PSR Using ANNs

Power system restoration Using ANNs

Seminar Presented by Rupak

MPSD-732-2K-15 Date: 9TH Nov. 2015

03/05/2023 10:08 AM 2

Outline

• Introduction• Power Outage• Causes of Power Outage• Types of Power Outage• Major Power Outages• Conventional Techniques

• Artificial Neural Networks• Introduction• ANN Structure• Training and Learning

• ANN Based Power Restoration• Introduction• Island Restoration Techniques• Advantages & Disadvantages

Rupak PSR Using ANN

Rupak PSR Using ANN

Power OutageCauses Types Of Power OutagesMajor Power OutagesConventional Techniques

Power Outage

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IntroductionArtificial Neural Networks

ANN Based Power System Restoration

• A power outage (also called a power cut, or a power blackout, power failure or a blackout) is a short- or long-term loss of the electric power to an area.

• Under certain conditions, a network component shutting down can cause current fluctuations in neighbouring segments of the network leading to a cascading failure of a larger section of the network. This may range from a building, to a block, to an entire city, to an entire electrical grid.

Causes

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• Faults at Power Stations• Damage to Electric transmission lines• Substations• Other parts of the distribution system• A short circuit or the overloading of electricity mains.• Outages may last from a few minutes to a few weeks depending on the nature of

the blackout and the configuration of the electrical network.• Effects on commerce, industry, and everyday life of the general population

Rupak PSR Using ANN




Types of Power Outage

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• Power outages are categorized into three different phenomena, relating to the duration and effect of the outage:

• A permanent fault is a massive loss of power typically caused by a fault on a power line.

• A brownout is a drop in voltage in an electrical power supply.

• A blackout is the total loss of power to an area and is the most severe form of power outage that can occur.

Rupak PSR Using ANN




Sites of Power Outage

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• Particularly critical at sites where the environment and public safety are at risk.• Institutions such as hospitals, • sewage treatment plants, • mines • The like will usually have backup power sources such as standby generators (which

will automatically start up when electrical power is lost.)• Telecommunication, are also required to have emergency power. • The battery room of a telephone exchange usually has arrays of lead–acid batteries

for backup and • Also a socket for connecting a generator during extended periods of outage.

Rupak PSR Using ANN



Power OutageCauses Types Of Power OutagesSites of power outageMajor Power OutagesConventional Techniques

Protecting the System From Outage

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• In power supply networks the power generation and the electrical load (demand) must be very close to equal every second to avoid overloading of network components, which can severely damage them.

• Protective relays and fuses are used.

• Modern power systems are designed to be resistant to this sort of cascading failure, but it may be unavoidable . Moreover, since there is no short-term economic benefit to preventing rare large-scale failures, researchers have expressed concern that there is a tendency to erode the resilience of the network over time, which is only corrected after a major failure occurs.

Rupak PSR Using ANN



Power OutageCauses Types Of Power OutagesProtecting the system from power outageMajor Power OutagesConventional Techniques

Major Power Outages

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Outage DateJuly 2012 India blackout 30 July 2012-31 July 2012

January 2001 India blackout 2 January 2001

November 2014 Bangladesh Blackout

1 January 2014

2015 Pakistan blackout 26 January 2015

2005Java Bail Blackout 18 Aug 2005

1999Southern Brazil Blackout 11 March 1999

2009 brazil & Paraguay Blackout 10-11 Nov 2009

2015 Turkey Blackout 31 March 2015

Northeast Blackout of 2003 14-15 Aug 2003

2003 Italy Blackout 28 Sept. 2003

Thailand Nation wise Blackout 1978 18 Mar 1978

Conventional Techniques

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Automated restoration: In this restoration technique,

• Computer Programs are responsible for the PSR plan development and implementation.• The PSR techniques acquire system data from the SCADA and EMS to develop a

restoration plan for the transmission system.

• Working: After developing the restoration plan, a Switching Sequence Program, which is also a part of the EMS, will be responsible for the transmission of control signals through SCADA to circuit breakers and switches to implement the plan.

• In this technique, the system operator plays the role of a supervisor.

Rupak PSR Using ANN





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Computer aided restoration: In this technique,• The PSR plan development and implementation is performed by the system operator.• The PSR technique acquire system data from the local SCADA/EMS develop a PSR plan.• The system operator can use the PSR procedures and power system analysis programs.• It will also use the local SCADA/EMS to transmit control commands to circuit breakers and switches in order to implement the chosen PSR plan.

Rupak PSR Using ANN





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Cooperative restoration: In this technique, •A computer program installed at the EMS will propose a PSR plan and use system data after the occurrence of a blackout. •The system operator is responsible for the implementation of the PSR plan. •Acquire power system data obtained from local SCADA/EMS.•Then system operator can send controlling signals through local SCADA/EMS to circuit breakers and switches to implement the plan.

Rupak PSR Using ANN




Limitations of Conventional Techniques

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• Generated very good results; however, few implementations exist at this time.• More time required to find the restoration plan.• The rule-based techniques can take several minutes to find the plan in large transmission

systems, because the number of rules is proportional to the size of the system. • The mathematical programming approach has similar performance characteristics.• Very time consuming when applied to a large transmission system.

