How to Speak by Prof. Patrick Winston

I have watched the full talk, 1 hour and three minutes. At the end of the talk, I'd like to solute to Prof. Winston. This is a MUST watch! It will make one a better writer, a much better speaker to persuade people, and help improve the skill of job hunting or getting recognized of one's ideas. 

There are many memorable notes. Here are a few things: 

1. How to start a talk? Give promises. Tell your audience or students what they can learn. For research talks, show problems by an example. For the case of resource allocation problem, the professor shows how to color a US map with neighboring states in different color. Normal algorithms may never end. On the other hand, a good optimization problem runs in a few seconds. This shows a stark contrast and demonstrates great benefits of an algorithm.  

2. How to give a successful job talk as faculty candidate? In the first 5 minutes, one needs to give a vision and demonstrate that he/she has worked on the research. The second part requires one to show steps of how to solve the problem even he/she has not worked out all the steps.

3. How to design slides? Be concise; use big fonts. 

4. How to get recognized for one's idea? Five elements: have symbols, have a slogan, give surprise, have a salient idea, and tell stories of the discovery. 

5. How to end slides? Use Conclusion page to show your unique contributions. 

6. How to end a talk? Do not say thank you. Rather salute the audience with somethings different. "It's been fun to be at MIT. I look forward to coming back again". Prof. Winston demonstrated two politicians good at speech on how they ended a talk: Governor Christie and Bill Clinton. In both cases, the speakers salute to the audience. They were fun to watch. 

Spring 2017 conference papers for NAPS submission

Often times, Ph.D. students with psychological burden on paper writing would request that they be excused from paper submission to a conference. The excuse is that time can be saved for more in-depth study and for the final journal paper writing. The responsible and correct answer is, as always, no.  Any Ph.D. student survival guide would show that paper writing is an essential skill and there is no reason to trust there will be a journal paper before a conference paper. Usually, there are three stages to have a solid paper out. Stage 1: notes; Stage 2: conference paper; and Stage 3: journal paper. 

The spring 2017 semester sees a total of 11 conference papers. Coverage of the papers includes 1) dynamics and control; 2) optimization; and 3) system identification.

On the topic of dynamics, the following papers address converter droop control, synchronous machine modeling, converter cascaded control, and interconnected power system dynamics.

1.  I. Alsaleh and L. Fan, "DQ Current-based Droop Control for Microgrids."

2. Z. Wang and L. Fan, "Space Vector based Synchronous Machine Modeling."

3. Y. Zhou, Z. Miao, Y. Li and L. Fan, " Robust Cascaded Control for Converters. "

4. L. Bao, Z. Miao and L. Fan, "Smart Grid Cyber Attacks to Excite Interarea Oscillations."

On the topic of optimization, the following papers address PMU placement formulation as MIP and nonlinear programming, SDP relaxation AC OPF’s two applications, Using Benders’ decomposition to solve a model predictive control for multi-level modular converters, AC OPF-based financial transmission right auction model while considering loss allocation, and distributed computing using alternating direction method of multipliers (ADMM) for nonconvex AC OPF.

1. A. Almunif and L. Fan, "Optimal PMU Placement for State Estimation in Power Systems."

2. A. Alassaf and L. Fan, "Economic Dispatch with Heavy Loading and Maximum Loading Identification using Convex Relaxation of AC OPF."

3. M. Ma and L. Fan, "Bender's Decomposition for Model Predictive Control of an MMC."

4. A. Alburidy and L. Fan, " Loss Allocation in AC OPF-based FTR Auction Models."

5. R. Kar, M. Zhang, Z. Miao, and L. Fan, "ADMM for nonconvex AC OPF."

On the topic of system identification, the following two papers address how to estimate a synchronous generator’s parameters using PMU data and how to estimation a battery’s SoC and circuit parameters using measurements.

1. Y. Xu, Y. Li, and Z. Miao, "Nonlinear Least-Square Estimation-based Parameter Identification of a Synchronous Generator," submitted, NAPS 2017.

2. M. Zhang, Z. Miao and L. Fan, "Battery Identification based on Real-World Data," submitted, NAPS 2017.

All students are commented for their ability of project management, hardwork, and team spirit. 

