Articles
<![CDATA[On Active Surge Control of Compression Systems via Characteristic Linearization and Model Nonlinearity Cancellation ]]>
<![CDATA[Simamora, Yohannes S.M.]]>;
<![CDATA[Tjokronegoro, Harijono A.]]>;
<![CDATA[Leksono, Edi]]>
<![CDATA[ Journal of Engineering and Technological Sciences Vol 46, No 3 (2014) ]]>
Publisher : <![CDATA[ITB Journal Publisher, LPPM ITB ]]>
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DOI: 10.5614/j.eng.technol.sci.2014.46.3.8
<![CDATA[A simple approach of active surge control of compression systems is presented. Specifically, nonlinear components of the pressure ratio and rotating speed states of the Moore-Greitzer model are transferred into the input vectors. Subsequently, the compressor characteristic is linearized into two modes, which describe the stable region and the unstable region respectively. As a result, the systemâs state and input matrices both appear linear, to which linear realization and analysis are applicable. A linear quadratic regulator plus integrator is then chosen as closed-loop controller. By simulation it was shown that the modified model and characteristics can describe surge behavior, while the closed-loop controller can stabilize the system in the unstable operating region. The last-mentioned was achieved when massflow was 5.38 per cent less than the surge point.]]>
<![CDATA[Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes ]]>
<![CDATA[Haq, Irsyad Nashirul]]>;
<![CDATA[Kurniadi, Deddy]]>;
<![CDATA[Leksono, Edi]]>;
<![CDATA[Yuliarto, Brian]]>;
<![CDATA[Soelami, F.X. Nugroho]]>
<![CDATA[ Journal of Engineering and Technological Sciences Vol 51, No 2 (2019) ]]>
Publisher : <![CDATA[ITB Journal Publisher, LPPM ITB ]]>
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DOI: 10.5614/j.eng.technol.sci.2019.51.2.1
<![CDATA[In this work, a historical data based battery management system (BMS) was successfully developed and implemented using an embedded system for condition monitoring of a battery energy storage system in a smart microgrid. The performance was assessed for 28 days of operating time with a one-minute sampling time. The historical data showed that the maximum temperature increment and the maximum temperature difference between the batteries were 4.5 °C and 2.8 °C. One of the batteries had a high voltage rate of change, i.e. above 3.0 V/min, and its temperature rate of change was very sensitive, even at low voltage rate of changes. This phenomenon tends to indicate problems that may deplete the battery energy storage system’s total capacity. The primary findings of this study are that the voltage and temperature rates of change of individual batteries in real operating conditions can be used to diagnose and foresee imminent failure, and in the event of a failure occurring the root cause of the problem can be found by using the historical data based BMS. To ensure further safety and reliability of acceptable practical operating conditions, rate of change limits are proposed based on battery characteristics for temperatures below 0.5 °C/min and voltages below 3.0 V/min.]]>
<![CDATA[Pengembangan Sistem Kontrol Traksi Mobil Elektrik Berbasis Rekonstruksi Keadaan Kecepatan Model Roda ]]>
<![CDATA[Pratikto, Pratikto]]>;
<![CDATA[Nazaruddin, Yul Yunazid]]>;
<![CDATA[Leksono, Edi]]>;
<![CDATA[Abidin, Zainal]]>
<![CDATA[ Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 1, No 2 (2010) ]]>
Publisher : <![CDATA[Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences ]]>
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<![CDATA[In this paper the development of electric vehicle traction control based on state of speed reconstruction of vehicle model that has the same acceleration condition between tire and chassis is studied. Vehicle is accelerated if the friction force takes place between tire and road. However, the traction force decreases even tends to zero on slippery road and torque input produces a large slip. Evidently, tire slip can be reduced by decreasing the applied torque to the tire. So the basic principle of the proposed method here compares the real vehicle tire speed condition with the model to determine the torque in order to reduce the slip. Tire speed is controlled in order to follow the reference value that is calculated from the model. Tire torque input then can be controlled by applying the feedback that is obtained from the difference value of speed between model and real tire. Implementation of this method on a real vehicle shows that the control method effectively controls the tire speed of vehicle to follow the reference and reducing the slip. From the experiment the control system performance in reducing slip has the result of 9.8% for maximum overshoot, 3.1 second for rise time, and 8 second for settling time. ]]>
