Harmonic Source Location Research Articles

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.

Harmonic State Estimation for Distribution Networks Based on Multi-Measurement Data

Accurate harmonic state estimation (HSE) is necessary to mitigate harmonic issues in power distribution networks. However, existing HSE methods face difficulties in solution of state equation that the measured data is insufficient with limited monitors. Therefore, this study proposes a HSE algorithm for distribution networks based on multi-measurement data. Firstly, a harmonic source location method based on mutual information and distribution network partition is proposed to reduce the number of state variables and to solve the state equation. Thus, HSE with low resolution (15 min) is realized. Secondly, a HSE method with enhanced resolution is developed. This method establishes maximum entropy principle models for harmonic currents injected into buses, calculates the probability density function (PDF) of the harmonic current, and realizes optimal interpolation of the harmonic data to enhance the resolution of the HSE (3 min) according to the PDF. Finally, the applicability and effectiveness of the proposed method are verified using an IEEE-33 test system simulation and a real power system in South China.

Harmonic source localization method for the port distribution system based on the stagewise regularized orthogonal matching pursuit algorithm

The application of power electronic technology which is widely used in large mechanical equipment of ports causes inevitable harmonic pollution issues. Due to the small capacity of the port power system, harmonic interference will bring huge security risks to its normal operation. Therefore, it is necessary to carry out the effective localization of the harmonic sources in the grid. In this paper, a harmonic source localization method based on the stagewise regularized orthogonal matching pursuit (SROMP) is proposed by improving the orthogonal matching pursuit (OMP) in a compressed sensing theory. It can achieve accurate localization of harmonic sources only through measurement information analysis, which is significantly superior to traditional methods. First, according to the grid topology and line parameters, a system equation is constructed that takes the node harmonic voltage as the measurement variable and the harmonic current injected to the node as the state variable. Next, the equation is solved to obtain the harmonic current value injected to each node. The result can reflect the harmonic injection situation of each node, so as to determine the position of each harmonic source. In order to test the localization effect of the algorithm under different measurement configurations, the SROMP method is compared with other algorithms in considerable simulation experiments on the improved IEEE-33 model, which verifies the stability and accuracy of the proposed algorithm.

Harmonic Source Location and Characterization Based on Permissible Current Limits by Using Deep Learning and Image Processing

Identification of harmonic sources contributing to harmonic distortion, and characterization of harmonic current injected by them, are crucial tasks in harmonic analysis of modern power systems. In this paper, these tasks are addressed based on the permissible current limits recommended by IEEE 519 Standard, with a determination of whether or not injected harmonics are within these limits. If limits are violated, the extent of the violations are characterized to provide information about harmonic current levels in the power system and facilitate remedial actions if necessary. A novel feature extraction method is proposed, whereby each set of harmonic measurements in a power system are transformed into a unique RGB image. Harmonic State Estimation (HSE) is discretized as a classification problem. Classifiers based on deep learning have been developed to subsequently locate and characterize harmonic sources. The approach has been demonstrated effectively both on the IEEE 14-bus system, and on a real transmission network where harmonics have been measured. A comparative study indicates that the proposed technique outperforms state-of-the-art techniques for HSE, including Bayesian Learning (BL), Singular Value Decomposition (SVD) and hybrid Genetic Algorithm Least Square (GALS) method in terms of accuracy and limited number of monitors.

Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

Sound Source Localization (SSL) has numerous applications namely machinery noise control, machinery fault localization, localization of explosives and weaponry in defense, development of hearing aids and robot’s auditory system. Beamforming and holographic SSL methods require numerous microphones and measurements for predicting source location; hence are highly expensive and time consuming. The simpler alternatives require less microphones; but are inaccurate, require high SNR, and have space of non-convergence where source cannot be uniquely localized. There is also a lack of method to localize pure harmonic sources. This paper proposes a method that addresses these limitations by accurately and quickly identifying the exact location of a harmonic sound source in the complete 3D space in acoustic free-field using just five acoustic signals. The novelties of this method are: new array design, spatially averaged Time Delay Estimation (TDE), TDE-ER (Energy Ratio) based localization, ER to remove mirror points, spectral de-noising, and solution of non-convergence. The method is rigorously tested through numerous simulations and real-life free-field experiments. A performance comparison shows that proposed method is more accurate (6.7% error) compared with the existing 3D SSL technique (11.7% error). Overall, it accurately identifies the coordinates of dominant sound source with 9.17% mean error, in presence of inherent sensor noise (14 dB SNR onwards) and background noise (8 dB SNR onwards) in complete 3D space. Interestingly, the sound source location, and sound source frequency is found to effect the consistency of TDE-based SSL methods. The presented work paves way for further advancement in 3D SSL.

