Nowadays, data analysis widely used in many fields especially in engineering. Clustering is one of data analysis methods to organize the amount of data into groups with similarity characteristics. One powerful analysis method to learn information by grouping data is clustering algorithms. The clustering advantages for electrical power utilities is to learn load behavior and provide information for power plant operation and also generation cost. In this paper, a simulation concept is proposed for analysis of peak load data by K-means clustering algorithm based on historical dataset. The results show electrical peak loads clustering by K-means algorithm are optimum classified into three clusters. This cluster evaluated by silhouette scores which high, intermediate, and low load level interpretation. One cluster has centroid during January, June, and July are relatively lower than another cluster caused by Indonesia national holiday. This concept also evaluates the load level affected by Covid-19 pandemic condition.

No abstract

The stability and economic level of the power system operation during the penetration of Wind Power Plants (WPPs) are much determined by the variability and uncertainty of the wind power output. The characteristics of seasonal wind power output can be used to define the optimal operating reserves of a stable and cost-effective power system operation. This paper proposes a comprehensive algorithm of hybrid Artificial Intelligence (AI) approach that combines the Seasonal Autoregressive Integrated Moving Average (SARIMA) and selected Neural Network Variants (NNVs) in Seasonal Daily Variability and Uncertainty (SDVU) scheme. Among all NNVs, Long Short-Term Memory (LSTM) shows the most consistent and accurate results. With the hybrid AI approach, this algorithm calculates the Dynamic Confidence Level (DCL) to determine hourly operating reserves on a daily basis. The proposed algorithm has been successfully tested using historical data of real-world WPPs that operated in Indonesia. Furthermore, the comparison toward non-seasonal with a Static Confidence Level (SCL) in several percentile scenarios is made to prove the cost-effectiveness advantages of this new algorithm that may save up to 4.2% of total daily energy consumption. An interface application is added so that the results of this research can be directly utilized by users both on the observed power system and generally in Indonesia.INDEX TERMS dynamic confidence level, neural network, operating reserve, wind power, SARIMA.

Wind Power Plant (WPP) is part of renewable energy sources, with rapid expansion worldwide. It has the advantages of clean and green energy, but its uncertainty leads to an additional grid integration cost. The uncertainty of wind power output is much dependent on the accuracy of the wind power forecast (WPF) result. Since there is no perfect wind power forecast, understanding the current system's forecast accuracy characteristics is essential in expecting typical errors faced in the future. This paper proposed a new algorithm of the statistical approach method to evaluate characteristics of wind power forecast errors (WPFE) from an observed power system with high-penetration WPP. This method combined the approach of scatter diagram, statistical distribution, standard error performance, and score weighting in a multi-stage algorithm. It consists of serial and parallel processes to check the consistency of the results. In this study, a comprehensive analysis was made of various scenarios based on location and timescale. This proposed algorithm has been successfully tested on statistical data of Sidrap WPP and Jeneponto WPP in the Southern Sulawesi power system. The result showed that the scenario with the aggregation of both WPPs in hour-ahead timescale has the most accurate and consistent performance among all scenarios. It demonstrated specific characteristics of WPFE in the observed power system that can be used as an essential starting point in conducting future wind integration expansion studies.

No abstract

Children are the future and the nation's next generation who have limitations in understanding and protecting themselves from various influences of the existing system. Sexual violence against children often occurs, so it impacts the child's physical and psychological aspects, which can be carried over to the child's maturity and interfere with the child's development. The description of a child as a victim can use in proving a crime. The method used in this study is normative juridical research. The nature of the research used is descriptive analysis, and data analysis carries out qualitatively using library data collection techniques. The results of this study found that the position of a child as a witness in a criminal case has legally recognized in the Criminal Procedure Code. However, the statement of a child witness cannot be accounted for in criminal law by the provisions of Article 160, paragraph (3) of the Criminal Procedure Code, Article 185, paragraph (7) of the Criminal Procedure Code, and Article 184 Criminal Procedure Code. The strength of the child's testimony can be said not to have reliable evidentiary power to determine whether the defendant has committed a crime but rather to strengthen the judge's conviction. In the judge's consideration of sentencing decisions, in general, the information given by the child is used by the judge as a guide and reinforcement of other legal evidence based on adjustments and links with evidence and facts that occurred in court, which use as a reinforcement for the judge's conviction and as a consideration in imposing a criminal sentence. In giving sentencing decisions in the decision file No.39./Pid.Sus/2016/PN Sbg the statement of the child as a victim witness adds to the confidence of the Panel of Judges.

No abstract

No abstract