A Neural Multi-agent-based Approach for Preventing Blackouts

in Power Systems

Michael Negnevitsky

, Nikita Tomin

, Daniil Panasetsky

, Ulf Haeger

, Nikolay Voropai

Christian Rehtanz

and Victor Kurbatsky

School of Engineering, University of Tasmania, Churchill Ave, Sandy Bay TAS 7005, Hobart, Australia

Department of Electric Power Systems, Melentiev Energy Systems Institute, Lermontov str. 130, Irkutsk, Russia

Energy Systems, Energy Efﬁciency and Energy Economics, TU Dortmund, Emil-Figge-Str. 70, Dortmund, Germany

Keywords: Blackout, Pre-emergency Control, Voltage Stability, Multi-agent Control System, Artificial Neural

Networks.

Abstract: A neural multi-agent-based approach for system monitoring and preventing large-scale emergencies in

power systems is presented in this paper. The automatic emergency control process is represented as a

neural multi-agent system with hierarchical architecture. The proposed system consist of two main parts: the

alarm trigger, a Kohonen neural network-based system for early detection of possible alarm states in a

power system, and the competitive–collaborative multi-agent control system. For demonstration purposes,

we investigated conventional and neural multi-agent automatic control schemes. Results are presented and

discussed.

1 INTRODUCTION

The ongoing deregulation and restructuring in power

system worldwide require more complex control and

decision making. In many cases, the current

generation of automatic emergency control systems

is ineffective and unreliable. Moreover, in

emergency condition, a power system operator has

to deal with a large amount of data and apply most

appropriate remedial actions. At such times it

becomes difficult to reach a correct diagnosis of the

problem or to formulate the correct decision when

actions must be taken. As a result, large scale

blackouts still happen (PSDP 2007, Wang 2005)

Computational intelligence techniques in power

systems provides a way forward to give new

possibilities for energy management systems,

especially in the field of preventing large scale

emergencies. There are many benefits to using a

multi-agent system as automatic control system,

such as the ability to perform multiple

computationally intensive tasks in parallel such that

effective optimized real-time control can be

achieved. These parallel tasks include neural

network training, parameter optimization, and

system monitoring. The multi-agent system

approach also allows for intelligent control that is

robust and flexible in that it can autonomously make

decisions and adjust to partial control system failure

to maintain control with minimal performance

degradation, to name a few of the potential benefits.

What’s more decentralized emergency control is

showing important advantages over centralized

control, especcialy with large data, calculation and

communication.

Several intelligent approaches have been

proposed for preventing large-scale emergencies. On

the one hand, there have been some previous

attempts to take advantage of agents and multiagent

systems as control systems (Lehnhoff 2011, Häger

2012, Panasetsky 2012, Negnevitsky 2008). On the

other hand, some different machine learning models

– including artificial neural networks (ANNs) – have

been successfully applied for power system security

assesment, as for example (Kalyani 2012, Voropai

2012, Niebur 1994).

The use of ANN models as a trigger system of

the multi-agent control systems let take advantage of

some of the properties of ANNs (such as pattern

recognition) and agents (reactivity, proactivity and

sociability) making preventing large-scale

565

Negnevitsky M., Tomin N., Panasetsky D., Haeger U., Voropai N., Rehtanz C. and Kurbatsky V..

Neural Multi-agent-based Approach for Preventing Blackouts in Power Systems.

DOI: 10.5220/0004906505650570

In Proceedings of the 6th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2014), pages 565-570

ISBN: 978-989-758-015-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

emergencies is more effective and reliable (Tomin

2013, Negmevitsky 2013) . The paper proposes a

neural multi-agent-based system for preventing

blackouts in power systems. This system includes

some experience and developments obtained at the

University of Tasmania (Australia), the Melentiev

Energy Systems Institute (Russia) and the TU

Dortmund (Germany) in developing intelligent

systems for a disaster management in modern power

systems.

2 PROBLEM DESCRIPTION

Several studies identified voltage instability as one

of the major reasons of blackouts (PSDP 2007,

Tomin 2013, CAMS 2008). A typical blackout

scenario develops as follows: high system loading

(due to heavy transfers across the grid) is followed

by events that initiate protection system actions. As

a result, some lines are disconnected, the grid

becomes even more overloaded, and consumption of

reactive power is increased, causing a cascading

effect in which voltages drop even further. Practical

experience demonstrates that most blackouts begin

with a large disturbance (a disturbance, which may

or may not cause cascading failures), which leads to

a slow deterioration of the system conditions (PSDP

2007, Tomin 2013).

Failures of protection and emergency control

devices as well as human errors are the two biggest

causes of large-scale blackouts. Most blackouts

begin with a large disturbance, which leads to a slow

deterioration of the system conditions. The system

parameters may still remain within specified limits,

but many of these parameters are on the boundary of

stability. If such conditions are identified as pre-

emergency, preventive actions can be taken, and

major events avoided.

