g-I Chen

A Two-Stage ADALINE for Harmonics and Interharmonics Measurement

Harmonics and interharmonics may introduce operational problems of electrical and electronic equipment. Therefore, monitoring harmonics/interharmonics for improving the power quality is of importance for both electric utilities and their customers. In this paper, a cascade two-stage adaptive linear element (ADALINE) structure for both harmonics and interharmonics measurement is proposed. In addition, a simple laboratory setup implemented by MATLAB and the dedicated hardware for measuring power signals is built to verify the performance of proposed method. Results are compared with those obtained by short-time Fourier transform (STFT) and two other conventional ADALINE-based methods. It shows that the proposed method is with a better accuracy, even if the power frequency deviation and interharmonic components are present in the measured signal. The proposed method also can be adopted for harmonic/interharmonic compensation devices in real-time.

Radial-Basis-Function-Based Neural Network for Harmonic Detection

The widespread application of power-electronic loads has led to increasing harmonic pollution in the supply system. In order to prevent harmonics from deteriorating the power quality, detecting harmonic components for harmonic mitigations becomes a critical issue. In this paper, an effective procedure based on the radial-basis-function neural network is proposed to detect the harmonic amplitudes of the measured signal. By comparing with several commonly used methods, it is shown that the proposed solution procedure yields more accurate results and requires less sampled data for harmonic assessment.

Extended Real Model of Kalman Filter for Time-Varying Harmonics Estimation

The effective harmonics estimation for measuring power signals has become an important issue in the power quality assessment. By reviewing those commonly used Kalman filter-based models, some limitations for harmonics estimation can be observed. In this paper an extended real model of Kalman filter combined with a resetting mechanism for accurately tracking time-varying harmonic components of power signals is presented. The usefulness of the proposed algorithm is demonstrated by a simple laboratory setup with LabVIEW program and the dedicated hardware for harmonics monitoring. Results show that the proposed method can achieve more accurate and robust measurement of harmonic amplitudes and phase angles for the time-varying power signals among compared methods while the uncertainty testing performances required by IEC standard 61000-4-30 are satisfied.

Virtual Instrumentation and Educational Platform for Time-Varying Harmonic and Interharmonic Detection

Selection of a suitable method among various techniques for assessment of harmonic and interharmonic components of a measured voltage or current waveform is not an easy task for the students. A good start is to understand the relationship between the analysis techniques and the measured parameters. In this paper, several commonly used methods for time-varying harmonic and interharmonic detection of measured waveforms are reviewed and implemented in an integrated virtual instrumentation. Compared from the aspect of frequency identification for the reviewed methods, general guidelines for performing harmonic and interharmonic detection are also developed for the educational purpose.

Virtual Multifunction Power Quality Analyzer Based on Adaptive Linear Neural Network

Monitoring of electrical quantities is an important task for the evaluation of power quality (PQ). However, analysis methods for PQ disturbances are quite different. This circumstance would make the design of a multifunction PQ-measuring instrument difficult. In this paper, the design and implementation of a virtual multifunction PQ analyzer based on the adaptive linear neural network are discussed. The main advantages of the realized analyzer are the simplification and integration for the harmonic/interharmonic analyzer, flickermeter, and voltage event identifier by adopting the same computational mechanism. Finally, some tests are made to verify the performance of the proposed virtual multifunction analyzer.

On Tracking the Source Location of Voltage Sags and Utility Shunt Capacitor Switching Transients

This paper presents a new procedure to track the disturbance source location of voltage sags and shunt capacitor switching transients in a power system based on branch current measurements. In the proposed method, the power-quality metering locations are first determined by the sensitivity of the system fault-current level to the fault voltage. Then, a current deviation index for representing the change of each measured branch current rms magnitudes before and after the disturbance is calculated to track the disturbance source location within an area confined by the directions of the top-priority branches. The IEEE 30-bus benchmark system and an actual transmission system with recorded power-quality disturbance events are used to illustrate the usefulness of the proposed method. Simulation results obtained by the proposed method are also compared with those obtained by a previously developed approach and by actual recorded data. By observing the results, it shows that the proposed method is efficient and accurate to track the source area of voltage sags or shunt capacitor switching transients.

A Neural-Network-Based Method of Modeling Electric Arc Furnace Load for Power Engineering Study

It is known that artificial neural network is a powerful scheme for function learning and modeling nonlinear loads. However, a direct application of artificial neural network for modeling time-varying loads may lead to inaccuracies. This paper presents an accurate neural-network-based method for modeling the highly nonlinear voltage-current characteristic of an ac electric arc furnace (EAF). The neural-network-based model can be effectively used to assess waveform distortions, voltage fluctuations, and performances of reactive power compensation devices associated with the EAF in a power system. Simulation results obtained by using the proposed model are compared with the actual measured data and two other traditional neural network models. It is shown that the proposed method yields favorable performance and can be applied for modeling similar types of nonlinear loads for power engineering studies.

A Digital Implementation of Flickermeter in the Hybrid Time and Frequency Domains

Voltage fluctuations caused by rapidly changing loads in the power systems may give rise to noticeable illumination flickers of lighting equipment. The voltage flickers can also cause malfunctions in many electric devices. This paper presents a digital implementation of flickermeter in the hybrid time and frequency domains based on IEC standard 61000-4-15. In addition, this paper proposes a new demodulation method to extract the voltage envelope. Simulations and actual measurements show that the digital implementation with the proposed method yields relatively accurate flicker measurements.

Measurement techniques for stationary and time-varying harmonics

Frequency is an important factor for power system harmonics measurement. Accurate spectral analysis relies much on the correct identification of frequencies of the measured signals. In this paper, several commonly used methods for power system stationary and time-varying harmonics measurement are reviewed and compared according to the aspect of frequency identification. Recommendations for adopting proper harmonics detection methods under different measuring conditions are then suggested.

Modeling voltage-current characteristics of an electric arc furnace based on actual recorded data: A comparison of classic and advanced models

Field measurements of voltage and current is the most effective way for characterizing the electric response of an EAF that describe the nonlinear behavior of AC EAF loads. Sufficient measured information can be adopted to develop an appropriate EAF model. In this paper, two classic methods based on measured data, harmonic current injections and equivalent harmonic voltage sources, for the EAF load modeling are reviewed. For comparison, two advanced methods based on actual recorded data, cubic spline interpolation and radial basis function neural network (RBFNN), are also proposed to model the EAF load. A steel plant power system with EAF loads is used for field measurements and computer simulations. Comparisons between the results of measured data and simulations for the four EAF models are being made according to the voltage/current waveforms and voltage-current characteristics. It is shown that the advanced models yield better performance than classic models of the EAF.