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Voltage stabilization is an important task for the microgrid power control. The long transient response of voltage caused by the grid transition from the grid-connected mode to the islanded mode or the power variations would deteriorate the operation of the voltage protective relay. This would lead to the difficulty of integration for the renewable energy and the storage system. To solve this problem, the asymmetric membership function based wavelet petri fuzzy neural network (AMFWPFNN) controller is proposed for the voltage stabilization control of storage system in this paper to provide fast response speed and mitigate the transient impact. To investigate the performance of the proposed microgrid controller and examine the compliance with the settings in IEC Std. 60255, Cimei Island in Taiwan is studied. Through the hardware in the loop (HIL) mechanism built with OPAL-RT real-time simulator, the effectiveness of proposed controller can be verified.

To realise the resonant characteristics of motor drive system, a wavelet multiresolution filter (WMF)-based scheme is proposed in this study to perform the detection of the mechanical resonant frequencies for interior permanent magnet synchronous motor servo drives. In a conventional motor drive system, the compliance in the coupling between motor and load may cause resonance. Moreover, the parasitic torque ripples resulting from the harmonics of a pulse-width modulation inverter may excite mechanical vibrations. In the proposed scheme, the localisation analysis of signal can be completed with the WMF at any time and frequency domains for the extraction of resonant frequencies. Then, a band-pass filter is applied to perform the frequency sweeping for the detection of resonant frequencies. In the final stage, the signal smoothing is implemented with the calculation of absolute and average values. To verify the effectiveness of the proposed resonant-frequency detection scheme using the WMF, some experimental results at different rotor speed and load conditions are provided. In addition, a two-channel dynamic signal analyser is also adopted in the experimentation for the comparison of resonant-frequency detection.

In recent years, the use of renewable energy has been increasing and energy storage equipment is standard equipment for renewable energy. Under the strong development of green energy by the government, it has also driven the application of a large number of energy storage equipment. It uses batteries as energy storage. In the island strategy, only the internal energy storage and green energy are used. To perform the energy management for the building microgrid, the energy scheduling algorithm designed in this paper is based on the remaining battery power and solar power as the benchmark, because all commercially available energy storage equipment has been abandoned. The method is used to reduce the state of virtual charging. The main reason is that the load control requires additional hardware and control methods. Therefore, when using the abandoned method, insufficient power consumption is present at night. This has led to a reduction in battery life, and this aspect has been taken as the research direction for this purpose. It is planned to apply the optimization algorithm for controlling the load, so that the power generation and battery power can be effectively used. Besides, the loads can be graded to perform the demand management when power is interrupted.

With the penetration of renewable energy, the power quality (PQ) becomes the important issue for operation stability of power system. The efficient monitoring mechanism for different multiple PQ disturbances is the fundamental infrastructure for modernization of power system. However, the diverse PQ signals result in numerous analysis techniques and make the integration of PQ monitoring difficult. To resolve the above-mentioned problem, the Prony-based PQ analysis mechanism is proposed in this paper. The commonly seen PQ disturbances, such as the fundamental frequency deviation, harmonics, interharmonics, voltage fluctuations (flickers), and PQ events, can be easily analyzed and detected with Prony-based technique and integrated for the design of monitoring system. The performance of proposed analysis mechanism can be verified by the implementation in the Pingtung ac microgrid of Taiwan.

Detection of fundamental and harmonic components is important to the control, protection, and compensation applications of grid-connected converters for the power-quality purposes. The conventional frequency-domain methods often suffer from the problems of at least one-cycle delay and dependence on frequency resolution. On the other hand, the numerical stability and robustness to interference are the necessary factors in the time-domain methods. To resolve the aforementioned issues, an adaptive model-based scheme with short sliding analysis window is proposed to track the frequency deviation and modify the analysis model with the online-sampled data to extract time-varying harmonics. Through the synchronized detection and accurate extraction of harmonics, the performance of proposed technique can be verified with several field measurement cases.

