Sutanto Hadisupadmo

Wireless sensor network for single phase electricity monitoring system via Zigbee protocol

Development of audit and energy management system in the industrial automation and intelligent building installation has been observed over many years. Electrical energy monitoring system is now using real-time measurement facility via wireless network applications. In this paper we consider an application of star topology wireless sensor network for single phase electricity monitoring system based on Energy Meter IC ADE7753. Zigbee is used as wireless protocol. It has many benefits such as low cost, low power, low data rate, and supports on mesh-networking IEEE 802.15.4. For web-server monitoring system, Ext-JS framework is used as client-side programming which is integrated with IDE Arduino. Statistically, for sample of 30 data measurement, it is shown that current measurement gave guarantee of accuracy value until 5.3μArms with average precision error of 0.03%, and voltage measurement gave guarantee of accuracy value until 226.5μVrms with average precision error of 1.23%. Effectively, data measurement occurs in the intervals of 10 seconds and a transmission maximum of 40 meters with barrier and 60 meters without barrier.

Inverse learning control using neuro-fuzzy approach for a process mini-plant

This paper is concerned with a development of an inverse learning control method designed using adaptive neuro-fuzzy controller and its real-time implementation for controlling a process mini-plant. The adaptive neuro-fuzzy approach is implemented to model the dynamic inverse of the plant where, during the learning phase, an off-line and on-line technique will be performed, while in the design of the neuro-fuzzy controller, an adaptive network will be employed as a building block. A hybrid learning rule is also used to minimize the difference between the actual and a given desired trajectory. Experimental results of real-time control of a laboratory-scaled process mini-plant show that the designed on-line inverse learning control technique performs well to the changing dynamics of the plant and tracks the given desired set-points. Performance comparison was also made between the designed and PI controller.

Optimization of capacity and operational scheduling for grid-tied microgrid using pumped-storage hydroelectricity and photovoltaic

This paper deals with the optimization of capacity and operation scheduling for a grid-tied microgrid system. Photovoltaic power system is used as renewable energy source and pumped-storage hydroelectricity is used as energy storage. The physical models of the photovoltaic system and pumped-storage hydroelectricity are developed. The objective function is formulated based on capital and operational costs of individual systems. The constraints for the optimization are defined considering the model of systems, operational limitations, and performance requirements. The capacity and the operation scheduling of the photovoltaic and pumped-storage hydroelectricity are optimized based on the solar insolation data and average load demand tied to the microgrid. The performances required are high renewable energy penetration and low curtailed renewable energy. The optimization problem is solved using the mixed integer linear problem (MILP). With the proposed scheme, the renewable energy penetration ratio achieves 50%, which is acceptable. Moreover, the curtailed renewable energy ratio of 1.22% is obtained, which is very low. Both achievements, the acceptable renewable energy penetration ratio and the low curtailed renewable energy ratio, are the key outcomes of this research.

Performance analysis of PID controller, fuzzy and ANFIS in pasteurization process

Pasteurization engine uses a heat exchanger to heat the milk up to 71.6 °C. Therefore, the suitable type of controller for pasteurization process must be obtained. In this paper, there are five types of controller to be compared. These controllers are a PID controller tuning Ziegler Nichols, PID tuning Cohen Coon, Cascade PID, Fuzzy Inference System and Adapative Neuro Fuzzy Inference System (ANFIS). The system is also got disturbances that is caused by changes in flow of the product and changes of a step and impulse input for the system. This controller is created in MATLAB, which is connected to the PLC using OPC. The output of MATLAB will determine the control signals which serve as an input for the heater. It is shown from the data that the system that is controlled using ANFIS has an overshoot is only 0.27 percent, which is the smallest overshoot compared to other controllers. The required power for heater is only 0.48 kW which is the smallest one compare to other controllers. The MSE (mean squared error) value after the rise time had been reached is 0.114. The MSE when the system is given the flow disturbance is 1.86. When the systems step input is changed, the system that is controlled by ANFIS has a smaller overshoot and MSE compared to when the system is being controlled by PID cascade. However, when exposed to impulse disturbance ANFIS system controlled is highly reactive and has a lower performance than PID Cascade. Overall, it can be concluded that the performance of the ANFIS controller is better compare to other controllers.

Temperature control for a pasteurization miniplant using multiloop and cascade controllers

In order to obtain a temperature range of a pasteurization process, a good controller that can reject the unidentified disturbance which may occur at any time is needed. In this paper, control structure of both multi-loop and cascade controllers are designed for a pasteurization mini plant Armfied PCT23 MKIL The control algorithm uses proportional-integral-derivative (PID) controller. Some tuning methods are simulated to obtain the best controller performance. The two controllers are simulated and tested on real plant and their performances are compared. From experiments, it is found that the multiloop controller has a superior set point tracking performance whereas the cascade controller is better for disturbance rejection.

Modeling and control strategy analysis of gasoline level dynamic in storage tank with nonsimultaneous product filling and withdrawal scenario

Fuel oil availability holds a vital role for economic activity of a country. Gasoline is a type of fuel oil which is quite high in demand. Most fuel engine vehicles, which in charge for commodity distribution, depends on gasoline in order to operate. In Indonesia, gasoline distribution system is controlled mostly by the government via national oil company (Pertamina). The government control most of oil activity, i.e. exploration, production and distribution. Those activities related to one another, and form a vast and complicated supply chain system. An effective and proper management of such large scale system is essential to achieve optimal result. Supply chain management mostly depends on intuition and experience, hence the system become less efficient, including the gasoline supply chain management system in Indonesia. In addition, research related to gasoline supply chain modeling which can accommodate specific condition such as, non simultaneous product filling and withdrawal, up to this time, has not yet been studied. This paper describe the dynamics of gasoline stock in a depot which satisfy the non simultaneous product filling and withdrawal scenario. Furthermore, it is available to accommodate disturbances such as transport delay and demand fluctuation. The model also shows the queue dynamics when depot is in out-of-stock condition. A two-state controller, as an initial trial to anticipate more advance control method, is proposed to overcome such phenomena.