Intelligent Ships Symposium May 20-21, 2015

Ranjit Amgai, PhD

Dept. of Electrical and Computer EngineeringMississippi State University

AUTONOMIC MANAGEMENT OF SHIPBOARD POWER SYSTEMS AND ITS

CHALLENGES

MOTIVATION AND CHALLENGES

Increasing power demand with Quality of Service(QoS) requirements Uncertain power demand profiles requiring the continuity in power

supply. Meeting the design specifications to overcome several challenges for

normal and faulty conditions via manual tuning is not only exhausting and fallible, but also infeasible.

Handling such complexities for uncertain operating conditions require the generic management framework that adds the self-managing and self-healing capabilities given the high-level objectives.

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Deliver Right Power to Right Load at Right Time

AUTONOMIC COMPUTING

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LITERATURE REVIEW

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Expert System [Karim-2002]: Optimal power flow, optimal load shedding, and

voltage controls using rule based system [Morse-2012]: Load and Configuration management verified

through expert system rules on physics based simulation.

Feedback Control Theory [Radan-2008]: Observer based fast load reduction.

Advanced Artificial Intelligence [Solanki-2005]: Multi-agent based implementation for data

sharing, communications, and reconfiguration [Qunying-2012]: Multi-agent based framework for power

management functionalities. [Mitra-2011]: Modified PSO algorithm for reconfiguration and

critical load balancing

LITERATURE REVIEW

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Model Predictive Approach for Management Framework [Seenumani-2010]: Reference governor based hierarchical

controller using trajectory optimization [Licheng-2011]: MPC based power system control architecture

for protection [Torstein-2012]: MPC based framework for drilling ship with load

sharing control

Distributed Architecture [Qunying-2012]: Multi-agent based distributed framework for

Eship power management functionalities. [Venkat-2008, Moradzadeh-2011]: Distributed MPC applied to

terrestrial power system applications

MODEL-BASED CONTRIBUTIONS

It is highly configurable and can be tuned for multiple performance specifications.

The framework is generic, modular and scalable with respect to system components.

It provides flexibility for using additional algorithms and the idea can be extended further to smart grid applications, thus strengthening the automation.

Novel model-based distributed management control framework provides fault-tolerant and self-configurable platform.

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MANAGEMENT FRAMEWORK

SYSTEM CONTROLS FRAMEWORK

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Key Components of Framework Model: System Abstraction Control Algorithm: Limited

Lookahead Control (LLC) Optimizer: Search Space Modules: SPS Diagnosis,

Dispatch Control, Shunt compensation control, etc

SPS DIAGNOSIS SUPPORT

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Goal: To develop the intelligent diagnosis support module that classifies the Shipboard Power System operation states to assist the supervisory controls architecture.

Computation Burden Fast training Adaptive Uncertainties Relative ease of system

development

Normal, Alert, Emergency Assists the control framework Screening of contingencies help for

preventing rescheduling Time to trigger control actions Situational awareness for Operator Supports SPS reliability

Motivation SPS Assessment

EVALUATION INDEX

Offline Data processing Post Contingency Screening Identification of vital/ Non-vital load loss Objective results amount of Real Power cut

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FLOWCHART FOR DIAGNOSIS

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Signal Conditioning

Training Complete?

Data Measurement

Feature Selection

Training Data Set Testing Data Stream

Bayes’ Model

Update

Classification o/p

N

Classification steps Implemented on Matlab/Weka

API Supports the incremental

Training

CASE-STUDY

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300 representative samples 60% to 140% load variation 97.67% classification accuracy 25ms to test the data 10ms to build/update the model

N Events Evaluation Index Value

Result

1 Generator Outage(MTG1) 34 Emergency

2 One Line Outage (L10) 0.5 Normal

3 Double Line Outage (L1+L10) 34 Emergency

4 Double Line Outage(L2+L7) 0 Normal

5 One Generator + One Line Outage (MTG1+L1)

15 Emergency

6 One Generator + One Line Outage(ATG1+L2)

3 Alert

VOLTAGE CONTROL MODULE

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Goal: Develop a module for handling system faults through predictive optimal tuning of real time control parameters for voltage control from the finite set of control actions

Lookahead Horizon: ‘Limit Horizon’ can be estimated by offline simulation.

