Application of ANNs model with the SDSM for the hydrological trend prediction in the sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM (statistical downscaling model) and ANNs (artificial neural networks) models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs (global climate models). The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the mean of temperature for overall months, except the month of August and November

Prediction of ZnO Surge Arrester Degradation Based on Temperature and Leakage Current Properties

Temperature and leakage current on the ZnO arrester are interrelated with each other. In low conduction region, voltage-current characteristics of ZnO surge arrester are highly dependent on temperature. The leakage current will increase as the temperature increases and experience thermal runaway when the temperature exceeds the acceptable limit. This phenomenon is associated with the increase of resistive leakage current due to degradation. Therefore the temperature and leakage current are good indicator to evaluate the condition of ZnO arrester. This paper proposed the degradation  prediction of ZnO surge arrester by analyzed the temperature and leakage current. The 132 kV station type ZnO surge arrester was employed. Temperature profile of ZnO arrester was obtained using thermal camera. The leakage current was measured simultaneous with the temperature measurement to attain the leakage current at the actual temperature. The results shows the leakage current continue increasing by increasing the temperature. Keywords : Resistive leakage current, temperature, degradation, prediction, zinc oxide surge arreste

The Correlation of Statistical Image and Partial Discharge Pulse Count of LDPE-NR Composite

High voltage insulation must be designed in such a way that it is very resistant to ageing including that from partial discharge (PD). Many studies were previously carried out on composites based on low density polyethylene (LDPE). However, the use of natural rubber (NR) and nanosilica (SiO2) in the LDPE-NR based composites is relatively new. Furthermore, the PD resistant performance of the composites is yet to be extensively researched. This work aims to analyze the correlation between PD pulse count and its related image to interpreting the effect of PD signals. The results show there is a strong correlation between PD pulse count and the statistical image. The results indicate that the surface image statistical analysis can be used as a tool to justify the total of the PD pulse count on the surface for different samples of composite

Prediction of ZnO Surge Arrester Degradation Based on Temperature and Leakage Current Properties

Temperature and leakage current on the ZnO arrester are interrelated with each other. In low conduction region, voltage-current characteristics of ZnO surge arrester are highly dependent on temperature. The leakage current will increase as the temperature increases and experience thermal runaway when the temperature exceeds the acceptable limit. This phenomenon is associated with the increase of resistive leakage current due to degradation. Therefore the temperature and leakage current are good indicator to evaluate the condition of ZnO arrester. This paper proposed the degradation prediction of ZnO surge arrester by analyzed the temperature and leakage current. The 132 kV station type ZnO surge arrester was employed. Temperature profile of ZnO arrester was obtained using thermal camera. The leakage current was measured simultaneous with the temperature measurement to attain the leakage current at the actual temperature. The results shows the leakage current continue increasing by increasing the temperature

Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia

The increase in global surface temperature in response to the changing composition of the atmosphere will significantly impact upon local hydrological regimes and water resources. This situation will then lead to the need for an assessment of regional climate change impacts. The objectives of this study are to determine current and future climate change scenarios using statistical downscaling model (SDSM) and to assess climate change impact on river runoff using artificial neural network (ANN) and identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) models, respectively. This study investigates the potential of ANN to project future runoff influenced by large-scale atmospheric variables for selected watershed in Peninsular Malaysia. In this study, simulations of general circulation models from Hadley Centre 3rd generation with A2 and B2 scenarios have been used. According to the SDSM projection, daily rainfall and temperature during the 2080s will increase by up to 2.23 mm and 2.02 °C, respectively. Moreover, river runoff corresponding to downscaled future projections presented a maximum increase in daily river runoff of 52 m3/s. The result revealed that the ANN was able to capture the observed runoff, as well as the IHACRES. However, compared to the IHACRES model, the ANN model was unable to provide an identical trend for daily and annual runoff series

Paper ageing analysis in power transformers

For decades until now, manufacturers and end users alike have been absorbed by the challenge of extending the life span of power transformers. The life span of power transformers averages between 50 to 70 years, as the high capital cost of its replacement is astronomical. It is therefore essential that the utilization of the transformer can be optimized to the fullest extent of its maximum lifespan. It is generally accepted that the reliability of power and distribution transformers decreases with the ageing of their insulation system. This ageing is mainly due to the degradation of the characteristics of the insulating materials. In determining the life consumption of the transformer, measuring the degree of polymerization of the insulating paper is the more reliable way. However, this intrusive test requires a sample of the paper which means that the transformer has to be taken out from service and that portion of the unit be destroyed in the process. Therefore this method is not suitable for the transformers in service. The operating conditions of power and distribution transformers and other oil filled electrical equipment are usually monitored by measuring dissolved gasses in the insulating oil using gas chromatography. The analysis of oil samples does not represent a significant difficulty. Most electrical equipment is provided with sampling valve and a number of physical and chemical analyses can be performed both in the field and in the laboratory to determine the oil condition. Moreover, the insulating oil is widely used in predictive maintenance because its degradation under faulty operating conditions such as thermal defects, arcing or partial discharges, will produce gases that partially dissolved by the oil and their analysis may indicate the type or severity of the fault. The deterioration of transformer insulation is primarily a function of temperature and time, but it is also influenced by other factors such as moisture and oxygen content. Therefore, most predictive maintenance techniques of the transformer are focused on the monitoring of these factors. This is quite satisfactory for the assessment of the insulation conditions of the liquid. However, the techniques adopted such as the gas-in-oil analysis cannot give an account on the condition of the paper insulation. Similar sampling technique cannot be easily implemented with cellulosic paper due to the bad accessibility from the outside of the transformer tank. Therefore, a method of assessing the condition of the paper without involving the paper sample itself is required. An in-depth oil analysis had shown the presence of 2-fulfuraldehyde and related compounds, where these compounds are known to be specific to the degradation of cellulose and other paper constituents [1-3]. One such in-depth oil analysis is known as the High Performance Liquid Chromatography (HPLC). When the cellulosic insulation materials within a transformer undergo degradation, either by normal aging or by being involved with an incipient fault, among the by-products formed are carbon monoxide and carbon dioxide gases and derivatives of the aromatic compound called furan. Thus the amount of furans present in the oil might be a good indication of the cellulosic insulation condition. In this work, the degradation of Kraft transformer insulation paper is examined by means of monitoring the increase in the concentration of furan compounds as well as the increase of moisture content

Carrier wave optimization for multi-level photovoltaic system to improvement of power quality in industrial environments based on Salp swarm algorithm

The use of multi-level inverters is increasing in different structures, high power and medium power applications due to advantages such as low switching losses, harmonic distortion and electromagnetic interference at the output which could be used in microgrid systems. A microgrid can be defined as groups of renewable energy sources such as photovoltaic and wind turbine i.e. The switching technique for inverter control plays a significant role in reducing or eliminating the harmonics of inverter output voltage and reducing the switching losses. To minimize the distortion of the output voltage of the cascaded H bridge multi-level inverter due to low-order harmonics, an optimization method used for frequency selection, i.e. the carrier wave amplitude in the SPWM strategy within this study. The proposed method is called OSPWM, which employs a new optimization method based on the Salp swarm algorithm. The proposed method applied to a cascade H bridge five-level inverter. The simulation results show the reduction of the low-frequency harmonics amplitude and THD output voltage by optimizing the OSPWM carrier wave parameters with the optimization algorithm. The proposed method also compared with the classical SPWM method

Frequency Response Analysis for Three-Phase Star and Delta Induction Motors: Pattern Recognition and Fault Analysis Using Statistical Indicators

This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification

Frequency response analysis: An enabling technology to detect internal faults within critical electric assets

Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines