For obtaining efficient stability and good regulation of different devices in power system and industrial applications, automatic voltage regulators (AVR) are increasingly used. However, AVR without any controller will provides slow responses due to disturbance and may cause instability. In this study therefore, the objective is to consider a generator AVR system without PID controller and with PID controller, where the PID controller was tuned with a view for improving the response of the system and make comparison between the frequency deviation step response and the tuned PID controller block performance using linear block model and control techniques in MATLAB / Simulink environment, in which the design configuration and automated PID turning was used to tuned the PID controller for AVR system without and with PID controller for generator 1 and generator 2 respectively. Simulation results indicates that generator 1 shows higher frequency overshoot and oscillation as compared to generator 2, which shows low frequency overshoot and minimum oscillation. The performance response of the tuned AVR system in generator 2 with PID controller gives satisfactory settling time which was recorded at 1.44 second as against 5.54 second.

This article provides a way of accurately predicting one-hourahead load of a utility company located in the North Eastern region of Nigeria based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS are the next-hour temperature, next-hour humidity, day of the week, hour of the day, and the current-hour load. The output is the next-hour load of the entire system. All the data used span the period 2009 to 2012 (4 years). These parameters are non-linear, stochastic (random) and uncertain in nature. Adaptive Neurofuzzy based Inference System (ANFIS), an integrated system, comprising of fuzzy logic and Neural Network was used to model the next hour load, because it can address and solve problems related to non-linearity, randomness and uncertainty of data. 75% of the data was used for training and 25% for checking. From the analysis carried out on the ANFIS-based model; Mean absolute percentage error (MAPE) for a typical Monday, Wednesday and Friday was found to be 12.61%, 12.76% and 12.12%. The Mean absolute error (MAPE) on the entire test data was 24.76%. The analysis shows satisfactory level of accuracy with regards to the ANFIS-based model developed in forecasting the next hour load especially with a correlation (r) value of 84.64%. General TermsLoad Forecasting.