Journal articles: 'Multiple harmonic sources identification'

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M., H. Jopri, R. Abdullah A., Manap M., Sutikno T., and R. Ab. Ghani M. "An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram." Bulletin of Electrical Engineering and Informatics 7, no. 2 (2018): 244–56. https://doi.org/10.11591/eei.v7i2.1188.

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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.

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Hjertberg, Tommy, and Sarah K. Rönnberg. "Identifying Harmonic Sources by Profiling Discrete Harmonic Cycle Time." Energies 18, no. 4 (2025): 853. https://doi.org/10.3390/en18040853.

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Harmonic emissions cause power quality issues in electrical systems, making source identification necessary to determine the best way of remedying them. Existing methods rely on measuring variations in harmonic amplitude, angle, and phase which require multiple measurement points or extensive system knowledge. We identified a methodological research gap because Discrete Harmonic Cycle Time (DHCT) has not been evaluated as a measuring principle for harmonic source identification. We propose a method using DHCT to enable single point measurements to profile harmonic sources. To determine the cycle length, we used a combination of sifting, a filter bank of cascaded high-pass and notch filters, and zero-crossing detection. For comparing the devices, we extracted motifs from the time series of discrete cycle lengths and applied principal component analysis. While it has previously been known in other contexts that harmonics can have varying cycle lengths, our laboratory results show that this is also true for the emissions of power electronic devices and that the differentiation can be used to identify the devices, thus bridging this knowledge gap. This method is independent of system impedance and topology. While further validation in more complex environments is needed, our results suggest that devices can be identified using this measurement principle. Since the measuring principle is orthogonal to other methods, it has potential as a complementary tool in harmonic source identification.

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H. Jopri, M., A. R. Abdullah, M. Manap, T. Sutikno, and M. R. Ab Ghani. "An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram." Bulletin of Electrical Engineering and Informatics 7, no. 2 (2018): 244–56. http://dx.doi.org/10.11591/eei.v7i2.1188.

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Abstract:

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.

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Jopri, M. H., A. R. Abdullah, M. Manap, M. R. Yusoff, T. Sutikno, and M. F. Habban. "An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 4 (2016): 1355. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1355-1365.

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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.

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M., Hatta Jopri, Rahim Abdullah A., Manap Mustafa, Rahimi Yusoff M., and Sutikno Tole. "A Fast Localization of Multiple Harmonic Sources for Rectifier Loads by Utilizing Periodogram." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 71–78. https://doi.org/10.12928/TELKOMNIKA.v15i1.3120.

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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 (Z1) and harmonic impedance (Zh) 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.

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Mohd, Hatta Jopri, Rahim Abdullah Abdul, Karim Rony, Nikolovski Srete, Sutikno Tole, and Manap Mustafa. "Accurate harmonic source identification using S-transform." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2708~2717. https://doi.org/10.12928/TELKOMNIKA.v18i5.5632.

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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 (ZTFR) 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 (ZS) that extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). 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.

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Wang, Yue, Yonggang Li, Yinan Yang, et al. "A gray-box harmonic resonance frequency identification method of multiple-inverter-fed power system oriented to system designers." Wind Engineering 46, no. 2 (2021): 545–55. http://dx.doi.org/10.1177/0309524x211037918.

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This paper presents a gray-box harmonic resonance frequency identification method of multiple-inverter-fed power system, which enables modal analysis oriented to system designers based on only frequency response data provided by diverse vendors or measured by frequency scanning. First, admittance transfer functions of all grid-connected inverters (GCIs) are fitted using Matrix Pencil Method-Vector Fitting (MPM-VF) combined method. Then, node admittance matrix (NAM) is formed according to the topology of whole system. Finally, harmonic resonance frequency along with changes in number of GCIs are identified by NAM-based modal analysis (MA). The proposed gray-box identification method is implemented in a typical multiple-inverter-fed power system. The correctness of harmonic resonance frequency identification results and the effectiveness of the presented method are verified by simulation results obtained in Matlab/Simulink platform and OPAL-RT digital real-time simulation platform. Based on the identification results, a more stable and better power quality multiple-inverter-fed power system can be built by system designers though avoiding the appearance of harmonic sources with corresponding resonance frequency.