<![CDATA[Modelling and Identification of Oxygen Excess Ratio of Self-Humidified PEM Fuel Cell System ]]>
<![CDATA[Leksono, Edi]]>;
<![CDATA[Pradipta, Justin]]>;
<![CDATA[Tamba, Tua Agustinus]]>
<![CDATA[ Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 3, No 1 (2012) ]]>
Publisher : <![CDATA[Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences ]]>
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DOI: 10.14203/j.mev.2012.v3.39-48
<![CDATA[One essential parameter in fuel cell operation is oxygen excess ratio which describes comparison between reacted and supplied oxygen number in cathode. Oxygen excess ratio relates to fuel cell safety and lifetime. This paper explains development of air feed model and oxygen excess ratio calculation in commercial self-humidified PEM fuel cell system with 1 kW output power. This modelling was developed from measured data which was limited in open loop system. It was carried out to get relationship between oxygen excess ratio with stack output current and fan motor voltage. It generated fourth-order 56.26% best fit ARX linear polynomial model estimation (loss function = 0.0159, FPE = 0.0159) and second-order ARX nonlinear model estimation with 75 units of wavenet estimator with 84.95% best fit (loss function = 0.0139). The second-order ARX model linearization yielded 78.18% best fit (loss function = 0.0009, FPE = 0.0009).]]>
<![CDATA[Compensation of time-varying clock-offset in a LBL navigation ]]>
<![CDATA[Yohannes S. M. Simamora]]>;
<![CDATA[Harijono A. Tjokronegoro]]>;
<![CDATA[Edi Leksono]]>;
<![CDATA[Irsan S. Brodjonegoro]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i4.1996
<![CDATA[This paper presents compensation of the clock-offset in a long baseline (LBL)navigation. It departs from the existing literature mainly in dealing with a time-varyingclock-offset, i.e. the clock-rate drifts over the time. Specifically, the clock-offsetdynamics are introduced to the ToFs as an autoregressive filter.Subsequently,interactions among the now biased ToFs and the kinematics of an autonomousunderwater vehicle (AUV)–the navigation subject–are represented in a state-spaceform. Implementing the so-called graphic approach, minimum sensor requirementfor this system’s observability is then explicated. Finally, a standard discrete Kalmanfilter is deployed as the state estimator. By simulation, it is demonstrated that theestimator manages to compensate the offset and to provide localization with less than1 m accuracy]]>
<![CDATA[Pengembangan Sistem Kontrol Traksi Mobil Elektrik Berbasis Rekonstruksi Keadaan Kecepatan Model Roda ]]>
<![CDATA[Pratikto Pratikto]]>;
<![CDATA[Yul Yunazid Nazaruddin]]>;
<![CDATA[Edi Leksono]]>;
<![CDATA[Zainal Abidin]]>
<![CDATA[ Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 1, No 2 (2010) ]]>
Publisher : <![CDATA[National Research and Innovation Agency ]]>
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<![CDATA[In this paper the development of electric vehicle traction control based on state of speed reconstruction of vehicle model that has the same acceleration condition between tire and chassis is studied. Vehicle is accelerated if the friction force takes place between tire and road. However, the traction force decreases even tends to zero on slippery road and torque input produces a large slip. Evidently, tire slip can be reduced by decreasing the applied torque to the tire. So the basic principle of the proposed method here compares the real vehicle tire speed condition with the model to determine the torque in order to reduce the slip. Tire speed is controlled in order to follow the reference value that is calculated from the model. Tire torque input then can be controlled by applying the feedback that is obtained from the difference value of speed between model and real tire. Implementation of this method on a real vehicle shows that the control method effectively controls the tire speed of vehicle to follow the reference and reducing the slip. From the experiment the control system performance in reducing slip has the result of 9.8% for maximum overshoot, 3.1 second for rise time, and 8 second for settling time. ]]>
<![CDATA[Modelling and Identification of Oxygen Excess Ratio of Self-Humidified PEM Fuel Cell System ]]>
<![CDATA[Edi Leksono]]>;
<![CDATA[Justin Pradipta]]>;
<![CDATA[Tua Agustinus Tamba]]>
<![CDATA[ Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 3, No 1 (2012) ]]>
Publisher : <![CDATA[National Research and Innovation Agency ]]>
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DOI: 10.14203/j.mev.2012.v3.39-48
