Integration of a cascaded multilevel
inverter (CMLI) with AC mains
By
Muhammad Arslan Fida
Muhammad Muzammil Nawaz
Noman Khan
Thesis submitted to the faculty of Engineering in partial
fulfillment of requirements for the Degree of BS Electrical
Engineering.
Department of Electrical Engineering,
Pakistan Institute of Engineering and Applied Sciences,
Nilore, Islamabad 45650, Pakistan.
January 06, 2017.iii
Department of Electrical Engineering
Pakistan Institute of Engineering and Applied Sciences (PIEAS)
Nilore, Islamabad 45650, Pakistan
Declaration of Originality
I hereby declare that the work contained in this thesis and the intellectual content
of this thesis are the product of my own research. This thesis has not been
previously published in any form nor does it contain any verbatim of the published
resources which could be treated as infringement of the international copyright law.
I also declare that I do understand the terms copyright and plagiarism, and that
in case of any copyright violation or plagiarism found in this work, I will be held
fully responsible of the consequences of any such violation.
Signature:
Name: Muhammad Arslan Fida
Muhammad Muzammil Nawaz
Noman Khan
Date: January 06, 2017.
Place: PIEASiv
Certificate of Approval
This is to certify that the work contained in this thesis entitled
Integration of a cascaded multilevel inverter (CMLI) with AC mains"
was carried out by
Muhammad Arslan Fida
Muhammad Muzammil Nawaz
Noman Khan
under my supervision and that in my opinion, it is fully adequate,
in scope and quality, for the degree of BS Electrical Engineering
from Pakistan Institute of Engineering and Applied Sciences
(PIEAS).
Approved By:
Signature:
Supervisor: Mr. Tanveer Abbas
Verified By:
Signature:
Head, Department of Electrical Engineering
Stamp:v
'' Copyright by Muhammad Arslan Fida
Muhammad Muzammil Nawaz
Noman Khan, 2017.
All rights reserved.vi
Table of Contents
List of Figures viii
List of Tables viii
1 Introduction 1
1.1 Goals and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Approach and Methodology . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Overview of remainder of the report . . . . . . . . . . . . . . . . . . 4
2 Literature Review 5
2.1 Synchronization Techniques . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Zero Crossing detection . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.3 Discrete Fourier transform (DFT) . . . . . . . . . . . . . . . 6
2.1.4 Phase Locked Loop (PLL) . . . . . . . . . . . . . . . . . . . 6
2.1.5 Adaptive Notch Filter . . . . . . . . . . . . . . . . . . . . . 6
2.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Adaptive Notch Filter (ANF) 8
3.1 Simulations: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 Results: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11vii
4 Accomplishments and Future Plan 12
Appendices 13
Appendix A Code for ANF Simulations 14
References 16viii
List of Figures
1.1 Block Diagram of System . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1 Variation in '' at '' = 0:85 . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Variation in '' at '' = p2=2 . . . . . . . . . . . . . . . . . . . . . . . 10
A.1 ANF Differential Equations . . . . . . . . . . . . . . . . . . . . . . 14
A.2 Simulation code using ode45 . . . . . . . . . . . . . . . . . . . . . . 15
List of Tables
2.1 Comparison of Synchronization Methods . . . . . . . . . . . . . . . 7
4.1 Accomplishments Summary . . . . . . . . . . . . . . . . . . . . . . 121
1. Introduction
Renewable energy resources will play an important role in the world's future. The
energy resources have been split into three categories: fossil fuels, renewable resources and nuclear resources [1]. Renewable energy resources include solar energy,
wind energy, biomass energy, geothermal energy. The ever-increasing demand of
energy cannot be supplied by the conventional grid alone. Thus, renewable energy resources are of prime importance and generation of energy from renewable
resources will be an integral part of the future grid.
In present day, one of the major sources of renewable energy is sun and this energy
is harvested in DC form and hence is not readily available to be used as majority
of our appliances work with AC. Hence, a conversion of this solar energy to AC is
needed and this is done by an inverter. On the basis of overall system structure,
inverters could be divided into two categories: (1) Standalone Inverters, (2) Gridconnected Inverters.
Stand-alone inverters produce power from a single independent source. These are
more suitable for remotest locations where the grid cannot penetrate and there is
no other source of energy [2].
Grid-connected inverter, however, functions alongside with the utility grid. It not
only converts DC into AC but it also injects that energy into the utility grid. The
injection of the solar energy into the grid is impossible without synchronization
of the inverter output with the grid. This is done by a grid-side controller which
can have the following tasks [3]:2
1. Grid synchronization;
2. Control of active power delivered to the grid;
3. Control of reactive power transfer between the inverter and utility grid;
4. Ensure high quality of the injected power.
This project aims to addresses the problem of grid synchronization by designing
a controller.
1.1 Goals and Objectives
The goal of this project is to develop a controller which should be able to synchronize a cascaded multi-level inverter (CMLI) with the utility grid.
This goal is set to be achieved through the following objectives:
' Literature review;
' Selection of a suitable technique;
' Simulations of selected technique;
' Hardware Implementation of the controller;
' Testing of the controller;
' Thesis writing.