Preface of "Control and Dynamics in Power Systems and Microgrids" by L. Fan

The main part of the book was written in Spring 2016 when the author taught Power Systems II, a
graduate course at University of South Florida, Tampa Florida. The course was designed to focus on
control and dynamics of power systems. Bergen and Vittal's book, Power System Analysis, was
adopted as the textbook. Dynamics and control, especially in the area of power systems applications
where rotating magnetic fields are involved, is a formidable subject to students. Hence, a set of
class notes was developed in that semester to offer a tutorial approach of learning. Many examples
and codes were developed to facilitate understanding and hands-on training. A highlight of this
textbook is its many tutorial examples.

The first version of Bergen and Vittal's book was written in 1981 by Professor Bergen. Thirty-six
years have passed since then. This classic textbook has been highly recognized and helped to
educate a generation of power system engineers. Professor Bergen passed away in July 2014. As a
power systems engineer, this author would like to contribute to the field by reinterpreting the
classics of power system control and dynamics. This textbook is also a tribute to Professor Bergen.

There will be several things different from the classic textbook.

 The generator model derivation is very sophisticated in Bergen's book. In  Bergen's book, Park's transformation was employed to derive generator models. Park's transformation was conducted using matrix in real domain. Handling matrix is a sophisticated task. The alternative of Park's transformation is space vector and complex vector transformation, a concept used much more often in machines and power electronics after the 1980s. Space vector concept makes Park's transformation straightforward. In this textbook, the author will explain synchronous generator dynamics, the most formidable dynamics in power systems, using space vector concept. Readers will find that modeling dynamics in space-vector and further in the rotor reference frame an interesting and easy task. The second highlight of generator modeling part is torque derivation. Classic textbooks all use coenergy's derivation against rotor angle. In this book, the author started from physics taught in high school and evolved to the expressions in rotating machines.

 In the 1980s, power electronics and microgrids were yet to be developed. This field is well developed in the 21st century. Many techniques used in power systems for power sharing, e.g., droop control, can also be found in power electronic converter coordination. The part is now related and put into the textbook to help readers understand converter control and coordination. This is another highlight of the textbook.

Advanced control such as networked control (consensus control) were developed after 2000. Many classic engineering implementations follow advanced control framework. It is appealing to find them and interpret the intuitive engineering design with concepts and ideas from networked control. In this textbook, inter-area oscillations are explained using consensus control.

Professor Bergen's book has steady-state analysis and dynamics all together. This textbook focuses
on dynamics and control only. This author would also like to have a better flow to focus on power
system control. Starting from the beginning, ordinary differential equation, the building block of
dynamics and control, is explained using examples. Dynamic simulation and linear system analysis
are conducted for the examples. With the fundamental concept on dynamics built, readers can then
pursue the learning tasks related to power system control and dynamic stability with ease. Readers, if not familiar with coding, learn coding for dynamic simulation.

The flow of the text is to treat frequency or voltage control as control problems. For control  problems, first we discuss the plant model and its related steady-state and dynamic responses. The plant model should be identified with inputs and outputs specified. In the frequency control case, it is obvious that the output of the plant model should be frequency. The inputs are from a generator's mechanical system inputs. After setting up the plant model, we then think about how to design feedback controls to realize control objectives. After control design is conducted, we then employ dynamic simulation to verify controller performance.

The author is grateful to have the opportunity to write and publish this books. The author would like to acknowledge University of South Florida Electrical Engineering Department for providing a great environment to conduct research and teaching. The author wishes to acknowledge her family for countless support.

The book was developed from the author's class notes of Power Systems II for Spring 2016. Minyue
Ma, a Ph.D. student, was the teaching assistant for that course and helped work out examples and
homework problems for the class. A few students in the class, e.g., Abdullah Alassaf, highly
complimented the class notes, which encouraged the author to contact the CRC press for publication.
Yin Li, a Ph.D. student, built MATLAB/Simulink models in Chapter 6 Frequency and voltage control in microgrids. Yangkun Xu, another Ph.D. student drew many for Chapter 3 and Chapter 5. Graduate
students at the USF Power Systems Smart Grid Lab reviewed the book during the holiday season in
December 2016. The author wishes to acknowledge Minyue Ma and Yin Li as reviewers. The author also wishes to acknowledge Yi Yang from Eton Cooperation as a reviewer.

