„Kompiuterininkų dienos – 2019“ VU Kauno fakultete, 2019-10-03–04 d.

1. Kaliningrad Branch of the Federal Research Center “Computer Science
and Control“ of the Russian Academy of Sciences
Sergey Listopad
Kaunas, KoDi-2019, October 3-4, 2019
ARCHITECTURE OF THE HYBRID INTELLIGENT MULTI-
AGENT SYSTEM OF HETEROGENEOUS THINKING FOR
PLANNING OF DISTRIBUTION GRID RESTORATION

2. Distribution power grid recovery after accidents
To reduce economic and social losses
from power outages, energy supply
companies develop guidelines and
operational procedures for restoring
electricity. Such instructions are created,
among other things, based on the analysis
of previous accidents by teams of experts
from power engineers of the power supply
organization, representatives of design
institutes that developed the generation and
power grid complex of this organization, as
well as representatives of manufacturers of
operating equipment.
The emergency conditions can
significantly differ from those adopted during
the development of the recovery plan, which
reduces the likelihood of success of actions,
leading to unacceptable loads, voltage levels
or the operation of protection systems. It is
not possible to organize a comprehensive
collective solution to the problem due to the
limited decision-making time.

3. An example of the power grid scheme for
"game" restoration problem

4. Non-factors of distribution power grid recovery
planning after accidents
fuzziness of the timing of operations to
restore power supply elements
incomplete distribution power grid model
accident location uncertainty inaccuracy of the amount of power consumed by
each client and generated by each source of
distributed generation
ложный
сигнал
False
signal
False
signal

5. Distribution power grid recovery problem
 It is required to find a plan for restoring the power system, which includes the
sequence of switching the switches on and off, the sequence of trips of repair teams
to perform switching and restoration work.
 Plan Optimality Criteria :
 minimization of time for disconnecting priority load;
 maximization of the total restored load power;
 maximization of the reliability indicator of the power system (resistance to subsequent
accidents).
 Restriction system:
 preservation of the radial structure of the network of powered lines;
 permissible load limits for each line;
 balances of active and reactive power must be observed;
 voltage and frequency must be within acceptable limits;
 consumers unaffected by the initial outage should not be disconnected;
 the work must be carried out by teams having the appropriate clearance if the necessary
resources are available in their vehicle;
 vehicle capacity is never exceeded;
 crew working hours are limited;
 vehicles must return to base;
 communication lines between islands must have synchronization equipment.

6. Decomposition of the distribution grid restoration
problem
Localization of the
accident site
Relay protection and
automation engineer
Assessment of requirements for
restoration actions
Power equipment
repair engineer
Building routes for field
crews
Electrical network
area head
Recovery plan
development
Dispatcher
Power consumption
forecasting
Engineer in analyzing and
forecasting energy
consumption modes
Determining switching
order
Engineer on power
operational modes
Result:
Recovery plan
Input
problem data

7. Collective decision making modelling
Collective decision-making is more democratic and takes into account
the interests of many stakeholders.
With collective decision-making, the task can be comprehensively
analyzed, as experts in various fields of knowledge are invited.
This allows the team to solve problems that one person cannot cope
with.
Компьютерное
моделирование
коллективного
принятия
решений
Computer
modelling of
collective
decision
making

8. The functional structure of the hybrid intelligent multi-
agent system of heterogeneous thinking

9. Agent-facilitator functions
Agent-facilitator (AF) organizes collective processes, and the
corresponding relations between it and the expert agents, each of which is
responsible for solving one of distribution grid restoration problem’s tasks
mentioned above. Modeling collective heterogeneous thinking suggests that AF
initiates various methods of collective interaction of agents, “thinking styles”
depending on the current situation. For this purpose, it has to identify the stages
of the problem-solving process, the composition of expert agents and their
“thinking style”, the current situation in hybrid intelligent multi-agent system of
heterogeneous thinking (HIMASHT), the positive and negative group effects.
Also it acts on expert agents activating relevant to the situation “thinking style”
to minimize negative effects and to reinforce positive ones, using, among other,
the “diamond of participatory decision-making” model:

10. The interface agent architecture

11. The architecture of the decision-making
agent

12. The architecture of the agent-facilitator

13. The architecture of the intelligent technology
agent

14. The architecture of the expert agent

15. The architecture of the intermediary agent

16. Comparative analysis of the features of
intelligent systems for solving
heterogeneous problems
Features AGRO TRANSMAR HIMASHT
Handling problem heterogeneity + + +
Handling tool heterogeneity + + +
Modelling expert reasoning + – +
Autonomy of elements / agents – + +
Ontology-based reasoning – + +
Modelling collective
heterogeneous thinking
– – +
Self-organization type – Weak Strong

17. Conclusions
 The general description of the problem of restoration of the distribution
network after accidents is considered.
 In view of the heterogeneous nature of this problem, it is currently being
resolved by small teams of experts.
 A new class of intelligent systems is proposed - HIMASHT, relevant to
real teams of experts and group effects arising in them.
 The proposed HIMASHT class moves the imitation of collective work to
the field of synergetic informatics, when in order to obtain a result
greater than the sum of the work performed individually by individual
agents, their interaction is necessary.
 A comparative analysis of the approaches proposed in HIMASHT and
implemented in a hybrid intelligent multi-agent system, which increased
the efficiency of solving complex transport and logistics problems by
more than 7%, shows the advantages of the former and their relevance
for solving problems in dynamic environments.