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The development of electric power systems has become more complex. Consequently, electric power systems are operating closer to their limits and are more susceptible to instability when a disturbance occurs. Transient stability problems are especially prevalent. In addition, the identification of transient stability is difficult to achieve in real time using the current measurement data. This research focuses on developing a convolutional neural network—long short-term memory (CNN-LSTM) model using historical data events to detect transient stability considering time-series measurement data. The model was developed by considering noise, delay, and loss in measurement data, line outage and variable renewable energy (VRE) integration scenarios. The model requires PMU measurements to provide high sampling rate time-series information. In addition, the effects of different numbers of PMUs were also simulated. The CNN-LSTM method was trained using a synthetic dataset produced using the DigSILENT PowerFactory simulation to represent the PMU measurement data. The IEEE 39 bus test system was used to simulate the model under different loading conditions. On the basis of the research results, the proposed CNN-LSTM model is able to detect stable and unstable conditions of transient stability only from the magnitude and angle of the bus voltage, without considering system parameter information on the network. The accuracy of transient stability detection reached above 99% in all scenarios. The CNN-LSTM method also required less computation time compared to CNN and conventional LSTM with the average computation times of 190.4, 4001.8 and 229.8 s, respectively.

PT. PLN (Persero) has planned to develop a new renewable energy which has a minimum energy mix of 23% in 2025 and 31% in 2030. An intermittent renewable energy plant, which is uncontrollable and unpredictable, will begin to be massively used. Associated to the intermittent nature of the of intermittent renewable energy source (IRES), adequate system flexibility is necessary. This study determined the penetration level IRES generating unit using the systems load and existing generating unit ramping rate characteristic, IRES generating unit’s ramping rate and system’s technical minimum load (TML) on the Java Bali System based on the 2017 operation condition. The results showed that the TML value of the operating plant was 12164,69 MW. The ramping up and down capabilities of conventional power plants are 945.04 MW / 30 min and 4006.08 MW / 30 min where ± 5% of penetration of IRES was still applicable for the Java Bali System.

The leather industry's production activity affects liquid waste containing a high amount of organic and chemical compounds. This study aims to determine Bacillus cereus LS2 B's survival ability in the medium with the presence of fresh untreated tannery wastewater. The growth characterization was made and observed in the agar medium with 0; 25; 50; 75 and 100% tannery wastewater. Growth profile in a liquid medium was observed with the addition of 0; 25; and 50% tannery wastewater. The survival ability of Bacillus cereus LS2B was observed by a visual colony formed in the agar medium, the optical density (OD600) of cell bacteria in the liquid medium, and the cell viability. The growth of Bacillus cereus LS2B could not be confirmed at both solid and liquid medium with tannery wastewater more than 25%. The survival ability and cell activity were observed in the agar medium containing 25% tannery wastewater after incubation time at 48 hours. The growth curve was also observed at a liquid medium containing tannery wastewater at 25% during 36 hours observation since at the 12th hour. Thus, Bacillus cereus LS2B could tolerate up to 25% of fresh untreated tannery wastewater in the solid and liquid medium.

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