How to start research for a newbie?

New students often time asked me this question: How do I start research.

There are two critical elements that lead to the success. The first element is the ability to use tools. The second element is the ability of synergy or connecting dots.

The first element is easy to be improved as long as there is a good curriculum setup for students and as long as you are following a great instructor. If the subject area is optimization related, for every example discussed in the class, examine it and be able to extend this example to a different system or a large-scale system. This exercise trains a student to be a good programmer.

The second element requires many years of training. If you are lucky to follow a great researcher, this process can be made shorter. Synergy requires experience, good judgement, a big picture of the research areas, the ability of appreciating others' work, and the ability of capturing subtle details. Such a capability usually is not there for Ph.D. students yet. Thus, whether you are following a great researcher or not directly impact a student's outcome. We see many times, students under the guide of a greater researcher becomes a great researcher. On the other hand, many bright students end up with nothing significant after many years of study.

A colleague summarizes it well:  To have a successful career, just make sure these two things: make a correct choice of your advisor; be humble and follow his/her words.

On Programming

MATLAB is usually used for Power Systems Analysis teaching. If there is something to do with control system analysis, MATLAB is excellent. Other times, if we focus only on computing, then other tools might be better. One significant advantage of Python over MATLAB is that Python is free. Secondly, MATLAB occupies huge memory and starting it up takes time. On the other hand, unless you use a module, that module won't be loaded. A Python program is much easier to load and run. The third reason is related to job market. Many employers would ask students: Do you know Python? Familiarity with Python is an advantage.

Starting from Fall 2016, Python will be adopted in class teaching. In fact, Python was introduced in Fall 2015 already though not until we started to do optimization, Python was used. This time, Python will be used for every demonstration.

Programming is almost nature. There are only 3 to 4 things to be concerned:
1. how do we store data
2. How to do repeated work.
3. How to make visual display.
4. How to make things modular

1. In MATLAB, vectors and matrices are stored by one or two-dimensional array. In Python, a little bit complicated. We can still use just one line to define a vector. For example:

tt = [i*0.0001 for i in range(0, 1000)]
This is to define a vector of 1000 elements starting from 0, to 0.1. Further, we can create a null array, and during iterative process, append the array with something. Python uses .append.

2. Python has For loop.


3. Load pylab module, we can plot figures similar as Matlab's plot.
4. Python's functions are under def.

With that a Python program is ready.

Use Python to generate figures

From Stackflow:

If you are using matplotlib and trying to get good figures in a latex document, save as an eps. Specifically, try something like this after running the commands to plot the image:

plt.savefig('destination_path.eps', format='eps', dpi=1000)

I have found that eps files work best and the dpi parameter is what really makes them look good in a document.

UPDATE:

To specify the orientation of the figure before saving simply call the following before the plt.savefig call, but after creating the plot (assuming you have plotted using an axes with the name ax):

ax.view_init(elev=elevation_angle, azim=azimuthal_angle)

Where elevation_angle is a number (in degrees) specifying the polar angle (down from vertical z axis) and the azimuthal_angle specifies the azimuthal angle (around the z axis).

I find that it is easiest to determine these values by first plotting the image and then rotating it and watching the current values of the angles appear towards the bottom of the window just below the actual plot. Keep in mind that the x, y, z, positions appear by default, but are replaced with the two angles when you start to click+drag+rotate the image.

=================================

Just to add my results, also using matplotlib.

.eps made all my text bold and removed transparency. .svg gave me high-res pictures that actually looked like my graph. 

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Do the plot code
fig.savefig('myimage.svg', format='svg', dpi=1200)

I used 1200 dpi because a lot of scientific journals require images in 1200 / 600 / 300 dpi depending on what the image is of. Convert to desired dpi and format in GiMP or Inkscape. 

EDIT: Obviously the dpi doesn't matter since .svg are vector graphics and have "infinite resolution".

Curriculum setup for Ph.D. study

Number of Ph.D. graduates indicates a group's research capability. Nurturing process is time consuming. With a systematic curriculum setup, we expect to see an improved and effective outcome if not mass production of Ph.D. graduates.

