Honggeng Yang

Assessing Utility Harmonic Impedance Based on the Covariance Characteristic of Random Vectors

This paper presents a new method to calculate the utility harmonic impedance and the harmonic voltage emission level at the point of common coupling (PCC). Since harmonic current at the PCC commonly exhibits negligible dependence on the utility side background harmonic, the variable item of the background harmonic in the deviation formulation can be eliminated by using the covariance characteristic of random vectors. And then, the utility harmonic impedance, the customer harmonic voltage emission level, and the background harmonic can be calculated in real time. Based on the analysis results of the simulation and the field test case, it shows that the proposed method is effective to reduce the calculation errors caused by the fluctuation of background harmonic, and satisfactory results can be achieved.

Utility Harmonic Impedance Measurement Based on Data Selection

Determination of the utility harmonic impedance based on measurements is a significant task for utility power-quality improvement and management. Compared to those well-established, accurate invasive methods, the noninvasive methods are more desirable since they work with natural variations of the loads connected to the point of common coupling (PCC), so that no intentional disturbance is needed. However, the accuracy of these methods has to be improved. In this context, this paper first points out that the critical problem of the noninvasive methods is how to select the measurements that can be used with confidence for utility harmonic impedance calculation. Then, this paper presents a new measurement technique which is based on the complex data-based least-square regression, combined with two techniques of data selection. Simulation and field test results show that the proposed noninvasive method is practical and robust so that it can be used with confidence to determine the utility harmonic impedances.

Failure Probability Analysis of Sensitive Equipment Due to Voltage Sags Using Fuzzy-Random Assessment Method

The impact of voltage sags on equipment is usually described by equipment failure probability, and it is in general very difficult to assess and predict because of uncertainties with both the nature of voltage sags at the power supply side and the voltage tolerance level of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainties is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable with the use of -cut set and it is thus valuable in theoretical analysis and engineering application. The validity of the developed model has been verified by Monte Carlo stochastic simulation using personal computers as the test equipment.

A New Method to Calculate the Utility Harmonic Impedance Based on FastICA

Based on fast independent component analysis, a new method is proposed to calculate the utility harmonic impedance. Since the utility harmonic change is negligibly dependent on the customer, four independent components are obtained by demixing X-Y orthogonal components of voltage and current fluctuations at the point of common coupling. Then, mixing coefficients can be figured out by ordinary least square. Finally, the utility harmonic impedance is calculated according to the linearity of mixing coefficients. The disturbance of the utility harmonic is reduced to a certain extent by this method, which is because the joint impact of both sides is taken into account. According to analyses of simulation and field test cases, it is proved that utility harmonic disturbances can be effectively restrained by the proposed method; thus, more accurate utility harmonic impedance can be obtained.

A Method to Improve the Interharmonic Grouping Scheme Adopted by IEC Standard 61000-4-7

The interharmonic group concept adopted by the IEC 61000-4-7 is very useful for addressing the need to monitor interharmonics in power systems. This paper shows that grouping spectral bins into interharmonics and harmonic components of 5 Hz resolution could yield misleading information for cases where an interharmonic components frequency is very close to a harmonic frequency. Since interharmonics in such cases can cause more severe waveform modulation, the interharmonic grouping scheme needs to be improved. A signal-processing method that can separate interharmonic and harmonic components in close proximity is, therefore, proposed in this paper to address this need. The method is based on the estimation of leakage values caused by interharmonics, at harmonic frequencies. Simulation studies and field data analysis showed that the proposed method was able to extract interharmonic and harmonic components correctly. Moreover, this paper also discusses how to apply the method to enhance the spectral grouping scheme of the IEC 610004-7.

A Method to Determine the Existence of Genuine Interharmonics

Interharmonic components can often be seen from the discrete Fourier transform (DFT) results of measured voltage or current waveforms. These components may be caused by various factors, such as transient waveform distortions or noises. They are not necessarily related to the real interharmonics. This letter presents a simple method to verify the existence of a genuine interharmonic component contained in the DFT results. The method can also reveal the characteristics of the component. The method has been demonstrated on various field measurement data that contain genuine and false interharmonic components.

The Investigation of Residential Load Identification Technologies

Electrical energy, as a major source energy of production and life, plays an important role in national economic development. In recent years, the demand of electrical energy is increasing, the shortage of electrical energy becomes serious. In order to alleviate this problem, people has proposed the idea of load identification. Load identification system can determine the type of the load, provide the detailed electricity consumption and running state of equipments for supply-side and user. This paper gives a review of monitoring and identification methods for electrical loads, compares their applicability and accuracy on different loads, and updates direction for future research. And then, we propose fuzzy algorithm to implement the load identification of residential load . One of the key advantages of the fuzzy method is its capability to identify the major residential loads accurately. Actual trial data and results are presented in the paper to demonstrate the performance of the identification method.

Non-Intrusive Load Identification by Fuzzy Cluster Analysis Based on Active Power

The objective of load identification is to manage power consumption by the determination of the operation schedule of individual appliances. We applied fuzzy clustering analysis and fuzzy pattern recognition algorithm to load identification in this paper. In the method, the most ideal state is to treat each household appliance as a classification, then cluster them, and get a clustering result about home appliances. One of the critical advantages of fuzzy clustering analysis algorithm is its capacity to identify the main loads accurately.

Application of 3G Technology in Power Quality Monitoring System

At the present time the dedicated wired network is used widely to transfer power quality monitoring data. This transmission mode has some shortcomings, for example, the accessing terminals scope and the bandwidth resources are limited; the investment of the construction and maintenance is larger and the period is long. In view of the advantages of 3G technology, this paper proposed a 3G-based power quality monitoring system communication network, uses the VPN technology based on the L2TP and IPSec combination to build a virtual private monitoring data transmission network channel. Currently the 3G wireless networks is successfully applied in Sichuan power grid unified power quality monitoring system in China as an important supplement to the wired network and the actual field test results prove the reliability and practicality of this solution.

A Method Based on K-Means and Fuzzy Algorithm for Industrial Load Identification

At present, electrical energy becomes more and more important, and the shortage of electrical energy becomes serious. In order to develop power efficiency and alleviate the problem, people have planned the methods of load identification. This paper is on the basis of many different kinds of methods, and used K-means classification and fuzzy algorithm. This kind of method also needs to store the templates in database in advanced, and compare the features of samples which are extracted from the experiment with templates. Then it utilizes K-means classification to classify and uses fuzzy algorithm to calculate the closeness degree of every cluster. The industrial environment is very complex. E.g. the number of load types, states, some interference factors etc. This method we proposed can handle large amount of data and has another key of advantage. It is the capability to identify major industrial loads accurately. The results are presented in this paper. All in all, industrial load identification needs to be consummated.