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The frequency instability observed in the power transmission network was mainly as a result of the per unit volts not falling within 0.95 through 1.05 P.U, volts. This has caused constant power failure in our transmission net work. This sad situation of power failure noticed in the power transmission network is contained by introducing an improvement in frequency stability of the Nigerian 330kV transmission network using fuzzy controller. It was achieved by first characterizing the 330kv transmission network by running load flow on the network, designing conventional SIMULINK model for improving frequency stability of the Nigerian 330kv transmission network, designing a rule base that makes these faulty buses to attain stability, integrating the designed rule to the conventional SIMULINK model for improving frequency stability of the Nigerian 330kv transmission network. The results obtained are conventional bus 1 per unit volts at 4s through 10s is 0.94. On the other hand, when fuzzy controller is incorporated in the system it is 1.043P.U volts. This shows that there is frequency stability when fuzzy controller is incorporated in the system since the per unit volts fall within the range of 0.95 through 1.05 P.U. volt and conventional per unit volts is 0.944 which makes the frequency unstable since the volts does not attain stability. Meanwhile, when fuzzy controller is incorporated in the system the per unit volts is 1.047. With these results, it shows that there is frequency stability when fuzzy controller is imbibed in the system. Since the per unit volt fall within the stability range of 0.95 through 1.05P.U. Volts.

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Energy efficiency is the use of technology that requires less energy to perform the same task. It was considered to introduce optimized energy efficiency by using machine learning to reduce power consumption at communication base station (BTS) sites. This process involves reviewing relevant work to identify defects, characterizing and determining the power consumption of the cell site under investigation, developing a SIMULINK model for the cell site under investigation, and identifying the module. It also includes optimizing high power consumption; design a machine learning rule base to monitor the power consumption of the module. Train artificial neural network (ANN) on machine learning rules designed to reduce cell power consumption, thereby improving network performance. The next step is developing an algorithm to implement it, and finally, to design a power consumption model for the network under investigation. The results obtained after a large simulation show that the traditional maximum power consumed at the cell site is 5746 kW, while the power when machine learning is injected into the system is 4733 kW. Integrating machine learning into the system resulted in 4731 kW, an 8.9% performance improvement.