Control Set: For non finite control sets, “discretized” approximation

Input Set: Complexity ( )

Where, b is the size of input set and d is the horizon depth. Enhanced Search Techniques(A*), search space reduction Approximation of control input domain

Modeling Abstraction

( )db

Controller Performance

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LOOK-AHEAD CONTROL SCHEME Key Components

System model and control objective System input forecast Controlling parameters Constraints and performance specifications Lookahead Horizon:

System Model Set of difference equations relating the objective Function to the

finite set of constrained input set( 1) ( ( ), ( ), ( ( 1, )), ( ))kx k f x k u k w k r e k

LOOK-AHEAD CONTROL SCHEME

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For each step k:

And picks the best:

Selection of the next step is based on a map that defines how close the current state is to Xs

Controller constructs a tree of all future states up to certain depth. A path that minimizes the distance to Xs is traced back to current state

and the initial step is selected.

Selection of the next step is based on a map that defines how close the current state is to Xs

Controller constructs a tree of all future states up to certain depth. A path that minimizes the distance to Xs is traced back to current state

and the initial step is selected.

],1[

:horizon Prediction

hkk

k+hk+3k+2k+1

)1(ˆ kx

)2(ˆ kx )(ˆ hkx )3(ˆ kx

x(k)

sX

OPTIMAL CONTROL PROBLEM

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Subject to:

Dynamics:

Input space:

Constraints:

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Component wise

Differential Equations

Coupling Linearization Discretize

IMPLEMENTATION STRUCTURE

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RTDS TEST-BED SETUP

TCP/IP based communication Runtime Scripts commands Flexible setup Applicable to multiple control algorithms Matlab/C for Interface

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COMPLEXITY REDUCTION METHOD Number of explored states: A* Algorithm Offline computed hash table Pre-compute the knowledge base using existing model Utility function assists the computation of heuristics table Significant computation speedup Use of existing system model

[Abdelwahed-2009]

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PERFORMANCE COMPARISON

Capacitor Tuning

Performance Comparison for Various Search Strategies

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SYSTEM PERFORMANCE

RTDS Open Loop Fault Scenario RTDS Closed Loop: 1% Load Change

RTDS Closed Loop RTDS Closed Loop: 2.5% Load Change

DISTRIBUTED MANAGEMENT FRAMEWORK

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Goal: To enhance the computation efficiency and address the system management issues by keeping the frequency in limits by managing No-load frequency and fuel rate.

Complexity of system design increases as it grows bigger Minimize the single point of failure Enhance computation time Formally define the problem Flexible modular approach Adds robustness towards failure

Motivation

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SPS APPLICATION

Coordinator influences the first level control units in certain manner to satisfy the coordination condition.

Changes the infimal performance function Finds the set of coordination variables The applied controls depends upon the infimal

units output and the control inputs Computes an optimal set of control input u

[ωNL,1, ωNL,2 ] for each subsystem by satisfying the operational constraints of the subsystems.

β1……. βn

System Modeling

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1. Load sharing ratio2. Utility value3. Computational Aspect

1. Hierarchical Units Interaction2. Subsystem Processing time

The ratio of explored states:

where, U is the set of control input, Ik is the iterations among the subsystem

and coordinator, N is the number of subsystems, m is the

input pair, H is the prediction horizon steps.

Performance Parameters

1. Frequency2. Load Sharing3. No-load frequency4. Utility value5. Latency6. Scale of Implementation7. Controller Interaction8. Interaction error

Performance Comparison

PERFORMANCE OF CONTROL APPROACH

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SYSTEM PERFORMANCEWith constant No-load frequency With the application of IBP

PERFORMANCE EVALUATIONNo load frequency

Interaction Errors

Power sharing

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It can be applied to a general class of power system applications due to the generic nature of the approach.

It efficiently manages the power management specifications by handling the load sharing and fault minimization simultaneously.

It also provides better utility of the deployment and reduces the computational overhead compared to a centralized approach.

The developed approach shows robustness towards the varying load conditions.

BENEFITS OF THE APPROACH

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Keeping the Model simple and Accurate Abstraction is the key

Control Inputs Impact Long term system performance Analysis

Computation Speed For transient level control Benefit/Cost Analysis

Algorithm Selection Open Architecture Framework Application specific algorithm

CHALLENGES OF THE APPROACH

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Human Interface Iterative approach to setting policies

Policy Goal-based and Utility-function based Not just low-level policies

Demonstrate the effectiveness Relatively new scheme on research community More research effort helps to mature

CHALLENGES OF THE APPROACH

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Extension of Modules Add other modules to consolidate the power management

concept in SPS. Formally analyze the approach further and proven for stability Extensive application for MVDC systems Scale to real-time cases for validation

FUTURE RESEARCH

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Thank You!!!

Questions ?