<![CDATA[One essential parameter in fuel cell operation is oxygen excess ratio which describes comparison between reacted and supplied oxygen number in cathode. Oxygen excess ratio relates to fuel cell safety and lifetime. This paper explains development of air feed model and oxygen excess ratio calculation in commercial self-humidified PEM fuel cell system with 1 kW output power. This modelling was developed from measured data which was limited in open loop system. It was carried out to get relationship between oxygen excess ratio with stack output current and fan motor voltage. It generated fourth-order 56.26% best fit ARX linear polynomial model estimation (loss function = 0.0159, FPE = 0.0159) and second-order ARX nonlinear model estimation with 75 units of wavenet estimator with 84.95% best fit (loss function = 0.0139). The second-order ARX model linearization yielded 78.18% best fit (loss function = 0.0009, FPE = 0.0009).]]>
<![CDATA[On Active Surge Control of Compression Systems via Characteristic Linearization and Model Nonlinearity Cancellation ]]>
<![CDATA[Yohannes S.M. Simamora]]>;
<![CDATA[Harijono A. Tjokronegoro]]>;
<![CDATA[Edi Leksono]]>
<![CDATA[ Journal of Engineering and Technological Sciences Vol. 46 No. 3 (2014) ]]>
Publisher : <![CDATA[Institute for Research and Community Services, Institut Teknologi Bandung ]]>
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DOI: 10.5614/j.eng.technol.sci.2014.46.3.8
<![CDATA[A simple approach of active surge control of compression systems is presented. Specifically, nonlinear components of the pressure ratio and rotating speed states of the Moore-Greitzer model are transferred into the input vectors. Subsequently, the compressor characteristic is linearized into two modes, which describe the stable region and the unstable region respectively. As a result, the system's state and input matrices both appear linear, to which linear realization and analysis are applicable. A linear quadratic regulator plus integrator is then chosen as closed-loop controller. By simulation it was shown that the modified model and characteristics can describe surge behavior, while the closed-loop controller can stabilize the system in the unstable operating region. The last-mentioned was achieved when massflow was 5.38 per cent less than the surge point.]]>
<![CDATA[Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes ]]>
<![CDATA[Irsyad Nashirul Haq]]>;
<![CDATA[Deddy Kurniadi]]>;
<![CDATA[Edi Leksono]]>;
<![CDATA[Brian Yuliarto]]>;
<![CDATA[F.X. Nugroho Soelami]]>
<![CDATA[ Journal of Engineering and Technological Sciences Vol. 51 No. 2 (2019) ]]>
Publisher : <![CDATA[Institute for Research and Community Services, Institut Teknologi Bandung ]]>
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DOI: 10.5614/j.eng.technol.sci.2019.51.2.1
<![CDATA[In this work, a historical data based battery management system (BMS) was successfully developed and implemented using an embedded system for condition monitoring of a battery energy storage system in a smart microgrid. The performance was assessed for 28 days of operating time with a one-minute sampling time. The historical data showed that the maximum temperature increment and the maximum temperature difference between the batteries were 4.5 °C and 2.8 °C. One of the batteries had a high voltage rate of change, i.e. above 3.0 V/min, and its temperature rate of change was very sensitive, even at low voltage rate of changes. This phenomenon tends to indicate problems that may deplete the battery energy storage system's total capacity. The primary findings of this study are that the voltage and temperature rates of change of individual batteries in real operating conditions can be used to diagnose and foresee imminent failure, and in the event of a failure occurring the root cause of the problem can be found by using the historical data based BMS. To ensure further safety and reliability of acceptable practical operating conditions, rate of change limits are proposed based on battery characteristics for temperatures below 0.5 °C/min and voltages below 3.0 V/min.]]>
<![CDATA[Data Driven Building Electricity Consumption Model Using Support Vector Regression ]]>
<![CDATA[FX Nugroho Soelami]]>;
<![CDATA[Putu Handre Kertha Utama]]>;
<![CDATA[Irsyad Nashirul Haq]]>;
<![CDATA[Justin Pradipta]]>;
<![CDATA[Edi Leksono]]>;
<![CDATA[Meditya Wasesa]]>
<![CDATA[ Journal of Engineering and Technological Sciences Vol. 53 No. 3 (2021) ]]>
Publisher : <![CDATA[Institute for Research and Community Services, Institut Teknologi Bandung ]]>
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DOI: 10.5614/j.eng.technol.sci.2021.53.3.13
<![CDATA[Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.]]>