Rupak PSR Using ANN




Introduction ANN structure Training and Learning

Introduction

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Artificial Intelligence

Developing machines that exhibit intelligent behavior

Systems that learn from past experiences and respond with appropriate decisions to unseen situations

Examples: Self driving Cars, Automatic Speech Recognition

Tools to Develop Intelligent Systems:

Different methods are used to teach systems to make them intelligent: 1. Rule Based Systems: Decision is taken based on predefined deterministic rules. 2. Logical Reasoning: Logic is used for knowledge representation and problem solving. 3. Statistical Methods: Training systems based on past/historical data 4. Artificial Neural Networks: Systems duplicating the human brain’s decision making process

Rupak PSR Using ANN



Artificial Neural Networks

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Fig. 1. ANN is an interconnected group ofNodes, similar to network of neurons in human brain

Inspired by Biological Neural Networks

Used to estimate a function dependent on large number of input variables.

Structure of ANN : NN is composed of interconnected nodes. Each connection carries a weight.

Nodes are arranged in form of layers. Outermost layer is the output layer. First layer is Input and intermediate are hidden layer

Rupak PSR Using ANN




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Output of nodes in a layer acts as input to the nodes in next layer.

Fig2. Shows the output of a neuron/node i is obtained by applyingActivation function, g to weighted sum S of inputs to the nodes.

Output of hidden and output layer Is obtained by applying activation function on weighted sum of inputs

1 1 2 2 3 3i i i iS w I w I w I

1 1 2 2 3 3i i i iS w I w I w I

Weighted sum to a node is defined as

Where w’s are the weights of the connections

Each node applies an function called Activation function to weighted sum to produce its output O

( )i iO g S where g is the activation function Most widely used form of activation function is sigmoid function where

1( )1 zg ze

Rupak PSR Using ANN




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For n inputs the output O of ith hidden node can be written as:

The output y of network, in a similar way, can be written as:

1

i h

i ii

y g wO

1

j n

i ji jj

O g w I

where, h is the number of hidden nodes and is the weight from ith hidden node to Output node. All the weights in network form the weight matrix

iw

Hence the output for a given input feature vector can be determined if weights of interconnections are known.

The optimal weights are obtained by training the NN on already seen data.

For a simpler illustration, assuming activation function to be Identity. (T is the transpose of matrix)

Toutput inputy w x

w

Rupak PSR Using ANN




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Training An Artificial Neural Networks

Training data consists of inputs for which actual output is known.

Error of the NN is defined as avg. difference between predicted output and actual output for an input.

1 1

1 1( ) ( )m m

Tavg actual output actual input

i i

Error y y y w xm m

( )0avg

optimal

d Errorw

dw

Rupak PSR Using ANN




IntroductionIsland Restoration TechniquesAdvantage & Disadvantage s

ANN in Power System Restoration

ANN based PSR is employed in Island Restoration Schemes (IRS).

We will define what an ISR is and how the configuration of ANN based ISR is developed:Island Restoration Scheme (IRS) After a wide-area disturbance in power system, the region is divided into small island each recovering on its own. 1. Number of islands is predefined based on offline studies and load –generation balance. 2. Fast & Efficient for parallel restoration of large transmission systems. 3. “All open” switching strategy is used for IRS where all circuit breakers of the system are open

PSR Through IRS In order to restore power, each IRS will generate local restoration plans composed of: 1. Switching sequences of local circuit breakers 2. Forecast Restoration Load

Next we will show, how ANN is employed to achieve these two steps

Rupak PSR Using ANN



ANN in Power System Restoration

To generate, switching sequence and forecast load each IRS is composed of: 1. 2 ANNs 2. A Switching Sequence Program (SSP): SSP determines the energizing sequence of transmission paths.

The first ANN is responsible for island’s restoration forecast. Input: Normalized vector of pre-disturbance load. Output: Forecasted Restoration Load

ANN 1 ANN 2Pre-disturbance LoadForecasted Load

Constraints: Unavailable Paths

Island Configuration& Restoration Load Pick-up percentage

SSPEnergizing Sequence of Transmission Paths

Second ANN is responsible for island restoration configuration

Input: (i) Forecast Load (ii) Unavailable Paths Output: Configuration for recovery & Load Pick-up % for feasible operational condition Fig 3. Structure of ANN based IRS

The output of 2nd ANN is sent to SSP. SSP is connected to energizing sequence database. For SSP: Input: Final Restoration island configuration generated by 2nd ANN & Energizing sequence database. Output: Sequence of transmission paths to be energized to achieve operational configuration

Rupak PSR Using ANN





ANN based restoration is faster because of processing speed of neural networks

Since ANN are trained before use, they have the ability to learn and adapt to new data.

No requirement for knowledge of system to generate a model between input and output

Can handle incomplete information, noisy and complex data.

Increasing the number of hidden layers increases network’s intelligence by learning more patterns

Advantages

Disadvantages Large dimensionality

A neural network trained for a certain task can’t be generalized for other tasks, unless trained again.

Rupak PSR Using ANN




ANN Based PSR Conclusion

Island Restoration Scheme is an efficient way of achieving parallel restoration in large transmission systems.

Neural Networks are a method of approximating input-output functional forms.

Neural Networks are trained on data and optimal weights are achieved by minimizing error on training data

Trained networks can be used efficiently in IRS to restore power quickly by forecasting load and restoration configuration.

Rupak PSR Using ANN

Summary

• PSR has become a field of growing interest

• Several techniques based on artificial intelligence have been proposed to improve power system restoration since it has generalization capability and high processing speed.

• These techniques propose the use of the computer as an operator aid instead of the use of predefined operating procedures for restoration.

• The stressful condition following a blackout and the pressure for achieving a restoration plan in minimum time can lead to misjudgement by system operator.

• The large number of possible faulty conditions and the need to provide a restoration plan in minimum time are arguments in favour of these techniques.

Rupak PSR Using ANN

Rupak PSR Using ANN

Thank You

PSR Using ANNs

Engineering

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