scholarly journals A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1523
Author(s):  
Cornelis Jan Kikkert
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Block Diagram ◽  
Rate Of Change ◽  
Measurement Unit ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Phasor Measurement ◽  
Ieee Standard

Phasor measurement units (PMU) are increasingly used in electrical power transmission networks, to maintain stability and protect the network. PMUs accurately measure voltage, phase, frequency, and rate of change of frequency (ROCOF). For reliability, it is desirable to implement a PMU using an FPGA. This paper describes a novel algorithm, suited to implementation in an FPGA and based on a simple PMU block diagram. A description of its realization using low hardware complexity infinite impulse response (IIR) filters is given. The IEC/IEEE standard 60255-118-1:2018 Part 118-1: Synchrophasor measurements for power systems, describes “reference” Finite Impulse Response (FIR) filters for implementing PMU hardware. At the 10 kHz sampling frequency used for our implementation, each “reference” FIR filter requires 100 multipliers, while an 8th order IIR filter only requires 12 multipliers. This paper compares the performance of different order IIR filter-based PMUs with the performance of the same PMU algorithm using the IEC/IEEE FIR reference filter. The IIR-based PMU easily satisfies all the requirements of IEC/IEEE standard and has a much better out of band signal rejection performance than a FIR-based PMU. Steady state errors for a rated voltage ± 10% and a rated frequency ± 5 Hz are < 0.000001% for total vector error (TVE) and < 1 µHz for frequency, with a latency of two mains cycles.

Design and Applications of Digital Filters

2011 ◽  
pp. 1016-1023
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
High Reliability ◽  
Rapid Development ◽  
Digital Signal ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Circuit Fabrication ◽  
Analog Systems

Digital signal processing (DSP) is an area of engineering that “has seen explosive growth during the past three decades” (Mitra, 2005). Its rapid development is a result of significant advances in digital computer technology and integrated circuit fabrication (Jovanovic Dolecek, 2002; Smith, 2002). Diniz, da Silva, and Netto (2002) state that “the main advantages of digital systems relative to analog systems are high reliability, suitability for modifying the system’s characteristics, and low cost”. The main DSP operation is digital signal filtering, that is, the change of the characteristics of an input digital signal into an output digital signal with more desirable properties. The systems that perform this task are called digital filters. The applications of digital filters include the removal of the noise or interference, passing of certain frequency components and rejection of others, shaping of the signal spectrum, and so forth (Ifeachor & Jervis, 2001; Lyons, 2004; White, 2000). Digital filters are divided into finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR digital filters are often preferred over IIR filters because of their attractive properties, such as linear phase, stability, and the absence of the limit cycle (Diniz, da Silva & Netto, 2002; Mitra, 2005). The main disadvantage of FIR filters is that they involve a higher degree of computational complexity compared to IIR filters with equivalent magnitude response (Mitra, 2005; Stein, 2000).

FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Aladin Kapić ◽  
Rijad Sarić ◽  
Slobodan Lubura ◽  
Dejan Jokić
Keyword(s):  
Transfer Function ◽  
Impulse Response ◽  
Discrete Time ◽  
Time Domain ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Role ◽  
Digital Implementation ◽  
Iir Filter

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

scholarly journals Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1468
Author(s):  
Xiang An ◽  
George K. Stylios
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Median Filter ◽  
Infinite Impulse Response ◽  
Ecg Signal ◽  
Motion Artefact ◽  
Iir Filter ◽  
Adaptive Motion ◽  
Artefact Reduction

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

scholarly journals Transformations for FIR and IIR Filters’ Design

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 533
Author(s):  
V. N. Stavrou ◽  
I. G. Tsoulos ◽  
Nikos E. Mastorakis
Keyword(s):  
Impulse Response ◽  
Transfer Functions ◽  
Finite Impulse Response ◽  
Transformation Method ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Two Dimensional ◽  
Bilinear Transformation ◽  
One Dimensional ◽  
Iir Filters