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A.S., Hussin, Abdullah A.R., Jopri M.H., Sutikno T., Saad N.M., and Tee Weihown. "Harmonic Load Diagnostic Techniques and Methodologies: A Review." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 690–95. https://doi.org/10.11591/ijeecs.v9.i3.pp 690-695.

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This paper will review on the existing techniques and methodologies of harmonic load diagnostic system. The increasingly amount of harmonic producing load used in power system are the main contribution in quantifying each harmonic disturbance effects of the multiple harmonic producing loads and it became very important. Literature proposes two different techniques and methods on the harmonic source identification under the soft computing technique classification. The advantages and disadvantages of harmonic load identification techniques and methods are discussed in this paper. In the proposed method, the issue on the harmonic contribution is determine and transformed to a data correlation analysis. Several techniques to identify the sources of harmonic signals in electric power systems are described and reviewed based on previous paper. Comparative studies of the methods are also done to evaluate the performance of each technique. However, without sufficient information in this inconsistent environment on the property of the power system, accurate harmonic producing load diagnosis methods are important and further investigations in this regard assumes great implication.

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Ujile, Awajiokiche, and Zhengtao Ding. "A dynamic approach to identification of multiple harmonic sources in power distribution systems." International Journal of Electrical Power & Energy Systems 81 (October 2016): 175–83. http://dx.doi.org/10.1016/j.ijepes.2016.02.038.

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Hussin, A. S., A. R. Abdullah, M. H. Jopri, T. Sutikno, N. M. Saad, and Weihown Tee. "Harmonic Load Diagnostic Techniques and Methodologies: A Review." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 690. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp690-695.

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This paper will review on the existing techniques and methodologies of harmonic load diagnostic system. The increasingly amount of harmonic producing load used in power system are the main contribution in quantifying each harmonic disturbance effects of the multiple harmonic producing loads and it became very important. Literature proposes two different techniques and methods on the harmonic source identification under the soft computing technique classification. The advantages and disadvantages of harmonic load identification techniques and methods are discussed in this paper. In the proposed method, the issue on the harmonic contribution is determine and transformed to a data correlation analysis. Several techniques to identify the sources of harmonic signals in electric power systems are described and reviewed based on previous paper. Comparative studies of the methods are also done to evaluate the performance of each techniques. However, without sufficient information in this inconsistent environment on the property of the power system, accurate harmonic producing load diagnosis methods are important and further investigations in this regard assumes great implication.

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11

Saxena, D., Sayak Bhaumik, and S. N. Singh. "Identification of Multiple Harmonic Sources in Power System Using Optimally Placed Voltage Measurement Devices." IEEE Transactions on Industrial Electronics 61, no. 5 (2014): 2483–92. http://dx.doi.org/10.1109/tie.2013.2270218.

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Moradifar, Amir, Asghar Akbari Foroud, and Khalil Gorgani Firouzjah. "Comprehensive identification of multiple harmonic sources using fuzzy logic and adjusted probabilistic neural network." Neural Computing and Applications 31, S1 (2017): 543–56. http://dx.doi.org/10.1007/s00521-017-3022-8.

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Fernando, Rashen, Manan Mittal, Yongjie Zhuang, Ryan M. Corey, and Andrew C. Singer. "Physics informed sound field interpolation using an acoustic sensor network." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A276. http://dx.doi.org/10.1121/10.0027490.

