Multiple Harmonic Sources Research Articles

Multi-harmonic sources identification and evaluation method based on cloud-edge-end collaboration

With the increase of power electronic devices such as distributed renewable energies, energy storage, and flexible loads, the disturbance sources are rapidly raised and distributed in multiple voltage levels in the distributed network. The harmonics they produce are random and uncertain, and the massive amounts of data propose high demands on server computing capability, which restrict harmonic sources evaluation and significantly influence the judgments of the power quality in the distribution network. In order to improve the power quality and the harmonic source evaluation function of the distribution network, in this paper, a framework for multi-harmonic source identification and evaluation based on cloud-edge-end collaboration is proposed. Firstly, based on the analysis of the functional requirements harmonic sources evaluation in multiple voltage levels, a novel framework of cloud-edge-end collaboration is proposed. Secondly, the cloud-edge-end collaborative harmonic sources evaluation method which executes different service strategies according to voltage levels and grid operation based on the multi-level interaction of edge computing is proposed. Further, the working principle of the proposed novel framework with multiple evaluation methods including the dominant harmonic source identification, concentrated multiple harmonic sources evaluation (CMHSE) and hierarchical harmonic sources evaluation (HHSE) is described in detail in order to show the completed work processes of the proposed framework. Finally, the performance of the proposed method is verified based on the real data measured from different realistic urban power grids in China. The proposed method can effectively evaluate harmonic contributions in distribution systems and in turn provide a basis for harmonic mitigation measures.

Counting and Localization of Multiple Unknown Harmonic Sound Sources in Entire 3D Space Using Just Five Microphones

Counting and localization of multiple sound sources in an unknown environment has many applications, namely fault diagnosis of complex machineries, hearing aids' development, and localization of enemy vehicles in defense. However, multiple sound source localization is extremely challenging because it requires numerous microphones, which increases the cost, computational time, and installation difficulties. Here, we present a new method to count the number of stationary harmonic sound sources and localize them in a free-field environment using a novel oblique square pyramid shaped array of only 5 microphones. We find FFT peaks of microphone signals and compare the ratio of the magnitude of the most dominant peak with remaining peaks' one-by-one. If these ratios are below 25 dB difference, they are considered as sound source and counted. A new time delay and intensity ratio computation is applied to the segregated source signals to calculate exact coordinates of each source. Simulation studies of four concurrent sources show that our method predicts all sources' coordinates at every octant location with mean error of 3.4%, and less than 5% error beyond 20 dB Signal-to-Noise ratio. Mean computational time is 0.92 second. Therefore, our method is low-cost, quicker, more accurate and more reliable than existing techniques.

Research on Supraharmonics Suppression Strategy of Distribution Network based on Dynamic Cooperative Control of Multiple Harmonic Sources

In view of the increasing number of supraharmonic sources in distribution network and the increasing distortion of power grid waveform, a strategy of supraharmonic suppression in distribution network based on dynamic coordinated control of multi-harmonic sources is proposed. The characteristic harmonic current components related to carrier frequency in the output current of each supraharmonic source are shifted by several angles through carrier phase shifting, so that the total current harmonics of all supraharmonic sources merged into point of common coupling (PCC) are obviously reduced to meet the requirements of PCC supraharmonic control. This strategy dynamically adjusts the phase-shift angle according to the running number of supraharmonic sources to avoid the problem that the harmonics of the remaining supraharmonic sources cannot be cancelled after some supraharmonic sources exit the system. The simulation results show that the supraharmonic suppression strategy based on dynamic cooperative control of multi-harmonic sources in distribution network can significantly suppress the supraharmonic current component and voltage distortion rate of a certain number of parallel points.

고조파원의 등가 전류원 모델 추정에 기반한 고조파 전류 기여도 평가

This paper presents a method for evaluating harmonic contributions to current distortion injected into a utility system from multiple harmonic sources at the point of common coupling (PCC). For effective harmonic management, it is necessary to identify harmonic sources and to evaluate their contribution to harmonic current distortion. Basically, to evaluate the harmonic contribution, the equivalent current source model of each harmonic source is required. The proposed method estimates the harmonic current source and impedance parameters of the equivalent model using the data measured at PCC and the recursive least squares (RLS) algorithm with variable forgetting factor. The harmonic current contribution can be also evaluated using the principle of current superposition with the estimated harmonic equivalent current model. The performance of equivalent model parameter estimation and harmonic contribution evaluation of the proposed method was analyzed through a case study using the PSCAD/EMTDC test system.

