Power quality problem such as unbalanced voltage have adverse effects on the satisfactory performance of three phase induction motors which are the main work horses in many industrial, commercial and residential applications. The new models of three phase induction motor and adjustable speed drives are highly susceptible to unbalance voltage. Detecting whether the supplied voltage to these machine is balanced or unbalance is very vital, if the service life and motor efficiency will be maintained for a reasonably numbers of years. This paper applied Artificial Neutral Network for detection of voltage unbalance. ANN was satisfactorily trained with 100 samples of real-time voltage readings supplied to three phase induction motors. The collected data were the fed to the trained ANN for classification. The results of the analysis showed that 26.25% were detected to be balanced voltage while 73.75% were tagged unbalanced. It was observed that Mean Squared Error (MSE) was found to be 0.084321, Root Mean Squared Error (RMSE) was found to be 0.781885 and percentage accuracy was 100% for balanced and unbalanced voltage.
Static Synchronous Compensator (STATCOM) is one of the members of flexible alternating current transmission systems (FACTS) devices that controls one or more network parameters to increase system power transfer capability. However, it needs be optimally allocated to fully maximize its usage. Allocation of STATCOM simply refers to optimal placement and size of a STATCOM to solve some of the transmission network problems. This paper therefore utilizes particle swarm optimization (PSO) to allocate STATCOM to enhance the system voltage magnitudes and minimize the active power loss. A Matlab programme was developed for the proposed method and applied to IEEE 14 bus network and the results presented. The results showed that the total active power loss was reduced by 6.90%. The voltage magnitudes at buses 7 and 13, which were above the upper voltage limit, were reduced and brought to within acceptable voltage limits and hence improvement in the system voltage profile. Therefore, the optimal allocation of STATCOM improves the efficiency and operation of the network.
Accurate and timely detection of power quality (PQ) events is imperative for adequate corrective measures to be taken. This paper presents a method of PQ event detection development called joint triggering point detection (JTPD) scheme with a view to achieving a more accurate PQ event detection in a voltage waveform. The JTPD combines the advantage of cumulative sum (CUSUM) algorithm for the statistical distribution of a signal waveform and the discrete wavelet transform (DWT) for change-point detection. The performance of JTPD scheme using detection rate was compared with CUSUM and DWT schemes and based on the analysis, the detection rate of JTPD, CUSUM and DWT were 100%, 95% and 50% respectively. With this, the proposed approach outperformed CUSUM and DWT schemes by 5% and 50% respectively. Hence, the proposed approach is suitable for accurate detection of voltage dip, swell and interruption PQ events Keywords: Cumulative Sum, Discrete Wavelet Transform, Joint Triggering Point Detection, Power Quality, Voltage Waveform
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.