates, ensuring the long-term efficient and stable operation of the system.

[0032] Example 5 This embodiment is explained in Example 4. Please refer to Figure 1 and Figure 2 ,Specifically: the operation load evaluation module includes an execution rate analysis unit and a load evaluation unit; The execution rate analysis unit is used to perform summary calculation based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl, which is specifically: , where zxl i Represents the load execution rate of the i-th unit.

[0033] The load evaluation unit counts the load execution rates zxl of all historical normal load and abnormal load units based on the historical steady-state data group and load fluctuation data group in the data repository, and uses the statistical method to calculate the mean of the historical load execution rates zxl of normal units and abnormal units. , based on the mean value as the standard, the first load execution adaptive threshold A and the second load execution adaptive threshold B are preset according to the unit operation safety fluctuation range, where, , c represents the unit operation safety fluctuation range, and the unit load evaluation is performed with the real-time load execution rate zxl. The specific evaluation scheme is as follows; When the load execution rate zxl is less than the first load execution adaptive threshold A, the unit load is abnormal. At this time, the unit load is insufficient, and a control instruction is generated to increase the load of this unit; When the first load execution adaptive threshold A ≤ load execution rate zxl ≤ the second load execution adaptive threshold B, the load distribution is reasonable and monitoring is maintained; 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, and a control instruction is generated to reduce the load of this unit. The unit is then subjected to a secondary iterative operation analysis through the operation steady-state analysis module.

[0034] In this embodiment, the introduction of the operation load evaluation module enables the system to dynamically monitor the load execution of each unit after load distribution, and to perform intelligent load adjustment through the parallel operation evaluation unit. Based on the load execution rate zxl calculated by the execution rate analysis unit, the system can accurately identify the load state 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, so as to realize the rapid identification and adjustment of the abnormal load of the unit. When the unit is underloaded or overloaded, the system can automatically generate control instructions to increase or decrease the load of the corresponding unit to ensure that the load distribution of the entire unit group is always kept within a reasonable range. Compared with the traditional static threshold judgment method, this scheme establishes a more scientific and reasonable load evaluation 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 the overloaded unit to further ensure its operation stability and avoid equipment damage or power supply fluctuations caused by overload operation. This adaptive load adjustment mechanism not only improves the overall operation efficiency of the unit, but also effectively reduces energy waste, prolongs equipment life, and improves the safety and stability of power supply.

[0035] Example 6 This embodiment is explained in Example 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 zone when the unit is overloaded and the secondary iterative operation analysis shows that the state of this unit does not meet the parallel requirements. Specifically: , where n represents the total number of remaining units; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb ≤ the load fluctuation peak fb max When the offline requirement is met, the offline instruction is executed; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb > the load fluctuation peak fb max When the load fluctuation rate fb is stable, the offline command is executed; When the total load capacity of the remaining units sf is less than the total demand load to of the industrial zone, the offline requirement is not met. At this time, the operating load of this unit is reduced, and the offline command is executed after the standby unit is started.

[0036] In this embodiment, the offline decision module intelligently analyzes the abnormal load of the unit and combines it with the secondary iterative operation analysis to ensure that the offline decision is executed only when the unit does not meet the parallel requirements, thereby avoiding unnecessary shutdowns and improving the stability of the system. The module is based on the total load capacity sf of the remaining units and the total demand load to of the industrial zone to make an accurate match, ensuring 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 zone and the load fluctuation rate fb is in a stable range, which effectively reduces the risk of insufficient power supply due to 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 zone, but fb exceeds the safe range, the system can intelligently wait for the load fluctuation to stabilize before executing offline, avoiding the problem of system instability caused by excessive load fluctuations. When the total load capacity sf is less than the total demand load to of the industrial zone, the system will not immediately execute offline, but will take the approach of reducing the operating load of the faulty unit and waiting for the standby unit to start, so as to ensure the continuity of the overall power supply. Compared with the traditional offline mode that relies on manual decision-making, the module's adaptive adjustment mechanism improves the power supply system's ability to respond to sudden load changes, reduces power outages caused by erroneous offline operations, and optimizes the scheduling efficiency of standby units, ultimately improving overall energy utilization and system operation reliability.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A parallel control system for multiple fuel-fired generator sets, characterized by: It includes operation data acquisition module, operation steady-state analysis module, parallel management module, operation load assessment module and offline decision-making module; 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, and to build a data processing platform for preprocessing, to obtain the operation steady-state data group, and then store it in the data storage library; The operation steady-state analysis module is used to perform an operation steady-state analysis on the fuel generator according to the operation steady-state data group, and to perform a preliminary parallel operation compliance assessment with a preset operation steady-state safety threshold Z; The parallel management module is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the parallel conformity is evaluated to meet the parallel requirements, obtain the load fluctuation data group after pre-processing, and calculate the load distribution index fhf and the actual output power sjg of the unit; The operation load evaluation module calculates the load execution rate zxl according to the load distribution index fhf and the actual output power sjg of the unit, and performs unit load evaluation with the preset first load execution adaptive threshold A and second load execution adaptive threshold B; The offline decision module is used to analyze the total load capacity sf of the remaining units and the total required load to of the industrial zone when the unit load is abnormal, and execute corresponding offline and parallel instructions.

2. A parallel control system for multiple fuel-fired generator sets according to claim 1, characterized in that: The operation 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 on 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, 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 analysis, so as to obtain a steady-state operation data group; The fault detection analysis is calculated based on the number of maintenance records and the number of failures during maintenance to obtain the fault probability gl; The steady-state operation data group includes the unit speed zs, the unit temperature T, the vibration amplitude zf and the failure probability gl; The data storage unit constructs a data storage library based on the data processing platform, and transmits the operation steady-state data group to the data storage library for storage in real time.

