artificial intelligence applications in power...

Artificial Intelligence Applications in Power Systems

Om P. Malik

IEEE SA & NC Sections, May 7/8, 2018

What is Artificial Intelligence?

Artificial Intelligence (AI) has recently emerged as a science even though it may still be considered in its early stages of development. Depending on the goals and methods employed in research, its definition varies. As a broad description, it may be described as the science of making machines do things that would require intelligence if done by humans.

AI applications are now being considered in a very wide variety of disciplines, ranging from humanities to natural and applied sciences. In the context of power systems, application of artificial neural networks (ANNs) and fuzzy logic is commonly referred to in the literature as AI applications in power systems.

Over the past 25 years or so, feasibility of the application of AI for a variety of topics in power systems has been explored by a number of investigators. Topics explored vary from load forecast to real-time control and protection, and even maintenance.

Artificial Neural Networks

Natural Nerve Cell

Artificial Nerve Cell

Networks Based on Artificial Nerve Cell Model

- Multi-layer feed-forward

perceptron

- Recurrent

- Radial basis function

- Adaline

- Bayesian

- Hopfield

- Boltzman

- Kohonen

- Generalized Regression network

Types of Neuron Models

•Artificial Neuron Cell Model

•Multiplicative neuron

Reacts to product of activation of pairs of synapses

•Generalized neuron

Contains both summation and aggregation functions with sigmoid and Gaussian activation functions

Output Opk

s_bias

OutputInput

Aggregation

Thresholding

Function Function

Bias

Output OpkInputs,

Xi

Biass

1 f1

2

f2

Simple and Generalized Neuron Models

10

- ANN

(Conventional ANN)

Input

layer

-hidden

layer

-output

layer

P - ANN Input

layer

P-hidden

layerP -output

layer

-P -ANNInput

layer -hidden

layer

P -output

layer

P - -ANNInput

layerP-hidden

layer

-output

layer

DEFERENT ANN MODELS

Training of Neural Networks

Neural networks need to be trained. Based on the type of network, it may be:

• Supervised learning

•Unsupervised learning

•Competitive

Although most networks are trained off-line using available data, in some cases the weights can be up-dated on-line in real-time to track the system operating conditions.

Neural Network Controllers

Copying an existing controller with a network.Inverse plant modeling using a network.

Back propagating through a forward model of the plant.

Bayesian Networks

A Bayesian network (BN), also known as a Bayesian belief network, is a

graphical model for probabilistic relationships among a set of variables.

They have a qualitative component represented by the network

structure and a quantitative component represented by the assignment

of the conditional probability (CP) distributions to the nodes of the

network.

BNs can learn from observations. Learning of BNs can be parameter

learning and structure learning. With parameter learning, the structure

of the BN is given and only the CP parameters are learned. With

structure learning, the BN structure itself is learned. Bayesian learning

calculates the probability of each of the hypotheses given the data.

Insulation Deterioration Estimation of a Transformer

Using a Bayesian Network

Insulation LoL estimation by BN versus other methods for unit #64

Classical Direct Torque Control of an Induction Motor

Udc

abc to Switching Table

1

23

65

4

N

Sector calculator

Torque and Flux Estimation

+

+-

-

IP+

-

ANN Based DTC of an Induction Motor

Udc

abc to

1

23

65

4

N

Sector calculator

Torque and Flux Estimation

+

+-

-

SMC+

-

ANN

PI-DTC versus ANN-SMC-DTC

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

120

140

160

180

Time (sec)

Rot

or S

peed

(ra

d/se

c)

0 0.2 0.4 0.60

50

100

150

X: 0.3459

Y: 149.2

X: 0.1507

Y: 149.2

0 0.5 1 1.5 2150

152

154

156

158

160

DTC-ANN-SMC

Reference

DTC

Block Diagram of an Adaptive Controller

Controller Structure with MLFF NNs

Neuro-Adaptive PSS

Table 1:Dynamic Stability Margin* for Different Stabilizers.