In medical imaging, positron emission tomography (PET) shows metabolic changes of an organism in pseudo color and magnetic resonance (MR) imaging presents anatomical structures in gray level. The PET and MR brain medical image fusion produces one composite image rendering the anatomical structures with metabolic changes to help doctors effectively diagnose a possible disease. For this purpose, a new fusion method based on Intensity-Hue-Saturation model and log-Gabor wavelet transform is proposed. First, MR image and the intensity component of PET image are decomposed by log-Gabor wavelet transform with suitable decomposition scale. Then, maximum selection’ fusion rule for the high-frequency sub-band and two-stage fusion rule based on weighted-averaging scheme and visibility measure for the low-frequency sub-band are employed. Finally, a new intensity component, obtained by applying reverse log-Gabor wavelet transform to the fused high-and low-frequency sub-bands, along with original hue and saturation components of PET image are converted to obtain our fused color image. In our fused images, both anatomical structures and color changes are rendered with effectively-reduced color distortion. Experimental results on twelve sets, including normal axial, normal coronal, and Alzheimer’s disease brain images, demonstrate that our fusion method outpaces framelet-transform-based method both visually and quantitatively.

This paper is aimed to develop the control strategy of harmonic current compensation for shunt active power filter (SAPF). When analyzing harmonic signal through adaptive linear filtering (ADALINE), the estimation accuracy of phase angle would be easily reduced due to power system frequency deviation. Therefore, the model of improved ADALINE with the frequency detection method is proposed to overcome this problem and then perform the compensation control. From the simulation results, the performance of proposed SAPF for the compensation of nonlinear harmonic currents can be verified and the requirements in IEEE Std. 519-2014 are met.

An accurate model of nonlinear load is important for the evaluation of power quality (PQ). Among different PQ disturbance sources, alternating current electric arc furnace (AC EAF) is one of most complicated and serious loads. To provide effective operation prediction of AC EAF, a data-driven modeling approach based on time- and frequency-domain voltage-current $(v -i)$ characterization is proposed in this paper. With the prediction of the proposed model in the time domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasistationary and long-term features of AC EAF would be extracted via the frequency-domain phase of the proposed model. From the comparison on the field measurement data, the performance of the proposed model can be verified in the applications of PQ studies.

Since the renewable energy is intermittent power source, feeding a lot of renewable energy into the grid would cause voltage variations affecting the stability of the power system. As a result, the effective detection and identification of voltage variation events become important tasks for the protection of microgrid integrated different distributed energy systems. In this paper, a rule-based technique based on IEEE Std 1159-2009 is proposed to deal with the estimation of voltage variations, and a prototype of a supervisory control and data acquisition system with filter bank and adaptive filtering is developed. With the help of the implemented technique, the monitoring of voltage variation events can be efficiently performed and practically applied in the microgrid system of the Taiwan Institute of Nuclear Energy Research.

Voltage flicker is one of many serious power quality disturbances, which would deteriorate the stability and operation efficiency of a power system. Effective evaluation of flicker severity plays an important role in the monitoring and control of developing advanced metering infrastructure. Since the evaluation process for voltage flicker is complicated, some inaccurate intermediate results may give rise to the propagation of estimation errors. Therefore, an accurate solution procedure for the calculation of voltage flicker components is proposed in this paper. With the extraction technique for envelope signal and high-frequency resolution mechanism for flicker spectral analysis, the superior performance to achieve the instantaneous flicker severity evaluation can be obtained.

Frequency is one of the most important factors for power system harmonic and interharmonic estimation. 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 harmonic and interharmonic estimation are reviewed and compared according to the viewpoint of the frequency identification. It is expected to provide suitable guidelines for the future studies.

This paper describes an artificial neural network (ANN)-based method for the design of a digital power meter, which is able to evaluate the power quantities suggested in IEEE Standard 1459-2010 for a single-phase system. The solution procedure contains two stages. In the first stage, the spectral analysis of voltage and current signals is performed separately with the ANN-based method in a real-time manner. Then, the power quantities and evaluation indices are calculated in the second stage according to the requirements in IEEE Standard 1459-2010. Finally, a personal-computer-based instrument implemented with LabVIEW is developed to verify the usefulness of the proposed mechanism.

Phasor estimation is of great importance for many applications in power systems, such as power quality and protection. However, when the power frequency deviation is present, the phasor would undergo a rotation in the complex plane. This phenomenon will introduce difficulty for the interconnection of power grids and distributed generation systems. In this paper, a synchrophasor estimator based on the combination of harmonic components is introduced for the grid synchronization. With the benchmark and compliance tests in IEEE Standard C37.118-2005, the performance of the proposed solution structure is verified.

