EE PhD Thesis Colloquium — Francis C Joseph — 28th July, 4 PM

Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures in Power System Applications

Speaker: FRANCIS C JOSEPH . of Ph.D. (Engg) in Electrical Engineering under Electrical Engineering

Date/Time: Jul 28 / 16:00:00

Location: Room 303, 2nd Floor, EE

Research Supervisor: Dr. Gurunath Gurrala

Abstract:
Computer hardware capabilities have been enormously increasing over the years. Multi-core processors, graphic processing units (GPUs), and field programmable gate array (FPGA) accelerators have seen significant growth in recent years. They have opened new computational paradigms such as edge computing, fog computing, grid computing, distributed computing, cloud computing, and exascale supercomputing. However, efficient utilization of most of these computational paradigms in traditional engineering disciplines such as power engineering is very challenging. In this thesis, we develop efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications, power system stability assessment and power quality measurements respectively.

Faster than real-time transient stability assessment of large power grids using time-domain simulations with detailed models is comp utationally challenging. Today the commercial tools being used for this application in Energy Management Systems (EMS) across the world rely on parallel batch processing methods which don’t utilize the architecture of the computational paradigms efficiently. In this thesis for transient stability simulations, we explore a time parallel algorithm, Parareal in Time, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches, Master Worker and Distributed, are analysed for large systems, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialization and subsequent correction steps. A potential coarse solver, Modified Euler (ME), a well-known solver for transient stability simulations even in commercial packages, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM), which gives an approximate solution to a set of non-linear equations in the form of a power series, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is found to be faster with comparable accuracy when compared to the conventional fixed and adaptive Modified Euler solver for large systems transient stability simulations. Since MHAM is lighter than the ME solver and LTE estimate is sufficient for step size adaptation, an adaptive MHAM coarse solver is proposed for the Parareal. However, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence, an optimisation framework is also proposed to select this parameter based on the system’s dynamics automatically. Based on many case studies on test systems of different sizes, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm, however for the distributed implementation maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal.

In Parareal implementation, each coarse time segment is assigned to one processor in the MPI environment. In order to improve speedup, multiple processors in a node is to be assigned to a coarse time segment. Therefore, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed in which both the space parallelisation and task parallelisation are explored. It is shown that the ideal speedup can be closely matched by the space parallelism and can be exceeded by space + task parallelism while the network is well-partitioned and it can be further improved when combined with time parallelism. The current state-of-the-art chips provide multicore architectures for edge computing applications also. One such low-cost, open-source, heterogeneous, resource-constrained hardware platform is called “Parallella”. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids which could sample 3 voltages and 4 currents at 32 kHz sampling rate. One application of such a device to measure the harmonics and compute various Power Quality (PQ) indices is explored in the thesis. In this regard, we have developed a parallel implementation of multichannel FFT on Epiphany for the streaming data. Epiphany 16-core architecture has very limited memory resources and the order in which the cores are to be accessed has a significant impact on the execution. Proper decomposition of the FFT algorithm tasks and scheduling of the tasks for efficient core and memory usage are crucial which requires a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are found to be comparable to commercial power analyser measurements.

Acknowledgements: This work is funded by the • SERB Science and Technology Award for Research (SERB-STAR) grant, File No: STR/2020/000019 titled Hybrid Parallel Solvers for Faster than Real-time Transient Stability Analysis of Large Power Grids. • Bosch Research and Technology Centre, Bangalore, India and by the Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bangalore, India (under Project E-Sense: Sensing and Analytics for Energy Aware Smart Campus) • DST Young Scientist Grant DST-YSS/2015/001371, India



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