An artificial neural network based harmonic distortions estimator for grid- connected power converter-based applications

Grid-connected solar Photovoltaic (PV) systems are predicted to cause significant harmonic distortions in today’s power networks due to the increase utilization of power conversion systems widely recognized as harmonic sources. Estimating the actual harmonic emissions of a certain harmonic source can be a challenging task, especially with multiple harmonic sources connected, changes in the system’s characteristic impedance, and the intermittent nature of renewable resources. A method based on an Artificial Neural Network (ANN) system including the location-specific data is proposed in this paper to estimate the actual harmonic distortions of a solar PV inverter. A simple power system is modelled and simulated for different cases to train the ANN system and improve its prediction performance. The method is validated in the IEEE 34-bus test feeder with established harmonic sources, and it has estimated the individual harmonic components with a maximum error of less than 10% and a maximum median of 5.4%.

Multiple harmonic sources identification including inverter‐based distributed generations using empirical Fourier decomposition

AbstractThis paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter‐based distributed generations (DGs) like microturbine (MT), battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly‐fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.

Harmonic Contribution Quantification for Multiple Harmonic Sources Based on Minimum Impedance Fluctuation

Harmonic Contribution Quantification for Multiple Harmonic Sources Based on Minimum Impedance Fluctuation

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.

Harmonic Contribution Evaluation Based on the Distribution-Level PMUs

With the rapid development of renewable energy resources, harmonics in modern distribution systems have become more serious owing to the wide adoption of power electronic devices. Today, distribution-level phasor measurement units (D-PMUs), which can provide synchronized harmonic data with high-time resolution, are being rapidly developed. To utilize the D-PMUs, a hierarchical harmonic contribution (HC) determination method is proposed in this paper for systems with multiple harmonic sources. The HCs were determined using a widely linear complex partial least squares (WL-CPLS) method, which is both accurate and efficient. In an actual power system, the D-PMUs are not fully installed at all buses and the harmonics monitored by each D-PMU are actually the aggregate effects of multiple harmonic sources in the subarea. Based on the above consideration, a hierarchical HC strategy is proposed to locate the main harmonic contributors, layer by layer. The proposed method can be applied to a radial distribution system with any number of D-PMU placements. The methodologies are explained and verified using the IEEE 37-bus system. The real case study results based on the demonstration project are also analyzed in this paper.

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.

A Novel Method for Assessing the Contribution of Harmonic Sources to Voltage Distortion in Power Systems

This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC). The proposed method quantitatively represents harmonic contributions based on the equivalent voltage models of harmonic sources. The equivalent voltage models can be estimated using the recursive least square (RLS) algorithm and measured voltage and current at the PCC. The assessment of harmonic contribution heavily depends on the accuracy of equivalent model estimation. Many existing studies have assumed that the equivalent model parameters of harmonic sources are not changed. However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in harmonic contribution assessment. In this paper, we propose a new assessment method based on variable forgetting factor RLS and parameter change detection to solve the problem. The proposed method is also effective for evaluating comprehensive harmonic contributions as well as individual harmonic contributions. The case studies show that the proposed method is superior to the previous one even under parameter changing conditions.

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.

Identification of multiple harmonic sources in power system containing inverter‐based distribution generations using empirical mode decomposition

This paper proposes an identification method of multiple harmonic sources in the presence of different inverter-based distributed generations (DG) such as photovoltaic (PV), wind turbine with doubly fed induction generator (DFIG), and microturbine (MT) at the point of common coupling (PCC). To classify the linear loads, non-linear loads (NL) and inverter-based DGs, K-nearest neighbours (KNN) classifier was employed. The features were extracted using empirical mode decomposition (EMD), just from voltage waveforms. To reduce the redundant data, dimension of features vector, and time, the Relief-F method was implemented on the extracted features. Utilising only the voltage waveform for extraction of features increases the speed of the process and decreases the number of measurement equipment. To verify the effectiveness of the proposed method, a number of scenarios such as different harmonic sources with various harmonic orders, load and DG levels were simulated. The results on two test systems showed that the proposed method has a high accuracy even in the presence of different inverter-based DGs. It can be used as an added tool for power-quality engineers and can be integrated into monitor instruments. Also, the speed and precision of this method make it suitable for real-time applications in the power-quality issues.

An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram

The identification of multiple harmonic sources (MHS) is vital to identify the root causes and the mitigation technique for a harmonic disturbance. This paper introduces an identification technique of MHS in a power distribution system by using a time-frequency distribution (TFD) analysis known as a spectrogram. The spectrogram has advantages in term of its accuracy, a less complex algorithm, and use of low memory size compared to previous methods such as probabilistic and harmonic power flow direction. The identification of MHS is based on the significant relationship of spectral impedances, which are the fundamental impedance (Z1) and harmonic impedance (Zh) that estimate the time-frequency representation (TFR). To verify the performance of the proposed method, an IEEE test feeder with several different harmonic producing loads is simulated. It is shown that the suggested method is excellent with 100% correct identification of MHS. The method is accurate, fast and cost-efficient in the identification of MHS in power distribution arrangement.