People usually have the wrong impression that research has nothing to do with courses. Wrong. It has everything to do with courses. Top schools such as MIT, Stanford or Caltech have four-year engineering curricula and the BS students can smoothly transfer into researchers. One MIT circuit course is equivalent to four courses in a tier-2 university. A Caltech undergraduate course in a quarter semester can lift students to the level of semi-experts. In ten weeks twenty lectures and ten intensive homework sets, anybody with a goal of learning and disciplines can be lifted to a level that their peers in tier-2 universities will be awed.

That said, the key for effective production of Ph.D. students is curriculum design. Which set of courses should be taken and which books should be read. Without experience, an advisor may have difficulty to answer. Novice advisors feed students with papers and have weekly meetings. Experienced ones spend much less time on meetings and yet receive much better outcomes.

Design a curriculum for power engineering Ph.D. study is challenging. Power engineering itself requires a breadth and the integration of three subareas: power systems, machines and power electronics. In the previous generation, many educators and researchers, are experts in one of the three subareas. On top of power engineering fundamentals, the advanced study has to include math:  controls, operations research, etc.

Thanks to the public sharing of top schools, we now can see the contents and videos of Stanford, MIT or Caltech's theoretic courses. With such resources, all other universities and application areas can catch up, to a level that the previous generation cannot imagine.

A big picture on power engineering research, education and industry

A talk given to IEEE PES/IAS USF student chapter by Prof. Lingling Fan, Associate Professor, Smart Grid Power System Lab, EE, USF (http://power.eng.usf.edu)

The traditional power industry, or the utility industry has a socialism flavor. Let’s just say, the job is stable. Here are some quotes from a recent New Yorker article (Power to People, Why the rise of green energy makes utility companies nervous, by Bill McKibben, June 29, 3015)

“’If you’re in a business where the customer is the public-utility commission, and after that your profits are locked in by law, it’s the sleepiest business sector there is, if you could even call it a business sector. They build power plants, sit back, and the money comes in.’ The entire realm is protected, he added, by `a huge force field of boringness.’”

Education can be timeless. In fact, a professor can use his/her notes developed 30 years ago for Power System courses. All the fundamentals are timeless, be it KCL/KVL or Faraday’s Law.

Not anymore. Today, the power industry is full of exciting R&D opportunities. Research and education follow up the change. 

The big motivating force is environmental concerns and fossil fuel deficiency. Global warming and climate change result in inhabitable environment which can further leads to hunger, civil wars, and genocides. A 2008 article from The New York Times (Rwanda as an Example of the Damagers of Climate Change) indicates that global warming is the key cause of genocide in Rwanda. 

Fossil fuel consumption is a main contributing factor to global warming. Curtailing fossil fuel is a challenge posed to the power industry. Development and use of renewable energy is the driving force of the emerging new technologies in the power area. 

While wind generators employ the classic rotating electric machine mechanism to convert mechanical energy to electric energy, solar PV and energy storage rely on first of all the breakthrough in material science. 

The next stage of applications is grid integration of those energy sources. That is the job of power engineers. The two important new technologies happening in the power area are hardware (Power Electronics) and software (Information Technology). 

Even back in 1980s, there were wind farms built in Nevada and California responding to the rising oil price. But today’s wind turbines have 15 times of capacity (eg., 1.5 MW per turbine manufactured by GE doubly fed induction generator wind turbine ) compared to 1980s’ turbine (100 kW). The technology behind is Power Electronics. Voltage source converter is able to handle high power which makes the use of variable speed constant frequency wind turbine possible. By comparison, the 1980s’ turbines were not equipped with voltage source converters and cannot capture wind energy in a wide range of wind speeds. 

The advance of Power Electronics Technology relies on material science and electrical engineering. Recent advance include wide-band semiconductors, which can enhance the efficiency of power electronic switches. For example, CREE at North Carolina is a manufacturer. Then at the application level, how to assemble the switches to make a circuit for electricity conversion (dc/dc, dc/ac, ac/dc), this is the job of Power Electronics engineers. Further, how to make the assembled systems (converters, for example) to work as designed, this is the job of various electrical engineers. Low power applications such as computer power supply are very different from the high power applications (motor drive, High-voltage direct current system) in terms objectives and system requirements. In fact, the IEEE Power Electronics community has two separate conferences for low power and high power applications. APEC is for low power applications while ECCE is for high power applications. 

