Harmonic Source Identification Research Articles

Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers

The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.

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.

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 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.

Harmonic source identification method based on sinusoidal approximation

The proportion of nonlinear electronic devices such as electric arc furnace is increasing day by day, and the proportion of harmonic content in power grid is also more and more cannot be ignored. Therefore, the identification of harmonic source has received the attention of experts and scholars in the industry at present. Therefore, how to accurately locate and reduce harmonic content from power system source becomes the first step to improve power quality. This paper firstly analyzes the causes of harmonics and points out that nonlinear load is the fundamental cause of harmonics in power system. Secondly, the load equivalent model, the composition of the model and the method to determine the model parameters are established. Meanwhile, the current distortion coefficient is introduced to determine the harmonic source. Finally, an example is designed to verify the feasibility and rationality of the proposed method. Compared with the existing analytical methods at present, the proposed method has significantly improved the accuracy of harmonic source identification.

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.

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>

Compressive Sensing-Based Harmonic Sources Identification in Smart Grids

Identifying the prevailing polluting sources would help the distribution system operators in acting directly on the cause of the problem, thus reducing the corresponding negative effects. Due to the limited availability of specific measurement devices, ad hoc methodologies must be considered. In this regard, compressive sensing (CS)-based solutions are perfect candidates. This mathematical technique allows recovering sparse signals when a limited number of measurements are available, thus overcoming the lack of power quality meters. In this article, a new formulation of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization algorithm for CS problems, with quadratic constraint, has been designed and investigated in the framework of the identification of the main polluting sources in smart grids. A novel whitening transformation is proposed for this context. This specific transformation allows the energy of the measurement errors to be appropriately estimated, and thus, better identification results are obtained. The validity of the proposal is proven by means of several simulations and tests performed on two distribution networks for which suitable measurement systems are considered along with a realistic quantification of the uncertainty sources.

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.

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

&lt;span&gt;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.&lt;/span&gt;

Harmonic Source Identification in Power Distribution System and Meter Placement using Network Impedance Approach

The growing use of non-linear loads in the electrical systems has made harmonics a serious problem. Harmonic disturbance leads to degradation of power quality by deforming the current or voltage waveforms, thus, necessitating the effective techniques for harmonics detection. The purpose of this study is to propose a method for a single harmonic source identification in power distribution system by implementing a network impedance technique, and optimize the meters allocation by optimum meter placement algorithm (OMPA). The main advantage of this technique is that it results in enhanced accuracy with minimum vulnerability towards deviations in the measurements. Moreover, it minimizes the number of nodes for meter allocations, thereby resulting in economic advantages. To validate the results and effectiveness of the proposed methodology, a standard IEEE 13-Bus industrial network is designed using ETAP software and the algorithm is developed in MATLAB software. The validation of proposed algorithm OMPA is done by comparing its results with Monte Carlo Algorithm (MCA) technique. The results show that without any deviation in the network impedances, OMPA gives 89% accuracy as compared to 75% accuracy of MC. With the deviations in the harmonic impedances, the accuracy of both algorithms is decreased. For the deviation value ꝺ = 1-13 in the harmonic impedances, the overall accuracy of OMPA stays at 75%, while that of MCA drops down to 56%. The developed algorithm OMPA is not only better in performance in harmonics identification with minimum number of meters, but also shows more resistance to the variations in the harmonic impedances as compared to MCA.

An improved active power direction method for harmonic source identification

Due to significant increment in harmonic polluting loads in the power system, there has been enhanced attention of the power professionals towards the estimation of harmonic signals and identification of their sources in the system. Harmonic source identification is an important step for proper accountability, monitoring, and mitigation of any harmonic pollution. The active power direction (APD) method is one of the conventional approaches for harmonic source detection in the distribution system. Although it is simple and easy to implement, serious concerns were raised on its validity, as the direction of active power is dependent on the phase angle. In this paper, APD is augmented with distorting and non-distorting power to improve its accuracy and reliability for harmonic source identification. The distorting and non-distorting portions of the loads are separated, and the distorting and non-distorting powers are calculated at each node. These calculated powers, in addition to the direction of the harmonic active power, are used to formulate the logic required for deciding the severity index at each node. The validity of the method has been tested on a single-phase network, an IEEE-5 bus system, and an IEEE-14 bus system. It has been observed that the proposed method provides good results than conventional APD with the same measurement requirement.

