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Special Project:Artificial Neural Networks for Load Flow Studies -- MSU-IIT
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An Electrical Engineering Special Project
ANN-BASED LOAD-FLOW STUDIES FOR MSU-IIT ELECTRICAL SYSTEM
ABSTRACT
CON
TEN
TS INTRODUCTION REVIEW OF RELATED
LITERATURE METHODOLOGY RESULTS AND DISCUSSIONS CONCLUSION AND
RECOMMENDATIONS
ABSTRACT
CON
TEN
TS INTRODUCTION REVIEW OF RELATED
LITERATURE METHODOLOGY RESULTS AND DISCUSSIONS CONCLUSION AND
RECOMMENDATIONS
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
GENERAL INTRODUCTION
Load Flow studies is performed to solve the steady state operating condition of a power system, by solving the static load flow equations (SLFE) for a given network.IN
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N
GENERAL INTRODUCTION
The main objective of power flow studies is to determine the bus voltage magnitude with its angle at all the buses, real and reactive power flows (line flows) in different lines and the transmission losses occurring in a power system.
INTR
OD
UCT
ION
P QV d
GENERAL INTRODUCTION
Power flow study is the most frequently carried out study performed by power utilities and it is required to be performed at almost all the stages of power system planning, optimization, operation and control.
INTR
OD
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ION
GENERAL INTRODUCTION
During last four decades, almost all the known methods of numerical analysis for solving a set of non-linear algebraic equations have been applied in solving power flow problems.IN
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GENERAL INTRODUCTIONIN
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However, what are computer systems good at? and not so good at?
GENERAL INTRODUCTIONIN
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N Good at Not so good atRule-based systems: doing what the programmer wants them to do.
Dealing with noisy data
Dealing with unknown environment data
Massive parallelism
Fault tolerance
Adapting to circumstances
…thenIf..
GENERAL INTRODUCTIONIN
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In other words, numerical methods cannot solve highly complex problems or it may require tedious mathematical iterations that will utilize high computational time and computer memory.
GENERAL INTRODUCTIONIN
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N In recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex problems.
GENERAL INTRODUCTIONIN
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N Artificial Neural Networks (ANN) is one of the AI methods..
Load Flow Studies itself is a highly complex problem.
GENERAL INTRODUCTIONIN
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Thus, Artificial Neural Network would be a very good method for Load Flow Studies.
GENERAL INTRODUCTIONIN
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Why?
GENERAL INTRODUCTIONIN
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N
In Artificial Neural Networks, power flow problems can be solved not by giving the computer a set of rules or instructions but by letting the system learn by experience (like humans).
GENERAL INTRODUCTIONIN
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N Numerical power flow methods are accurate but become unacceptable for on-line implementation due to high computational time requirements.
GENERAL INTRODUCTIONIN
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N
With the advent of artificial intelligence, in recent years, expert systems, pattern recognition, decision tree, neural networks and fuzzy logic methodologies have been applied to complex problems.
GENERAL INTRODUCTIONIN
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N
Amongst these approaches, the applications of artificial neural networks (ANNs) have shown great promise in power system engineering due to their ability to synthesize complex mappings accurately and rapidly.
GENERAL INTRODUCTIONIN
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N The composition of the input variables for the proposed neural network has been selected to emulate the solution process of a conventional power flow program.
GENERAL INTRODUCTIONIN
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N This special project shall utilize multi-layer perceptron model (MLP) based on Backpropagation (BP) Algorithm. A certain ANN software will be used.
GENERAL INTRODUCTIONIN
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N The effectiveness of the proposed ANN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes in a lumped, 5-bus power system in MSU-IIT.
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitation Definition of Terms
STATEMENT OF THE PROBLEM
INTR
OD
UCT
ION
Although Numerical Methods proven to be robust and reliable for Load flow studies, speed of solution is more important especially for online applications.
STATEMENT OF THE PROBLEM
INTR
OD
UCT
ION This is why decoupled power flow
methods are used over full AC numerical solutions because of its speed of solution. However, decoupled power flow methods are known for its high inaccuracies.
STATEMENT OF THE PROBLEM
INTR
OD
UCT
ION Recently, Artificial
Intelligence (AI) methods have been used to solve complex problems in medicine, business, sciences and engineering because of its speed and accuracy.
STATEMENT OF THE PROBLEM
INTR
OD
UCT
ION Hence, the methods of AI, like
Artificial Neural Network (ANN), shall be a great importance for load flow studies. The study shall evaluate the possibility of using ANN for Load flow studies and its accuracy compared to the numerical solution.
