The aim of this paper is to design a three-phase distance relay using an adaptive neuro-fuzzy inference system algorithm (ANFIS). The proposed relay is used to protect the power transmission lines where they are subjected to faults continuously. These faults may produce a high electric current which leads to high damage in power system equipment. The relay is used to detect the transmission line faults by measuring the voltage and current values for each phase. The line impedance is then calculated to detect the faults and issue instantaneous trip signal to circuit breaker, to separate the fault zone of the transmission line without affecting the work of other relays. To isolate the faulty line without affecting the other lines within the network the relays were trained using adaptive neuro-fuzzy inference system (ANFIS). The obtained results through this work show that the designated distance relay with (ANFIS) algorithm has the ability to detect the faults occurrence, recognize it from the cases of the disturbance and to isolate only the fault zone without affecting the work of other relays in system.
An energy audit was conducted on a university building located within the main campus of Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia. Some of the physical parameters particularly energy consumption, air velocity, airflow, operating temperature, relative humidity and lighting intensity were compared to the Malaysian Standards 1525:2014. This work aims at understanding the comfort level of the occupants and investigates the impact of lighting changes to the overall energy consumption. Based on the data collected, the team could estimate the current energy consumption for all floors. As expected, the air conditioning recorded the highest rate consumption at 72% of electricity usage in the building, followed by the consumption of lighting at 18% and other equipment only 10%. The average operating temperature was recorded at 22.758°C, which is less than that of the recommended range of 24°C - 26°C The average humidity was about 68.31% while the average lighting intensity was recorded at 461.422 lux. Additionally, the Building Energy Index (BEI) for the 2016-2017 period is 128.53 kWh/m²/year and 137.55 kWh/m²/year for 2017/2018. BEI values in 2017-2018 were a little higher than that specified in MS 1525:2014 Standards, which is 135 kWh/m²/year.
This study proposes an intelligent protection relay design that uses artificial neural networks to secure electrical parts in power infrastructure from different faults. Electrical transformer and transmission lines are protected using intelligent differential and distance relay, respectively. Faults are categorized, and their locations are pinpointed using three-phase current values and zero-current characteristics to differentiate between non-earth and ground faults. The optimal aspects of the artificial neural network were chosen for optimal results with the least possible error. Levenberg-Marquardt was established as the ideal training technique for the suggested system comprising the differential relay. Levenberg-Marquardt was the optimal training technique for the proposed framework consisting of the differential relay. Fault detection and categorization were performed using 20 and 50 hidden layers, and the corresponding error rates were 9.9873e-3 and 1.1953e-29. In the context of fault detection by the distance relay, the hidden layer neuron counts were 400, 250, and 300 for fault detection, categorization, and location; training error rates were 7.8761e-2, 1.2063e-6, and 1.1616e-26, respectively.
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