ANN for Load Flow Studies

An Electrical Engineering Special Project

ANN-BASED LOAD-FLOW STUDIES FOR MSU-IIT ELECTRICAL SYSTEM

ABSTRACT

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TS INTRODUCTION REVIEW OF RELATED

LITERATURE METHODOLOGY RESULTS AND DISCUSSIONS CONCLUSION AND

RECOMMENDATIONS

ABSTRACT

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RECOMMENDATIONS

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TS INTRODUCTION

General Introduction Statement of the Problem Objectives of the Study Significance of the Study Scopes and Limitations Definition of Terms

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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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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.

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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.

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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?


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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..


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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.


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N In recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex problems.


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N Artificial Neural Networks (ANN) is one of the AI methods..

Load Flow Studies itself is a highly complex problem.


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Thus, Artificial Neural Network would be a very good method for Load Flow Studies.


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Why?


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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).


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N Numerical power flow methods are accurate but become unacceptable for on-line implementation due to high computational time requirements.


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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.


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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.


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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.


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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.


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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.

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CON

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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

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Although Numerical Methods proven to be robust and reliable for Load flow studies, speed of solution is more important especially for online applications.


INTR

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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.


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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.


INTR

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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.


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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

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TS INTRODUCTION

General Introduction Statement of the Problem Objectives of the Study Significance Study Scopes and Limitation Definition of Terms

CON

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TS INTRODUCTION


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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2.) To evaluate the MSU-IIT bus voltages for different loading conditions using a conventional power flow program.

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3.) To train an ANN network based on Backpropagation Algorithm by using the voltage calculations from the power flow software.

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4.) To validate and test the neural network and compare the results to the calculations from the power flow program.

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SIGNIFICANCE OF THE STUDY

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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.


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The results of this study can be used for further hardware implementation for power control applications (i.e. OLTC and Shunt Capacitor control).


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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.

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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.


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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.


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N Maximum load per bus was assumed based on transformer ratings total connected loads present in the bus.


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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.


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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.

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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.


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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.


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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.


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N Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics.


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N Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics.

ABSTRACT

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RECOMMENDATIONS

ABSTRACT

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RECOMMENDATIONS

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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

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LOAD FLOW STUDIESRE

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RE Load Flow Study is an important tool involving numerical analysis applied to a power system.

INTRO

LOAD FLOW STUDIESRE

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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.

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LOAD FLOW STUDIESRE

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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

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RE It analyses the power systems in normal steady-state operation. There exist a number of software implementations of power flow studies.

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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

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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.

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INTROSTATIC LOAD FLOW EQUATIONS (SLFE)

LOAD FLOW STUDIESRE

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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

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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

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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

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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

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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

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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

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ARTIFICIAL NEURAL NETWORKS

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Artificial Neural Networks (ANN) Connectionist computation Parallel distributed processing Computational models

Biologically Inspired computational models

Machine Learning Artificial intelligence

INTRO


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processing paradigm inspired by biological nervous systems, such as our brain

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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

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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?


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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


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NEUROBIOLOGY


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SYNAPSE CONCEPT

“The synapse resistance to the incoming signal can be changed during a "learning" process” [1949]


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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


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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.


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Axon

Terminal Branches of Axon

Dendrites

Electro-chemical signalsThreshold output firing


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Binary classifier functionsThreshold activation function

Axon

Terminal Branches of Axon

Dendrites

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PERCEPTRON: THRESHOLD ACTIVATION FUNCTIONBinary classifier functionsThreshold activation function

Step Threshold


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LINEAR ACTIVATION FUNCTIONS

Output is scaled sum of inputs

Linear

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Nonlinear Activation FunctionsSigmoid Neuron unit function

Sigmoid

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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


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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


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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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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!

Documents

ANN for Load Flow Studies