Harmonic Responsibility Research Articles

Non-Invasive Approach to Harmonic Responsibility Assignment Based on Conservative Power Theory

Non-Invasive Approach to Harmonic Responsibility Assignment Based on Conservative Power Theory

Time‐domain harmonic source location and evaluation methods based on non‐linear and time‐varying properties of devices

AbstractMost of the existing methods for harmonic analysis are from frequency‐domain perspective. In fact, the essential factor for generating harmonics is non‐linear characteristic or time‐varying property. Therefore, the harmonic generation mechanism and the harmonic source location method are investigated from the time domain perspective in this paper. Firstly, a general model of non‐harmonic sources is established. The non‐harmonic sources will match well with the proposed general model while the harmonic source will not. Then correlation coefficient that reflects compliance degree between each device and the general model is defined for distinguishing harmonic sources from non‐harmonic sources. On this basis, a novel time‐domain harmonic source location method is proposed. Numerous simulations and experiments have demonstrated that the proposed method has good performance under fluctuations, saturations, pre‐distortions, transient process, and resonances. The defined correlation coefficient can reflect non‐linearity/time‐varying degree of the harmonic source, which can be used for evaluating harmonic emission level of each harmonic source. On this basis, a simple harmonic responsibility division method is proposed, which is immune to possible fluctuations, pre‐distortions, saturations, resonances, and impedance variation.

Causal Responsibility Division of Chronological Continuous Treatment Based on Change-Point Detection.

This paper introduces a novel approach, called causal relation quantification, based on change-point detection to address the issue of harmonic responsibility division in power systems. The proposed method focuses on determining the causal effect of chronological continuous treatment, enabling the identification of crucial treatment intervals. Within each interval, three propensity-score-based algorithms are executed to assess their respective causal effects. By integrating the results from each interval, the overall causal effect of a chronological continuous treatment variable can be calculated. This calculated overall causal effect represents the causal responsibility of each harmonic customer. The effectiveness of the proposed method is evaluated through a simulation study and demonstrated in an empirical harmonic application. The results of the simulation study indicate that our method provides accurate and robust estimates, while the calculated results in the harmonic application align closely with the real-world scenario as verified by on-site investigations.

Harmonic Responsibility Calculation with the System-side Harmonic dominated

Considering the features of the load, such as time-varying and randomness, it is hard to estimate the harmonic impedance. The computing method of harmonic impedance is proposed with the system-side harmonic fluctuation dominated, while the phase angle is taken into account, which improves the accuracy of the calculation. At the same time, this paper adopts the idea of piece-wise linear regression to decrease the influence of unusual data on the estimation results. Finally, the theoretical validity and application feasibility are verified by an example analysis.

Research on harmonics liability division considering the influence of user harmonics on economic loss

With the rapid development of power electronics technology, a large number of nonlinear devices are widely used in power network, industry and home, and the harmonic problem has attracted more attention. In the distorted power system, it is the premise of harmonic pollution control to clarify the shared harmonic responsibility of the system side and each user at the common connection point. The existing liability division methods mainly focus on a certain harmonic, and the results are difficult to reflect the actual harmonic influence of users. The direct effect of harmonic problem can be measured by economic loss, which is the main basis for the division of harmonic responsibility is beneficial to the implementation of reward and punishment measures. This paper presents a harmonic liability division method considering the effect of user harmonic on economic loss. Firstly, the harmonic compatibility level of users is graded considering different proportions and different types of equipment. Secondly, reference to the national standard to develop economic indicators to measure the impact of harmonics on users at all levels; Finally, each harmonic responsibility of the system side and the user side is calculated, and the final comprehensive harmonic responsibility index is obtained by combining the economic index of each user harmonic influence. The reasonableness of this method is verified by the measured harmonic data of users connected to a substation.

Study on the estimation of harmonic impedance based on Bayesian optimized Gaussian process regression

System harmonic impedance is a key parameter in the evaluation of harmonic emission level and harmonic responsibility allocation. In order to solve the problem of calculating the harmonic impedance when background harmonics are different, a method of calculating the harmonic impedance based on Bayesian optimized Gaussian process regression (O-GPR) is presented. The harmonic impedance calculation method is based on the harmonic voltage and harmonic current obtained at the point of common coupling (PCC), and the Bayesian optimization of the Gaussian process regression is used to calculate the harmonic impedance of the system. Considering the influence of background harmonics, the harmonic equivalent models with non-Gaussian distribution background harmonics and Gaussian distribution background harmonics are built in the simulation analysis. The accuracy of this method under different background harmonics is analyzed, and its robustness is evaluated by error comparative analysis. Case analysis further verifies the effectiveness of the method. Also, the method is evaluated through indicators such as mean value, standard deviation and root mean square error. It provides ideas for practical engineering applications.

