Abstract: A device and method for starting a pumped storage group, and a pumped storage system are disclosed. The device includes: an acquisition module configured to acquire starting data of the group in real time; a first determination module configured to determine a real-time speed versus time curve for the group based on the starting data; and a correction module configured to correct a starting speed of the group based on a deviation between a reference speed versus time curve cluster and the real-time speed versus time curve. The real-time speed of the group during starting is visualized through a curve fitting procedure to form the real-time speed versus time curve, the real-time speed versus time curve is compared with a favorable history operation curve, and the deviation between the real-time speed versus time curve and the favorable history operation curve is corrected to adjust the starting speed of the group.

Abstract: The present disclosure discloses a method and an apparatus for identifying an S-characteristic of a pump turbine and a control method. The method for identifying an S-characteristic of a pump turbine includes: collecting a set of state parameters of the pump turbine at a preset time interval, wherein the state parameters comprise a flow rate and a rotation speed of the pump turbine; calculating a slope of a relationship curve between the rotation speed and the flow rate of the pump turbine within a current preset time interval; and determining, according to the positivity or negativity and an absolute value of the slope, whether the pump turbine enters an S-characteristic region.

Abstract: The present disclosure provides a self-adaptive test method for an intelligent prediction algorithm of analog measured values. Firstly, an event recording sequence, an analog measurement point ID and an analog measurement point alarm value are read from a time sequence event record table, an analog measurement point table and an alarm threshold table. Next, operation records of a normal operation state of a unit within a statistical cycle are acquired to form historical statistics of measured values of the analog measurement point based on switching value signals. Then, simulated measured values of the analog measurement point with time scales are calculated based on the historical statistics, the analog measurement point alarm value and an analog measurement point current measured value. Finally, sensitivity is calculated; and an alarm is sent to remind a technician to adjust the algorithm when the sensitivity is greater than a threshold.

Abstract: Provided are a determination method and apparatus of a main shaft bearing state, an electronic device, and a storage medium. The method includes the following steps: Bearing swing data of a target main shaft bearing at multiple moments within a preset time period are acquired; at least one current data period and current-period bearing swing data in each current data period are determined according to the multiple pieces of bearing swing data; a first abrupt change value and a second abrupt change value are determined according to the current-period bearing swing data in each current data period; and a main shaft bearing state of the target main shaft bearing is determined according to the first abrupt change value and the second abrupt change value.

Abstract: The present disclosure provides a self-adaption faster-than-real-time method for estimating a working condition start-up state of a unit. Firstly, an event recording sequence, an analog measurement point ID, an analog measurement point first-level alarm value and a history scatter point distribution record are read from a time sequence event record table, an analog measurement point table, an alarm threshold table and a historical scatter record table. A measured value slope of the analog measurement point is calculated according to an event recording relative time. Then, an abnormal state estimated value is calculated based on the history scatter point distribution record, the analog measurement point first-level alarm value and the measured value slope of the analog measurement point. Finally, a pre-warning is sent to remind a watchman to perform a preventive operation when the abnormal state estimated value is above the threshold.

Abstract: The present disclosure provides a self-adaption faster-than-real-time method for estimating a working condition start-up state of a unit. Firstly, an event recording sequence, an analog measurement point ID, an analog measurement point first-level alarm value and a history scatter point distribution record are read from a time sequence event record table, an analog measurement point table, an alarm threshold table and a historical scatter record table. A measured value slope of the analog measurement point is calculated according to an event recording relative time. Then, an abnormal state estimated value is calculated based on the history scatter point distribution record, the analog measurement point first-level alarm value and the measured value slope of the analog measurement point. Finally, a pre-warning is sent to remind a watchman to perform a preventive operation when the abnormal state estimated value is above the threshold.

Abstract: The present disclosure provides a self-adaptive test method for an intelligent prediction algorithm of analog measured values. Firstly, an event recording sequence, an analog measurement point ID and an analog measurement point alarm value are read from a time sequence event record table, an analog measurement point table and an alarm threshold table. Next, operation records of a normal operation state of a unit within a statistical cycle are acquired to form historical statistics of measured values of the analog measurement point based on switching value signals. Then, simulated measured values of the analog measurement point with time scales are calculated based on the historical statistics, the analog measurement point alarm value and an analog measurement point current measured value. Finally, sensitivity is calculated; and an alarm is sent to remind a technician to adjust the algorithm when the sensitivity is greater than a threshold.