Unfortunately, in current competitive

environment, such conditions may not be easily

detected because different problems may

simultaneously occur in different parts of a large

network within different jurisdictions. The

liberalisation process in power systems has created

an additional interface which can adversely impact

communication and coordination activities between

operators on both sides.

Multi-agent models are oriented towards

interactions and collaborative phenomena. It is

perfect suitable for resolve so called the irony of

interconnected power grids that are owned by

separate and often competing companies. In this

case a technical cooperation between interconnected

grids to a certain extent militates against the pure

profit motive.

3 PROPOSED SYSTEM

The proposed system consists of two main parts: the

alarm trigger, which is an intelligent neural network-

based system for detecting possible alarm states in a

power system, and the competitive–collaborative

multi-agent control system (MACS).

3.1 The Hierarchy Multi-agent Control

System

The innovation here in using a decentralized

structure in which distributed “agents” operate in

either competitive or collaborative modes,

depending on the system security state, so that fast

and robust responses can be provided in both normal

and emergency conditions – responses directly

tailored to the very different needs of each of these

two conditions. Agents are hardware or software

entities operating in virtual or real environments,

and will be distributed among all serial devices in a

power system – generators, transmission lines,

transformers, and power flow controllers (PFCs).

The MACS is a hierarchy of agents located at

different levels (Fig. 1):

1) The top-level agent (Advisor) – the objective is

to initiate a collaborative protocol of agents

located on the middle level, setting up their

priorities, and coordinating their actions. In

practice this might be a joint security center of

several TSOs.

2) Middle-level agents (the transmission system

operator-level) – the objective is to initiate

control actions according to the goals set by the

Advisor. These actions include PFCs between

different systems.

3) Low-level agents (i.e. device-level agents –

generators, transmission lines, transformers and

loads) – the objective is to achieve the goals set

by the respective middle-level agents within their

jurisdictions. The low-level agents are

specialized devices responsible for specific areas

of power generation, transmission and

distribution.

The Advisor receives messages from middle-level

agents about the current state of the interconnected

system, and if required proposes appropriate actions

to control power flows between different systems. If

the Advisor receives an alarm message from the

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Figure 1: Block diagram of the MACS.

security alarm system, it assesses the severity of the

situation, and if required takes control over the

middle-level agents.

For normal conditions, we organise competitive

control by the middle-level agents as follows:

optimal power flow (OPF) is used to determine the

optimal settings of power-flow controlling devices

in the area of responsibility of each agent. Using

PFC devices as an example, representing them by

phase-shifting transformers and flexible alternating

current transmission system (FACTS) devices

(Häger 2012). The PFCs were installed to increase

transmission capacity and controllability of the grid.

In normal operation conditions, each transmission

system operator (TSO) used OPF methods to

optimise settings of their PFC devices, to reduce

internal congestions as required by market rules. The

objective function is to minimise the generation

costs by optimising power flows according to the

market situation.

However, in emergency conditions, all TSOs will

need to coordinate their PFC devices to stabilise the

system. We achieve this coordination through the

use of MACS. In the collaborative mode, the

objectives of the agent operation changes: a middle-

level agent seeks and receives help from the low-

level agents that belong to the neighbouring middle-

level agents. For example, under an emergency in

System A due to voltage instability, a middle-level

agent A will redefine the objective function of low-

level agent B1 of System B (Systems A and B are be

connected via a tie transmission line) to increase

reactive power input from the neighbouring system.

3.2 a Neural Multi-agent-based

Approach

In order to distinguish between competitive and

collaborative mode, we need to overcome an issue of

identifying pre-emergency conditions. This paper is

concerned with the real time identification of alarm

states that are dangerous for the system security. We

examined a clustering approach based on the self-

organized Kohonen neural network. The Kohonen

alarm trigger identifies pre-emergency conditions

using enormous amounts of data with incomplete

and distorted alarm patterns and activates the MACS

(Fig. 2).

Multi-agent control system

Figure 2: Diagram of a neural multi-agent-based system.

The security alarm system is trained using a set of

training examples based on randomly generated

events in a power system. The clusters is identified

using test cases representing a set of normal and

emergency conditions in the power system. As a

result, a Kohonen ANN-based clustering system is

able to classify power system states in real time and,

if required, to produce an alarm. The main objective

is to rank power system states with respect to their

potential for causing voltage instability.

The Kohonen network is trained off-line and

used on-line to classify the system operating state

based on the patterns created in the off-line mode.

The Kohonen network is divided into power system

states as follows: normal, alarm, emergency

(correctable) and emergency (non-correctable).

Here, a normal state implies that all parameters of

the power system are maintained within specified

normal operation limits:

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NeuralMulti-agent-basedApproachforPreventingBlackoutsinPowerSystems

567

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Real-time measurements are used to assess the

system state. Kohonen network-based monitoring

provides a warning when the system security is

under threat (Fig. 2).