Comprehensive identification of multiple harmonic sources using fuzzy logic and adjusted probabilistic neural network

This paper presents a comprehensive approach based on fuzzy logic and probabilistic neural network (PNN) to identify location, relative level, and type of multiple harmonic sources in power distribution systems. The location and relative level of harmonic sources were determined in the fuzzy stage by interpreting harmonic powers together with network impedances. Then, the type of the harmonic sources was classified in the neural stage using adjusted PNN. In the proposed method, the harmonic powers were considered as classification features. Then, ReliefF feature selection method was used to reduce the redundant data and dimension of features vector. A new modified adaptive imperialist competitive algorithm (MAICA) was proposed to determine the only adjusted parameter of the PNN classifier. Furthermore, a deep belief network (DBN) was applied in the neural stage, and its results were compared with the PNN classifier. The proposed approach was evaluated on IEEE 18-bus and IEEE 69-bus test systems. Unlike the single point methods, the presented method provides information on multiple harmonic sources in the whole of the distribution system. The results show that the comprehensive approach identifies the multiple harmonic sources with high accuracy.

Research on the Differentiation and Evaluation Strategy of the Precision of the Harmonic Responsibility

In the power system with multiple harmonic sources, there is interaction and influence between harmonic sources. Norton equivalent circuit is proposed. Then, the analysis of the harmonic coupling factor to distinguish the negative influence brought to the responsibility of using partial least squares method is given. Finally, through the establishment of the IEEE 13 node simulation model to verify the above algorithm in the correctness of the analysis of multi harmonic sources.

A Fast Localization of Multiple Harmonic Sources for Rectifier Loads by Utilizing Periodogram

This paper introduces a fast method to localize the multiple harmonic sources (MHS) for rectifier based loads in power distribution system by utilizing periodogram technique with a single-point measurement approach at the point of common coupling (PCC). The periodogram is a fast and accurate technique for analyzing and distinguishing MHS location in power system. Matlab simulation is carried out several unique cases on IEEE test feeder cases due to validate the proposed method. The identification of MHS location is based on the significant relationship of spectral impedance which are fundamental impedance (Z 1 ) and harmonic impedance (Z h ) that's extracted from an impedance power spectrum. It is verified that the proposed method is fast, accurate, and cost efficient in localizing MHS. In addition, this method also contributes 100% correct identification of MHS location.

An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram

This paper introduces an improved of multiple harmonic sources identification that been produced by inverter loads in power system using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is a very applicable method to represent signals in time-frequency representation (TFR) and the main advantages of spectrogram are the accuracy, speed of the algorithm and use low memory size such that it can be computed rapidly. The identification of multiple harmonic sources is based on the significant relationship of spectral impedances which are the fundamental impedance (Z1) and harmonic impedance (Zh) that extracted from TFR. To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases with different harmonic producing loads on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior with 100% correct identification of multiple harmonic sources. It is envisioned that the method is very accurate, fast and cost efficient to localize harmonic sources in distribution system.

Localization and Characterization of Multiple Harmonic Sources

We introduce a new and intuitive algorithm to characterize and localize multiple harmonic sources intersecting in the spatial and frequency domains. It jointly estimates their fundamental frequencies, their respective amplitudes, and their directions of arrival based on an intelligent non-parametric signal representation. To obtain these parameters, we first apply variable-scale sampling on unbiased cross-correlation functions between pairs of microphone signals to generate a joint parameter space. Then, we employ a multidimensional maxima detector to represent the parameters in a sparse joint parameter space. In comparison to others, our algorithm solves the issue of pitch-period doubling when using cross-correlation functions, it estimates multiple harmonic sources with a signal power smaller than the signal power of the dominant harmonic source, and it associates the estimated parameters to their corresponding sources in a multidimensional sparse joint parameter space, which can be directly fed into a tracker. We tested our algorithm and three others on synthetic data and speech data recorded in a real reverberant environment and evaluated their performance by employing the joint recall measure, the root-mean-square error, and the cumulative distribution function of fundamental frequencies and directions of arrival. The evaluations show promising results: Our algorithm outperforms the others in terms of the joint recall measure, and it can achieve root-mean-square errors of 1 Hz or 1 $^\circ$ and smaller, which facilitates, e.g., distant-speech enhancement or source separation.

A dynamic approach to identification of multiple harmonic sources in power distribution systems

This paper provides a novel algorithm for identifying harmonic sources in power distribution systems. This algorithm is developed based on an observer design to carry out harmonic estimation for a combination of suspicious nodes. The estimation error is analyzed to determine the existence of harmonic sources in the specified node combinations. This approach is used to determine the source of both single and multiple harmonic sources in distribution systems with time varying load parameters. For systems with a large number of suspicious nodes, the system may be divided into sub-systems and the algorithm is applied to each sub-system to identify the harmonic sources present. Simulations are carried out on a benchmark IEEE distribution test feeder for both single and multiple harmonic sources and a number of scenarios are simulated to verify the accuracy and robustness of the proposed approach. The results show that the node combinations which represent the harmonic sources yield an estimation error which approaches zero asymptotically.