Identification of harmonic source location in power distribution network

This paper presents the experimental set-up of identification of harmonic source location in the power distribution network using time-frequency analysis, known as S-transform (ST) at the point of common coupling (PCC). S-transform offers high frequency resolution in analyzing the low frequency component and able to represent signal parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR). The proposed method is based on IEEE Std. 1459-2010, ST, and the significant relationship of spectral impedances components (ZS) that been extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). This experiment was conducted out on an IEEE 4-bus test feeder with a harmonic producing load in numerous different scenarios. The experimental was tested and verified for three consecutive months. The findings of this study reveal that the proposed method provides 100 percent correct identification of harmonic source location.

Harmonic Source Location and Identification in Radial Distribution Feeders: An Approach Based on Particle Swarm Optimization Algorithm

The location and identification of harmonic sources in radial distribution feeders are essential tasks since they improve the power quality monitoring and assist in mitigating issues resulting from harmonic distortions. Thus, this article presents an approach based on particle swarm optimization algorithm with adaptive and individual inertia, which was used to locate and estimate the parameters (harmonic magnitudes, real and reactive power) of the harmonic source with the highest contribution. The proposed approach needs only 20 cycles in steady state, considering the harmonic source pre- and postconnection as input data. In addition, this approach uses few meters to reduce the cost of investment. As a result, it was possible to locate the harmonic source, in the IEEE 34-bus test feeder, with absolute errors less than 1.78 km, using only 3 m. Moreover, the harmonic source identification algorithm reached mean percentage relative errors of less than 3.7%.

K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.

Evaluation of Power Quality and Reliability of Distributed Generation in Smart Grid

With the continuous development of new energy power generation technology, the access capacity of distributed photovoltaics continues to increase, and its impact on the power quality of the distribution network is gradually increasing. The access of distributed power sources brings many problems to the system planning, power quality, and relay protection of the power grid. From the perspective of power quality, this paper analyses the power quality problems caused by the grid-connected distributed power sources and the commonly used improvement methods, and expounds the role of microgrid technology in the hybrid access of multiple distributed power sources to the grid. At the same time, it expounds the technical hotspots of harmonic research on distributed new energy access to the power grid from the aspects of harmonic measurement, harmonic source location and responsibility assessment, harmonic resonance and its instability, and puts forward the future large-scale distributed new. The new harmonic research trend after energy integration provides a theoretical reference for the study of harmonic problems in the distributed new energy grid.

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

<span>The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources. The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (H<sub>V</sub>) and harmonic current (H<sub>C</sub>) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.</span>

Switch Status Identification in Distribution Networks Using Harmonic Synchrophasor Measurements

Topology identification (TI) in distribution networks is a challenging task due to the limited measurement resources and therefore the inevitable need to use pseudo-measurements that are often inaccurate. To address this issue, a new method is proposed in this paper to integrate harmonic synchrophasors into the TI problem in order to enhance TI accuracy in distribution networks. In this method, topology identification is done jointly based on both fundamental synchrophasor measurements and harmonic synchrophasor measurements. This is done by formulating and then solving a mixed-integer linear programming (MILP) problem. Furthermore, an analysis is provided to capture the number of and the location of harmonic sources and sensors that are needed to ensure full observability. The benefits of the proposed TI scheme are compared against those of the traditional scheme that utilizes only the fundamental measurements. Finally, through numerical simulations on the IEEE 33-Bus power system, it is shown that the proposed scheme is considerably accurate compared to the traditional scheme in topology identification.

Harmonic Source Region Location Based on Fast ICA and Scene Clustering

The widespread application of power electronic devices and renewable power generation has resulted in significant harmonic pollution. Harmonic source location is of great importance since it is a prerequisite to mitigate harmonic pollution. In this paper, a scene-based harmonic source region location method is proposed. The proposed method employs the fast independent component analysis (ICA) to locate the harmonic source region as the harmonic measurement matrix is unknown and the measuring equation is not observable. In this way, the range of suspect harmonic sources is narrowed and the cost of harmonic source location is reduced. Furthermore, the improved K-means algorithm is adopted to divide the measurement data into several scenes to improve location accuracy. Finally, a case study is conducted to verify the validity and accuracy of the proposed method.

