Differential protection fault detection method, device and system of energy storage tramcar super-capacitor charging device
A technology for charging devices and trams, applied to fault locations, measuring devices, measuring current/voltage, etc., can solve problems such as increased workload, inability to judge faults, and difficult maintenance work, so as to ensure reliability and shorten troubleshooting time, high detection accuracy
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Embodiment 1
[0055] This embodiment is a differential protection fault detection method for an energy storage tram supercapacitor charging device, including:
[0056] Collect real-time voltage and current data on the input side of the charging device, real-time voltage and current data on the output side, voltage and current fault data on the input side, and voltage and current fault data on the output side;
[0057] Based on the collected real-time data, calculate the input-output current differential data;
[0058] Use the collected real-time data and current differential data as the input layer neuron data of the pre-built BP neural network model to obtain the output data of the BP neural network model;
[0059] According to the output data of the BP neural network model, the location of the fault point is judged.
[0060] The above input side voltage and current fault data and output side voltage and current fault data can be obtained through the control platform of the charging devic...
Embodiment 2
[0081] Based on the same inventive concept as Embodiment 1, this embodiment is a differential protection fault detection device for an energy storage tram supercapacitor charging device, including:
[0082] The data collection module is used to collect real-time voltage and current data on the input side of the charging device, real-time voltage and current data on the output side, voltage and current fault data on the input side, and voltage and current fault data on the output side;
[0083] The differential calculation module is used to calculate the input-output current differential data based on the collected real-time data, and then determine the current differential flag bit according to the calculation result;
[0084] The neural network calculation module is used to obtain the output data of the BP neural network model by using the collected real-time data and the current differential flag as the input layer neuron data of the pre-built BP neural network model;
[008...
Embodiment 3
[0088] This embodiment is a differential protection fault detection system for an energy storage tram supercapacitor charging device, which includes a detection device and an integrated control platform connected and communicated through a network; The fault detection method of the differential protection of the capacitor charging device is to use the BP neural network model to determine the location information of the fault point with the set detection cycle;
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