Model-based design process for product development of substation IEDs

Yang, Xue‐Xia; Kirby, Brian; Zhao, Qiang; Ma, Yongyue; Xu, Fangfu

doi:10.1109/energycon.2012.6348290

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…As an example, the development of a controller for a low power singlephase transformerless inverter was presented. The development, verification, and implementation stages of automatic code generation to implement over/under-power protection functions and MATLAB/Simulink active power filter control blocks are given in [4] and [5], respectively. The active power filter allows the rapid on-line tuning of the filter's parameters, selection of control algorithms, and can be applied to generate code for microcontrollers.…”

Section: Modelling Input Test Signals and Defining Reference Responsementioning

confidence: 99%

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Rapid algorithm prototyping and implementation for power quality measurement

Kołek

Piątek

2015

EURASIP J. Adv. Signal Process.

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This article presents a Model-Based Design (MBD) approach to rapidly implement power quality (PQ) metering algorithms. Power supply quality is a very important aspect of modern power systems and will become even more important in future smart grids. In this case, maintaining the PQ parameters at the desired level will require efficient implementation methods of the metering algorithms. Currently, the development of new, advanced PQ metering algorithms requires new hardware with adequate computational capability and time intensive, cost-ineffective manual implementations. An alternative, considered here, is an MBD approach. The MBD approach focuses on the modelling and validation of the model by simulation, which is well-supported by a Computer-Aided Engineering (CAE) packages. This paper presents two algorithms utilized in modern PQ meters: a phase-locked loop based on an Enhanced Phase Locked Loop (EPLL), and the flicker measurement according to the IEC 61000-4-15 standard. The algorithms were chosen because of their complexity and non-trivial development. They were first modelled in the MATLAB/Simulink package, then tested and validated in a simulation environment. The models, in the form of Simulink diagrams, were next used to automatically generate C code. The code was compiled and executed in real-time on the Zynq Xilinx platform that combines a reconfigurable Field Programmable Gate Array (FPGA) with a dual-core processor. The MBD development of PQ algorithms, automatic code generation, and compilation form a rapid algorithm prototyping and implementation path for PQ measurements. The main advantage of this approach is the ability to focus on the design, validation, and testing stages while skipping over implementation issues. The code generation process renders production-ready code that can be easily used on the target hardware. This is especially important when standards for PQ measurement are in constant development, and the PQ issues in emerging smart grids will require tools for rapid development and implementation of such algorithms.

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Section: Modelling Input Test Signals and Defining Reference Responsementioning

confidence: 99%

“…and P x is the x-th percentile of the values of P inst logged during a specified time interval, where x is 0.1, 0.7, 1, 1.5, 2.2, 3,4,6,8,10,13,17,30,50,80. The interval can vary from 1 to 15 m, however, the EN 61000-4-30 standard assumes 10 m for a typical flicker severity evaluation.…”

Section: Flicker Implementationmentioning

confidence: 99%