Intelligent low-voltage power distribution monitoring system for hydropower station

Abstract

The invention discloses an intelligent low-voltage power distribution monitoring system for a hydropower station, which can accurately predict future load change in advance and adjust a power supply strategy in advance before a load peak period so as to ensure continuous and stable operation of the hydropower station and reduce energy consumption. According to the system, a load prediction model is introduced, so that the system can recognize and cope with load fluctuation in advance, the operation stability of the system is remarkably improved, production interruption and equipment damage caused by unstable power supply are avoided, and the reliability of the system is enhanced; meanwhile, through a self-adaptive control algorithm, the system can dynamically adjust operation control parameters according to the real-time load condition, unnecessary energy consumption is reduced, in addition, the system has the real-time response and dynamic adjustment capacity, and dependence on manual intervention is remarkably reduced. The intelligent control mode improves the management efficiency of the system, ensures safe operation under complex working conditions, and reduces the frequency and cost of daily maintenance. The method is suitable for popularization and application in the power technology field.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and particularly to an intelligent low-voltage power distribution monitoring system for a hydropower station. Background Art

[0002] In traditional hydropower stations, as the core link of power transmission, the design and operation of the low-voltage power distribution system face severe challenges. However, the traditional low-voltage power distribution system exposes many deficiencies in practical applications and is difficult to meet the high standards of hydropower stations. First of all, the traditional system lacks real-time monitoring and dynamic regulation capabilities and usually relies on fixed control strategies and manual monitoring means, resulting in slow responses when dealing with load fluctuations and sudden failures. For example, when the power demand suddenly increases, the traditional system often causes excessive voltage and current fluctuations and affects system stability because it cannot quickly adjust control parameters. Secondly, the fault detection and handling efficiency of the traditional system is low, and the average response time is as high as 15 minutes, seriously prolonging the operation time of equipment in an unstable state and increasing the risk of equipment damage and shutdown. In addition, the energy efficiency utilization rate of the traditional system is relatively low. In the Yeba Tan Hydropower Station, the power utilization rate is only 85%, and when the load fluctuates greatly, the voltage fluctuation range can reach ±8%, further reducing the energy efficiency of the system. High maintenance costs are also a major problem. The traditional system mainly relies on manual inspection and maintenance, and the annual maintenance cost is very high. Frequent manual operations also increase the risk of errors and affect the reliability and safety of the system. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent low-voltage power distribution monitoring system for a hydropower station that can accurately predict future load changes in advance and adjust the power supply strategy in advance before the peak load period, thereby ensuring the continuous and stable operation of the hydropower station and reducing energy consumption.

[0004] The technical solution adopted by the present invention to solve its technical problems is as follows: The intelligent low-voltage power distribution monitoring system for hydropower stations includes a sensor network module, a data acquisition and communication module, a central control unit, an execution device module, a user interface and display module, a data storage and history record module, and a remote monitoring and maintenance module. The sensor network module, the execution device module, and the remote monitoring and maintenance module are respectively connected to the central control unit through the data acquisition and communication module. The user interface and display module and the data storage and history record module are respectively connected to the central control unit. The sensor network module is used to monitor electrical parameter data in real time and transmit the electrical parameter data to the central control unit through the data acquisition and communication module. The data storage and history record module is used to store the operation data and log information of the monitoring system. The user interface and display module is used to display the operation status of the monitoring system. The remote monitoring and maintenance module is used for remote access and management functions. The central control unit processes and analyzes the electrical parameter data to generate the control parameter C(t + k) at the future time t + k. The control parameter C(t + k) at the future time t + k is sent to the execution device module through the data acquisition and communication module. The execution device module adjusts the voltage and switches the load according to the control parameter C(t + k) at the future time t + k. After completion, the result is fed back to the central control unit through the data acquisition and communication module. The specific process of the central control unit processing and analyzing the electrical parameter data to generate the control parameter C(t + k) is as follows:

[0005] A. Obtain the electrical parameter data at the current time t and in the past p + 1 time periods;

[0006] B. Calculate the load data L(t), L(t - 1), …, L(t - p + 1) at the current time t and in the past p + 1 time periods;

[0007] C. Predict the load L(t + k) at the future time t + k using the following formula;

[0008]

[0009] where, represents the load prediction model parameters, The value range of is between 0 and 1;

[0010] ∈(t + k) represents the random disturbance term, and the value range of ∈(t + k) is between -0.1×L 平均 and +0.1×L 平均 ; L 平均 represents the average load at the current time t and in the past p + 1 time periods;

[0011] D. Calculate the control parameter C(t + k) at the future time t + k;

[0012] C(t + k)=C 0 ×(1 + β×ɑ(t + k))

[0013] ɑ(t + k)=(L(t + k)-L 平均 ) / L 平均

[0014] Where ɑ(t + k) represents the load response coefficient, indicating the change amplitude of the load at the future time t + k relative to the average load L 平均 ; C 0 represents the initial control parameter, that is, the standard control parameter value when the load does not change. β represents the control sensitivity coefficient, which is used to adjust the response intensity of the control parameter to the load change. The value range of β is between 0.1 and 2.0.