A novel approach for harmonic responsibility assessment based on an optimization model

A novel approach for harmonic responsibility assessment based on an optimization model

Blind source separation methods applied to evaluate harmonic contribution

SummaryOne of the greatest challenges found in power quality has been, and still is, the establishing of a reliable noninvasive method for quantifying the harmonic contributions of an electric utility and a consumer at the point of common coupling (PCC). This difficulty arises from the fact that the harmonic distortions measured on the PCC are unidentifiable mixtures from both parties. Currently, some work has focused on exploring the use of blind source separation (BSS) techniques to promote the sharing of harmonic responsibilities in a noninvasive way. However, the usage of this methodology is very recent and still requires thorough evaluations, not only from the point of view of the BSS techniques available in the literature, but also from the perspective of the electrical power systems and the nature of their loads. Thus, seeking to contribute to the consolidation of the use of BSS techniques in the sharing of harmonic responsibilities, this work aimed to investigate the impact of the variation of harmonic current sources on the accuracy of BSS methods. In addition, three algorithms suitable for complex‐valued signals were evaluated computationally and experimentally; the results obtained computationally were compared with the results of the method based on the covariance characteristic of the random vectors, and the experimental results was compared to that obtained through an invasive procedure.

A harmonic impedance estimation method based on AR model and Burg algorithm

With the development of smart grid, a lot of nonlinear loads and new energy power stations are connected into the power network. There are a large number of harmonic sources in the power grid, harmonic distortion has become one of the main power quality concerns in recent years. Utility side harmonic impedance estimation is important for evaluating harmonic emission levels and determining the harmonic responsibility. In the traditional methods, the harmonic voltage and current at the PCC are considered to satisfy the normal distribution and the timing correlation of them is ignored which contains important information that can be used to estimate the harmonic impedance. This paper proposes a new method for harmonic impedance estimation based on the Burg algorithm and AR model. In this paper, harmonic voltage and current are considered to satisfy AR model including timing correlation. The reflection coefficient is calculated by minimizing the average power of the forward and backward errors in the AR model. Then, the utility side harmonic impedance is estimated by minimizing the apparent power. The proposed method is robust, and the error of the estimation result is small by the proposed method. Whats more, the proposed method is not limited by some traditional assumptions, such as the background harmonics are stable and small, the customer harmonic impedance is much larger than the utility, and the harmonic sources of both sides are independent, and so on.

A Method for Responsibility Division of Multi-Harmonic Sources Based on Canonical Correlation Analysis

The variable background harmonic data and incomplete phasor information make multi-harmonic source responsibility division in three-phase symmetrical power system a significant challenge. In this paper, a background harmonic data selection method based on canonical correlation analysis is proposed to deal with multi-harmonic source responsibility division without phasor information. Firstly, the canonical correlation coefficient between harmonic voltage and harmonic current is used to characterize the fluctuations of background harmonic voltage. Then, the sliding window method is adopted to select the harmonic voltage and harmonic current with small fluctuations. Next, the canonical correlation results for selected data are used to calculate the harmonic responsibility index via the linear regression method. The harmonic responsibility index in the form of percentage represents the harmonic responsibility division. Finally, several experimental results demonstrate that the proposed method has a high accuracy in calculating the harmonic responsibility division, particularly when the user side contains fluctuations of unknown harmonic sources.

Background Harmonic Probabilistic Model for Harmonic Responsibility Assessment Based on Nonparametric Methods

As the increasing integration of inverter‐interfaced renewable energy generators intensifies the volatility and uncertainty of background harmonics in the utility system, it is crucial to construct a precise background harmonic model and determine the harmonic responsibility to promote harmonic management. This paper proposes a probabilistic model of background harmonics for harmonic responsibility assessment based on nonparametric methods. First, the amplitude data of background harmonic voltage are segmented in chronological order considering the stochastic features. Then, the nonparametric Bootstrap resampling is introduced to handle the case of insufficient samples, and the Kernel density estimation with optimal bandwidth is used to depict the probability distribution characteristics in each time interval. For the sake of identifying the piecewise probability density function with optimal fitting effect, the goodness‐of‐fit test is subsequently applied. Finally, based on the proposed background harmonic probabilistic model, the harmonic responsibility assessment is conducted without the involvement of phase angle information. The detailed performance verification of the proposed model is carried out in light of the field‐measured data. The results validate that the proposed model outperforms the conventional parametric models in handling insufficient data and fitting accuracy and is helpful to achieve the effective quantification of the overall harmonic responsibility within a period of time. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Novelty method for the utility harmonic impedance estimation based on Gaussian mixed model