* Dynamic Stability Margin is defined as the maximum power output at which the generator loses synchronism while input torque reference is gradually increased

Response to a three phase to ground fault, p=0.7 pu, pf=0.62

OPEN CPSS NAPSS

Maximum Power 2.65 pu 3.35 pu 3.60 pu

Maximum Rotor Angle 1.55 rad 2.14 rad 2.36 rad

ADALINE Network as an Identifier

Radial Basis Function Network

RBF-Identifier & Pole-Shifting Controller

Stability Margin Test

APSS CPSS APSS

Experimental Power System Model

Plant

GN

IdentifierUnit Delay

Σ

Disturbance

u-vector

w vector

w(t)+

-PLANT

wGN

Controller

Learning Algorithm GN-identifier

Output wi(t+T)

u_vector

ω_vector

Identifier Controller

28

Performance of GN identifier

Results of GN identification for a 3-Phase to Ground fault at generator bus for 100 ms at P=0.7, Q=0.3 (lag).

Experimental Results of GN identification under 23 % step change in torque reference and trained on-line.

29

Performance of GNPSS and GNAPSS under three phase to ground fault for 100ms at the middle of one line in a double

circuit system at P=0.7pu and Q=0.3 pu (lag) .

Performance of GNPSS and GN based adaptive PSS when one line is removed at 0.5 sec. and re-energized at 5.5 sec and

then again same line is removed at 10.5 sec. and re-energized at 15.5 sec. at P=0.8 pu and Q=0.4 pu (leading).

Fuzzy LogicGeneral Concept

Fuzzy Logic Membership Functions

18-May-8

Examples of Membership Functions distributions

-1 0 10

1

NB NM NS ZO PS PM PB

(a) Initial

-1 0 10

1

(b) Contracted

-1 0 10

1

(c) Expanded

-1.5 -1 -0.5 0 0.5 1 1.50

0.5

1

(a) =2.5

-1.5 -1 -0.5 0 0.5 1 1.50

0.5

1

(b) =1.0

-1.5 -1 -0.5 0 0.5 1 1.50

0.5

1

(c) =0.6

Fig. 2. Linear scaling

32/15

Fig. 3. Nonlinear scaling

.

Initial

Contracted

Expanded

Initial

Fuzzy Rules Table

We

NB NM Z PM PB

∆We

NB NB NB NB Z Z

NM NM NB NS Z Z

Z NS NS Z PS PS

PM Z PS PM PM PM

PB Z PM PB PB PB

Conventional and Fuzzy PID Algorithm

Fuzzy Logic Self-tuning PI Algorithm

Hybrid Micro-Grid Configuration

Synchronous

Generator

Diesel

Engine

Clutch

Diesel Generator

PDiesel

PHydro

PWind

PDumpLoad

PLoad

Pump-Turbine

Dynamics

Synchronous

Generator

Tunnel and

Penstock Water

Dynamics

Head

Flow

Hydro-Pumped Storage Facility

Induction

Generator

Wind Turbine

Dynamics

Wind Generator

Dump Load

ControllerResistor Bank

Dump Load

Consumer Load

Dump Load Frequency Control

Fuzzy IF-THENInference engine

Δf

dΔfdt

ΔP

23/42

DL frequency control

Previous fuzzy frequency control

[4]

Proposed fuzzy controller

1 Large membership functions reduce the regulation time

2 Small membership functions reduce oscillations around settling point24/42

HPS Turbine Mode Controller

Simulink Fuzzy PID Controller Model

Linear Fuzzy PID Controller Non-Linear Fuzzy PID Controller

HPS Pumping Mode Controller

Simulink Fuzzy Logic Controller Schematic

Fuzzy Logic Controller Control Surface

Generator Loads

Power system diagram including SVC

Device

SVC

Transmission Lines

Generator Loads

+

-

Δω / ΔPeSVC

Controller

Vref

Vm

u(t)

Transmission Lines

Classical control approach for

a FACTS device

Proposed Solution

Proposed adaptive control system structure for SVC device

In a SMIB system

Generator Loads

+

-

ΔPSVC

SVC

Adaptive Controller

System Identification

Vref

Vm

u(t)

Transmission Lines

Single Machine Infinite Bus System Simulation Results

μ (

e)