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Sound field estimation is the process of analyzing and characterizing the distribution of sound waves in a particular physical space. The applications of sound field estimation extend to various areas, including the visualization of acoustic fields, interpolation of room impulse responses, identification of sound sources, capturing sound fields for spatial audio, and spatial active noise control, among other potential uses. In a previous study, a directionally weighted kernel has been used to estimate the sound field, where the priori information of source directions is employed to improve the estimation accuracy. In another separate study, spherical harmonics have been used to represent the sound field. However, the order of spherical harmonic coefficients was limited due to the limited number of microphones. This research introduces a novel method for sound field estimation using multiple microphones to sample a source-free volume. A physics-informed neural network is used to predict the spherical harmonics coefficients and locations of unknown sources to estimate the sound field.

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Lin, W. M., C. H. Lin, K. P. Tu, and C. H. Wu. "Multiple Harmonic Source Detection and Equipment Identification With Cascade Correlation Network." IEEE Transactions on Power Delivery 20, no. 3 (2005): 2166–73. http://dx.doi.org/10.1109/tpwrd.2004.843462.

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15

Moradifar, Amir, Asghar Akbari Foroud, and Milad Fouladi. "Identification of multiple harmonic sources in power system containing inverter-based distribution generations using empirical mode decomposition." IET Generation, Transmission & Distribution 13, no. 8 (2019): 1401–13. http://dx.doi.org/10.1049/iet-gtd.2018.5382.

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Setti, Mohammed, and Mohamed Cherkaoui. "Low switching frequency modulation for generalized three-phase multilevel inverters geared toward Grid Codes compliance." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (2021): 2349. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2349-2357.

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In this paper, a generalized three-phase multilevel power inverter (MLI) structure is proposed under asymmetric configurations. The operating mode and the switching combinations are briefly exposed according to the parity of the number of direct current (DC) voltage sources in use. Subsequently, the proposed topology is evaluated in terms of commonly used factors and then benchmarked against some of the state-of the-art cascaded MLIs featuring multiple DC voltage sources (MDCS-CMLIs) while putting emphasis on the reduction of power switching devices. Moreover, a new nearest level control (NLC)-based modulation technique is designed for the purpose of better comply with some quality grid codes, namely the European EN 50160 and the International IEC 61000-2-12. The identification of the optimal control thresholds is realized by a constrained optimization algorithm (e.g., particle swarm optimization (PSO)) which is implemented in python script and validated through SIMULINK fast fourier transform (FFT) analysis tool. Lastly, the harmonic performance of the proposed technique is compared side-by-side with that of the conventional NLC scheme and exhibits significant reduction in harmonic distortion.

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Mohammed, Setti, and Cherkaoui Mohamed. "Low switching frequency modulation for generalized three-phase multilevel inverters geared toward Grid Codes compliance." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (2021): 2349–57. https://doi.org/10.11591/ijpeds.v12.i4.pp2349-2357.

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In this paper, a generalized three-phase multilevel power inverter (MLI) structure is proposed under asymmetric configurations. The operating mode and the switching combinations are briefly exposed according to the parity of the number of direct current (DC) voltage sources in use. Subsequently, the proposed topology is evaluated in terms of commonly used factors and then benchmarked against some of the state-of the-art cascaded MLIs featuring multiple DC voltage sources (MDCS-CMLIs) while putting emphasis on the reduction of power switching devices. Moreover, a new nearest level control (NLC)-based modulation technique is designed for the purpose of better comply with some quality grid codes, namely the European EN 50160 and the International IEC 61000-2-12. The identification of the optimal control thresholds is realized by a constrained optimization algorithm (e.g., particle swarm optimization (PSO)) which is implemented in python script and validated through SIMULINK fast fourier transform (FFT) analysis tool. Lastly, the harmonic performance of the proposed technique is compared side-by-side with that of the conventional NLC scheme and exhibits significant reduction in harmonic distortion.

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Xu, Qunwei, Feibai Zhu, Wendong Jiang, et al. "Efficient Identification Method for Power Quality Disturbance: A Hybrid Data-Driven Strategy." Processes 12, no. 7 (2024): 1395. http://dx.doi.org/10.3390/pr12071395.

