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Ability to organize data spatially while conserving the topological relation between data features makes the Self Organizing Map (SOM) a very useful tool for analysis and visualization of high dimensional data such as a power transformer's Dissolved Gas Analysis (DGA). Past SOM application required large historical data for its training and has limited fault detection sensitivity. In this paper, the effects of input features and data normalization are studied to enhance SOM's clustering. SOM is trained using DGA results extracted from actual faulted transformers. Combination of input features and data normalization methods are tested on SOM before the best SOM is identified. Validation is conducted using several datasets i.e. the IEC Technical Committee 10 database. Compared with past SOM applications, the proposed SOM required lesser training data, improved SOM's sensitivity in incipient fault detection and has good diagnosis accuracy. The proposed SOM is also compared with other AI-based DGA interpretation method i.e. Support Vector Machine (SVM) for benchmarking.

Distribution transformer in Malaysian electricity network is categorized as major element on delivering electricity throughout the nation. Advanced diagnostic tools are used to monitor any technical problem of transformer to sustain its capability in the long run. One type of it which is currently being used in worldwide is Frequency Response Analysis (FRA). The reliability from this method is proven worldwide in diagnosing transformer condition especially a mechanical movement. FRA technique is done by comparing the response result in transformer initial condition to its current condition. The interpretation of transformer condition from the response is based on frequency sub-bands. Each frequency sub-bands are indicating any movement parts of transformer and electrical faults. Currently, there are three worldwide standards established for interpreting the FRA results but both of them are different in frequency sub-bands range. Up to now, there are no research papers comparing the fault detection capabilities from frequency sub-bands range for both standards. In this paper, comparative study is done to visualize and interpret the capabilities from three standards (IEEE Std C57.149 2012, DL/T911-2004 and Cigre WG A2/26); by focus on the frequency sub-bands range.

Examples of solid and liquid electrical insulations for power transformer are mineral oil and cellulose based paper. As transformers performed their function to step-up or step-down voltage level, its’ insulations will be degraded. Paper insulation is considered the most critical component in a transformer insulation system because it is not easily replaced if compared to oil where it is easily reconditioned in-order to remove water and contaminants. Studies show that temperature, moisture contents and oxygen contributed to paper insulation degradation. Moisture and furanic compound were produced from these deterioration processes. This paper is focused on the formation and effect of moisture to Kraft paper’s life. Levels of moisture contents were obtained from in-service power distribution transformers. These data then is used to assess the Kraft paper’s life by means of Weibull plot. This study shows that level of moisture contents can be used to assess the life of Kraft paper insulation.

Power transformers studies have shown that the main cause of power transformers failures is the On Load Tap Changer (OLTC). OLTC failure occurs due to the insulation degradation and contacts failure inside it. Other than that, contacts wear influences the OLTC performance and this phenomenon has not been deeply investigated. The worn contacts will cause hotspots in the OLTC tank and cause the insulating oil to degrade faster. Consequently, this will lead to the damage of complete transformer unit. Thus, the research work that has been carried out applied three different stages related to the diagnosis of OLTC. Firstly, fault detection of power transformer's OLTC is done mainly based on the results of the DGA interpretation by using the latest Duval Triangle method. Then, the results that obtained from latest Duval Triangle method are compared with the results carried out from Dynamic Current Measurement (DCM). Finally, research validation through visual inspection is applied to validate the results obtained from both DGA and DCM.

Distribution transformer is the key element in the electricity transmission throughout the nation. Hence, serious attention for the transformer condition monitoring is a crucial. With an occurrence of short-circuit in the power system, the transformer active parts such as core and winding will experience a mechanical movement created by the electromagnetic forces. There are various types of transformer condition monitoring measurement in market, but the monitoring of the mechanical movement in transformer is still lacking. Therefore, Sweep Frequency Response Analysis (SFRA) has been introduced to assess any mechanical movement especially of the transformer's core and winding. The frequency response of the transformer provides mechanical information of core and winding conditions. The SFRA measurement results were validated using the actual transformer untanking process.

