Quantum mechanics

Control (management)

Engineering

Network topology

Phasor

Control engineering

Units of measurement

Stability (learning theory)

Operating system

Power (physics)

Computer science

Fault (geology)

Seismology

Geology

Physics

Politics

Political science

Electric power system

Artificial intelligence

Representation (politics)

Generator (circuit theory)

Topology (electrical circuits)

Phasor measurement unit

Control theory (sociology)

Artificial neural network

Convolutional neural network

Machine learning

Electrical engineering

IEEE Transactions on Power Systems

A continuous Online Monitoring System (OMS) for power system stability based on Phasor Measurements (PMU measurements) at all the generator buses is proposed in this paper. Unlike the state-of-the-art methods, the proposed OMS does not require information about fault clearance. This paper proposes a convolutional neural network, whose input is the heatmap representation of the measurements, for instability prediction. Through extensive simulations on standard IEEE 118-bus and IEEE 145-bus systems, the effectiveness of the proposed OMS is demonstrated under varying loading conditions, fault scenarios, topology changes, and generator parameter variations. Two different methods are also proposed to identify the set of critical generators that are most impacted in the unstable cases.

An Online Power System Stability Monitoring System Using Convolutional Neural Networks