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The massive integration of distributed renewable energy sources and nonlinear power electronic equipment has given rise to power quality issues such as waveform distortion, voltage instability, and increased harmonic components. Nowadays, the pollution of power quality is becoming increasingly severe, posing a potential threat to the security of the power grid and the stable operation of electrical equipment. Due to the presence of significant noise interference in the collected signals, existing methods still face issues such as low accuracy in disturbance identification and high computational complexity. To address these problems, this paper proposes a hybrid data-driven strategy that can significantly improve the accuracy and speed of identification. Firstly, the wavelet packet transform (WPT) method is employed to denoise the power disturbance signals. Subsequently, the local mean decomposition (LMD) algorithm is used to adaptively decompose the nonlinear and complex time series into multiple product function components. Feature extraction of the disturbance signals is then achieved by calculating entropy values after local mean decomposition, and a feature matrix is constructed from the entropy values of each component for analysis in disturbance identification. Finally, an extreme learning machine (ELM) is employed for the identification and classification of transient power disturbance signals. The verification of numerical examples demonstrates the feasibility and effectiveness of the proposed method in this paper.

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Fenyvesi, Bence, and Csaba Horváth. "Identification of Turbomachinery Noise Sources via Processing Beamforming Data Using Principal Component Analysis." Periodica Polytechnica Mechanical Engineering 66, no. 1 (2021): 32–50. http://dx.doi.org/10.3311/ppme.18555.

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Complex turbomachinery systems produce a wide range of noise components. The goal is to identify noise source categories, determine their characteristic noise patterns and locations. Researchers can then use this information to quantify the impact of these noise sources, based on which new design guidelines can be proposed. Phased array microphone measurements processed with acoustic beamforming technology provide noise source maps for pre-determined frequency bands (i.e., bins) of the investigated spectrum. However, multiple noise generation mechanisms can be active in any given frequency bin. Therefore, the identification of individual noise sources is difficult and time consuming when using conventional methods, such as manual sorting. This study presents a method for combining beamforming with Principal Component Analysis (PCA) methods in order to identify and separate apart turbomachinery noise sources with strong harmonics. The method is presented through the investigation of Counter-Rotating Open Rotor (CROR) noise sources. It has been found that the proposed semi-automatic method was able to extract even weak noise source patterns that repeat throughout the data set of the beamforming maps. The analysis yields results that are easy to comprehend without special prior knowledge and is an effective tool for identifying and localizing noise sources for the acoustic investigation of various turbomachinery applications.

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Matthews, Michael L., Jeff Bos, Jacquelyn M. Crebolder, and Sharon McFadden. "Modeling the Effectiveness of Tools to Assist Sonar Operators." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 12 (2005): 1124–28. http://dx.doi.org/10.1177/154193120504901205.

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The task of building the underwater picture from sonar data is made complex by high volumes of noise and multiple data that arrive from a variety of acoustic sources detected at great distances by modern, sonar equipment. Typically, acoustic sources from ships have a complex spectrum consisting of several base frequency components and related harmonics. The task for operators is to analyse the data to determine if there is a pattern that represents the signature of a known source, thereby leading to identification of a vessel. Since the task can be highly labour intensive automated decision aids may be of value to the operator. This project addresses how to predict and optimise the impact of new technologies in system re-design by using a modeling/simulation approach to operator-system functionality. A generic sonar analysis process was simulated and the effectiveness of a decision aid evaluated. The improvement in performance predicted by the aid was then validated experimentally.

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Cole, M. O. T., P. S. Keogh, and C. R. Burrows. "Control of multifrequency rotor vibration components." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 216, no. 2 (2002): 165–77. http://dx.doi.org/10.1243/0954406021525106.

