No abstract

Power systems employ measures of reliability indices to indicate the effectiveness a power system to perform its basic function of supplying electrical energy to its consumers. The amount of energy required in a generating system to ensure an adequate supply of electricity is determined using analytical and simulation techniques. This study focuses on reviewing the generation reliability assessment methods of power systems using Monte Carlo simulation (MCS) and variance reduction techniques (VRTs). MCS is a very flexible method for reliability assessment of the power systems, by the sequential process it can imitate the random nature of the system components and can be broadly classified into two, sequential and non-sequential simulations. A brief introduction to MCS is provided. Unlike analytical methods, MCS can be used to quantitatively estimate the system reliability in even the most complex system generating capacity situations. The major drawback of the MCS is that it requires more computational time to reach for converging with estimated the values of reliability indices. This paper presents an effective methods for accelerating MCS in power system reliability assessment. VRT used is to manipulate the way each sample of an MCS is defined in order to both preserve the randomness of the method and decrease the variance of the estimation. In addition, the study presents detailed descriptions of generation reliability assessment methods, in order to provide computationally efficient and precise methodologies based on the pattern simulation technique. These methodologies offer significantly improved computational ability during evaluations of power generation reliability.

Energy is a basic necessity in every country. The worldwide demand for energy will rise due to the developments of power generation in industrial, service, and residential sectors. A healthy power system is therefore very important to guarantee continuous electricity supply to the end users and this can be achieved through asset management. A proper asset management will allow asset managers to conduct quality assessment of conditions and to develop future management strategies of the electrical assets such as transformers. The execution of transformer asset management involves an investigation of the transformer's condition by employing Transformer's Health Index (THI). Mathematical equation/algorithm or expert judgment has been investigated by many previous studies as one of the technique to determine health index (HI). Some of the established methods of HI determination such as scoring and ranking method, tier method, matrices and multi-feature assessment model have led to the different interpretations of the final condition of a transformer. This paper critically examines and explores the previous studies related to transformer health index by using mathematical equation/algorithm or expert judgment. The concept of HI and its formulation are presented in this study. Generally, there are three parts of HI formulation which are input, algorithm for HI and the output of HI. The application of HI is discussed in terms of the performance of in-service transformer. The limitations of the available methods are also discussed and future works to overcome the problems are suggested.

Vegetable oils have emerged as insulating fluids in transformer applications and as a prominent and effective alternative for traditional dielectric fluids. However, most of vegetable oils are edible causing their application on a large scale to be limited. In the present work, a novel non-edible vegetable oil is developed as an insulating fluid. The developed oil is oxidation-inhibited cottonseed oil (CSO) based nanofluids. Tertiary butylhydroquinone was used as antioxidant. The concept of nanofluids was used to overcome the limited dielectric and thermal properties of cottonseed oil. Hexagonal Boron Nitride (h-BN) nanoparticles at low weight fractions (0.01 -0.1 wt%) were proposed as nanofillers to achieve adequate dielectric strength and improved thermal conductivity. Stability of prepared CSO based nanofluids was analyzed using Ultraviolet-visible (UV-Vis) spectroscopy. Then, the prepared nanofluids were tested for dielectric and thermal properties under a temperature range between 45 • C and 90 • C. The dielectric properties include breakdown strengths under AC and lightning impulse voltages, dielectric constant, dissipation factor, and resistivity, while thermal properties include thermal conductivity and thermogram analysis. The dielectric and thermal properties were significantly improved in CSO based nanofluids. The creation of electric double layer at nanoparticle/oil interface and the lattice vibration of nanoparticles were used to clarify the obtained results. The proposed CSO based h-BN nanofluids open up a great opportunity in both natural ester insulating fluid applications and thermal energy management systems. INDEX TERMSVegetable oils, transformers, nanofluids, dielectric properties, thermal properties. NOMENCLATURE b Absorbance in y intercept BDV Breakdown voltage [kV] c p Specific heat capacity [J/(kg.K)] CSO Cottonseed oil EDL Electric Double Layer Enh. Enhancement h-BN Hexagonal Boron Nitride k B Boltzmann constant [1.3806505e −23 J/K] LI Lightning impulse m Coefficient of molar extinction [M −1 .cm −1 ] NEIO Natural ester insulating oil The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney.

No abstract