• Energy Research

This paper proposes a solar tracker design as both a solar energy harvester and a rainwater collector on the hybrid of photovoltaic-pumped hydro storage (PV-PHS) system design in small scale, called a solar tracker-rainfall collector (STRC). The system proposed in this work is designed for implementation in a high-rise building. Increasing the storage energy of the PHS system is the objective of the system design in this study. The STRC system consists of six PV panels. During the day, when the sun is shining, the solar tracker works to harvest solar energy by positioning the panel according to the position of the sun. Meanwhile, during rainy or nighttime hours, the solar tracker configuration will form a funnel to collect rainwater so that it flows into the upper tank of the PHS system. Simulation results to validate the system design show that the PHS system with STRC is able to store 24.78% more total energy than the PHS system without STRC in a two-day test, with the average stored energy of the PHS system with STRC being 24.84% higher than the PHS system without STRC. Meanwhile, for a one-month test in January 2020, it was found that the total energy stored in the PHS system with STRC was 25.10% greater than in the PHS system without STRC, with the average stored energy in the PHS system with STRC increasing by 25% compared to the PHS system without STRC.

This paper proposes a new formulation for transient stability analysis for electric power systems. Different from existing methods, a minimization problem is formulated for obtaining critical clearing time (CCT) for transient stability. The method is based on the computation of a trajectory on the stability boundary, which is referred to as critical trajectory in this paper. The critical trajectory is defined as the trajectory that starts from a point on a fault-on trajectory at CCT and reaches a critical point of losing synchronism. The new proposal includes a modified trapezoidal formulation for numerical integration, the critical conditions for synchronism, and the unified minimization formulation. It will be demonstrated that the solution of the minimization problem successfully provides the exact CCT that agrees with the conventional numerical simulation method.

Intelligent-based protective relays in the power system is currently growing very rapidly with the use of embedded systems and artificial intelligence algorithms. Overcurrent relays operated based on the standard characteristic curve will move toward digital overcurrent relay (DOCR) using a flexible characteristic curve based on the user settings. DOCR with the standard characteristic curve cannot be used if the user must make modifications to the curve for field implementation purpose. In this paper, a new DOCR based on modeling a characteristic curve using Artificial Neural Network (ANN) will be presented. Four types DOCR with the standard and user-defined characteristic curve were developed and tested. Results demonstrate their successful operation with highly accurate results. The results also prove that the proposed DOCR with user-defined characteristic curve may serve as a convenient alternative to the conventional DOCR particularly on the implementation phase.

Abstract In order to achieve the optimization of the production process, power continuity is needed. The most important is the protection system, overcurrent relays are widely used as a primary relay to protect overload and short circuit. However, in some cases due to the nonlinearity pattern of the system, overcurrent relays does not grant the protection system. To overcome this problem, under-frequency and under-voltage relay are installed as a backup relay, but it also do not give the best result. This paper focused on the addition of reverse power relay to improve the system performance. Transient stability analysis is necessary to set reverse power relay because optimal power flow analysis cannot give proper parameter when a failure in grid happen. By arranging the delay time between those relays according to IEEE 242-2001, adequate coordination can be done. Moreover, the frequency and the voltage of the system stable at 100.04%, and 100.01%.

The distribution transformer is one of the vital components in the power system distribution, which deliver electricity power to the consumer. Various disturbances on the transformers can cause a decrease of their performance, so that they cannot reach the operation life. This study proposes a simulation study to predict the transformer oil age by using wavelet transform and backpropagation neural network. Transformer's current measurement was carried out in North Surabaya with a rating of 20 KV/380-220V and capacity of $100~\mathrm {k}\mathrm {V}\mathrm {A}$. The secondary current of the distribution transformer has been processed using the haar wavelet to obtain the detail coefficients, which is used to calculate the energy and PSD (power spectral density) value. Energy value and PSD are the input data on training and testing of back propagation neural network, while the output (target) is the transformer oil age. The simulation results show that the proposed method can predict the transformer oil age with an accuracy rate of 89.5795%.

One obstacle to the energy industry’s tendency toward adopting renewable energy is the requirement for a monitoring system for energy transactions based on microgrids in the wheeling scheme (shared use of utility networks). The quantity of transaction expenses for each operational generator is not monitored in any case. In this project, a mobile phone application is developed and maintained to track the total amount of fees paid and received by all wheeling parties and the amount of electricity produced by the microgrid. In the wheeling case system research, the number of transaction costs, such as network rental fees, loss costs, and profit margins, must be pretty calculated for all wheeling participants. The approach created in this study uses a blockchain system to execute transactions, and transactions can only take place if the wheeling actor and the generator have an existing contract. The application of energy trading is the main contribution of this research. The created application may track energy transfers and track how many fees each wheeling actor is required to receive or pay. Using a security system to monitor wheeling transactions will make energy trades transparent.

This paper presents a new computation method for transient stability analysis for electric power systems. Different from existing methods, a minimization problem is formulated for obtaining critical clearing time (CCT). The method is based on the computation of a trajectory on the stability boundary, which is referred to as critical trajectory in this paper. The critical trajectory is defined as the trajectory that starts from a point on a fault-on trajectory and reaches a critical point of losing synchronism. The new proposal includes the critical conditions for synchronism and the unified minimization formulation using a modified trapezoidal formulation for numerical integration. It will be demonstrated that the solution of the minimization problem successfully provides the exact CCT that agrees with the conventional numerical simulation method.

Abstract The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included.