In this paper, the transfer functions related to one-dimensional (1-D) and two-dimensional (2-D) filters have been theoretically and numerically investigated. The finite impulse response (FIR), as well as the infinite impulse response (IIR) are the main 2-D filters which have been investigated. More specifically, methods like the Windows method, the bilinear transformation method, the design of 2-D filters from appropriate 1-D functions and the design of 2-D filters using optimization techniques have been presented.

scholarly journals Design of One-Dimensional Linear Phase Digital IIR Filters Using Orthogonal Polynomials

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Orthogonal Polynomials ◽  
Impulse Response ◽  
Phase 1 ◽  
Infinite Impulse Response ◽  
Linear Phase ◽  
One Dimensional ◽  
Iir Filters ◽  
Iir Filter ◽  
Amplitude Characteristics ◽  
Zeros And Poles

In the present paper, we discuss a method to design a linear phase 1-dimensional Infinite Impulse Response (IIR) filter using orthogonal polynomials. The filter is designed using a set of object functions. These object functions are realized using a set of orthogonal polynomials. The method includes placement of zeros and poles in such a way that the amplitude characteristics are not changed while we change the phase characteristics of the resulting IIR filter.

An Adaptive Feedback Noise Control Approach Using Q-Parameterization

2002 ◽  
Author(s):  
Mark A. McEver ◽  
Daniel G. Cole ◽  
Robert L. Clark
Keyword(s):  
Impulse Response ◽  
Gradient Descent ◽  
Noise Control ◽  
Finite Impulse Response ◽  
Open Loop ◽  
Infinite Impulse Response ◽  
Feedback Signal ◽  
Control Approach ◽  
Iir Filters ◽  
On Line

An algorithm is presented which uses adaptive Q-parameterized compensators for control of sound. All stabilizing feedback compensators can be described in terms of plant coprime factors and a free parameter, Q, which can be any stable function. By generating a feedback signal containing only disturbance information, the parameterized compensator allows Q to be designed in an open-loop fashion. The problem of designing Q to yield desired noise reduction is formulated as an on-line gradient descent-based adaptation process. Coefficient update equations are derived for different forms of Q, including digital finite impulse response (FIR) and lattice infinite impulse response (IIR) filters. Simulations predict good performance for both tonal and broadband disturbances, and a duct feedback noise control experiment results in a 37 dB tonal reduction.

Research of Sea Clutter Suppression Algorithm Based on GPU

2012 ◽  
Vol 239-240 ◽  
pp. 1194-1201
Author(s):  
Yan Guo ◽  
Shi Dan Li ◽  
De Sheng Wang
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Graphics Processing Unit ◽  
Main Idea ◽  
Fir Filter ◽  
Infinite Impulse Response ◽  
Processing Unit ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Iir Filter

This paper presents an algorithm of sea clutter suppression using graphics processing unit (GPU) to meet the real-time requirement in the general radar terminal system. The main idea is to convert an infinite impulse response (IIR) filter to a finite impulse response (FIR) filter, which is suitable for the parallelization processing of GPU. Finally, the converted FIR filter algorithm is implemented on the GPU efficiently, achieving a speed approximately twice as fast as that of the previous IIR filter algorithm implemented on the CPU.

GOCE SGG filtering with FIR, IIR and wavelet MRA  

2021 ◽  
Author(s):  
Eleftherios A. Pitenis ◽  
Elisavet G. Mamagiannou ◽  
Dimitrios A. Natsiopoulos ◽  
Georgios S. Vergos ◽  
Ilias N. Tziavos
Keyword(s):  
Gravity Field ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Gravity Gradient ◽  
Satellite Gravity ◽  
Iir Filters ◽  
Field Spectrum ◽  
Lower Accuracy ◽  
Goce Satellite

