Adi Soeprijanto

Coordination of PID based power system stabilizer and AVR using combination bacterial foraging techique — Particle swarm optimization

The damping of oscillation in power system commonly use known controller such as power system stabilizer (PSS). Proportional integral derivative (PID) controller tuning based on power system stabilizer and AVR is presented in this paper. The parameters of PID controller such as proportional gain, integral factor, differential coefficient and gain AVR are selected and optimized by BF-PSOTVAC. The proposed method is applied to PID controller tuning and is compared to another method. The integral time absolut error standards of optimization design as objective function. The results of simulations show that the proposed method has the capability to damn optimally and suppresses error to minimum

Type 2 fuzzy adaptive binary particle swarm optimization for optimal placement and sizing of distributed generation

This paper proposes a new method for optimizing the placement and size of distributed generation (DG) using type-2 fuzzy adaptive binary particle swarm optimization with single mutation operator, called T2FABPSOM. The objective function of the proposed method to minimize active power losses in transmission line with the bus voltage system constraints is allowed. Type-2 fuzzy logic system (type-2 FLS) is used for tuning the inertia weight w, the learning factors c1 and c2 parameters of particle swarm optimization to control the particle velocity. Single mutation also used in the proposed method as a combination to improve and strengthen the ability of particle to search for candidate solutions globally and avoid convergence to local optima. To evaluate the performance of the proposed method, the method is applied on IEEE 30 bus system. The proposed method compared with the binary PSO (BPSO) and fuzzy adaptive binary PSO (FABPSO). The simulation results indicated that the proposed method can determine the size and location of the optimal DG with a total active power losses are minimum compared to other methods.

Optimal placement and sizing of Distributed Generation using Quantum Genetic Algorithm for reducing losses and improving voltage profile

In this paper Quantum Genetic Algorithm (QGA) is combined with The Newton Raphson power flow (NR power flow) to optimize the placement and sizing of Distributed Generations (DGs) in electrical power systems. QGA is used to find the optimal placement and generate real power of DG in accordance with mathematical calculations and NR Power Flow is used to calculate the loss on the network and determine the voltage at bus. The goal is to minimize the losses, while at the same time still maintain the acceptable voltage profiles. DGs may be placed at any load bus. Which load buses to have the DGs and of what size they are respectively are determined using this proposed method. Observations are based on standard IEEE 14 buses input and results are compared to the results of network without DG and network with DG by other methods.

Dynamic economic dispatch of hybrid microgrid with energy storage using quadratic programming

Electrical power demand increases rapidly due to the development of technology. In the contrary, the availability of non-renewable energy sources surely decreases. This problem will impact on the national energy security. To meet the need of a large electric power, it is required to develop a large area small scales of distributed generations. Distributed generations utilize renewable energy sources, such as PV, in order to minimize the use of non-renewable energy sources. To maximize the utilization of renewable resources, it is necessary to apply energy storage. This storage is needed to store excess energy generated by renewable energy based power plants. With the distributed generations and energy storage which are connected to the main grid through microgrid, it is important to optimize the operation of the power system in order to meet daily load. In this paper, the optimization problem is formulated as Dynamic Economic Dispatch which is applied on hybrid microgrid with energy storage. The problem is solved using Matlab-based quadratic programming

Controlling chaos using ANFIS-based Composite Controller (ANFIS-CC) in power systems

Chaos appears as nonlinear oscillations in a power system and these phenomena are caused by Disturbing of Energy (DE) when a power system is in critical loading. Chaos causes instability and voltage collapse and must be avoided. The ANFIS-based Composite Controller (ANFIS-CC) is proposed to solve these phenomena. The ANFIS-CC has a more efficient computation than a Mamdani fuzzy logic controller. A three-bus power system with DE and additional reactive load are used to validate the proposed method performance. The proposed method is very effective in the control and suppression of chaos in power systems.

