{
  "$type": "site.standard.document",
  "description": "A method for diagnosing a fault in an electric power distribution network includes measuring three-phase current signals from local and remote terminals of a protected zone on the network, and computing differential current signals based on the measured three-phase current signals. The differential…",
  "path": "/patents/1407633",
  "publishedAt": "2026-06-16T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "H02J3/001",
    "King Fahd University of Petroleum and Minerals"
  ],
  "textContent": "A method for diagnosing a fault in an electric power distribution network includes measuring three-phase current signals from local and remote terminals of a protected zone on the network, and computing differential current signals based on the measured three-phase current signals. The differential current signals are preprocessed using a filter to smooth the differential current signals. A maximal overlap discrete wavelet transform is applied on the smoothed differential current signals to obtain a plurality of detail coefficients. Observation signals comprising the plurality of detail coefficients are provided to a deep reinforcement learning (DRL) agent comprising a temporal convolution attention-based neural network (TCAN). The TCAN DRL agent is trained using a proximal policy optimization (PPO) method to propose a trip action corresponding to a trip or no trip command for a fault condition. Responsive to receiving a trip command, a trip signal is transmitted to a circuit breaker.",
  "title": "Deep reinforcement learning differential protection system for electric power networks"
}