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A method is developed for the control of rotor lateral vibration using multiple frequency components. The control strategy uses a generalized algorithm for the real-time calculation of the amplitude and phase of the vibration components. The complex amplitudes are evaluated successively at the controller sample frequency and can therefore be used for dynamic feedback control. Parallel control of all frequency components is achieved using frequency-matched control signals with amplitude and phase dictated by the control algorithm. The strategy is evaluated experimentally using a flexible rotor system with magnetic bearings. The controller gain matrices are calculated from frequency response identification. The controller sample frequency is the rotational frequency, but the vibration frequencies that are controlled simultaneously include harmonics and subharmonics of the rotational frequency. The controller is shown to be effective in reducing rotor vibration arising from various sources and having a number of discrete frequency components.

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Lu, Zhiye, Lishu Wang, and Panbao Wang. "Microgrid F36ault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling." Energies 17, no. 11 (2024): 2699. http://dx.doi.org/10.3390/en17112699.

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The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system’s topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model’s troubleshooting decision-making process, reduces the model’s subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model’s effectiveness and adaptability.

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Pessentheiner, Hannes, Martin Hagmuller, and Gernot Kubin. "Localization and Characterization of Multiple Harmonic Sources." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 8 (2016): 1348–63. http://dx.doi.org/10.1109/taslp.2016.2556282.

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Hartana, R. K., and G. G. Richards. "Constrained neural network-based identification of harmonic sources." IEEE Transactions on Industry Applications 29, no. 1 (1993): 202–8. http://dx.doi.org/10.1109/28.195908.

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Kalyuzhniy, D. "IDENTIFICATION AND EVALUATION OF HARMONIC VOLTAGE SOURCES IN POWER SUPPLY SYSTEMS." Municipal economy of cities 1, no. 161 (2021): 157–62. http://dx.doi.org/10.33042/2522-1809-2021-1-161-157-162.

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Non-sinusoidal voltage in power supply systems leads to economic losses that need to be distributed and compensated. This problem is solved on the basis of the problem of identification and evaluation of the influence of voltage distortion sources. To date, existing methods for identifying and assessing the impact of harmonic voltage sources have significant limitations of practical implementation. This is due to their basic mathematical model and local approach to measuring the parameters of the network. In the given research the new mathematical model of identification and an estimation of harmonic voltage sources that is focused on the distributed measuring system is presented. The criterion for identifying harmonic voltage sources is the distorting nodal current. To adequately determine it, it is necessary not only to measure the parameters of the network mode in the base nodes by the currents of the higher harmonic components, but also to control the structure and parameters of the replacement circuit of the entire power supply system. To achieve this goal, it is proposed to use a distributed measuring system, which is based on vector measurement systems and control systems. The estimation of the influence of harmonic voltage sources is directly proportional to the distorting nodal current, where the coefficient of proportionality is either mutual or intrinsic resistance of power supply systems, that connects the location of the distortion source and the place for which its influence is estimated. In order to control the influence of measurement errors and determine the parameters of circuits for replacing elements of the power supply system, the method and algorithm for identifying and assessing the impact of harmonic voltage sources should be developed based on the principle of excluding one of the voltage distortion sources.

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Conde Mones, José Julio, Carlos Arturo Hernández Gracidas, María Monserrat Morín Castillo, José Jacobo Oliveros Oliveros, and Lorenzo Héctor Juárez Valencia. "Stable Numerical Identification of Sources in Non-Homogeneous Media." Mathematics 10, no. 15 (2022): 2726. http://dx.doi.org/10.3390/math10152726.

