The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ([1,2,7,16,18,35,46],1,[1,3,13,21,27,46])(1,1,1)48(0,0,1)336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
Dynamic stability analysis is one of important issue in electrical power system study. This paper aims to analyze and design the control of power generation operation system in small disturbance events. This condition is affected by changes in the prime mover of mechanical power input in generator system due to power fluctuation in load, so that the system becomes unstable. In the analysis of electrical power distribution, generation unit provides power output based on regulations of fluctuating power. This distribution system that provides continuous data periodically is actually performing a pattern of dynamic time series model. Within the statistical methods analysis, the presentation of load data will be analyzed through clustering method based on the average distribution and peak loads. This kind of pattern description is purposed to enable the control system for anticipating the changes in the load model, where each of load cluster represents one dynamic system model in appropriate operation condition. The solution of dynamic models control systems, performed by Takagi-Sugeno Fuzzy Inference System (TS-FIS) as multiple soft switching controllers and optimal control gain for each dynamic model. Those can be distributed into TS-FIS outputs, to achieve robustness of power generation system that affected by changes in huge variation of load power. In this study, the cluster analysis technique has produced seven data's groups with interval of 18 MVA. By performing Robust-Fuzzy control through TS-FIS as multiple soft switching, can be proved that the power generating performance is better than using Linear Quadratic Regulator (LQR) optimal control, since Robust-Fuzzy control Integral Absolute Error (IAE) is better than LQR optimal control IAE.
Forecasting short-term electrical load is very important so that the quality of the electrical power supplied can be maintained properly. The study was conducted to measure the results of electrical load forecasting based on parameter estimates and the presentation of time series data. It is important to manage stationary data, both in terms of mean and variance. Data presentation is done by determining the value of variance through the Box-Cox transformation method and the mean value based on the ACF and PACF plots. This study considers the pattern of electricity consumption which contains double seasonal patterns. The results of previous studies show the electric power prediction model, the DSARIMA model with a MAPE of 2.06%. The condition of the model used to predict the electrical load still has a tendency not to be normally distributed and it is estimated that there are outliers. Improvements to the AR and MA parameters that meet the standard error tolerance value of 5 percent are increased in this study. The results showed improvement of parameters to predict electrical load with DSARIMA model. The significance of this study was obtained by the MAPE value of 1.56 percent when compared to the actual data.
This chapter describes the process of identifying a power generation system. This is important because in principle the system parameters as a whole are not linear and uncertain. For this reason, it is necessary to carry out an identification process using an experimental approach that is able to represent the system as a whole. The technique used in this identification process is Prediction Error Minimization (PEM) as a tool available in Matlab. Identification is done by simulating changes in the value of frequency, voltage and electrical power due to changes in load. The change in load over time is a characteristic of the time series pattern. Through descriptive analytic approach, the cluster load is patterned for each load operating condition. Through load clusters, the identification results of power generation systems are obtained based on their operating conditions. This chapter presents validated parameter estimates for each change in instantaneous load conditions. The simulation results obtained better performance between the actual output and the identification model, namely the calculation of the Intergal Absolute Error (IAE), with MAPE for the average frequency value of 73.95 percent, nominal voltage of 0.23 percent, and electric power of 23.46 percent.
Electricity consumption always changes according to need. This pattern deserves serious attention. Where the electric power generation must be balanced with the demand for electric power on the load side. It is necessary to predict and classify loads to maintain reliable power generation stability. This research proposes a method of forecasting electric loads with double seasonal patterns and classifies electric loads as a cluster group. Double seasonal pattern forecasting fits perfectly with fluctuating loads. Meanwhile, the load cluster pattern is intended to classify seasonal trends in a certain period. The first objective of this research is to propose DSARIMA to predict electric load. Furthermore, the results of the load prediction are used as electrical load clustering data through a descriptive analytical approach. The best model DSARIMA forecasting is ([1, 2, 5, 6, 7, 11, 16, 18, 35, 46], 1, [1, 3, 13, 21, 27, 46]) (1, 1, 1)48 (0, 0, 1)336 with a MAPE of 1.56 percent. The cluster pattern consists of four groups with a range of intervals between the minimum and maximum data values divided by the quartile. The presentation of this research data is based on data on the consumption of electricity loads every half hour at the Generating Unit, the National Electricity Company in Gresik City, Indonesia.
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