STATEMENT OF THE PROBLEM
INTR
OD
UCT
ION
In connection to this, a small power system shall be used for us to conduct the load flow calculation.This will be the MSU-IIT power system lumped into a 5-bus system.
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance Study Scopes and Limitation Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
OBJECTIVESIN
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N The Study has the following main objectives:
1.) To model MSU-IIT’s power system into a 5-bus system.
OBJECTIVESIN
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N
2.) To evaluate the MSU-IIT bus voltages for different loading conditions using a conventional power flow program.
OBJECTIVESIN
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3.) To train an ANN network based on Backpropagation Algorithm by using the voltage calculations from the power flow software.
OBJECTIVESIN
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N
4.) To validate and test the neural network and compare the results to the calculations from the power flow program.
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
SIGNIFICANCE OF THE STUDY
INTR
OD
UCT
ION
Since the study shall evaluate the possibility of ANN as a method for Load flow studies, the results shall be of a great use for planning, optimization, operation and control.
SIGNIFICANCE OF THE STUDY
INTR
OD
UCT
ION
The results of this study can be used for further hardware implementation for power control applications (i.e. OLTC and Shunt Capacitor control).
SIGNIFICANCE OF THE STUDY
INTR
OD
UCT
ION
Numerical methods are accurate but requires high computation time and memory. ANN, as an AI method, shall outweigh numerical methods when used in on-line power system applications.
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
SCOPES AND LIMITATIONSIN
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N The study is centered only on MSU-IIT Electrical System.
Some of the data were gathered from reliable sources and some the data were assumed.
SCOPES AND LIMITATIONSIN
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N PowerWorld GSO 14 was the power flow simulator used.
The whole MSU-IIT power system was lumped and reduced into a 5-bus system.
SCOPES AND LIMITATIONSIN
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N Maximum load per bus was assumed based on transformer ratings total connected loads present in the bus.
SCOPES AND LIMITATIONSIN
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N Although power factor between to different buses is different. The power factor of each bus was assumed to be constant.
SCOPES AND LIMITATIONSIN
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N Two ANN programs/software were used to train, test and validate the calculations from the power flow program. These are: Neural Connection 2.1 and Neurosolutions 5.
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
CON
TEN
TS INTRODUCTION
General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms
DEFINITION OF TERMSIN
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N Bus – a node or a common point of connection of elements; a conductor; or a group of conductors, that serves as a common connection for two circuits.
DEFINITION OF TERMSIN
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N Bus – a node or a common point of connection of elements; a conductor; or a group of conductors, that serves as a common connection for two circuits.
DEFINITION OF TERMSIN
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N Artificial Neural Network - is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks.
DEFINITION OF TERMSIN
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N Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics.
DEFINITION OF TERMSIN
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N Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics.
ABSTRACT
CON
TEN
TS INTRODUCTION REVIEW OF RELATED
LITERATURE METHODOLOGY RESULTS AND DISCUSSIONS CONCLUSION AND
RECOMMENDATIONS
ABSTRACT
CON
TEN
TS INTRODUCTION REVIEW OF RELATED
LITERATURE METHODOLOGY RESULTS AND DISCUSSIONS CONCLUSION AND
RECOMMENDATIONS
CON
TEN
TS Review of Related Literature
Load Flow Studies Intro, Numerical Methods, Decoupled Methods Artificial Neural Networks Intro, Neurobiology, Synapse Concept, Mathematical Representation, Perceptron Activation Function, BP
CON
TEN
TS Review of Related Literature
Load Flow Studies Intro, Numerical Methods, Decoupled Methods Artificial Neural Networks Intro, Neurobiology, Synapse Concept, Mathematical Representation, Perceptron Activation Function, BP
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE Load Flow Study is an important tool involving numerical analysis applied to a power system.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE
Unlike traditional circuit analysis, a power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (ie: reactive, real, and apparent) rather than voltage and current.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE
Unlike traditional circuit analysis, a power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (ie: reactive, real, and apparent) rather than voltage and current.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE It analyses the power systems in normal steady-state operation. There exist a number of software implementations of power flow studies.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE The great importance of power flow or load-flow studies is in the planning the future expansion of power systems as well as in determining the best operation of existing systems.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE The principal information obtained from the power flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line.
INTRO
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE
INTROSTATIC LOAD FLOW EQUATIONS (SLFE)
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE Newton Raphson Method – Newton's method can often converge remarkably quickly, especially if the iteration begins "sufficiently near" the desired root.