3. A parallel control system for multiple fuel-fired generator sets according to claim 2, characterized in that: The operation steady-state analysis module includes an operation steady-state analysis unit and a parallel operation compliance evaluation unit; The operation steady-state analysis unit is used to perform summary calculation based on the operation steady-state data group to obtain the operation steady-state function index ywt; The running steady-state function index ywt is calculated and obtained by the following formula: ； In the formula, ywt i represents the steady-state function index of the i-th unit, zs min Indicates the minimum speed of the unit set by personnel, T opt Indicates the optimal temperature of the unit during normal operation, zf max It indicates the peak value of vibration amplitude during safe operation of the unit set by the manufacturer. and represent the standard deviation of unit temperature and failure probability respectively.

4. A parallel control system for multiple fuel-fired generator sets according to claim 3, characterized in that: The paralleling conformity evaluation unit is used to count all historical normal and abnormal steady-state function indexes ywt based on all historical steady-state data groups in the data storage library, and calculate the mean value of the historical steady-state function index ywt using a statistical method. , based on the mean value, the preset operation steady-state safety threshold Z is used, and the operation steady-state function index ywt obtained in real time is used to conduct a preliminary parallel operation compliance assessment. The specific assessment scheme is as follows; When the operation steady-state function index ywt> the operation steady-state safety threshold Z, the unit status meets the paralleling requirements; When the operation steady-state function index ywt ≤ the operation steady-state safety threshold Z, the unit status does not meet the parallel requirements.

5. A parallel control system for multiple fuel-fired generator sets according to claim 4, characterized in that: The parallel management module includes a load data acquisition unit and a load analysis unit; The load data acquisition unit is used to collect the load data of the fuel generator in real time according to the SCADA system of the fuel generator set when the paralleling conformity is evaluated to meet the paralleling requirements, and perform preprocessing to obtain the load fluctuation data group, and transmit the load fluctuation data group to the data storage library in real time through the Internet for storage; 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 analyze the load standard deviation of the computer group based on 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, It represents the average load in the sampling interval; The load fluctuation data set includes load fluctuation fb.

6. A parallel control system for multiple fuel-fired generator sets according to claim 5, characterized in that: The load analysis unit includes a load pre-distribution unit and a power analysis unit; The load pre-distribution unit is used to distribute the load to all the generator sets in parallel after the units are connected in parallel, and to obtain the load distribution index fhf by summarizing and calculating the load fluctuation data group and the operation steady-state function index ywt, and to distribute the load to each unit according to the current total load demand and the status of each unit; The load distribution index fhf is calculated and obtained by the following formula: ； In the formula, to represents the total load demand of the industrial area, n represents all parallel units, j represents the ergodic variable, and fh i，max represents the peak load capacity of the i-th unit under normal operation, fb max Indicates the peak load fluctuation that the system can withstand for normal operation.

7. A parallel control system for multiple fuel-fired generator sets according to claim 6, 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, and to build a power analysis model through the data processing platform. ywt and load response time to the power analysis model to calculate and obtain the actual output power sjg of the unit, and analyze based on the results of load distribution to determine the actual output power of the unit; The actual output power sjg of the unit is calculated and obtained by the following formula: ； In the formula, w ywt The power reduction factor representing the operating steady-state function index, t i represents the load response time of the i-th unit, t max Indicates the peak time required to adjust the load of the specified unit.

8. A parallel control system for multiple fuel-fired generator sets according to claim 6, characterized in that: The operation load evaluation module includes an execution rate analysis unit and a load evaluation unit; The execution rate analysis unit is used to perform summary calculation based on the obtained load distribution index fhf and the actual output power sjg of the unit to obtain the load execution rate zxl, which is specifically: , where zxl i Represents the load execution rate of the i-th unit.

9. A parallel control system for multiple fuel-fired generator sets according to claim 8, characterized in that: The load evaluation unit counts the load execution rates zxl of all historical normal load and abnormal load units based on the historical steady-state data group and load fluctuation data group in the data storage library, and calculates the mean of the historical load execution rates zxl of normal units and abnormal units using a statistical method. , based on the mean value as the standard, the first load execution adaptive threshold A and the second load execution adaptive threshold B are preset according to the unit operation safety fluctuation range, where, , c represents the safe fluctuation range of unit operation, and is used together with the real-time load execution rate zxl to evaluate the unit load. The specific evaluation scheme is as follows; When the load execution rate zxl is less than the first load execution adaptive threshold value A, the unit load is abnormal, and the unit load is insufficient at this time; When the first load execution adaptive threshold A ≤ load execution rate zxl ≤ the second load execution adaptive threshold B, the load distribution is reasonable; When the load execution rate zxl> the second load execution adaptive threshold B, the unit load is abnormal, and the unit load is overloaded.

10. A parallel control system for multiple fuel-fired generator sets according to claim 9, characterized in that: 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 zone when the unit is overloaded and the secondary iterative operation analysis shows that the unit status does not meet the parallel requirements, specifically: , where n represents the total number of remaining units; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb ≤ the load fluctuation peak fb max When the offline requirement is met, the offline instruction is executed; When the total load capacity of the remaining units sf ≥ the total demand load of the industrial area to and the load fluctuation rate fb > the load fluctuation peak fb max When the load fluctuation rate fb is stable, the offline command is executed; When the total load capacity of the remaining units sf is less than the total demand load to of the industrial zone, the offline requirements are not met.

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