Harmonic load modeling: a case study of 33 kV Abuja Steel Mill Feeder

An in-depth study of the harmonic orders inherent in a power system network is required before mitigation techniques are adopted. This paper studied the harmonic orders of the 33 kV Abuja Steel Feeder modeled as a harmonic source using measured data. Readings of kW, kVar, kV and Hz were obtained using power quality analyzer (PQube) to identify the trend and the harmonic unbalance on the feeder. Electrical Transient Analyzer Program (ETAP) software package was deployed to perform Discrete Fast Transform (DFT) while the input parameters were the resultant relative amplitudes and phase angles for both the current and voltage source models. The current source model spectrum of the feeder under study revealed that the 11th and 13th harmonic orders have the highest percentage of amplitude relative to the fundamental compared to the other harmonics. On the other hand, the voltage source model spectrum showed that the 14th, 15th, 16th, 17th, 32nd, 33rd and 34th harmonic orders have higher percentage of relative amplitude. However, only the 3rd and 5th harmonic orders were found to cause severe harmonic distortion of voltage and current after harmonic analysis and frequency scan were performed on the ETAP platform.Keywords: Harmonic order; Power quality; Transmission grid; ETAP; Tuned filter

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods