Abstract
The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the structure “if…then’’. This proposed study is simulated using MATLAB simulation. The IEEE 13-bus system, the recorded real data based on PQube, and the experiment based on the laboratory environment are applied to verify the effectiveness.
Highlights
Power quality (PQ) is a significant issue that has become increasingly important to both electric power utilities and their customers because of financial losses caused by insufficient power quality (PQ) [1].PQ disturbance issues and the resulting problems are the consequences of using light flickers in the 1930s; air conditioners in the 1950s; sensitive electronics, computers, power electronics and microelectronics in the 1990s; and nowadays, based on the modern power electronics industry, connecting the distributed generation based on energy sources, controlling heavy non-linear loads, and using the rectifies and inverters in the industrial plant [2]
PQ disturbances in power systems introduced as combination methods of discrete wavelet transform (DWT) and two types of machine learning-based algorithms [29], an improved method from the iterative adaptive kernel regression method [30], a method based on multi-resolution Stockwell transform (ST) and decision tree (DT) [31], and the method based on image enhancement techniques and feature selection [32]
This paper proposes a combination algorithm between ST and DT methods for solving the detection and classification of PQ disturbances in the power system under the consideration
Summary
Power quality (PQ) is a significant issue that has become increasingly important to both electric power utilities and their customers because of financial losses caused by insufficient PQ [1]. PQ disturbances in power systems introduced as combination methods of DWT and two types of machine learning-based algorithms [29], an improved method from the iterative adaptive kernel regression method [30], a method based on multi-resolution ST and DT [31], and the method based on image enhancement techniques and feature selection [32] Based on these facts, this paper proposes a combination algorithm between ST and DT methods for solving the detection and classification of PQ disturbances in the power system under the consideration. The ST method is used to detect five statistical features such as the high frequency of oscillatory transient, the distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption of the input PQ disturbance signals.
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