A Variable N Synchronous Sampling Method for Grid Aperiodic Harmonic Detection and Analysis

The pillar porcelain insulator is an important protection device that is related to the safety of the entire power grid. Small damage of it may even cause a disaster. Nonlinear output frequency response functions (NOFRFs) can well reflect the nonlinear characteristics of early damage in the system. This paper uses the NOFRF entropy-based harmonic excitation detection method to detect the damage of the structure. Its effect has been verified by using metal specimens. Then, the pulse hammer detection and harmonic detection are carried out to detect the early-stage damage of the pillar porcelain insulator, and they achieve a good result, which verifies that the harmonic detection method can detect the early-stage damage in the pillar porcelain insulator as well. In addition, the orthogonal test of the simulated breathing crack model is used to search the greatest influence of the parameters of crack on the detection index. Through the orthogonal analysis, the results show that among the lengths, positions, and angles of the crack, the length of crack is the main factor that affects the detection index.

Aiming at the low measurement accuracy and poor time lag of traditional power system harmonic and interharmonic detection methods, a new power harmonic detection method is proposed, namely, the neural network power system harmonic detection analysis based on big data. It combines wavelet transform with artificial neural network, uses wavelet transform to extract the characteristic components in the signal. And then separates and extracts the different frequency band signals, and then uses the corresponding artificial neural network to analyze and identify the extracted frequency band signals to obtain each Harmonic type, amplitude and phase parameters in the hierarchy. The final simulation experiment shows that this research proposes to combine wavelet transform with artificial neural network to give full play to the time and frequency locality advantages of wavelet transform, as well as the advantages of artificial neural network self-adaptability and model maximum value advantages, which are effective for periodic changes and interference in electric power. It has strong tracking performance and recognition ability, which further shows that the power harmonic detection method proposed in this study is feasible and effective.

This paper describes Detecting Methods for Harmonics of power system has been considered one of the serious harms for power system. The research of harmonics has obtained high attention among people. The researches of harmonics detection have many methods, such as Based on Fryze theory of harmonic power detection, Instantaneous reactive power theory detection method, Fourier Transformation harmonic detection method, wavelet transform detection method, neural networks harmonic detection method etc. Aiming at harmonic detection, the different detection methods of power system harmonics are summarized and compared. This paper reviews the existing harmonic detection methods, and discuss their advantages and disadvantages in terms of detection accuracy and response speed, and finally summarizes the development trend of the harmonic detection method.

It is of great significance to study the weak harmonic signal detection from strong chaotic background. Current detection methods mainly use the chaotic phase space reconstruction method based on Takens theory, among which the neural network method has attracted the most attention. However, these methods require high signal-to-interference-plus-noise ratio (SINR) and are sensitive to Gaussian white noise, etc. Noticing the fact that the second-order statistical properties of chaotic signals are stationary, we propose a harmonic signal detection method from strong chaotic background based on optimal filter. We first construct a data matrix, whose rows are the detection signal and reference signals. The reference signals only contain chaotic interference. Then we calculate the one-dimensional fast Fourier transformation of the data matrix to make each column of the matrix form a frequency channel. The harmonic signal can be detected by searching each frequency channel in the frequency domain, thus the signal detection problem is converted into an optimization problem. Further, we use the optimization theory to design a filter such that it can maintain the gain of the signal from the current frequency channel and suppress signals from other frequency channels as far as possible. Finally, the harmonic signal can be obtained by calculating the output SINR of each frequency channel. In order to reduce the calculation, we can further design a local region optimal filter. We choose part of frequency channels to constitute a local area, thus the dimension of the chaotic interference covariance matrix is greatly reduced. Theoretically speaking, the more the number of auxiliary frequency channels, the better the detection results are. However, in the practical application, choosing two channels on the left and right side of current channel each can obtain a very good detection effect. After obtaining the chaotic interference covariance matrix, we can further achieve the output SINR of each frequency channel. Compared with the traditional methods, the proposed method has the following advantages: 1) it can detect a weak harmonic signal under lower SINR; 2) it can detect a greater range of signal amplitude; 3) it is robust against white Gaussian noise. The simulation results with taking Lorenz system as the strong chaotic background show that the proposed method still has a very good detection effect when SINR =-81.03 dB, and the stronger the harmonic signal, the better the detection effect is, while the neural network method can work under the condition of SINR higher than -67.03 dB; the proposed method still can correctly detect the target signal in the case that the SNR is as low as -20 dB, but the neural network method has a poor detection effect under the same condition.