[0015] Furthermore, the is determined by historical data regression analysis or time series analysis.

[0016] Furthermore, β = 0.1.

[0017] The beneficial effects of the present invention are as follows: The intelligent low - voltage power distribution monitoring system for hydropower stations of the present invention enables the system to identify and respond to load fluctuations in advance by introducing a load prediction model, effectively reducing the risk of equipment failures caused by sudden load changes. The accurate prediction ability of this model significantly improves the operating stability of the system, avoids production interruptions and equipment damage caused by unstable power supply, and enhances the reliability of the system; at the same time, through the adaptive control algorithm, the system can dynamically adjust the operating control parameters according to the real - time load conditions, reducing unnecessary energy consumption. Compared with the traditional fixed control strategy, the present invention significantly optimizes the energy utilization efficiency, reduces the overall energy consumption of the system, and makes an important contribution to energy conservation and emission reduction in large - scale hydropower projects. In addition, the intelligent low - voltage power distribution monitoring system for hydropower stations of the present invention has the ability of real - time response and dynamic adjustment, significantly reducing the dependence on manual intervention. The intelligent control mode improves the management efficiency of the system, ensures safe operation under complex working conditions, and reduces the frequency and cost of daily maintenance. Specific Embodiments

[0018] The following further illustrates the present invention with reference to embodiments.

[0019] The intelligent low-voltage power distribution monitoring system for hydropower stations described in the present invention comprises a sensor network module, a data acquisition and communication module, a central control unit, an execution device module, a user interface and display module, a data storage and history record module, and a remote monitoring and maintenance module. The sensor network module, the execution device module, and the remote monitoring and maintenance module are respectively connected to the central control unit through the data acquisition and communication module. The user interface and display module and the data storage and history record module are respectively connected to the central control unit. The sensor network module is distributed at each key node of the power distribution system, and real-time monitors electrical parameters such as voltage, current, and temperature, and transmits the electrical parameter data to the central control unit through the data acquisition and communication module. The data storage and history record module is used to store the operation data and log information of the monitoring system. The user interface and display module is used to display the operation status of the monitoring system and provide a real-time status display and control interface for the operator. The remote monitoring and maintenance module is used for remote access and management functions. The central control unit processes and analyzes the electrical parameter data to generate the control parameter C(t + k) at the future time t + k. The control parameter C(t + k) at the future time t + k is sent to the execution device module through the data acquisition and communication module. The execution device can be a voltage regulator, a circuit breaker, a relay, etc. The execution device module adjusts the voltage and switches the load according to the control parameter C(t + k) at the future time t + k, and after completion, feeds back the result to the central control unit through the data acquisition and communication module. The specific process of the central control unit processing and analyzing the electrical parameter data to generate the control parameter C(t + k) is as follows:

[0020] A. Obtain the electrical parameter data at the current time t and within the past p + 1 time periods;

[0021] B. Calculate the load data L(t), L(t - 1), …, L(t - p + 1) at the current time t and within the past p + 1 time periods;

[0022] C. Use the following formula to predict the load L(t + k) at the future time t + k;

[0023]

[0024] where, represents the load prediction model parameter, the value range of is between 0 and 1; the is determined by historical data regression analysis or time series analysis.

[0025] ∈(t + k) represents the random disturbance term, and the value range of ∈(t + k) is between -0.1×L 平均 and +0.1×L 平均 ; L 平均Represents the average load at the current moment t and over the past p+1 time periods; by introducing a load prediction model, the system can identify and respond to load fluctuations in advance, effectively reducing the risk of equipment failures caused by sudden load changes. The accurate prediction ability of this model significantly improves the operating stability of the system, avoids production interruptions and equipment damage caused by unstable power supply, and enhances the reliability of the system;

[0026] E. Calculate the control parameter C(t+k) at the future moment t+k;

[0027] C(t+k) = C 0 ×(1 + β×ɑ(t+k))

[0028] ɑ(t+k) = (L(t+k) - L 平均 ) / L 平均

[0029] where ɑ(t+k) represents the load response coefficient, indicating the change amplitude of the load at the future moment t+k relative to the average load L 平均 ; C 0 represents the initial control parameter, that is, the standard control parameter value when the load has not changed. β represents the control sensitivity coefficient, which is used to adjust the response intensity of the control parameter to load changes. The value range of β is between 0.1 and 2.0. Through the adaptive control algorithm, the system can dynamically adjust the operating control parameters according to the real-time load situation, reducing unnecessary energy consumption. Compared with the traditional fixed control strategy, the present invention significantly optimizes the energy utilization efficiency, reduces the overall energy consumption of the system, and makes an important contribution to energy conservation and emission reduction in large-scale hydropower projects. In addition, the intelligent low-voltage power distribution monitoring system for hydropower stations described in the present invention has the ability of real-time response and dynamic adjustment, significantly reducing the dependence on manual intervention. The intelligent control mode improves the management efficiency of the system, ensures safe operation under complex working conditions, and reduces the frequency and cost of daily maintenance.