1.2 Scope
This project deals with the complex problem of synchronizing a CMLI with the
utility grid. Designing a CMLI is not in the scope of this project and it will be
provided during the testing phase. A block diagram of the whole grid-connected
system is shown below in Figure 1.1.3
Figure 1.1 Block Diagram of System
1.3 Approach and Methodology
A literature review of the present grid synchronization techniques is to be carried out and a suitable technique is to be selected to design the controller. After
selection of the technique, simulations are to be carried out to assess the performance and working of the technique.The simulation results will enable us to
further enhance the performance of the selected technique. After that, hardware
implementation is to be carried out. This will complete the design of the controller. This controller is then to be tested by synchronizing a CMLI with the
utility grid. After this, thesis writing is to be done.4
1.4 Overview of remainder of the report
Chapter 2 describes the literature review carried out. Chapter 3 details our selected technique. Chapter 4 deals with current accomplishments and future work
plan.5
2. Literature Review
There are different practical methods used for synchronization. Some of them are
native and can only be used for some specific applications, while other techniques
are comparatively more complex and are used for the applications where the loss of
synchronization cannot be compromised. The remainder of this chapter presents
a comprehensive discussion on these techniques.
2.1 Synchronization Techniques
Following are some of the techniques used for synchronization of grid-connected
inverters.
2.1.1 Zero Crossing detection
Zero crossing detection is one of the most popular methods to determine frequency
and time period of a signal. When determining the frequency is our priority, we
usually calculate the frequency of a number of cycles of the signal because by
doing so the error will be reduced to a very small magnitude. Zero crossing is the
point of choice for measuring phase and frequency. The reference is usually easy
to establish. However, phase synchronized triggering requires placing additional
constraints on zero crossing detection [4].
Although the system looks very easy and simple but it has a disadvantage that it
is very welcoming to noise. That noise alters the results and the synchronization
can be compromised.6
2.1.2 Kalman Filter
The Kalman gain can be evaluated off-line, so that the filter can be implemented
using fixed gains, simplifying its implementation. This is capable of generating
the synchronization signals even when the input signal contains harmonics and
measurement noise. The computational complexity of this technique is more and
difficult to handle and covariance selection is also a problem.
2.1.3 Discrete Fourier transform (DFT)
In this case, the proposed algorithm takes samples in a window synchronous with
the exact rate as that of the nominal frequency of the network. If the frequency is
exactly equal to nominal value i.e., 50Hz the system will produce synchronization.
This method, however, produces a phase shift when the DFT sampling is asynchronous with power grid.
2.1.4 Phase Locked Loop (PLL)
The difference between phase angle of the input and that of the output signal
is measured by the phase detection and passed through the loop filter .The loop
filter output signal drives the voltage-controlled oscillator (VCO) to generate the
output signal. The method is very simple and easy to implement. It gives most
accurate results as compared to other techniques.
However, using PLL may have a negative impact on the performance of HVDC in
weak ac system and during unsymmetrical voltage faults, 2nd harmonic produced
by negative sequence will propagates through PLL system and will be reflected in
the '' _.
2.1.5 Adaptive Notch Filter
It is a notch filter with adaption mechanism that tracks the frequency and phase
of the input signal. This information can be used for the purpose of the synchronization. A second-order notch filter is used to update the frequency and the7
voltage-controlled oscillator (VCO) is replaced by two extra integrators.
In addition to a simple structure (no VCO) ANF offers higher immunity to noise as
compared with other conventional methods. The transient response is also faster
than the PLL-based method.
This technique has been chosen for this project and detailed discussion of this
technique will be carried out in Chapter 3.
Comparison table of different techniques:
A comparison table is given below with the purpose to give an overview of different
techniques:
Table 2.1 Comparison of Synchronization Methods
Synchronization method Single phase Three phase Harmonic detection Implementation Complexity
Zero crossing detection Yes Yes No Simple
Kalman filter Yes Yes No Complex
Discrete Fourier transform No Yes Yes Medium
Phase-locked loop Yes Yes Yes Medium
Adaptive notch filter Yes Yes Yes Simple
2.1.6 Summary
Several techniques are available for synchronization of grid connected inverters
among which Zero crossing is robust to the frequency variations but the results
are distorted by noise. In case of Kalman filter, the computational complexity is an
issue. Discrete Fourier transform produces a phase shift when the DFT sampling
is asynchronous with power grid. PLL is immune to noise as compared to other
techniques but ANF provides much higher noise immunity as compared to PLL. It
includes a second-order notch filter to update the frequency and replaces the VCO
of PLL with two integrators. Also, its response time is faster than that of PLL.
Faster response rate and simplicity in design makes ANF our preferred technique
to synchronize a CMLI with AC Mains. A detailed discussion of ANF is given in
the Chapter 3.8
3. Adaptive Notch Filter (ANF)
ANF is preferred over other techniques because of its simplicity and faster response. A Notch filter (NF) is basically band-Stop filter with very narrow stopband corresponding to high Quality Factor. NF basically gives a notch at particular frequency. For example if it is set to give a notch at 50 Hz frequency; whenever
a signal is applied with a 50 Hz component in it, it would give a notch. An adaptive filters work on feedback algorithm by adjusting the variable parameters of its
transfer function.