NUMERICAL METHODS
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE In NR method, the changes in real power (P) are very much influenced by the changes in load angle only and no influence due to the voltage magnitude changes.
NUMERICAL METHODS
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE Similarly the changes in reactive power are very much influenced by changes in voltage magnitudes and no change takes place due to load angle changes.
NUMERICAL METHODS
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE Gauss-Seidel Method - The iteration process begins with a flat voltage profile assumption to all the buses expect the slack bus.
NUMERICAL METHODS
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE The bus voltages are updated and the convergence check is made on updated voltages and the iteration process is continued till the tolerance value is reached.
NUMERICAL METHODS
LOAD FLOW STUDIESRE
VIEW
OF
RELA
TED
LITE
RATU
RE It is reliable and fastest method in obtaining convergence.
This method with branches of high (R/X) ratios, could not solve problems with regard to non- convergence and long execution time.
FAST-DECOUPLED METHODS
CON
TEN
TS Review of Related Literature
Load Flow Studies Intro, Numerical Methods, Decoupled Methods Artificial Neural Networks Intro, Neurobiology, Synapse Concept, Mathematical Representation, Perceptron Activation Function, BP
CON
TEN
TS Review of Related Literature
Load Flow Studies Intro, Numerical Methods, Decoupled Methods Artificial Neural Networks Intro, Neurobiology, Synapse Concept, Mathematical Representation, Perceptron Activation Function, BP
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Artificial Neural Networks (ANN) Connectionist computation Parallel distributed processing Computational models
Biologically Inspired computational models
Machine Learning Artificial intelligence
INTRO
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE Artificial Neural network - information
processing paradigm inspired by biological nervous systems, such as our brain
INTRO
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process
In a biological system, learning involves adjustments to the synaptic connections between neurons
INTRO
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
when we can't formulate an algorithmic solution.
when we can get lots of examples of the behavior we require.
‘learning from experience’
when we need to pick out the structure from existing data.
WHEN TO USE ANN’S?
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
A neuron: many-inputs / one-output unit
output can be excited or not excited incoming signals from other neurons
determine if the neuron shall excite ("fire")
Output subject to attenuation in the synapses, which are junction parts of the neuron
NEUROBIOLOGY
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
NEUROBIOLOGY
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
SYNAPSE CONCEPT
“The synapse resistance to the incoming signal can be changed during a "learning" process” [1949]
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
HEBB’S RULEIf an input of a neuron is
repeatedly and persistently causing the neuron to fire, a metabolic change happens
in the synapse of that particular input to reduce its
resistance
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
MATHEMATICAL REPRESENTATION
The neuron calculates a weighted sum of inputs and compares it to a threshold. If the sum is higher than the threshold, the output is set to 1, otherwise to -1.
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
MATHEMATICAL REPRESENTATION
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
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TURE
MATHEMATICAL REPRESENTATION
Axon
Terminal Branches of Axon
Dendrites
Electro-chemical signalsThreshold output firing
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
MATHEMATICAL REPRESENTATION
Binary classifier functionsThreshold activation function
Axon
Terminal Branches of Axon
Dendrites
S
x1
x2
w1
w2
wnxn
x3 w3
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
PERCEPTRON: THRESHOLD ACTIVATION FUNCTIONBinary classifier functionsThreshold activation function
Step Threshold
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
LINEAR ACTIVATION FUNCTIONS
Output is scaled sum of inputs
Linear
n
N
nnxwuy
1
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Nonlinear Activation FunctionsSigmoid Neuron unit function
Sigmoid
uhide
uy
1
1)(
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Learning
From experience: examples / training data
Strength of connection between the neurons is stored as a weight-value for the specific connection
Learning the solution to a problem = changing the connection weights
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Unsupervised Learning
No help from the outside No training data, no information available on
the desired output Learning by doing Used to pick out structure in the input:
Clustering Reduction of dimensionality compression
Example: Kohonen’s Learning Law
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Backpropagation Desired output of the training
examples Error = difference between actual &
desired output Change weight relative to error size Calculate output layer error , then
propagate back to previous layer Improved performance, very common!
ARTIFICIAL NEURAL NETWORKS
REVI
EW O
F RE
LATE
DLI
TERA
TURE
Backpropagation Desired output of the training
examples Error = difference between actual &
desired output Change weight relative to error size Calculate output layer error , then
propagate back to previous layer Improved performance, very common!
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