The second important technology happening in Power Area is, in my opinion, information technology. Many new applications such as PMU technology, demand response, smart building, vehicle to grid, power market and power system operation deal with large-scale system computing and operations research. In the power area, many jobs are related to software or computing. For example, a couple of DOE labs are working on projects related to V2G, smart building, etc. These labs hire applied mathematicians and control people. 

Aiming to provide competitive work force for the current job market, at USF, two graduate study tracks are offered with rigorous courses. The first track is Power Electronics focused Renewable Energy Integration, and the second track is computing focused Power System Operation. The course settings are listed as follows.

—  Renewable Energy Integration

◦       Power Electronics

◦       Energy Delivery Systems

◦       AC Machines and Drives

◦       Related control courses

—  Power System Operation

◦       Power System Analysis

◦       Power Systems II

◦       Power Market

◦       Related operations research courses (eg., LP, MIP)

We have many student success stories to share with the audience. However, due to time limit, please check our websites for more details. Thank you for your attention.

Introduction to the recently submitted NAPS papers

The set of the NAPS papers recently submitted showcases the breadth and depth of SPS's research and education. Ten research papers covers the following three research areas.

1. Optimization
L Fan, "Tutorial on Cutting Plane Methods for Economic Dispatch Problems."
M. Ma and L Fan, "Implementing Consensus Based Distributed Control in Power System Toolbox."  Z. Miao, L. Lakshan and L. Fan, "Capacitor Siting Using Benders Decomposition."
Z. Miao and L. Fan, "Power System Optimization Considering Stability Limits."

The set of the optimization papers demonstrates our capability in not only formulating optimization problems and shipping them to commercial solvers, but also our capability in developing customized algorithms for optimization. One example is the understanding of cutting plane methods and the implementation of Benders decomposition in handling nonlinear mixed integer programming problems.

2. System identification
Y. Xu, Z. Miao and Y. Li, "Identification of Synchronous Generator Parameters Using Continuous ARX Model and Least Square Estimation."
Y. Wehbe and L. Fan , "PMU-Based System Identification for a Modified Classic Generator Model."

Both papers have novel contributions to the area of measurement based system identification. The first adopts continuous ARX model for estimation while the latter one adopts subspace concept to analyze parameter sensitivity. 

3. Power Electronics and Microgrids
A. Tazay and Z. Miao, "Common Mode Voltage Reduction Schemes for Voltage Source Converters in an Autonomous Microgrid."
L. Xu, Z. Miao, L. Fan and G. Gurlaskie, "Unbalance and Harmonic Mitigation Using Battery Inverters."
Y. Li and L. Fan , "Modular Multilevel Converter Based Induction Machine Drive."
Y. Ma and L. Fan, "Circulating Current and DC Current Ripple Control in MMC Under Unbalanced Grid Voltage."

Needless to say, this is the traditional strong area of SPS. We continue to work on the most sophisticated converters (MMC converters, the last two papers) and look into variety of issues of microgrid operation, including harmonics handling and common mode voltage reduction. Both control methods and switching methods were investigated in the first two papers.  

Course projects
Two papers were produced as course projects from USF EE course Power System II.
A.H. Akca, L. Fan, "Modeling Free Acceleration of a Salient Synchronous Machine Using Two-Axis Theory."
Y. Li and L. Fan, "Determine Power Transfer Limits of an SMIB System Through Linear System Analysis with Nonlinear Simulation Validation."

Both papers extend the materials in Bergen's book Chapter 7 synchronous generator modeling. The first paper presents the modeling and simulation technique to show a synchronous generator works as an induction motor and be accelerated. Physical experiment experience is necessary to make the simulation work since there are a few tricks to play with the parameters to make the speed rise to full speed.
The second paper extends the linear analysis with nonlinear simulation. The second paper clearly shows for each model each assumption, what is happening in terms of system stability and power transfer level.