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In this work, we present a numerical algorithm to solve the inverse problem of volumetric sources from measurements on the boundary of a non-homogeneous conductive medium, which is made of conductive layers with constant conductivity in each layer. This inverse problem is ill-posed since there is more than one source that can generate the same measurement. Furthermore, the ill-posedness is due to the fact that small variations (or errors) in the measurement (input data) can produce substantial variations in the identified source location. We propose two steps to solve this inverse problem in some classes of sources: we first recover the harmonic part of the volumetric source, and, in a second step, we compute the non-harmonic part of the source. For the reconstruction of the harmonic part of the source, we follow a variational approach based on the reformulation of the inverse problem as a distributed control problem, for which the cost function incorporates a penalized term with the input data on the boundary. This cost function is minimized by a conjugate gradient algorithm in combination with a finite element discretization. We recover the non-harmonic component of the source using a priori information and an iterative algorithm for some particular classes of sources. To validate the numerical methodology, we develop synthetic examples both in circular (simple) and irregular (complex) regions. The numerical results show that the proposed methodology allows to recover the complete source and produce stable and accurate numerical solutions.

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Heydt, G. T. "Identification of harmonic sources by a state estimation technique." IEEE Transactions on Power Delivery 4, no. 1 (1989): 569–76. http://dx.doi.org/10.1109/61.19248.

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Carta, Daniele, Carlo Muscas, Paolo Attilio Pegoraro, Antonio Vincenzo Solinas, and Sara Sulis. "Compressive Sensing-Based Harmonic Sources Identification in Smart Grids." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2020.3036753.

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Li, Yaqiong, Zhanfeng Deng, Tongxun Wang, Guoliang Zhao, and Shengjun Zhou. "Coupled Harmonic Admittance Identification Based on Least Square Estimation." Energies 11, no. 10 (2018): 2600. http://dx.doi.org/10.3390/en11102600.

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Norton equivalent circuit is a commonly used model in estimating harmonic current emissions of harmonic sources. It however cannot reflect the mutual coupling relationships among voltage and current in different harmonic orders. This paper proposes a new method to identify parameters in a coupled harmonic admittance model. The proposed method is conducted using voltage and current measurements and is based on least square estimation technique. The effectiveness of the method is verified through time-domain simulations for a grid-connected converter and also through field data obtained from a ±800 kV converter station. The experimental results showed that the proposed method presents higher accuracy in terms of harmonic current emission estimation compared with three Norton-base methods.

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Huang, Cong Hui, Chia Hung Lin, Chung Chi Huang, and Chia Hung Wang. "Using Wavelet Hybrid Self-Organizing Feature Map Network for V-I Based Multiple Harmonic Sources Classification." Applied Mechanics and Materials 764-765 (May 2015): 545–49. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.545.

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This paper proposes a method using non-linear voltage-current characteristics for multiple harmonic sources classification using wavelet hybrid neural network (WHNN). Typical voltage-current characteristics of harmonic sources are non-linear closed curves in the time-domain, referring to the converters, reactors, and non-linear loads. The hybrid neural network is a two-subnetwork architecture, consisting of wavelet layer and a self-organizing feature map (SOFM) network connected in cascade. The effectiveness of the proposed method is demonstrated by numerical tests. The results of multiple harmonic sources show the computational efficiency and accurate classification.

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Jiang, Shan, Min You Chen, Hao Lin, Zhi Sheng Lv, and Ang Fu. "An Identification Method of Harmonic Source in the Power System Based on Independent Component Analysis." Applied Mechanics and Materials 380-384 (August 2013): 4088–93. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4088.

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In this research, an identification method of harmonic source, based on independent component analysis, was proposed. An unknown harmonic source in the equivalent circuit of power system was regarded as the signal source in the independent component analysis. The known voltage and current of PCC were taken as observed quantities. The independent component analysis and the optimization algorithm were utilized to construct a matrix that could linearly transform voltage and current of PCC into mutually independent signal sources. The harmonic impedance and harmonic source of both the user side and system side were obtained. Then the contribution of users to the harmonic distortion at PCC could be identified. With a good anti-jamming property, this algorithm only needs a condition that variations in the harmonic voltage sources at system side and user side are independent to each other. Simulation results indicated the effectiveness of the method to identify the harmonic source of PCC.