Example of membership function before and after adaptation

………. Before adaptation After adaptation

Consequent Parameters

Multi-machine System Simulation Results

G3

1

G2

1

G5

1

G1

1

G4

1Load 1

Load 2

Load 3

1 7 96 3

8

4

2

5

Schematic model of a multi-machine power system with an SVC

device installed at the middle of the tie-line

Sensorless Control of a Switched Reluctance Machine

Fuzzy Logic Controller for SRM

Speed Tracking SRM

0 5 10 15

0

50

100

150

200

250

300

350 a

Sp

eed

(ra

d/s

)

Time (s)

Wmes

Wref

0 5 10 15-10

-8

-6

-4

-2

0

x 10-6 b

S

peed

err

or

(rad

/s)

Time (s)

Estimated and Real Load Torque SRM

0 5 10 15-2

0

2

4

6

8

10

12 a

T

orq

ue (N

.m)

Time (s)

Tl

Tl-obs

0 5 10 15-0.05

0

0.05 b

To

rqu

e e

rro

r (

N.m

)

Time (s)

Permanent Magnet Synchronous Generator WECS

Field Oriented Control of Stator Side Converter of PMSG

•D-Q components of the stator reference voltages, that ultimately control the rectifier firing angle, are generated by two PI controllers with d-q components of the stator currents as inputs.

•Conventional PI controllers are replaced by trained ANFIS with d-q axes stator currents error and integral of error as inputs.

• Applied to a 1.5 MW wind turbine system with PMSG

Δώ

Δω

Sector B

Sector F

Sector E

Sector C

Min Axis

Sector D

R

θ

Six sector phase plane

O

Sector A

Fig. 6 Fuzzy sets for input variable

Two Area Power System for LFC

0 10 20 30 40 50-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

Time (sec)

df1

Both PI

PI in Area #1 and PFC in Area #2

Frequency variation of area- 1 in a Two Area Thermal System without Reheat unit when disturbance in both areas

Frequency variation of area- 2 in a Two Area Thermal System without Reheat unit when disturbance in area - 1

-

0 10 20 30 40 50-0.016

-0.014

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

Time (sec)

df2

PI

PID

Fuzzy

Polar Fuzzy

Short Term Load Forecast• Statistical methods• AI based methods employing both neural networks and fuzzy logic- Neural networks need to be trained- Using heuristic optimization techniques, e.g. GA, that employ

random search and fuzzy rules to guide search, performance can be improved.

- A generalized neural network (GNN) with four wavelet components of the historic load data as input and fuzzy logic guided random search GA as a learning tool for the GNN is used for short term load forecast.

- RMS error with: back propagation training – 0.0610 GAF training – 0.0486

Short Term Load Forecast with FL and GNN

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(Hours)

Loa

d (

kW)

Actual and predicted training and testing for winter season using GNN

actual

predicted

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(Hours)Lo

ad (

kW)

Actual and forecasted training (0:101) and testing (102:117) for the winter season using GNN-GAF model

actual

predicted

Self-Tuning Load Forecast using GNN-W-GAF

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(Hours)

Load

(kW

)

Actual and predicted training and testing for the winter season using GNN-W-GA-F

actual

predicted

Supervisory Control of a Cogeneration Plant

Generator Fuzzy Set-Point Control

Fuzzy Logic Self-Tuning PI Controller

Fuzzy Adaptive Control PSS

RLS identifier and a self-learning Mamdani fuzzy logic controller.

^y(k+1)

y(t) Mamdani FLC

RLS identifier

z-1

+

_

AVR

&

Exciter

TL Gridu(t)

APSS

Results

0.1 p.u. step increase in torque and return to initial condition

(power 0.30 p.u., 0.9 pf lead)

3 phase to ground fault at the middle of one transmission line and successful re-closure

-adaptive Mamdani fuzzy logic PSS (AMFLPSS

----fixed centers FLPSS

(power 0.9 p.u., 0.9 pf lag)

Adaptive Neuro-Fuzzy Inference System

General Schematic of ANFIS

Basic structure of a typical ANFIS with two inputs and two-rule fuzzy system

Adaptive Neuro Fuzzy Inference System

• An ANFIS is an integration of neural networks and fuzzy inference systems to determine the parameters of the fuzzy system.