4 CASE STUDY

The proposed neural multi-agent-based system was

implemented in JADE (Java Agent Development

Framework). The Kohonen security clustering

model is realized in STATISTICA 6.0. MATLAB

and Power System Analysis Toolbox are used as

modeling tools. In this paper, we demonstrate the

proposed approach on the modified IEEE One Area

RTS-96 power system. Active power flow is

directed from Subsystem B to Subsystem A.

Subsystem A is a low-voltage distribution subsystem

being in stressful conditions because of reactive

power shortage, which potentially may cause voltage

instability. Subsystem B is a high-voltage

transmission subsystem with surplus of reactive

power.

The modified system has 53 buses and dynamic

elements to represent generators and loads. In

Subsystem B, there is an excess of reactive power

produced by reactors at busses 107, 111, 113.

Subsystem A has a deficit of reactive power. In

Subsystem A, the sources of reactive power are

Non-controlled Reactive Power Sources (NRPS) –

capacitor. Each load is modeled as exponential

recovery load. The exponential recovery load model

can adequately represent the load behavior during

voltage instability.

To demonstrate the proposed approach, the test

system is subjected to the following sequence of

disturbance: t=10 s – the loss of transformer T101-

208. We assumed that two types of automatic

control can be used in the power system:

Conventional Automatic Control System (CACS)

(includes TGs, AVRs and OXLs on each generator,

and OLTCs on transformers connected to buses

204–210), and MACS (Fig. 3) – in addition to the

set of local controllers, it includes OLTCs on

transformers connected to busses 101, 102 and 103.

Figure 3: Subsystem A with installed Load Agents and

Generator Agents (the device level agents).

4.1 Conventional Automatic Control

Modeling

The loss of transformer T101-208 immediately leads

to an overload of generators connected to bus 201.

After about t=15s, the OLTC starts to change the

transformation ratio for boosting the secondary

voltage at the load. This leads to a gradual overload

of all generators in the subsystem. At t=300 s, the

rotor current limits are exceeded on all generators as

the system does not have any reactive power

reserves. At t=500 s, the primary voltage at the load

reaches 0.8 p.u. due to the OLTC actions and

insufficient reactive power, while the secondary

voltage is maintained close to the nominal (Fig.4).

Figure 4: The system voltage profile under the CACS

control.

In this case, the AVR fails to secure critical voltage

levels in the primary network, and at about t=500 s,

cascading voltage decrease takes place. As a result,

after t=600 s, stator currents of the generators

increase rapidly (Fig. 5), and the voltage at generator

busses decrease even further. This leads to the

disconnection of generators by their protection

systems, and as a result, the system voltage collapses

(Fig. 4).

0 100 200 300 400 500 600 700 800

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0.3

0.4

0.5

0.6

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0.8

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1.2

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U205

U401

U204

U402

U208

U403

U209

U404

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U405

U210

U406

U207

U407

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568

Figure 5: Stator current and voltage profiles under the

CACS control.

4.2 Neural Multi-agent Automatic

Control Modeling

The MACS coordinates main controllers of reactive

power in the system in order to prevent voltage

instability. The MACS detects dangerous levels of

excitation currents of a number of generators and

blocks the OLTCs on transformers.

The Kohonen network-based security alarm

system uses the following inputs: voltages at busses

204 – 210 (primary voltages); voltages at busses 401

– 406 (secondary voltages); AVR excitation voltages

for generators G301 – G309; OXL output signals for

generators G301 – G309 and stator currents of

generators G301 – G309. Fig. 6 represents a

topological map of the Kohonen network. The

network is trained off-line to identify clusters

corresponding to the following operating states of

the power system: normal (cluster A); alarm 1–5

(cluster B); emergency 1 and 2 (correctable) (cluster

C); and emergency 3 (non-correctable) (cluster D).

Figure 6: The Kohonen topological map.

After the loss of transformer T101-208, generators

connected to bus 201 are overloaded (Figs7,8).

When the multi-agent scheme is available, as soon

as the Kohonen network detects the alarm state at

time t=10 s, the MACS is activated in order to

prevent the system from further deterioration.

Figure 7: The system voltage profile under the MACS

control.

Figure 9: The sequence of messages between agents in the elimination of emergency.

0 100 200 300 400 500 600 700 800

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0.7

0.75

0.8

0.85

0.9

0.95

1.05

1.1

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Stator Voltages, p.u.

303 U

306 U

307

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0.7

0.8

0.9

1.1

1.2

1.3

1.4

I,p.u.

Stator Currents, p.u.

303 I

306 I

307

0 50 100 150 200 250 300 350 400

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

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U401

U204

U402

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U403

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U405

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U406

U207

U407

NeuralMulti-agent-basedApproachforPreventingBlackoutsinPowerSystems

569

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

1.02

1.04

1.06

1.08

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1.12

1.14

t,сек.

U,отн.ед.

Напряжение Статора, отн.ед.

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306 U

307

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0.7

0.8

0.9

1.1

1.2

I,отн.ед.

Ток Статора, отн.ед.

303 I

306 I

307

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