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32

Filyanin, D., and A. Voloshko. "ANALYSIS OF HARMONIC DISTORTION SOURCES IDENTIFICATION METHODS IN DISTRIBUTION SYSTEMS." POWER ENGINEERING: economics, technique, ecology, no. 1 (November 26, 2020): 29–38. http://dx.doi.org/10.20535/1813-5420.1.2020.217561.

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33

Carta, Daniele, Carlo Muscas, Paolo Attilio Pegoraro, and Sara Sulis. "Identification and Estimation of Harmonic Sources Based on Compressive Sensing." IEEE Transactions on Instrumentation and Measurement 68, no. 1 (2019): 95–104. http://dx.doi.org/10.1109/tim.2018.2838738.

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34

Du, Z. P., J. Arrillaga, N. R. Watson, and S. Chen. "Identification of harmonic sources of power systems using state estimation." IEE Proceedings - Generation, Transmission and Distribution 146, no. 1 (1999): 7. http://dx.doi.org/10.1049/ip-gtd:19990061.

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35

Bulycheva, Evgeniia, and Sergey Yanchenko. "Real-time harmonic identification under varying grid conditions." Serbian Journal of Electrical Engineering 18, no. 1 (2021): 29–48. http://dx.doi.org/10.2298/sjee2101029b.

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One of the challenges of the power quality management is a need for reliable harmonic identification in grids with multiple non-linear loads. This paper proposes a novel method to accurately determine time-varying harmonic contributions of non-linear loads to the total grid voltage distortion. The use of the invasive measurement approach and ternary pulse sequence as a stimuli guarantees an accurate assessment of harmonic contribution with the account for timevariating harmonic impacts. The application of proposed approach is demonstrated by means of time-domain grid simulation with implemented white-box model of a pulse sequence generator. Statistical estimation of the accuracy of the proposed approach as well as comparison with typical harmonic identification methods justify its effectiveness under non-stationary network conditions.

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36

de Oliveira, Mateus M., Leandro R. M. Silva, Igor D. Melo, Carlos A. Duque, and Paulo F. Ribeiro. "Independent Component Analysis-Based Harmonic Transfer Impedance Estimation for Networks with Multiple Harmonic Sources." Energies 18, no. 1 (2024): 85. https://doi.org/10.3390/en18010085.

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This paper presents a novel methodology to estimate the harmonic transfer impedances in electric power systems with multiple harmonic sources (HSs). The purpose is to determine the responsibility of each HS for the total harmonic distortion at a specific bus within the system, addressing a critical issue in the power quality field. To achieve this objective, it is necessary to estimate not only the individual HS, but also the transfer impedances between each source and the bus under analysis (BUA). Most methods for solving this problem are based on proper network modeling or restrict variations in harmonic sources to a single source at a time. The proposed methodology has overcome this limitation. For this, synchronized current and voltage phasors are measured at the BUA. Once the measurements are gathered, the Independent Component Analysis (ICA) method is applied to estimate the Norton equivalent. The harmonic transfer impedance (HTI) is then determined using the information provided by the ICA. To enhance the accuracy of HTI estimation, three procedures are employed for data mining the parameters provided by ICA over time to generate a well-conditioned system. Once the HTI is satisfactorily determined, the individual harmonic contributions (IHCs), i.e., the harmonic responsibility, can be estimated accurately. The effectiveness and performance of the method are demonstrated based on computational simulations using distribution and transmission systems. Additionally, the methodology is validated with real data collected from a Brazilian transmission system monitored by synchronized power quality measurement units. Simulated results show that the Total Vector Error (TVE) is less than 0.4%, and for the field data test, the TVE is less than 2%.

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Jopri, Mohd Hatta, Abdul Rahim Abdullah, Mustafa Manap, M. Badril Nor Shah, Tole Sutikno, and Jingwei Too. "Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 171–78. http://dx.doi.org/10.11591/eei.v10i1.2686.

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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.