Evaluation of power quality (PQ) becomes an important issue for the grid integration of wind energy systems. To provide accurate estimation of grid situations, a calibration test platform is designed in this paper to assess the capability of commercial PQ instruments. In accordance with the latest International Electrotechnical Commission standards, the test power signals can be generated, and the standard PQ indices can be precisely obtained by designed autoregressive-model-based synchronization and voltage envelope extraction processes. In this way, the commercial PQ instruments can be conveniently calibrated to meet the measurement requirements during the continuous operation of wind turbine.

Magnitude and duration are important characteristics when performing tracking of voltage variations. Accurate characterization of voltage variations relies much on the precise identification of frequencies of the measured signals. Although the Prony's method can provide high resolution for frequency estimation, the computational burden in the root-finding process is a crucial problem. A modified Prony-based solution procedure for voltage variation tracking is proposed in this paper. By providing a set of filters, the transfer polynomial with high estimation order in Prony's model can be efficiently reduced. The performance of the proposed method is validated by testing the generated and actual measured voltage signals. Results are compared with those obtained by typical Prony's method, namely, fast Fourier transform, adaptive linear neural network, and Kalman filtering. It shows that the proposed method is more accurate regardless of interferences of the power system frequency deviation, harmonics, and interharmonics, where both voltage variations and harmonics can be simultaneously detected.

Maintenance of power quality is an important issue in the interconnection and protection between distributed generations and a power system. With the development of measuring techniques and digital signal processors, multifunction monitoring devices are introduced. However, the installation of numerous terminal units usually would increase the cost for the wide-area measurement. In order to promote the deployment of distributed measurement, low-cost acquisition units with low operational burden are applied in this paper. By combining the proposed reconstruction technique for power signals, a simple remote power-quality measurement system is developed.

With the development of smart grid, the accurate estimation of phasor measurement is increasingly significant to help achieve the reliable transmission and distribution of power system. However, the power system frequency may deviate from its nominal value and lead to estimation errors for the most traditional approaches. To perform the accurate synchrophasor measurement, a technique based on adaptive linear neural network (ADALINE) is applied. With the high frequency resolution of autoregressive model, the variation of power system frequency can be effectively extracted. Through the testing results corresponding to different power quality disturbances, the total vector errors of several uncertainty examinations for synchrophasor measurement in IEEE Std. C37.118.1-2011 can be maintained in the permissible range.

In this paper, an improved non-isolated DC-DC boost converter is proposed, which can be used in the distributed generation or storage system in the smart grid. The proposed circuit is composed of three insulated gate bipolar transistor (IGBT) power switches and separated into two stages. With the complementary single-sided multiple pulse width modulation (MPWM) operation of power switches in the first stage, the input current ripple of DC-DC converter can be effectively reduced. And the switched inductor in the second stage can provide high voltage gain for the applications of distributed generation. The performance of proposed converter can be verified with PSIM simulation software.

Accurate calculation of power quantities is important to the control and management of energy system. To reduce the design cost and computational burden in the terminal power meters, an efficient data acquisition technique based on waveform reconstruction is proposed to transfer correct power signal for calculation of power quantities. In the structure of wide-area intelligent energy system, the effectiveness and performance of the proposed technique can be verified.

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.

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.

Frequency is an important indicator for the quality of electric power. Accurate spectral decompositions rely much on the correct identification of frequencies of the measured signals. An efficient procedure that includes a high-resolution Prony-based method in conjunction with the downsampling technique for harmonics and interharmonics detection of the measured power signal is proposed in this paper. It is shown that even when two or more closely adjacent spectral lines are present, the proposed method can precisely detect the harmonic and interharmonic components. The performance of the proposed method is validated by testing the simulated and actual measured power signals. Results are compared with those obtained by fast Fourier transform with and without synchronization, IEC subgrouping method, and other commonly used linear prediction approaches adopted in the Prony's method. It shows that the proposed method is more accurate on the detection of harmonics and interharmonics with high resolution.

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.

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.

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.

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.

Harmonics and interharmonics may introduce operational problems in 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 cascaded 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 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.

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.

The GM(1,1) model in the grey system theory is a prediction model. It aims to construct the first differential model, which uses the background value of two adjacent points. In the previous researches, they found that the most important factor which had great impact on prediction error was background value. Hence, many researches focus on background value adjustment, including alpha value's digital jumping changes or the alpha value's continuous changes. However, even in this form, it sometimes still fails to present discrete points' implied law which result in the impossibility of lowering GM(1,1) errors. Therefore, the paper proposed a new method, including three major elements. First, the background values were presented in fractional power type and combine with consist method as the basic to get the real value for reduce the prediction errors. Next, the GM(1,1) model was integrated, and developed a prediction error reduction method. As the result, the paper not only can got rid of the traditional background value method, but also created a new research direction of using GM(1,1) model to reduce prediction errors.