The accurate estimation of harmonic impedance on the utility side of a point of common coupling (PCC) is much important to harmonic control, harmonic emission level, and harmonic responsibility analysis. In this study, a novel estimation method for the utility harmonic impedance is proposed, which employs the Gaussian mixed model (GMM) to fit the measured data at a PCC, because the authors find that the statistical distribution of the measured data at a PCC is close to the Gaussian distribution. Also, in theory, GMM can approach the probability distribution of any data. In this method, the Norton equivalent circuit model at a PCC is first expressed as a GMM, and the utility harmonic impedance in the GMM is estimated by expectation maximisation iterative method. Compared to the existing estimation methods, an outstanding advantage of this method is that it is less affected by background harmonics; therefore, higher accuracy can be acquired in this method. The effectiveness and accuracy of this method are verified by simulation experiments and field data.

Relevant Factors for Evaluation of the Harmonic Responsibility of Utility and Customer

This paper yields a systematic scientific foundation for the evaluation of responsibility of utility and customer to the harmonic distortions at the point of common coupling (PCC). In this regard, this paper proposes a technical process for the evaluation of harmonic distortions. This technical process provides a way to answer some technical questions related to harmonic issues based on the following aim: identifying the dominant side, determining the responsibility of each side to harmonic distortions, determining the influence of the utility on harmonic current distortions at the PCC, and determining the influence of the customer on harmonic voltage distortions at the PCC. This paper deciphers these technical questions in the concrete “relevant factors” for the evaluation of harmonic distortions. These “relevant factors” are systematically classified based on the equivalent circuits of utility and customer side for harmonic studies. In this regard, six well-defined “relevant factors” for the Thevenin equivalent circuit and six analogue “relevant factors” for the Norton equivalent circuit are formulated. Some of these “relevant factors” are proposed, for the first time, in this paper. Furthermore, this paper explains the relations between the “relevant factors” to ascertain their interdependence. In addition, the usefulness of the “relevant factors” to answer the technical questions related to the harmonic issues is illustrated using a calculation example.

Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle

The excessive use of power electronics makes power quality problems in power grids increasingly prominent. The estimation of the harmonic parameters of harmonic sources in the power grid and the division of harmonic responsibilities are of great significance for the evaluation of power quality. At present, methods for estimating harmonic parameters and harmonic responsibilities need to provide the amplitude and phase information of the current and voltage of the point of common coupling (PCC). However, in practical engineering applications, the general power quality monitor only provides the amplitude information of the voltage and current of the measured point and the phase difference information between them. Missing phase information invalidates existing methods. Based on the partial least squares regression method, the present work proposes a method for estimating harmonic parameters in the case of monitoring data without phase. This method only needs to measure the amplitude information of the harmonic voltage and current of the PCC and the phase difference between them, then use the measurable data to estimate the harmonic parameters and the harmonic responsibility of each harmonic source. It provides a new way to effectively solve the problem that the measured data of the project has no phase information. The feasibility and effectiveness of the proposed method are proved by simulation data and measured engineering data.

A New Method for Determination of Harmonic Responsibility Based on Quality Engineering Theory

At present, the essence of traditional methods for determination of harmonic responsibility among different parts in power system is mainly based on the contribution of relevant physical quantity on the point of common coupling (PCC) of both utility side and customer side. However, theoretical basis of various methods is of much difference, leading to a large difference in the final calculation result of harmonic responsibility. Therefore, there is no reliable approach applied in actual cases. This project aims to fill the current gap in literature, proposing a new method to determine harmonic responsibility from the perspective of economic loss. Firstly, according to power theory of IEEE Std.1459-2010 and quality engineering theory, the index set of harmonic pollution evaluation on PCC is established, and the expressions of calculation of economic loss are obtained. Secondly, collecting relevant data on PCC, the parameters of Norton equivalent models of utility side and customer side are obtained by the reference impedance method, then contributions of corresponding physical quantity on PCC can be analyzed. Based on relevant expressions of economic loss, calculation results of economic loss are all obtained. At the same time, values of harmonic responsibility of utility side and customer side are calculated. Finally, new adjustment scheme of rewarding-penalizing electricity charge (RPEC) is discussed and compared with other traditional schemes. After comparison, Results of comparative analysis show that the new adjustment scheme can not only increase the penalty for harmonics but also reflect commodity characteristic of electricity energy. In this manner, calculation result of electricity charge is more reasonable. Hence, electricity environment on PCC can be improved.