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38

Mohd, Hatta Jopri, Rahim Abdullah Abdul, Manap Mustafa, Badril Nor Shah Mohd, Sutikno Tole, and Too Jingwei. "Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 171–78. https://doi.org/10.11591/eei.v10i1.2686.

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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.

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39

Liu, Shaojie, Yingying Zhao, Yue Tian, Yubo Fan, and Jifang Ye. "Research on Supraharmonics Suppression Strategy of Distribution Network based on Dynamic Cooperative Control of Multiple Harmonic Sources." Journal of Physics: Conference Series 2488, no. 1 (2023): 012047. http://dx.doi.org/10.1088/1742-6596/2488/1/012047.

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Abstract 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.

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40

Joshi, Pragya, and Sachin K. Jain. "An improved active power direction method for harmonic source identification." Transactions of the Institute of Measurement and Control 42, no. 13 (2020): 2569–77. http://dx.doi.org/10.1177/0142331220932638.

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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.

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41

Hartana, R. K., and G. G. Richards. "Optimum filter design for distribution feeders with multiple harmonic sources." Electric Power Systems Research 23, no. 2 (1992): 103–13. http://dx.doi.org/10.1016/0378-7796(92)90057-8.

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42

Fernandes, R. A. S., I. N. da Silva, and M. Oleskovicz. "Data Mining Applied to Identification of Harmonic Sources in Residential Consumers." IEEE Latin America Transactions 9, no. 3 (2011): 302–10. http://dx.doi.org/10.1109/tla.2011.5893776.

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43

Jopri, M.H, MR Ab Ghani, A.R Abdullah, T. Sutikno, M. Manap, and J. Too. "Naïve bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1626–33. https://doi.org/10.11591/ijeecs.v20.i3.pp1626-1633.

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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 (HV) and harmonic current (HC) 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.

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44

Jopri, M. H., MR Ab Ghani, A. R. Abdullah, Tole Sutikno, M. Manap, and J. Too. "Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1626. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1626-1633.

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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 (HV) and harmonic current (HC) 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.

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45

Kaprielian, S. R., A. E. Emanuel, R. V. Dwyer, and H. Mehta. "Predicting voltage distortion in a system with multiple random harmonic sources." IEEE Transactions on Power Delivery 9, no. 3 (1994): 1632–38. http://dx.doi.org/10.1109/61.311200.

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46

Kumar, Ashwani, Biswarup Das, and Jaydev Sharma. "Determination of location of multiple harmonic sources in a power system." International Journal of Electrical Power & Energy Systems 26, no. 1 (2004): 73–78. http://dx.doi.org/10.1016/j.ijepes.2003.08.004.

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47

Eslami, Ahmadreza, Michael Negnevitsky, Evan Franklin, and Sarah Lyden. "Harmonic Source Location and Characterization Based on Permissible Current Limits by Using Deep Learning and Image Processing." Energies 15, no. 24 (2022): 9278. http://dx.doi.org/10.3390/en15249278.

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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.

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48

Jopri, Mohd Hatta, Mohd Ruddin Ab Ghani, Abdul Rahim Abdullah, Mustafa Manap, Tole Sutikno, and Jingwei Too. "K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2021): 2650–57. http://dx.doi.org/10.11591/eei.v9i6.2685.

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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.

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49

Mohd, Hatta Jopri, Ruddin Ab Ghani Mohd, Rahim Abdullah Abdul, Manap Mustafa, Sutikno Tole, and Too Jingwei. "K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2650–57. https://doi.org/10.11591/eei.v9i6.2685.

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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.

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50

Usagawa, Tsuyoshi, Hiromitsu Miyazono, and Masanao Ebata. "Identification of multiple point sources using acoustic intensity." Journal of the Acoustical Society of America 84, S1 (1988): S33. http://dx.doi.org/10.1121/1.2026271.

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Journal articles on the topic 'Multiple harmonic sources identification'

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