Smart-Meter based on current transient signal signature and constructive backpropagation method

Increasing of electric power consumption and electricity price are making customers more sensitive in addressing the issues. Therefore, the accuracy of the recording device power consumption (kWh-meter) becomes an absolute necessity to reduce potential conflicts that may arise. This paper proposed prototype of smart-meter which combines transient peak value and steady state values to identify an activity of electrical appliances. These values are used as the identity of electrical appliances that will be taught to Constructive Backpropagation Neural Network (CBP-NN) to record power consumption in detail, including type appliance and time use. The proposed method has very simple structure, it only uses two input (transient peak value and steady state values) and single hidden layer with five neuron. The number of output is equal to the number of appliance. So that, the proposed method implement in microsprocessor system or in standalone product. Simulation and experimental results have validated the performance of the proposed method to operate in a real system.

Security constrained optimal power flow with FACTS devices using bender decomposition

This paper deals with security constraied optimal power flow (SCOPF) in which FACTS devices are employed to meet system constraints under both normal and contingency states. The considered constraints are power generation limit, voltage limit, transmision limit and FACTS devices operation limit. In normal state, the objective function is to minimize operation cost while satisfying system constraints. If contingency occurs, FACTS devices are optimally controlled to eliminate violation of generator ramp rate as well as to meet system constraints. The iterative process is applied to ensure that there will be no generator ramp rate violation. Initially, normal state is simulated to obtain optimal power dispatch as a basecase. Using this basecase, contingency state is simulated in order to minimize generation ramp rate violation. If ramp rate violation is failed to be eliminated, the violation will be fed back to normal state as a basis to re-arrange the output of generators that will be the next basecase. By this basecase, contingency state is again simulated. This iterative process involving normal and contingency states will stop if ramp rate violation is no longer exist. To decompose main problem into normal and contingency state, Bender decomposition technique is used with relation between power generation under normal and contingency states as a coupling equation. The power generation deviation of particular unit should be less than the corresponding generator ramp rate. Each optimization problem is solved by sequential quadratic programming (SQP). IEEE 14 bus will be used to show the ability of the proposed approach to solve the SCOPF.

Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speed V and service margin SM, with the corresponding output parameters comprise of engine speed nE, engine break horse power PB, propeller pitch P/D, and the fuel consumption FC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main enginess performance for trimaran patrol ship is satisfactory.

Dynamic DC optimal power flow using quadratic programming

This paper proposes a quadratic programming for solving the dynamic direct current optimal power flow (DDCOPF). The DDCOPF solves OPF with multi load levels in which ramp rate of committed units become coupling between two series load levels. To overcome this problem, a very large matrix may be required. The more number of load levels are considered, the larger matrix will be used. Consequently, it may take long computation time to solve. Therefore, the DC load flow is preferable than AC load flow. To show the effectiveness of the proposed approach, IEEE 14 bus test system is used. In addition, application of the proposed approach to real system Jawa Bali 500 kV 25-bus is presented.

Comparison methods of Fuzzy Logic Control and Feed Forward Neural Network in automatic operating temperature and humidity control system (Oyster Mushroom Farm House) using microcontroller

The productivity of oyster mushroom cultivation in low-lying areas are still not optimal. This is due to the cultivation of oyster mushrooms needs ideal temperature and humidity (temperature 22–28 ° C with a humidity of 60% – 80%), while nowadays temperature and humidity preservation process is done in a conventional manner. Given these problems, the researchers gave the solution by creating a tool that able to work automatically to monitor and control the temperature and humidity in oyster mushroom cultivation problem based on microcontroller. Inputs used in these system are the value of temperature and humidity data readings from DHT11. While the output of the system is two actuators, the first is the exhaust fan and the second is mist maker. In the operation of the appliance automatically there will be two choices of data processing methods are applied, the method of Fuzzy Logic Control (FLC) and Feed Forward Backpropagation Neural Network (BPNN). Performance tools based on the application of these two methods will be compared to determine the most optimal and effective method when it applied to the tool to automatically control temperature and humidity oyster mushroom farm house. Based on the test results and data analysis, the tools can work well and also perform that optimal and effective data processing method is Neural Network with an average conditioning response time of 69.8 seconds to reach the ideal temperature and 113.4 seconds for the ideal humidity.