Comprehensive Harmonic Responsibility Calculation Based on Different Weighting Methods

Dividing the responsibility of harmonics between customer and utility sides is necessary to improve the objectivity, rationality, and scientificalness of power measurement and power quality evaluation. On the basis of expert experience and customer needs and considering the background harmonic fluctuation, this study proposes two reasonable conditions to evaluate harmonic responsibility. At the same time, the reference index of harmonic responsibility calculation and the problem of different frequency harmonics are considered in the comprehensive calculation. On the basis of the IEEE Std. 1459–2010 power theory, the index set of harmonic responsibility is established, and several common subjective and objective weighting methods are used to weigh each index. On the basis of optimization theory, an optimization model is established by constructing a Lagrange function in finding the condition extremum to unify the subjective and objective information. Finally, the calculation method is verified by a Norton equivalent model, and the harmonic responsibility of each index is calculated by the harmonic current vector method; thus, calculating the actual impedance of customer and utility sides is unnecessary, making the calculation simple and effective. Results of the comparative analysis show that the comprehensive evaluation method of harmonic responsibility with the combinatorial weighting method can not only meet the different needs of different groups for harmonic indicators but also reflect the background harmonic fluctuations sensitively. In this manner, the subjective and objective information are unified, and the shortcomings of a single weighting method are overcome. Hence, the evaluation results are accurate, practical, and reasonable.

An improved complex ICA based method for wind farm harmonic emission levels evaluation

Wind farms, as typical renewable energy infrastructure, provide clean energy to the power grid but also inevitably generate harmonic pollution. In the case of a multi-wind farms system, evaluating the harmonic emissions for each farm is essential for the division of harmonic responsibility and the design of the harmonic mitigation scheme. However, this evaluation process faces two challenges. First, the background harmonics of a single wind farm might be unstable. In addition, the harmonic impedance of wind farm side is reduced by the installation of filters. As a result, traditional evaluation methods are no longer applicable. Complex independent component analysis is a well-known method for evaluating harmonic emissions. To make this method feasible in the above situations, we analyze its shortcomings. Furthermore, an improved method is proposed by introducing two screening mechanisms based on sparse component analysis and the maximization of hybrid negentropy. Additionally, the results of simulations and field case verification indicate that the traditional methods have large calculation errors when the background harmonics fluctuate greatly or the harmonic impedance of the wind farm side is not far larger than that of the utility side. In contrast, our novel evaluation method has high precision in the above cases.

Method for detecting harmonic responsibility misjudgements based on waveform correlation analysis

In this study, a method for detecting misjudgements in customer harmonic responsibility determination is proposed. The method is based on waveform correlation analysis. The quantitative relationship between waveform correlation coefficients and equivalent harmonic current injections is studied and discussed through theoretical derivations. The complete harmonic responsibility misjudgement detection method is then proposed on the basis of quantitative analysis and validated by means of simulation studies and field tests. The method has the ability of detecting harmonic responsibility misjudgements made on linear inductive loads using direct harmonic current measurement indices. On-line detection of misjudgements made by power quality monitoring systems in harmonic responsibility incentive management can be achieved by the proposed method.

Harmonic emission level assessment method based on parameter identification analysis

In this study, a new method for harmonic emission level assessment is proposed. The method is based on parameter identification analysis. The quantitative relationship between standard deviation values of equivalent parameters and harmonic emission levels is studied and discussed through theoretical derivations. The complete harmonic emission level assessment method is then proposed on the basis of quantitative analysis and validated by means of simulation studies and field tests. The method has the ability of distinguishing the active injection contents in load harmonic currents and determining the actual harmonic responsibilities of the customers in power systems. On-line power quality monitoring applications and incentive management schemes can be designed by the proposed method.

The study of the responsibility of persons who don’t have children as a promising area of psychological and pedagogical support of future parents

The article describes the problem of psychological support and training for such an untraditional category of people for a customized education system as people expecting or planning to have a child. The results of the study of responsibility as a personal property in the context of parent-child relationships are presented; in particular, the level of severity of the responsibility variables determined using the “Test of liability judgments” by A.I. Krupnova. The primary data underwent mathematical processing, which made it possible to determine the normality of the distribution of the sample, and subsequently establish statistically significant differences between harmonic and inharmonic responsibility variables. By mathematical processing of the data, the severity levels of the variables of the target, motivational, cognitive, productive, dynamic, emotional, regulatory and evaluative-reflexive components of responsibility are determined and the percentage of persons from the surveyed sample who have high, above average, average, below average and low levels is revealed parameters. Interpretation of the data obtained during the study, their analysis allow us to determine the features of the structure of responsibility of people who are expecting or planning to have a baby, as well as draw conclusions about the features of its development.