• Journal of Electrical Engineering and Technology
• /
• v.2 no.2
• /
• pp.172-177
• /
• 2007

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of the reduction method for expedient fault analysis and protection assessment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.271-274
• /
• 1996

In this paper we made a reliability analysis of power system pumps by using the dimensional reduction method which over comes the problem due to unavailability of enpugh data in the actual systems under many different operational environments. Hence a resonable method was proposed to determine the optimum maintenance interval of given pump in thermal power stations. This analysis was based on an actual data set of pumps for over ten years in thermal power stations belonged to Kyushu Electric Power Company, Japan.

• Journal of the Korean Solar Energy Society
• /
• v.37 no.1
• /
• pp.81-90
• /
• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Proceedings of the KIPE Conference
• /
• 2001.10a
• /
• pp.731-736
• /
• 2001

Nowadays various simulation tools are widely used for the design and the analysis of power electronic converters. From the engineering point of view it is rather difficult to parameterize power semiconductor device models without the knowledge of basic physical parameters. In recent years some data sheet driven behavioral models or so called 'wizard' tools have been introduced to solve this problem. In this contribution some experiences with some user-friendly power semiconductor models will be discussed. Using special simulation test circuits it is possible to get information on the static and dynamic behavior of the parameterized models before they are applied in more complex schemes. These results can be compared with data sheets or with measurements. The application of these models for power loss analysis of inverter type arc welding power sources will be described.

• 전기의세계
• /
• v.27 no.1
• /
• pp.49-55
• /
• 1978

This paper describes a computer method for power system planning. Power system planning was usually studied through individual programs. Because of the laborious work involved in data preparation, the increase of task for power system planning and the time required for the detailed analysis of results, the available time for assessment and decision making has been sacrificed. In order to improve the above situation, the use of data base techniques an the simplified evaluation of the presented programs were newly developed. This program has been used successfully for the routine of power system planning in Korea Electric Company. In addition, this paper describes some results of analysis and evaluation of power system planning in KECO.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.79-84
• /
• 2021

The fundamental element to be kept for big data analysis, artificial intelligence technologies and asset management system is a data quality, which could directly affect the entire system reliability. For this reason, the momentum of data cleaning works is recently increased and data cleaning methods have been investigating around the world. In the field of electric power, however, asset data cleaning methods have not been fully determined therefore, automatic cleaning algorithm of asset data for transmission cables has been studied in this paper. Cleaning algorithm is composed of missing data treatment and outlier data one. Rule-based and expert opinion based cleaning methods are converged and utilized for these dirty data.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.746-757
• /
• 2021

Side-channel attacks pose an inevitable challenge to the implementation of cryptographic algorithms, and it is important to mitigate them. This work identifies a novel data encoding technique based on 1-of-4 codes to resist differential power analysis attacks, which is the most investigated category of side-channel attacks. The four code words of the 1-of-4 codes, namely (0001, 0010, 1000, and 0100), are split into two sets: set-0 and set-1. Using a select signal, the data processed in hardware is switched between the two encoding sets alternately such that the Hamming weight and Hamming distance are equalized. As a case study, the proposed technique is validated for the NIST standard AES-128 cipher. The proposed technique resists differential power analysis performed using statistical methods, namely correlation, mutual information, difference of means, and Welch's t-test based on the Hamming weight and distance models. The experimental results show that the proposed countermeasure has an area overhead of 2.3× with no performance degradation comparatively.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.2
• /
• pp.301-307
• /
• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.1013-1016
• /
• 1999

In this paper, a real-time communication method using a PICNET-NP(Plant Instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS (Distributed Control System) in nuclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others. In addition, the method was used to analyze the data characteristics of the DCS in existing nuclear power plant. The result shows that use of this method meets the response time requirement(100ms)

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.10
• /
• pp.465-472
• /
• 2017

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.