&lt;p&gt;GOCE Satellite Gravity Gradiometry (SGG) data have been widely used in gravity field research in order to provide improved representations of the gravity field spectrum either in the form of Global Geopotential Models (GGMs) or grids at satellite altitude. One of the key points in utilizing SGG observations is their proper filtering, in order to remove noise and long-wavelength correlated error, while the signals in the GOCE measurement bandwidth (MBW) should be preserved. Due to the gradiometer&amp;#8217;s design, the GOCE satellite can achieve high accuracy and stable measurements in the MBW of 0.005 Hz to 0.1 Hz. The gravity gradient&amp;#160;in MBW are at an equivalent accuracy level, while &amp;#160; are of lower accuracy. Outside of the MBW, systematic errors, colored noise, and noise with sharp peaks are observed, especially in the frequencies lower than 0.005 Hz. With that in mind, the present work focuses on the investigation of various filtering options ranging from Finite Impulse Response (FIR) filters, Infinite Impulse Response (IIR) filters, and filtering based on Wavelets. The latter are employed given their inherent characteristic of being localized both in frequency and space, meaning that the signal can be decomposed at different levels, thus allowing multi-resolution approximation (MRA). The analysis is performed with one month of GOCE SGG data in order to conclude on the method that provides the overall best results. SGG observations are reduced to a GGM in order to account for the long- and medium-wavelengths of the gravity field spectrum. Then, various filter orders are investigated for the FIR and IIR filters, while selective reconstruction is employed for the WL-MRA. Evaluation of the results is performed in terms of the smoothness of the filtered fields and the Power Spectral Density (PSD) functions of the entire GOCE tensor.&lt;/p&gt;

Vibration Suppression Based on Adaptive Feedforward Control With Infinite Impulse Response Filter

2015 ◽  
Author(s):  
Shiying Zhou ◽  
Masayoshi Tomizuka
Keyword(s):  
Impulse Response ◽  
Vibration Suppression ◽  
Finite Impulse Response ◽  
Feedforward Control ◽  
Vibration Signal ◽  
Infinite Impulse Response ◽  
Least Mean Square ◽  
Parameter Adaptation ◽  
Iir Filter ◽  
Adaptive Feedforward

This paper presents adaptive feedforward control for vibration suppression based on an infinite impulse response (IIR) filter structure. The vibration signal and the output signal are available for the algorithm to adaptively update the parameters of the vibration transmission path (VTP) dynamics. Two designs for parameter adaptation are proposed. They provide different methods to get the necessary signals for parameter adaptation of the IIR filter which is different from the conventional finite impulse response (FIR) filter adaptation design. Performance of the proposed designs is compared with the conventional Filtered-x Least Mean Square (FxLMS) method on a hard disk drive (HDD) benchmark problem. The simulation results show that the proposed designs have smaller 3σ value and peak to peak value at steady state.

scholarly journals OPEN CORES FOR DIGITAL SIGNAL PROCESSING

2014 ◽  
Vol 25 (1) ◽  
pp. 53-62
Author(s):  
Juan Camilo Valderrama-Cuervo ◽  
Alexander López-Parrado
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Hardware Verification ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
On Chip

This paper presents the design and implementation of three System-on-Chip (SoC) cores, which implement the Digital Signal Processing (DSP) functions: Finite Impulse Response (FIR) filter, Infinite Impulse Response (IIR) filter and Fast Fourier Transform (FFT). The FIR-filter core is based on the symmetrical realization form, the IIRfilter core is based on the Second Order Sections (SOS) architecture and the FFT core is based on the Radix 22 Single Delay Feedback (R22SDF) architecture. The three cores are compatible with the Wishbone SoC bus, and they were described using generic and structural VHDL. In-system hardware verification was performed by using an OpenRisc-based SoC synthesized on an Altera FPGA. Tests showed that the designed DSP cores are suitable for building SoC based on the OpenRisc processor and the Wishbone bus.

Sign in / Sign up

Export Citation Format

Share Document