Lab projects
H.G. Aghamolki, Z Miao, L Fan, "A Hardware-in-the-Loop SCADA Testbed."
H.G. Aghamolki, Z. Miao, "Real-time Digital Simulator Enabled HIL Electric Machine Drive Lab."

The teaching lab is a great platform to get students familiar with the state-of-the-art smart grid technology and machine drive lab. Instead of presenting students as the traditional nonsexy power lab with heavy equipment and big machines, the two papers are showing the world that power systems research and teaching are IT heavy computing oriented involving software programming, hardware interface and communication. The teaching lab is one of the selling points of USF SPS.

MOOC course: Pascal Van Hentenryck's discrete optimization

So far, this is the best course on Coursera. Two reasons. The course itself is a tough one. It covers both linear programming and integer programming. Material wise, this is heavy stuff. The second reason, the format and presentation of the course: fantastic. Hentenryck is a dynamic speaker with insights to help learners understand. He is a great teacher. A quick search shows that Pascal Hentenryck had been with Brown University Computer Science for 20 years and then he left for University of Melbourne. An excellent researcher, programmer, group leader and entrepreneur, his website is also very neat.

Polyhedrons, cutting planes, etc., these concepts are very difficult to understand. But here, with proper 3D animation and enough visual aids, everything is explained with clarity.

It is fair to say, the quality of this course is among the best of online courses. If we count from MIT's open course to today, visual and audio quality of online courses have been greatly improved. This era is for motivated learners and researchers to keep up with new ideas and technologies. Internet is great.

How to build a strong education/research program

It should be a common sense of the education/research community that great research leads to great teaching and rigorous teaching leads to in-depth research. Do we need examples? Just check MIT Stanford courses. This lab is another example: http://power.eng.usf.edu.

Through the years, a curriculum of power and energy engineering has been setup and taught with rigorous scholarship. A state-of-the-art teaching lab was setup with sponsorship from Duke Energy, strong support from the department and countless effort from the Ph.D. students from this lab serving as TAs. Classic textbooks were chosen for the courses. http://pixotale.com/story/46644215/.

The curriculum is kept expanding to cover emerging topics in this field. We now have three focus areas: power electronics enabled renewable energy grid integration; decision making in the era of smart grid; and power system estimation . As a result of the integrated research and teaching, the program has multiple tracks of courses which are listed as follows. In this blog, two tracks are mentioned.

Track 1: Power electronics and related applications, one in electric drive and one in renewable energy grid integration systems. With an undergraduate course on Electric Machinery, three courses will get students into the professional level:

• Power Electronics, 
• AC Machine and Drive: induction machine control will be realized by power electronic converters.
• Energy Delivery Systems: converter control to fulfill grid integration need as well as renewable energy maximum efficiency need.

Understanding of machines will be enhanced in another graduate level course: Power Systems II where Bergen's book is used as the text. The first module of the course on synchronous machine modeling gives students an in-depth understanding of rotating magnetic field as well as applying dynamic modeling skills in machine modeling.

In addition, a monograph related to this track authored by the two faculty members of USF SPS group will be published  in May 2015 by Elsevier.

Track 2:  Decision making in power systems. Certainly this track requires many control and optimization courses as well as power system analysis courses. Below is the list of the power courses.

• Power system analysis: load flow, state estimation, economic dispatch, optimal power flow are explained
• Power systems II: system voltage/frequency control will be explained.
• Power market: connects power system economic dispatch/optimal power flow and unit commitment problems, optimization's Lagrangian multipliers, duality gap with power market's concepts and schemes such as locational marginal price, convex hull price, uplift and etc.

In addition to the power courses, optimization courses as well as control courses are necessities. In Spring 2013, USF SPS collaborated with IMSE's Mocha group and developed a course on optimization with applications in smart grid. In Spring 2015, the power market course was launched.

