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Pilot and Numeric Relaying

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Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
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Directional relays, essential for managing unidirectional fault currents, enhance the safety and efficiency of power systems. On power lines equipped with directional relays, faults downstream (to the right) of the current transformer typically cause the fault current to lag the bus voltage by approximately 90 degrees, known as the forward direction. In contrast, upstream (left-side) faults may result in the fault current leading the bus voltage by nearly 90 degrees, termed the reverse...
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Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
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Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Updated: Oct 21, 2025

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Flexible Transmitter Network.

Shao-Qun Zhang1, Zhi-Hua Zhou2

  • 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China zhangsq@lamda.nju.edu.cn.

Neural Computation
|September 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the flexible transmitter (FT) model, a more biologically realistic neuron model. The novel flexible transmitter network (FTNet) demonstrates potential in processing complex spatiotemporal data.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Current artificial neural networks predominantly use the McCulloch-Pitts (MP) model, which simplifies neuron function.
  • The MP model's limitations hinder biological realism and the ability to process complex data like spatiotemporal information.

Purpose of the Study:

  • To propose a novel, biologically plausible neuron model, the flexible transmitter (FT) model.
  • To develop a new neural network architecture, the flexible transmitter network (FTNet), utilizing the FT model.
  • To demonstrate the FTNet's capability in handling complex data, particularly spatiotemporal data.

Main Methods:

  • Introduced the flexible transmitter (FT) model, incorporating neurotransmitter dynamics and neurotrophin density.
  • Formulated the FT model as a two-variable, two-valued function, encompassing the MP model as a special case.
  • Developed the flexible transmitter network (FTNet) using a fully connected feedforward architecture with FT models.
  • Implemented FTNet using an improved backpropagation algorithm in the complex-valued domain for gradient calculation.

Main Results:

  • The FT model offers greater biological realism compared to the MP model.
  • FTNet demonstrated significant potential and power in processing spatiotemporal data across various tasks.
  • The FT model serves as a viable alternative building block for artificial neural networks.

Conclusions:

  • The flexible transmitter (FT) model enhances biological plausibility in artificial neurons.
  • Flexible transmitter networks (FTNets) show promise for advanced spatiotemporal data processing.
  • This research validates the development of artificial neural networks incorporating neuronal plasticity.