Dense frequency signal refers to the signal containing harmonic and interharmonic components that are very close to each other in frequency domain. Due to the limited frequency resolution and the existence of spectrum leakage and fence effects, it is difficult to accurately detect the harmonic and interharmonic components in dense frequency signals. The spectral interference under asynchronous sampling conditions can lead to the failure of measuring the multiple adjacent harmonics and interharmonics. Therefore, this article proposes a mixed dense signal harmonic and interharmonic analysis method. The dense frequency signal model is first established. Based on the model, the discrete Fourier transform results of the dense frequency signal are processed by local mean decomposition extension to construct that single multichannel signal as a virtual multichannel. Then, the parameters of each frequency component are obtained by means of principal component analysis and fast independent component analysis. Finally, the measurement of dense harmonic and interharmonic components is implemented. The simulation and experimental results show the high accuracy and the noise resistance ability of the proposed method in identifying and measuring the dense harmonic and interharmonic components.

The charger’s operation in the power grid will cause harmonic pollution, affecting the service life of the equipment and the quality of the power grid. The harmonic characteristics and detection during the charging process are studied. The simulation model of charger is established first using MATLAB, the results of the experiment show that odd harmonics are mainly generated during charging, and the distortion rate of the current harmonics remains between 0.231 and 0.443 during the charging process. A new wavelet neural network is proposed to detect the harmonic method. Experimental results indicate that, compared with previous detection methods, this network has better detection efficiency and accuracy for harmonics. But detection result of this network is unstable and does not even converge. A new adaptive bat algorithm with improved focusing distance is presented here to optimize the wavelet neural network, which effectively solves the defect of being sensitive to the node number of hidden layer, resulting in higher detection accuracy, faster detection speed, and stable convergence. The detection error range is reduced from −0.03 to 0.02, and the number of iterations is reduced to around 1000.

As the boost of power electronics technology, the harmonic problem in the power system is becoming increasingly prominent. Fourier decomposition is performed on the load current in the power system, and components with a frequency that is an integer multiple of the fundamental wave are referred to as harmonic components. Harmonic control is essential to establish a safe and reliable power grid environment and provide high-quality and clean electricity to power users. The study focused on parallel active power filters, proposed a specific harmonic detection method on the grounds of synchronous harmonic rotating coordinate system, and developed a phase-locked loop design on the grounds of order generalized integrator. Meanwhile, a compensation current control method on the grounds of space vector pulse width modulation was introduced. The results showed that in the full compensation simulation experiment, the compensated A-phase grid side current waveform was significantly improved and presented a sinusoidal shape. After 0.05 s, the actual output compensation current closely followed the command current. Meanwhile, after compensation, the total harmonic distortion rate decreased from 26.58 to 3.06%. In specific harmonic compensation simulation experiments, when the sum of 5th, 7th, and 11th harmonic components was used as the command current for compensation, the distortion of the current waveform was improved after the load undergoes a sudden change. After compensation, the 5th, 7th, and 11th harmonic content significantly decreased, and the total harmonic distortion rate decreased to 4.08%. This indicated that the proposed phase-locked loop design and harmonic detection method for active power filters had high stability and effectiveness. The study’s primary contribution is to enhance the utilization efficiency of DC voltage and improve the dynamic response ability of current. Additionally, it offers a new method for reducing the impact of harmonics on the power grid and improving power quality. It provided an effective method reference for technological progress in related fields such as power electronics and control engineering.

This paper proposes a new harmonic current detection method using an average DC voltage and current of voltage source nonlinear load instead of a typical load current and a source voltage. By using a MATLAB Simulink tool, a model of single-phase AC system connected to a full bridge diode rectifier with capacitive load is designed. A step by step simulation results towards a generation of sinusoidal reference current and filtered reference current are presented and analysed. As a conclusion, the proposed harmonic detection method successfully achieved its objective to generate the reference current signals for the single-phase active power filter circuit.

The detection of harmonics is an important foundation for analysis and control of harmonics of power systems. This paper puts forward a new harmonic detection method based on a rotating coordinate transformation of multi-frequency, and expatiates of the basic theory of rotating coordinate orthogonal transformation of multi-frequency and its actualization processes to detect harmonics. Finally, computer simulation of harmonic detection based the proposed method and simulative results are given. The proposed detection method can detect the instantaneous values of random harmonic components and random fundamental components in real time with none principle error, and has the characteristics of high detection precision, good real time and simple actualization.

Aiming at the influence of DC component and high-order harmonics on harmonic current detection accuracy, an improved ip-iq harmonic detection algorithm based on mixed second-and third-order generalized integrator is proposed in this paper. The MSTOGI filter is added to the traditional ip-iq structure, which effectively filters the DC component of the input signal and suppresses the high-order harmonics. The phase-locked loop of MSTOGI can provide a stable grid voltage frequency when the grid voltage contains high-order harmonics and DC components, so that the harmonic current can be detected more accurately. The traditional ip-iq method and the general SOGI-based ip-iq method are compared and analyzed by simulation examples, which verifies the effectiveness and reliability of the proposed harmonic detection method when the power grid contains DC component and high-order harmonic.