So, an adaptive filter combined with a notch filter can track down a change in frequency and phase. ANF is the most recent technique being used in power systems
for frequency estimation and phase detection.
A modified version of HSU's ANF has been proposed in [5] and [6].
The response of ANF proposed in [5] and [6] is characterized by following set of
differential equations:
x '' + 2''x _ + ''2x = 2''2y (3.1)
'' _ = '''x(''2y ' ''x _) (3.2)
Where y is the input signal, x is the estimated phase and '' is the estimated
frequency in radians per second.
As compared to PLL, ANF doesn't use a VCO which makes it simpler to instead
it uses two extra integrators which makes it simpler to implement. But as far
as digital implementation is concerned, ANF needs a higher sampling rate as
compared to PLL as integrators must operate on signals that are changing faster.9
The design parameters are '' and '' . The parameter '' determines the depth
of notch and hence the sensitivity of ANF. While '' defines the adaption speed
and hence the frequency tracking capability of ANF [7]. A trade-off between the
accuracy and speed of the estimation is done by adjusting the ANF parameters
'' and ''. By increasing the value of '', one can achieve a faster estimation of
frequency but at the same time '' should be increased to avoid oscillatory behaviors.
Following are the constraints for a pure sinusoidal input signal y(t) = A1sin(!1t +
''1) and for '' > 1:
A2
1''
2
< 1 (3.3)
For 0 < '' < 1, the stability condition is no longer independent from ''. However,
one can use the more restricted condition that:-
A2
1''
2
< 0:8 (3.4)
Here A2
1=2 is the power of the sinusoidal signal with amplitude A. So, from equation
3.4 it is clear that knowledge of input signal power is adequate for selecting ''
independently from ''. Choosing '' = p2=2 and '' in the range [0.8, 1] results in
three to four cycles of transient response for a unity amplitude, sinusoidal signal
of frequency of 50 or 60 Hz [7].
3.1 Simulations:
The differential equations presented in Equation 3.1 and 3.2 are simulated in Matlab for a unity amplitude sinusoidal signal. Matlab codes are given in Appendix A
for reference. The results of the simulations are shown below.
3.1.1 Results:
After setting the initial conditions, we '' and '' to obtain some optimum value.
Figure 3.1 shows the response observed after varying the '' parameter while keeping
'' = 0:85.10
Figure 3.1 Variation in '' at '' = 0:85
Figure 3.2 shows the response observed after varying the '' parameter while
keeping '' = p2=2.
Figure 3.2 Variation in '' at '' = p2=211
3.1.2 Summary
ANF has been employed for the estimation of system Frequency and Phase. ANF
gives faster response by increasing the value of '' but this would result in overshoots
and undershoots. These overshoots and undershoots can be reduced by increasing
'' but this would increase the settling time. Whenever there is change in system
frequency the ANF will take some time to adapt to new frequency and the output
of CMLI will be disconnected during that time. So, we don't need to worry about
'' and hence the overshoots and undershoots. We increased '' to a certain limit
(0.95) to decrease the tracking time. After this limit the response of ANF becomes
unstable.12
4. Accomplishments and Future
Plan
Following table summarizes the accomplishments so far and future work plan for
the next semester.
Table 4.1 Accomplishments Summary
Objectives Deliverable Schedule Status
Literature Review Chapter 2 September-October 2016 Completed
Technique Explanation Chapter 3 October 2016 Completed
Simulations Simulation Results November-December 2016 Completed
Hardware Implementation Controller Hardware February-March 2017 Not Completed
Testing Practical Results April 2017 Not Completed
Thesis Writing Thesis 7th and 8th Semester Partially Complete13
Appendices14
A. Code for ANF Simulations
Following codes were used for the simulation of ANF in Matlab.
1 function dy = rigid(t,y,f)
2 3
clc
4 Zeta=sqrt(2)/2;
5 gamma=0.85;
6 dy = zeros(3,1);
7 if (t>3)
8 f=60.5;
9 end
10 if (t>5)
11 f=61;
12 end
13 if (t>7)
14 f=61.5;
15 end
16 dy(1) = y(2);
17 dy(2) = '2*Zeta*y(3)*y(2)'(y(3))''2*y(1)+2*Zeta*(y(3))''2* …
(1*sin(f*t));
18 dy(3) = ('gamma * y(1)) *( (y(3))''2 * 1*sin(f*t) ' (y(3)*y(2)));
Figure A.1 ANF Differential Equations15
1 y0 = [0 0 59.5];
2 tspan = [0 10];
3 f = 60;
4 options = odeset('RelTol',1e'4,'AbsTol',[1e'4 1e'4 …
1e'5],'OutputFcn',@odeplot);
5 [T,Y] = ode45(@(t,y) rigid(t,y,f),tspan,y0);
6 7
p = plot(T,Y(:,3),''');
8 hold on;
9 p.LineWidth = 1;
Figure A.2 Simulation code using ode4516
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