回忆陈珩教授

距陈珩教授辞世一晃已快十四年，音容笑貌仍历历在目。当年刚上大学时，陈珩教授大名如雷贯耳。及上研究生，看到其著作《同步电机运行基本理论与计算机算法》扉页，赞叹不已。 写得是文言文，“十余年前，先师吴大榕教授曾有撰写本书之议，并嘱予附骥。不料天不假以年，哲人早逝 … 珩本不才，难以当此重任，加以杂事羁身，碌碌终日，以致一暴十寒，。。。缅怀往事，能不扼腕 …”

天不假以年，哲人早逝。陈珩教授悉心泽被后学，教诲深远，受益终身。 

陈珩教授教学严谨，享誉海内外，经常能请到国内外学者到东大演讲，印象中有倪以信，江晓东，又有来自英国德国的诸教授。 对于刚进研究领域，脑子一片空白的学生来说，这样的交流一下子开了眼界，起码知道世界是怎么一回事。 整个研究组学术气氛非常浓厚。

此外，教研室逢过节开party, 格外热闹，外国留学生都参加。陈老师常常是英文祝词。有时还来个英文歌,翠堤春晓的主题歌，when we were young. 唱得满面笑容。

90年代末，电脑刚普及，因特网刚到大学校园，陈老师基本就是IT 迷，自学成才，玩电脑，上网，比谁都先进。若他现在在世，博客微博微信，说不准能写无数随笔。陈老师名声在外，想申请国外学校念博士的学生，本系的外系的，都来要推荐信。陈老师可以说是来者不据，不厌其烦。当年我整了个自我陈述，请陈老师过目提提意见。希望陈老师看一眼划几处给点意见。陈老师说，好，我带回家看。没有想到的是过一天，陈老师把打印好的几页纸交给我：他不仅读了，改了，还从头到尾打字打了一遍并激光打印出来。改过的文稿自然是非常的好。英文极为妥贴。

陈老师属于非常有文化的那辈，保持着非常多的文化人礼节，为人处事从容不迫。任何一个伊妹儿都会得到他的回信，而且非常工整，抬头，结尾，无比工整。去年开始看钱钟书余英时，才知道家学渊源的学者们都是这样的做派。 一开始一个刚进社会的人理解不深，基本took it for granted. 有时碰见的同事或上级回信没有规律，也以为这是常态。后到大公司里工作，专门有EMAIL courtesy 的培训，说有信必得回，一定要客客气气。这才回想到陈老师的做派，真是非常周到非常讲究文化传统。

纽约客的作家何伟(Peter Hessler)说中国最让人感动的地方是整个社会对老师对知识的尊重，再穷再苦的地方你能看到小朋友们认真地学习。余英时说中国人称老师为恩师因为其影响是全方位的。陈老师的影响不仅在于专业精深，更在于提携后学，为人勘表典范。现在想来能做到他那样耐心从容不迫太不容易。以此勉励自己。

谨以此文纪念陈珩教授。

Latex: Evaluate a partial derivative at a certain condition

Here is an example to do that:

\left. \frac{\partial f}{\partial x}\right|_{x_0, u_0} \Delta x +  \left. \frac{\partial f}{\partial u}\right|_{x_0, u_0} \Delta u

The resulting equation looks like the following.

Journal paper: Dynamic Phasor-Based Modeling of Unbalanced Radial Distribution Systems

Authors: Z. Miao, L. Piyasinghe, J. Khazaei, and L. Fan
Accepted by IEEE trans. Power Systems.

The full paper can be accessed at http://power.eng.usf.edu/docs/papers/DP-Paper_final.pdf

Abstract: This paper develops an analytical model of an unbalanced radial distribution system consisting of a single-phase PV, a three-phase induction machine load, a three-phase power factor correction capacitor (PFC) and a load. The analytical model is based on dynamic phasors (DP) for abc phases. The single-phase PV model includes inverter current control (Proportional Resonance (PR) controller), an L or an LCL filter. The induction machine model is based on positive-, negative-,
and zero-sequence components’ dynamic phasors. The sequencebased induction machine model was converted to the DP-abc reference frame and interconnected with other grid components.
The developed analytical model is capable of small-signal analysis and can be used to identify variety of stability and/or harmonic issues in distribution networks, e.g., instability due to weak grid. Impact of unbalance on system dynamic performance can also be investigated using this model. The analytical model is benchmarked with a high-fidelity model built in Matlab/SimPowerSystems
where power electronic switching details are included. The small-signal analysis results are validated via Matlab/SimPowerSystems time-domain simulations.