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.

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid.

Several Sufficient Conditions for Harmonic Source Identification in Power Systems

The power direction method is a widely used method for harmonic source identification. This method can work with no harmonic impedance information but is not theoretically rigorous. To overcome this theoretical shortcoming of the traditional power direction method, six existence criteria and two dominance criteria are proposed in this paper. These are all sufficient conditions that are mathematically derived based on the widely used utility–customer equivalent circuit. The existence criteria can be used to determine whether some harmonic source exists on the customer or utility side, and the dominance criteria can be used to identify whether the customer side is the dominant harmonic source. These criteria can be regarded as an improved power direction method, and the key improvement is the establishment of a rigorous theoretical basis. These criteria were tested using a MATLAB program, and no counterexample was found. Their practical application for harmonic source identification in a transformer substation is also introduced and discussed.

IDENTIFICATION OF HARMONIC DISTORTION SOURCES IN DISTRIBUTION SYSTEMS USING THE DISCRETE FOURIER TRANSFORM ON PERIODS

The presence of non-linear loads and the increase in the number of systems of distributed generation of electricity lead to a distortion of the voltage and current waveform in distribution systems (DS), то есть к появлению гармоник тока и напряжения. In this case, the power system is obliged to supply electricity only to the fundamental frequency of 50 Hz with constant amplitude. Power supply organizations usually disclaim responsibility for the causes of harmonics by introducing standards or recommendations for limiting the levels of harmonic components in the points of common connection of consumers. These documents do not take into account the composition of DS equipment and, accordingly, the damage from this harmonics for network equipment and consumer equipment. The urgency of the work is due to the need to reliably identify the sources of harmonic distortion in the supply system for the effective functioning of the system of penalties and fines and to more effectively determine the list of measures to improve the electric power quality. The paper reviews the existing methods for distortion sources identification in supply systems. In most cases, the fast Fourier transform (FFT) is used as a method of harmonic analysis to determine the sources of distortion, which in the case of a rapidly changing non-linear load does not reliably determine the sources of distortion and the degree of participation of each DS’s subject in the power distribution of higher harmonics. In order to detail the frequency content of the signal at the measurement interval, the spectral points obtained as a result of the FFT were calculated using the Discrete Fourier Transform (DFT). A new approach to the harmonic sources identification is proposed using a method based on the measurement of harmonic power flux sense using the Discrete Fourier Transform on periods by the example a distribution network model.

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.

On the Dominant Harmonic Source Identification— Part I: Review of Methods

Analyzing the harmonic distortions at the point of common coupling is thoroughly addressed in this two-part paper. The main aim of such an analysis is to identify which of the utility and customer sides is significantly responsible for the harmonic distortions at the point of common coupling. In Part I, the meaning of the dominant side is challenged fundamentally. Here, two different definitions for the dominant side are discussed: The side with the larger equivalent harmonic “voltage” or “current” source. It is shown that these definitions are not identical. Subsequently, common methods are categorized in three general concepts based on: the direction of active power flow, the reactive power, and the voltage-current ratio. These three categories as well as their validity are analytically reviewed for both the definitions. The concept based on the voltage–current ratio has been explicitly presented in the original work for identifying the dominant equivalent harmonic voltage source. This concept is developed in this paper for identifying the dominant equivalent harmonic current source as well. In Part II, the application of the methods is illustrated using some common calculation examples. In addition, these methods are described verbally to provide a physical interpretation. Some practical aspects regarding the implementation of the methods are discussed as well.