• ETRI Journal
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• v.30 no.1
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• pp.47-58
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• 2008

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction-level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instruction-level simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz-compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN's high precision instruction-level energy prediction, users can accurately estimate a sensor network's energy consumption and achieve fine-grained optimization of their software.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.2 s.344
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• pp.42-50
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• 2006

Owing to the development of semiconductor processing technology, high density complex circuits can be integrated in a System-on-Chip (SoC). However, increasing energy consumption becomes one of the most important limiting factors. Power estimation at the early stage of design is essential, since design changes at lower levels may significantly lengthen the design period and increase the cost. In this paper, logic level circuits ire levelized and several levels are selected to build power model tables for efficient power estimation. The proposed techniques are applied to a set of ISCAS'85 benchmark circuits to illustrate their effectiveness. Experimental results show that significant improvement in estimation accuracy and slight improvement in efficiency are achieved when compared to those of a well-known existing method. The average estimation error has been reduced from $9.49\%\;to\;3.84\%$.

• Journal of Power System Engineering
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• v.13 no.5
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• pp.59-66
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• 2009

High temperature and pressure valves in power plant have been used for fluid flowing and leakage occurred owing to valve internal damage such as disc wear, crack and inserting of foreign objects etc. in these valves. Recently, multi-measuring technique applied both ultrasonic and acoustic method have been used for evaluation of valve internal leakage in order to raise measurement reliability. Therefore, we have performed various leakage tests using ultrasonic and acoustic measuring system and acquired leakage data for the various leakage conditions. In this study, we developed the estimation method of regression model through leakage data, and expectation method for valve opening ratio, which is directly proportion to leakage rate, using the established estimation model from the measured data, valve size and fluid pressure so as to enhance data reliability. As a result of this study, it was founded that expectation method of leakage rate by statistical analysis method is appropriate to valve leakage evaluation.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1422-1427
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• 2006

In order to estimate the power consumed by an embedded microprocessor during an execution of software, we measure and utilize the current consumed by the processor during the execution of each instruction. In this paper, we measure and analyse the current consumed by the microprocessor adc16s310 during the execution of arithmetic and logic instructions, and propose a power estimation model which estimates the current for all instruction executions precisely by using a small numbers of current measurements. The proposed model can estimate the current with an average 0.34% error by using only 5.84% of total current measurements for arithmetic and logic instructions of the processor.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1729-1738
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• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3152-3159
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• 2012

This paper presents the new instruction-level power model for an asynchronous processor. Until now, the various power models for estimating the power dissipation of embedded processor in SoC are proposed. Since all of them are target to the synchronous processors, the accuracy is questionable when we apply those power models to the asynchronous processor in SoC. To solve this problem, we present new power model for an asynchronous processor by reflecting the behavioral features of an asynchronous circuit. The proposed power model is verified using an implementation of asynchronous processor, A8051. The simulation results of the proposed model is compared with the measurement result of gate-level synthesized A8051. The proposed power model shows the accuracy of 90.7% and the simulation time for estimation the power consumption was reduced to 1,900 times.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.75-82
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• 2002

A robust signal transition density propagation method for a zero delay model is presented to obtain the signal transition density for estimating the power consumption. The power estimation for the zero delay model is a proper criteria for the lower boundary of power consumption. Since the input characteristics are generally unknown at design stage, robust estimation for wide range input characteristics is very important for the power consumption. In this paper, a conventional transition estimation method will be explored. And this exploration will be analyzed with the input/output signal transition behavior and used to propose the robust signal transition density propagation for the power estimation. In order to apply to practical circuits, the reconvergent path, which is crucial to affect the exactness of the power estimation, will be studied and an algorithm to take the reconvergent path into consideration will be presented. In experiment, the proposed methodology shows better robustness, comparable accuracy and elapsed time compared to the conventional methods.

• Journal of Power Electronics
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• v.16 no.2
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• pp.815-822
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• 2016

The efficiency of light-emitting diode (LED) devices is a significant factor that reflects the capability of these devices to convert electrical power into optical power. In this study, a method for estimating the efficiency of LED devices is proposed. An efficiency model and a heat power model are established as convenient tools for LED performance evaluation. Such models can aid in the design of LED drivers and in the reliability evaluation of LED devices. The proposed estimation method for the efficiency and heat power of LED devices is verified by experimentally testing two types of commercial LED devices.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.845-851
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• 2012

Power system loads have a significant impact on a system. Although it is difficult to precisely describe loads in a mathematical model, accurately modeling them is important for a system analysis. The traditional load modeling method is based on the load components of a bus. Recently, the load modeling method based on measurements from a system has been introduced and developed by researchers. The two major components of a load modeling problem are determining the mathematical model for the target system and estimating the parameters of the determined model. We use the composite load model, which has both static and dynamic load characteristics. The ZIP model and the induction motor model are used for the static and dynamic load models, respectively. In this work, we propose the measurement-based parameter estimation method for the composite load model. The test system and related measurements are obtained using transient security assessment tool(TSAT) simulation program and PSS/E. The parameter estimation is then verified using these measurements. Cases are tested and verified using the sample system and its related measurements.