This paper presents a synchronous detection with Fourier analysis (SDFA) method. The proposed method is used to calculate the reference current of single-phase shunt active power filters. The SDFA method is operated bases on a synchronous detection (SD) and the sliding window with Fourier analysis (SWFA) technique. The harmonic elimination in single-phase power system is considered in this paper. The performance comparison of the SD and SDFA methods is also presented. The simulation results by using the Simulink program confirm that the SDFA method can provide the better performance compared with the SD method. Furthermore, the total harmonic distortion percentage (%THD) of the source current after compensation is satisfied under the IEEE std 519–2014.

Nowadays, the Fast Fourier transform (FFT) has been widely adopted for harmonic analysis in power system. However, a direct application of FFT-based algorithms may bring about inaccurate results because of spectral leakage and picket-fence effects. In this paper, a high accuracy method based on the all-phase and interpolated FFT is presented for harmonic analysis. Firstly, the input signal is preprocessed to mitigate spectral leakage through all-phase. Secondly, the interpolation algorithm is adopted to eliminate the picket-fence effects. As a result, the analysis accuracy of the harmonic parameters has been greatly improved and the phase estimation is not biased. The formulas of amplitude, frequency and phase are also obtained based on all-phase and interpolated FFT. The simulation results reflect that this method can better meet the requirements of harmonic detection in power system.

Modeling the oscillation of a steel plate in water under the action of loads to assess the safety of the structure is an urgent task of modern times in the design of structures. The aim of this work was to study the behavior of a plate immersed in a fluid that makes forced oscillations under the action of an applied load; the mechanism of behavior of this interaction and the determination of the connected masses of the fluid. Knowledge of the connected masses helps to assess the effect of the liquid. In this work, the oscillations of a steel plate in water under the action of two types of loads were studied. For the harmonic analysis of the oscillations of the plate, our own frequency response was constructed, which was compared with the frequency response acquired by the method of solving a series of FSI problems. From the comparison of the obtained frequency response it is concluded that it is possible to use harmonic analysis to study the stress-strain state of the plate during its oscillations. Since FSI problems are quite resource-intensive due to their nature, solving a number of such problems about plate oscillations in a fluid under various loads to study the stress-strain state of an oscillatory system is not a very convenient method. The question arises as to whether it is possible to avoid solving FSI problems and to find a simpler way to solve the problems of vibration of structures in a fluid. Since the estimation of the attached water masses of the plate is known, it can be assumed that this attached water mass is distributed around the plate evenly and in view of this fact we can perform a harmonic analysis. Due to the viscosity, water dampens the amplitude of free oscillations of the plate. Thus, in order to be able to compare the results of a series of FSI and harmonic analysis problems, the latter must be modeled taking into account the damping in the model. The method of harmonic analysis with the addition of the attached mass of water can be used at low oscillation frequencies. At all other oscillation frequencies it is not recommended to use the method of harmonic analysis taking into account the connected mass of water.

The content of harmonic has a great influence on the electric power quality. It seriously influences power factor and aberrance extent of voltage. Therefore, it has become a necessary task to choose an economic and accurate measurement device. The device not only can detect harmonic, but also it is different from traditional measurement devices. Common electric energy meters can't distinguish fundamental harmonic from other times of harmonics. And some odd harmonics can make induction watt-hour meter reverse. Therefore, power administration can't measure electric energy accurately. In this paper, a novel harmonic detection and measurement device is presented. The device consists of A/D unit, FFT unit, LCD display unit and network communication unit. The system adopts FPGA and DSP processor. The experimental results demonstrate that the device has more accurate response, and the device meets electric work application's requirements. It's a promising harmonic power detection and measurement device.

The appearance of microgrid solves the shortcomings of distributed generation and attracts the attention of the whole world. What's more, it has brought both opportunities and challenges for the development of electric vehicle charging stations. But due to the increasing use of electric vehicle chargers, the harmonic pollution of microgrid is becoming more and more serious, and it causes harm to both electric equipments and large power grid. Taking measures to govern the harmonic pollution is imperative, but before governance, the accurate and real-time detection of harmonic is indispensible. So this paper puts forward to use the improved HHT to detect the harmonics. The improved HHT combines the support vector regression machine and the mirror extension to improve the end effect of HHT and make the instantaneous frequency and amplitude become more meaningful. What's more, the improved HHT can accurately detect the starting and ending time of harmonic disturbances. Through the simulation results, it can be confirmed that this method is efficient and applicable for the harmonic detection in microgrid electric vehicle charging stations.