This paper presents a study on the application of the Markov Model (MM) to determine the transformer population states based on Health Index (HI). In total, 3195 oil samples from 373 transformers ranging in age from 1 to 25 years were analyzed. First, the HI of transformers was computed based on yearly individual oil condition monitoring data that consisted of oil quality, dissolved gases, and furanic compounds. Next, the average HI for each age was computed and the transition probabilities were obtained based on a nonlinear optimization technique. Finally, the future deterioration performance curve of the transformers was determined based on the MM chain algorithm. It was found that the MM can be used to predict the future transformers condition states. The chi-squared goodness-of-fit analysis revealed that the predicted HI for the transformer population obtained based on MM agrees with the average computed HI along the years, and the average error is 3.59%.
This paper presents an investigation of the condition state distribution and performance condition curve of the transformer population under different pre-determined maintenance repair rates based on the Markov Prediction Model (MPM). In total, 3195 oil samples from 373 transformers with an age between one and 25 years were tested. The previously computed Health Index (HI) prediction model of the transformer population based on MPM utilizing the nonlinear minimization technique was employed in this study. The transition probabilities for each of the states were updated based on 10%, 20% and 30% pre-determined maintenance repair rates for the sensitivity study. Next, the HI state distribution and performance condition curve were analyzed based on the Markov chain algorithm. Based on the case study, it is found that the pre-determined maintenance repair rates can affect the HI state distribution and improve the performance condition curve. The 30% pre-determined maintenance repair rate gives the highest impact, especially for the transformer population at state 4 (poor). Overall, the average percentage of change for all HI state distributions is 16.48%. A clear improvement of HI state distribution is found at state 4 (poor) where the highest percentage can be up to 63.25%.
This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.
In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively.
Power system stability is the capability of power systems to maintain load magnitude within specified limits under steady state conditions in electrical power transmission. In modern days, the electrical power systems have grown in terms of complexity due to increasing interconnected power line exchange. For that, an inherent of controllers were essential to correct the deviation in the presence of external disturbances. This paper hence aims to review the basic concepts of power system stability in load frequency control. Various control techniques were analyzed and presented. Power system stability can be classified in terms of method to improve power system stability, which are rotor angle stability, frequency stability and voltage stability. It is found that each method has different purpose and focus on solving different types of problem occurred. It is hoped that this study can contribute to clarify the different types of power system stability in terms of where it occurs, and which is the best method based on different situation.
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