No abstract

Natural ester oils are the current target of many industries and electrical utilities as electrically insulating fluids to replace the conventional mineral oils. However, previously investigated most natural ester oils are based on edible products, causing a negative impact on the food crisis. Accordingly, nonedible green nanofluids based on cottonseed oil have been targeted in the present study. Additive graphene nanoparticles (0.0015 wt%, 0.003 wt%, 0.006 wt%, and 0.01 wt%) along with surfactant sodium dodecylbenzene sulfonate (SDBS) were used (1:1) due to their promising impact on dielectric and thermal properties. Experimental methods introduced were including characterization of graphene and preparation of dielectric nanofluids (DNFs). The main concern for any nanofluid to be usable in transformer applications is its long-term stability. The effect of various ultrasonication period (10, 20, 30 and 60minute) on short-term stability of nanofluids was preliminary investigated by visual inspection, highest short-term stability was obtained at 30-min and 60-min. Considering short-term stability results, the two most stable samples were investigated and compared for long-term stability through Ultraviolet visible (UV-Vis) spectroscopy to find the suitable ultrasonication time. In addition, dielectric and thermal properties of these samples were investigated and compared. Physical mechanisms were discussed for the obtained enhancements considering the effect of ultrasonication period on the number of dispersed nanoparticle sheets per unit volume and the corresponding effect on dielectric and thermal properties.

In this Letter, a hybrid particle swarm optimisation and gravitational search algorithm (PSO-GSA) to tune the parameters of proportional-integral (PI) controller operating in cascade with sliding mode controller (SMC) is proposed for the design of Cuk converter for low-voltage electric vehicle application. The PI controller in the outer loop regulates the voltage and SMC in the inner loop regulates the current of converter operating in continuous conduction mode. The main objective of Cuk converter with PSO-GSA tuned PI & SMC controller is to track the reference voltage amidst disturbances, and to reduce the performance indices such as integral absolute error (IAE) and integral time absolute error. Initially, PI controller was tuned using Routh Hurwitz and root locus method. Then to improve its steady-state performance index, PSO-GSA technique is applied to tune the proportional and integral gains. To validate the effectiveness of the proposed scheme, the performance indices such as IAE, integral squared error was measured and compared. The results obtained reveal that IAE is reduced to minimal value and the solution converges at a faster rate with the proposed controller than the conventional control methods. Moreover, the stability analysis was carried out using the Lyapunov method of stability.

This paper presents the high-impedance fault (HIF) detection and identification in mediumvoltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.