Harmonic Identification Method Research Articles

Identifying single‐phase to ground fault based on line multi‐dimensional data in cyber‐physical distribution system

Single-phase to ground fault is the most common line fault in power distribution systems, which may result in an electrical fire and personal safety problems. Particularly, it is more difficult to identify the fault occurring in the system in which the neutral point is grounded via Petersen coil. This paper proposes a new scheme based on line multi-dimensional data in cyber-physical distribution system, where line multi-dimensional data is used to extract the fault characteristics and thus to identify the faulty line. First, the circuit is analyzed and the difference-like algorithm is used to extract the current signal difference value from the line multi-dimensional data as the feature quantity. In order to reduce the impact of the measurement error on the result, the authors employ the deep neural networks to process the line multi-dimensional data, and demonstrate that this method can effectively resist the error interference. A simulation based on a previously studied model has been carried out and the results show the superiority compared to the fifth harmonic identification method. In addition, the test results obtained in the Dynamic Simulation Laboratory of Huazhong University of Science and Technology also demonstrate the practicality of the scheme.

Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table Based on a Nonlinear Adaptive Algorithm

Since the electro-hydraulic servo shaking table came into existence, many nonlinear elements, such as, dead zone, friction and backlash, as well as its acceleration response has higher harmonics which result in acceleration harmonic distortion, when the electro-hydraulic system is excited by sinusoidal signal. For suppressing the harmonic distortion and precisely identify harmonics, a combination of the adaptive linear neural network and least mean M-estimate (ADALINE-LMM), is proposed to identify the amplitude and phase of each harmonic component. Specifically, the Hampel’s three-part M-estimator is applied to provide thresholds for detecting and suppressing the impulse noise. Harmonic generators are used by this harmonic identification scheme to create input vectors and the value of the identified acceleration signal is subtracted from the true value of the system acceleration response to construct the criterion function. The weight vector of the ADALINE is updated iteratively by the LMM algorithm, and the amplitude and phase of each harmonic, even the results of harmonic components, can be computed directly online. The simulation and experiment are performed to validate the performance of the proposed algorithm. According to the experiment result, the above method of harmonic identification possesses great real-time performance and it has not only good convergence performance but also high identification precision.

The method for harmonic identification based on optimal empirical mode decomposition

The method for harmonic identification based on optimal empirical mode decomposition

Harmonic distortion monitoring for nonlinear loads using neural-network-method

Nowadays, harmonic distortion in electric power systems is a power quality problem that has been attracting significant attention of engineering and scientific community. In order to evaluate the total harmonic distortion caused by particular nonlinear loads in power systems, the harmonic current components estimation becomes a critical issue. This paper presents an efficient approach to distortion metering, based on artificial neural networks applied to harmonic content estimation of load currents in single-phase systems. The harmonic content is computed using the estimation of amplitudes and phases of the first five odd harmonic components, which are carried out considering the waveform variations of current drained by nonlinear loads, within previously known limits. The proposed online monitoring method requires low computational effort and does not demand a specific number of samples per period at a fixed sampling rate, resulting in a low cost harmonic tracking system. The results from neural networks harmonic identification method are compared to the truncated fast Fourier transform algorithm. Besides, simulation and experimental results are presented to validate the proposed approach.

Identification of noise modes for automobile operational modal analysis

In rotating machinery, the input excitation does not meet the requirement of operational modal analysis (OMA). In this article, an effective and improved method of distinguishing forced harmonic response from measured vibration signal is presented based on the differences of the statistical properties of a stochastic signal and a harmonic signal. It makes the OMA applicable to the situation of existing periodic excitation. In order to prove the effectiveness of the proposed method, a modal experiment of a steel plate excited by a motor is performed. Then, an OMA is performed to solve the vehicle interior noise problem. Combining the projection channel technique with the advanced method, most noise modes, especially harmonic modes are successfully separated. Precise modal parameters of the car are obtained in typical working conditions which provide guidance of the dynamic modification and show that the harmonic identification method has great practical value.

The DQ Axis With Fourier (DQF) Method for Harmonic Identification

This letter proposes the DQ axis with Fourier (DQF) method for harmonic identification. The method, when used with a parallel active power filter, provides much better results than its predecessors, namely, the DQ axis and the sliding window Fourier analysis methods, in terms of harmonic elimination and phase balancing