This paper is to describe the design process and methods of a programmable power-quality signal generator to help perform the power disturbance testing of consumer electronics. Programmable ability is that the arbitrary waveform can be generated and provide the alternating current (AC) power output. The power output with arbitrary waveform can be used to emulate a power supply of pollution with power harmonics, flicker, sags, swells and other disturbances. This system can be used to perform the training for the design of filter to filter off a power source of pollution into the clean sine wave or can be used to test equipment for power pollution interference. The rated power of power supply mentioned in this paper is set to 300 watt, where the D-class power amplifier is used as the design core. And a boost circuit is designed to provide power supply for amplifier to raise the output voltage. Details of the design process will be discussed in this paper.

The synchronized measurement is important to the applications in power system. With the obtained power information, the conditions of power system can be easily realized, such as power quality, transient stability, and efficiency of energy usage, etc. In this paper, the design of phasor measurement unit for power system monitoring based on neural network is proposed. Through the experiment, the performance of proposed phasor measurement can be verified according to IEEE Std. C37.118-2005.

Extended Kalman filter is an efficient method for harmonic analysis in power system because of its good noise immunity and ability to track the frequency variation. When the filter reaches steady-state, it becomes less sensitive to variations and begins losing tracking ability. This problem can be improved by setting the suitable noise covariance matrix. This paper discusses the effect on the level of noise covariance matrix.

Frequency is one of the most important factors for power system harmonic and interharmonic estimation. 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 harmonic and interharmonic estimation are reviewed and compared according to the viewpoint of the frequency identification. It is expected to provide suitable guidelines for the future studies.

In recent years, with the booming of high-tech industry, there are many high-precision power electronic equipment being used. Most of them can not afford the transient changes in electricity. Therefore, this paper presents adaptive linear filtering (ADALINE) method for detection of voltage fluctuation. General methods for detecting the voltage variation is to calculate its root-mean-square value, which needs to calculate an integer multiple of the signal period in order to display the complete the effective energy of signal. If the calculation is not performed with the integer multiple of the signal cycle, the estimation results would be inaccurate. In this paper, ADALINE method is applied to detect the variation of voltage in the power system. ADALINE can accurately detect changes in the condition of the voltage value and then displayed on the amplitude - time graph. Finally, Lab VIEW program is used to give a simple test.

In this paper, we present a PET and MR brain image fusion method based on wavelet transform for low- and high-activity brain image regions, respectively. Our method can generate very good fusion result by adjusting the anatomical structural information in the gray matter (GM) area, and then patching the spectral information in the white matter (WM) area after the wavelet decomposition and gray-level fusion. We used normal axial, normal coronal, and Alzheimer's disease brain images as the three datasets for testing and comparison. Experimental results showed that the performance of our fusion method is better than that of IHS+RIM fusion method in terms of spectral discrepancy (SD) and average gradient (AG). In fact, our method is superior to IHS+RIM method both visually and quantitatively.

Flicker is one of significant power quality disturbances, which may lead to serious voltage fluctuation. Therefore, the accurate estimation of flicker components plays an important role in the power quality monitoring and protection of power system. In this paper, a high-resolution technique is proposed to extract the flicker components effectively. Different from the conventional spectral analysis methods, the performance of proposed mechanism would not be influenced by the fundamental frequency deviation, the sampling frequency, and the length of sampled data. With the experimental test, the estimation accuracy and robustness are verified.

The accurate extraction of voltage envelope is an important process for the flicker estimation. According to the latest IEC standard for design of flickermeter, the factor of fundamental frequency deviation is necessary to be taken into account. Therefore, a Prony-based technique is proposed in this paper. With the characteristic of high-frequency resolution in this method, the extraction of voltage envelope can be accurately performed for the time-varying power signals.