Degradation phenomenon due to water treeing has been identified as one of the main causes of cable failures in Cross-Linked Polyethylene (XLPE) cables. This research work presents on the development for the analysis of water tree conditions in the XLPE cable. The water tree condition is divided into three stages which are beginning, intermediate and critical. The method used to measure and obtain the data to determine the condition of the cable is called high voltage dielectric spectroscopy. It involves the measurement of capacitance and dielectric loss as a function of frequency at high voltages. The voltage levels used initially are all below the service voltage level and at the low frequency range. Based on the dielectric response measurement signals obtained, observation and numerical analysis were conducted to study the response of the cable and determine their water tree stages. Initially, the observation and analysis of the results are carried out by referring to the theory of the dielectric response. Then the research concentrates on analyzing the data from the dielectric spectroscopy measurement to perform the numerical analysis to obtain the characteristics of each water tree stages. Four different parameters were extracted from the Dielectric Spectroscopy response in the frequency domain and these parameters were found to be able to categorize the three different stages of water tree condition in the XLPE cables.

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

Generally, the insulation degradation and contacts failure happened inside the On Load Tap Changer (OLTC). Besides, the OLTC performance can be dropped due to contact wear. Hence., the worn contact creates hotspots inside OLTC tank and affects the insulating oil. Due to this phenomenon, the insulating oil will degrade faster. Consequently, the whole unit of the transformer will be affected as well. Therefore, it is important to find new ways to detect the contacts wear inside the OLTC as it impacts the insulation oil and the age of the transformer. Thus, this paper demonstrates the application of Duval Triangle method during the diagnosis of OLTC to provide any significant indication related to the breakdown of OLTC. Besides, this paper introduced the advanced technique, Dynamic Winding Resistance Measurement (DWRM) to figure out the types and locations of faults occurred inside the OLTC. Finally, visual inspections will be carried out to validate the both results obtained from DGA and DWRM respectively. The analysis in this paper revealed that the findings from Duval Triangle method were similar to the outcomes carried out by DWRM.

The main purpose of this paper is to design an optimal electrical power generation system using Renewable Energy in Pulau Perhentian Besar Island in Malaysia. This island was chosen in this study particularly because of the lack of record on the energy power generation assessment of islands in peninsular Malaysia. One of the important aspects of this study, is to reduce energy consumption with the applications of some new methods (Passive House, HVAC, Natural ventilation) to design entire buildings (residential, services) in Pulau Perhentian Besar Island and as a result, cost of electrical power generation will be optimal and accomplishable. Moreover, based on the potential renewable energy in this island, this paper attempts to propose electrical power generation with the use of solar and wind energy to cover overall energy requirements in Pulau Perhentian Besar Island. In this investigation, for the estimation of design cost, HOMMER soft is used to analyze the required solar panels, batteries, converters and etc, which dependent on the amount of solar radiation. The aim was to illustrate, that renewable energy can be an appropriate alternative instead fuel and is capable of supplying the required energy.

Partial Discharges are major source of insulation failure in power transformers. Therefore, partial discharge (PD) measurement has been commonly carried out to detect and monitor PD activity in insulation in order to avoid from any catastrophic failure. In this research work, acoustic emission (AE) detection system is used to detect PD in an experimental tank filled with transformer oil. Three different types of PD sources to generate PD in the experimental tank are created from pressboards which are the plain pressboard, the floating metal in the pressboard and the bubble in pressboard. The AE signals due to the occurrence of PD from these sources are captured and recorded. Due to the large number of AE signals to be recorded by the Data Acquisition System, some of these signals may not be stored correctly in the system. Some of these signals may also be affected by the reflection and refraction phenomena, interference and attenuation of the AE signals. Hence, the preprocessing and validation of the acquired AE signals from the experimental setup are also explored in this paper. Finally, the valid AE signals due to three different types of PD sources are identified for further analysis.