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

<span>A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.</span>

Accurate harmonic source identification using S-transform

This paper introduces the accurate identification of harmonic sources in the power distribution system using time-frequency distribution (TFD) analysis, which is S-transform. The S-transform is a very applicable method to represent signals parameters in time-frequency representation (TFR) such as TFR impedance ( Z TFR ) and the main advantages of S-transform it can provide better frequency resolution for low frequency components and also offers better time resolution for high-frequency components. The identification of multiple harmonic sources are based on the significant relationship of spectral impedances ( Z S ) that extracted from the Z TFR , consist of the fundamental impedance ( Z 1 ) and harmonic impedance ( Z h ). To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior, with 100% correct identification of harmonic source location. It is proven that the method is accurate, fast and cost-efficient to localize harmonic sources in the power distribution system.

Multi‐harmonic sources location based on sparse component analysis and complex independent component analysis

With the large-scale deployment of power electronic equipment, harmonic sources in the power system are gradually increasing. Traditional harmonic source location methods are inaccurate and they usually require prior information of the location, which is difficult to obtain. This study presents a novel method of harmonic source location that can use less prior information to accurately locate the location of harmonic sources. First, a linear screening criterion is constructed using sparse component analysis. Then, the source signals separated by complex independent component analysis are filtered by linear and non-Gaussian secondary screening, improving the accuracy of source signals and mixing matrix. Finally, the precise location of the harmonic is obtained by matching the mixing matrix with the columns of the matrix, which related to the system topology. Three IEEE systems are used to validate the proposed method. The simulation results show the high accuracy of the proposed method in the harmonic source location.

Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy.

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

Multiple Harmonic Source Location Using the Least Median of Squares Method with the Presence of Outliers in High Voltage Electric Power Systems

A static multipoint method to locate harmonic sources in high-voltage electric power systems based on the least median of squares approach is presented. Because of statistical robustness, the least median of squares method is used, even in the presence of multiple erroneous measurements. The special features of the method are described in this article. IEEE-30 bus test system with three harmonic sources and uniformly distributed erroneous measurements is solved with the least median of squares, Heydt’s and Euclidean norm methods. Several harmonic sources are correctly located with the least median of squares method. The least median of squares approach estimated harmonic bus voltages and currents is close to the actual values. With Heydt’s and the Euclidean norm methods the harmonic sources cannot be located in the presence of uniformly distributed erroneous measurements.

High-Impedance Fault Detection Based on Nonlinear Voltage–Current Characteristic Profile Identification

High-impedance fault detection (HIFD) is crucial in an effectively grounded distribution system because of the potential threat of fire and electric shock. HIFD has been extensively researched for more than 30 years, and a harmonic component in zero-sequence current is typically used in detection algorithms since waveform distortion is often produced by arc flash. For a stable HIF, it would be invalid or has lower sensitivity because of the limited harmonic content in zero-sequence current waveform. To overcome this problem, in this paper, a novel detection algorithm is proposed for detecting waveform distortion in the time domain. First, for the analysis of arc flash, the solid dielectric electrical breakdown theory is verified to be more suitable than theories based on the heat accumulation theory, and a nonlinear arc model that can be used for analyzing arc flash is then proposed. Furthermore, a novel HIFD algorithm based on the identification of nonlinear voltage–current characteristic profiles (VCCPs) is proposed, with the focus on the quenching and restrike dynamic process of an arc flash. The high performance of the algorithm is verified by performing various simulations and examining field data. An HIFD prototype using the proposed algorithm was developed, and it showed excellent performance in real-time digital simulator tests. The VCCP based disturbance detection approach could also be popularized in the applications of relay protection, harmonic source location and early fault alarming in smart grid.

Harmonic Source Location in the Distribution Grid Using Time-Synchronized Measurements

A method for locating harmonic producing source in the low-voltage distribution network is presented. The method aims to employ natural load variability of the existing non-linear installations. By performing multi-point time-synchronized acquisition of the voltage and current waveforms and analysing spectral characteristics of the signals, harmonic impedance of the cable section associated with non-linear load is estimated. A certain rejection algorithm is applied in order to discard data sets with unreliable information. To validate the method, a field test was established in the low-voltage grid. The results demonstrate capability of the proposed technique to accurately estimate harmonic impedances and successfully identify location of harmonic current source in the grid.