![](bg6.png)
Figure 8: The system stator current and stator voltage
profiles under the MACS control.
The local automation reduces the AVR setting. GAs
at busses 202 and 203 begin to increase reactive
power output until the generator excitation currents
reach their near-critical values. Collaborative actions
of GAs and LAs are allowed to unload G301-303
(Fig. 9).
As a result, the system voltage profile improves,
as can be seen in Fig. 7, and the Kohonen network
does not detect any deterioration at t=130.63 s. From
t=206 s, the Kohonen network identifies the normal
state, however, the alarm 3 state is also still activated
because the system is still in the normalization of
post-emergency state.
Thus, as a result of the MACS control actions,
the subsystem can maintain its stability without load
shedding via coordinating available sources of
reactive power.
5 CONCLUSIONS
This paper proposes a neural multi-agent-based
approach to the system monitoring and control with
the goal of identifying potential voltage instability
problems before they lead to major blackouts. The
proposed MACS structure is hierarchical; it consists
of the top-level agent, middle-level agents and low-
level agents. Under normal operating conditions, the
MACS operates in a competitive mode; low-level
agents exchange information with other agents to
maintain their local conditions within specified
limits and to maximize profits of their respective
companies. An alarm state triggers a collaborative
mode in which the agents coordinate their actions to
prevent a system blackout.
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0 50 100 150 200 250 300 350 400
1
1.02
1.04
1.06
1.08
1.1
1.12
1.14
t,сек.
U,отн.ед.
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G
303 U
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306 U
G
307
0 50 100 150 200 250 300 350 400
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I,отн.ед.
Ток Статора, отн.ед.
I
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306 I
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