# Fast Support Vector Machine for Power Quality Disturbance Classification
###### Abstract  
The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves  
Combining the conventional idea and AI, as a typical example in [4], Fast Fourier Transformation (FFT) and Artificial Neural Network (ANN) were integrated to solve the POD problem. Note that the harmonic measurements are different from the ordinary power system measurements, where most harmonic measuring equipment were designed with FFT techniques and digital filtering, with a suitable bandwidth (3 kHz) for recording the waveforms. FFT is used to analyze distorted waves and filter out the fundamental waves to detect harmonic components. With FFT [3], the recorded waves were digitized and processed to extract the deformed waves; the Artificial Neural Network (ANN) then followed to detect the harmonics, as conducted in [4]. The FFT technique has limits with the number of samples, the memory and the processing time, while the ANN has difficulties in determining the number of hidden layers and nodes. Training an ANN is slow and time consuming, in the meantime, global optimum will not be guaranteed [3; 20; 21; 22]. ANN accuracy requires more samples in determining the architectural design, where the time consuming process becomes another burden, regardless of the proposed idea of using the partial connecting ANN network [11].  
Considering these limitations, a simplified form of the support vector machine (SVM) [23; 24; 25; 26; 27] is proposed in this paper for POD. SVM is regarded as a better classifier than conventional methods for pattern recognition [28; 29]. There are also some publications in the PQD field with SVM [30; 31]. An SVM is a learning machine with interesting theoretical characteristics [23; 24; 25; 26]. The so-called support vectors (SVs) could identify the decision boundaries of classes, which are near the separation surface of various classes, and it is a critical feature for correct classifications. SVM structure has renowned binary classification capability, which will be extended in this paper, for multi-class PQD problems. A simple linear SVM machine, nonlinearly related to the input space, allows fast training techniques, even with big training sets and a large number of input variables [25]. The simple binary classification SVM can be interpreted into the one-versus-one (OVO) structure [29] for a multi-class problem, called the Fast SVM (FSVM) in this paper. FSVM uses standard quadratic optimization. It is very fast and ensures global optimality, which is not easily attainable by other methods [20; 21; 22]. Many tests were conducted, and a sample IEEE 14 bus power network was provided to show the simulation results, and comparisons with other ANNs were provided to show the effectiveness