• Automatically realize the fuzzy system by using the neural network methods.

• Fuzzy Sugeno models are involved in the framework of adaptive system to facilitate learning and adaptation.

• Permit combination of numerical and linguistic data.

• Requires structural and parameter learning algorithms.

The Proposed Adaptive Neuro-Identifier

• A Multilayer Perceptron (MLP) network is constructed to represent the plant

Architecture of adaptive neuro-identifier

Adaptive Simplified Neuro-FuzzyController

Proposed control system structure

18-May-8

NFC architecture

NNN xxsignxf

73/15

Nonlinear Function (NLF):

Nx

18-May-8

Control system structure

74/15

Online Adaptive Neuro Fuzzy Controller for Nonlinear

Functions in the Input Layer for Damping Power

System Oscillations

A fuzzy PSS is usually made adaptive by adjustment of input membership functions (premise) and consequent parameters (CPs).

Number of controller parameters depend on the shape and number of membership functions.Scaling factors have received little attention in the adaptive fuzzy PSS design

18-May-8

System Configuration

.

w

77/15

18-May-8

Simulation results

78/15

Multi-machine power system.

Fig.14. 0.10 pu step inc-dec in torque of G3,

PSS on G3.

0 2 4 6 8 10 12 14 16 18 20-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Time (s)

w1- w

2 (ra

d/s)

No PSS

CPSS

ANFPSS

0 2 4 6 8 10 12 14 16 18 20-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Time (s)

w 2- w3 (

rad/s

)

No PSS

CPSS

ANFPSS

ω1-ω

2(r

ad/s

)ω

2-ω

3(r

ad/s

)

0 2 4 6 8 10 12 14 16 18 20-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Time (s)

w1- w

2 (ra

d/s)

No PSS

CPSS

ANFPSS

0 2 4 6 8 10 12 14 16 18 20-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Time (s)w

2- w3 (

rad/

s)

No PSS

CPSS

ANFPSS

Fig.15. 0.10 pu step inc-dec in torque of G3,

PSS on G1, G2 and G3.

1.5 MW VSWECS

Fig.2 Field oriented control scheme with speed sensor at generator

PI PI+_+_

PI+_

++

+_

SVMPWM

gen.

dq

dq

abc

PMSG

s

1P

min/

sec/

rot

rad --

isq

isd

0

i sd

i sq

v sd

v sq

isq

isdisa

isb

isc

vDC

v

w de

w qe

rn

*

rn

mechn

0 5 10 15 200

0.2

0.4

0.6

0.8

1

1.2

Time (Sec)

Genera

tor

Speed (

p.u

.)

Generator Speed with PI Controller

Generator Speed with ANFIS Controller

0 5 10 15 200

0.2

0.4

0.6

0.8

1

1.2

Time (Sec)A

ctiv

e P

ow

er (

p.u

.)

Active Power with PI Controller

Active Power with ANFIS Controller

•Applied to the 1.5 MW wind turbine system.

• The wind speed starts at 11m/s, is changed to 9 m/s after 12 s

Experimental Results of Applying the

ASNFC in a Real-Time System

Generator speed deviation in response to a 15% step increase

in the torque reference (P=0.80 p.u. and 0.75 p.f. lag)

200 km Transmission Lines

Generator speed deviation in response to a three-phase to ground

short circuit test at the middle of a 200 km transmission line with

an unsuccessful re-closure (P=0.97 p.u. and 0.93 p.f. lag)

Concluding Remarks

• A wide spectrum of AI applications in power systems, from load forecast to maintenance, is being explored.

• A general survey of the type of AI applications that have been and are being explored for application in power system has been attempted.

• This is not an exhaustive survey and some other applications are also being pursued.

• Actual application of AI techniques, particularly for real-time applications, is lagging. One application that seems to have been adopted by the utilities is neural network based load forecast algorithms.

Thank you

Questions?

Om Malikmaliko@ucalgary.ca