[0030] In the above embodiment, in order to obtain the optimal system stability and response speed, by observing the system response under different load conditions, β is preferably 0.1.

[0031] Example

[0032] In the low-voltage power distribution system of the Yeba Tan Hydropower Station in the upper reaches of the Jinsha River, the applicant invented the intelligent low-voltage power distribution monitoring system for hydropower stations. The system monitors voltage, current, and load conditions in real time through a sensor network. By collecting historical load data, the system uses a load prediction model to accurately predict the load demand at the future time t + k. Before the peak load period, the power supply strategy is adjusted in advance. Through this method, the system not only optimizes energy consumption but also reduces system instability caused by sudden load changes, ensuring the continuous and stable operation of the hydropower station. At the same time, through an adaptive control algorithm, the system monitors voltage, current, and load conditions in real time through a sensor network, realizes dynamic load management, and dynamically adjusts control parameters to ensure power supply stability and energy efficiency. Through this method, power loss is significantly reduced, and at the same time, the reliability of the system is improved.

[0033] Due to the lack of intelligent regulation, the power utilization rate of traditional power distribution monitoring systems is only 85%. In the low-voltage power distribution system of the Yeba Tan Hydropower Station in the upper reaches of the Jinsha River, after applying the intelligent low-voltage power distribution monitoring system for hydropower stations, through the optimization of adaptive control and load prediction models, the power utilization rate has been increased to 92%. In terms of fault handling, the average fault response time of the traditional system is 15 minutes, mainly due to relying on manual detection and operation. The intelligent system of this project can automatically detect and quickly isolate faults, shortening the response time to 3 minutes. In terms of system stability, during periods of large load fluctuations, the voltage fluctuation range of the traditional system is ±8%. The intelligent system reduces the fluctuation range to ±2% by adjusting voltage and load distribution in real time, greatly improving system stability. In addition, since the intelligent system can give early warnings and automatically handle potential problems, the annual failure rate has dropped from 12 times in the traditional system to 3 times, significantly reducing the frequency of faults. At the same time, relying on intelligent management and remote monitoring, the maintenance cost of the system has been reduced from the traditional 2 million yuan to 1.2 million yuan, a reduction of 40%. These comparison data fully demonstrate the significant advantages of the intelligent low-voltage power distribution monitoring system in improving energy efficiency, shortening fault response time, enhancing system stability, and reducing maintenance costs.

[0034] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent low-voltage power distribution monitoring system for a hydropower station, comprising a sensor network module, a data acquisition and communication module, a central control unit, an execution device module, a user interface and display module, a data storage and history record module, and a remote monitoring and maintenance module, wherein the sensor network module, the execution device module, and the remote monitoring and maintenance module are connected to the central control unit through the data acquisition and communication module, respectively, the user interface and display module, and the data storage and history record module are connected to the central control unit, respectively, the sensor network module is used to monitor electrical parameter data in real time, and transmit the electrical parameter data to the central control unit through the data acquisition and communication module, the data storage and history record module is used to store the operating data and log information of the monitoring system, the user interface and display module is used to display the operating status of the monitoring system, and the remote monitoring and maintenance module is used for remote access and management functions; characterized in that: The central control unit processes and analyzes the electrical parameter data to generate the control parameter C(t+k) at the future time t+k. The control parameter C(t+k) at the future time t+k is sent to the execution device module through the data acquisition and communication module. The execution device module performs voltage regulation and load switching according to the control parameter C(t+k) at the future time t+k. After completion, the result is fed back to the central control unit through the data acquisition and communication module. The specific process of the central control unit processing and analyzing the electrical parameter data to generate the control parameter C(t+k) is as follows: A. Obtain electrical parameter data at the current time t and the past p+1 time period; B. Calculate the load data L(t), L(t-1)…L(t-p+1) at the current time t and in the past p+1 time period; C. Use the following formula to predict the load L(t+k) at the future time t+k; in, represents the load prediction model parameters, The value range of is between 0 and 1; ∈(t+k) represents the random disturbance term, and the value range of ∈(t+k) is -0.1×L 平均 To +0.1×L 平均 Between; L 平均 Represents the average load at the current time t and the past p+1 time period; D. Calculate the control parameter C(t+k) at the future time t+k; C(t+k)=C0×(1+β×ɑ(t+k)) ɑ(t+k)=(L(t+k)-L 平均 ) / L 平均 Among them, ɑ(t+k) represents the load response coefficient, which means the load at the future time t+k is relative to the average load L 平均 C0 represents the initial control parameter, that is, the standard control parameter value when the load does not change, β represents the control sensitivity coefficient, which is used to adjust the response strength of the control parameter to the load change, and the value range of β is between 0.1 and 2.

0.

2. The intelligent low-voltage power distribution monitoring system for a hydropower station according to claim 1 is characterized in that: Said Determined through historical data regression analysis or time series analysis.

3. The intelligent low-voltage power distribution monitoring system for a hydropower station according to claim 1 is characterized in that: The β=0.1.

Images