Voltage fluctuations caused by the rapid changes of loads in the power system may give rise to noticeable illumination flickers of lighting equipments. Evaluation of voltage fluctuation becomes an important issue for the grid integration of wind energy systems. To provide accurate estimation of grid situations, a calibration test prototype is designed in this paper to assess the capability of commercial flickermeters. In accordance with the latest International Electrotechnical Commission (IEC) standards, the test power signals can be generated and the standard flicker indices can be precisely obtained by designed autoregressive (AR)-model-based synchronization and voltage envelope extraction processes. In this way, the commercial flickermeters can be conveniently calibrated to meet the measurement requirements during the continuous operation of wind turbine.

The estimation of synchrophasor is of great concern for power quality and protection in power system. Once the power frequency deviation is present, the phasor would undergo a rotation in the complex plane. This phenomenon will introduce difficulty for the interconnection of power grids and distributed generation systems. In this paper, a synchrophasor estimator with the two-stage ADALINE is introduced to deal with the estimation errors due to power frequency deviation. With the compliance tests in IEEE Std. C37.118-2005, the performance of proposed solution structure can be verified.

An artificial neural network (ANN)-based method for the design of virtual power meter, which is able to evaluate the power quantities for the single-phase system, is introduced in this paper. The solution procedure contains two stages. In the first stage, the spectral analysis of voltage and current signals are performed separately with the ANN-based method in the real-time manner. Then, the power quantities and evaluation indices are calculated in the second stage. Finally, a PC-based virtual instrument implemented with Lab VIEW is developed to verify the usefulness of proposed mechanism.

For the development of smart grid, the effective detection of power quality events is a very important task for the power system monitoring. Voltage sags, interruptions, and voltage swells which usually produce large losses in commercial and industrial consumers are the main events in power systems due to the sensitivity of equipments to these voltage variations. In this paper, a simple rule-based detection method for voltage events by applying the filter bank and adaptive filter is presented. The usefulness of the proposed algorithm is demonstrated by a simple laboratory setup with LabVIEW program and actual recorded waveforms. With the help of accurate detection time of the filter bank and correct event classification of adaptive filter, power system monitoring could provide accurate and useful information to power grids via the developing advanced metering infrastructure.

With the development of smart grids, power-quality monitoring becomes the one of the necessary functions. However, the installation of numerous terminal units usually would increase the cost for the wide-area measurement. In order to promote the deployment of distributed measurement, the low cost acquisition units are applied in this paper. By combining the proposed reconstruction technique for power signals, the embryo of wide-area power-quality measurement network is built.

For the development of smart grid, the effective detection of power quality events is a very important task for the power system monitoring. Voltage sags, interruptions, and voltage swells which usually produce large losses in commercial and industrial consumers are the main events in power systems due to the sensitivity of equipments to these voltage variations. In this paper, a hybrid detection method for power quality events by combining the wavelet analysis and adaptive linear combiner (ADALINE) is proposed. The usefulness of the proposed algorithm is demonstrated by a simple laboratory setup with LabVIEW program and actual recorded waveforms. With the help of accurate time locating of the wavelet analysis and correct event classification of ADALINE, power system monitoring could provide accurate and useful information to power grids via the developing advanced metering infrastructure.

This paper presents an efficient method for placement of shunt capacitor in a harmonic polluted power system. The planning problem is formulated as an unconstrained optimization problem. In the approach also considers probabilistic characteristics of voltage, current and impedance of fundamental and harmonics in a power system. The objective function is the total system power loss. The shunt capacitor siting indices are computed to find the most suitable shunt capacitor locations. Simulation results obtained by testing an actual distribution system show that the proposed method is robust and computationally efficient.

Effective detection of power quality events is an important issue for maintaining reliable operations of power systems. Under different measuring environment, detection methods usually need different requirements. In this paper, the performances of several commonly seen methods for the detection of voltage sags are evaluated and compared.

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 is to present an accurate neural network-based method for modeling the highly nonlinear voltage-current characteristic of an AC electric arc furnace. 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 electric arc furnace 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.

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.

This paper presents an approach of incorporating hardware-in-the-loop simulation and ADALINE for shunt active power filter (APF) design. Even when the three-phase source voltages are unbalanced and/or distorted and supply to a nonlinear load, the described compensation strategy can compensate the harmonic and neutral current, and thus improve the power factor. After verifying the efficiency of compensation strategy, the APF control strategy is embedded into the digital signal processor (DSP) to verify the feasibility of the proposed strategy on hardware in the loop (HIL) structure. Results show that the proposed approach is effective for shunt APF design.

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 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 harmonics assessment.