Jasrul Jamani Jamian

Comparative Study on Distributed Generator Sizing Using Three Types of Particle Swarm Optimization

Total power losses in a distribution network can be minimized by installing Distributed Generator (DG) with correct size. In line with this objective, most of the researchers have used multiple types of optimization technique to regulate the DGs output to compute its optimal size. In this paper, a comparative studies of a new proposed Rank Evolutionary Particle Swarm Optimization (REPSO) method with Evolutionary Particle Swarm Optimization (EPSO) and Traditional Particle Swarm Optimization (PSO) is conducted. Both REPSO and EPSO are using the concept of Evolutionary Programming (EP) in Particle Swarm Optimization (PSO) process. The implementation of EP in PSO allows the entire particles to move toward the optimal value faster. A test on determining optimum size of DGs in 69 bus radial distribution system reveals the superiority of REPSO over PSO and EPSO.

Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

Voltage Stability Prediction on Power System Network via Enhanced Hybrid Particle Swarm Artificial Neural Network

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

Optimal Performance of a Nonlinear Gantry Crane System via Priority-based Fitness Scheme in Binary PSO Algorithm

This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.

Economic dispatch with valve point effect using iteration particle swarm optimization

Economic dispatch (ED) is one of the optimization problems in power system operation and planning. The practical ED problems have non-smooth cost functions with equality and inequality constraints. It becomes more complicated problem when valve point effects of generator are considered and results multiple local minima. This makes the ED problems are difficult to find global optimum solution when using any mathematical approach due to non-convex cost function. The Classical particle swarm optimization (PSO) has ability to reach near global optimum solution by tuning some parameters but always stuck at local minima. In this paper, an iteration particle swarm optimization (IPSO) is proposed to solve ED problems with valve point effects. The proposed algorithm is introduced a new parameter in the original velocity equation of PSO in order to enrich searching behaviour, solution quality and avoid being trapped at local minima. The proposed IPSO algorithm has been implemented on two test power systems (consisting 3 and 13 generating units) to validate its effectiveness. The simulation results confirmed that IPSO algorithm has better convergence characteristic and more robust compared to PSO and some published results.

Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique

This paper presents development of an optimal PID controller for controlling the nonlinear gantry crane system. The PSO with linear weight summation approach is implemented for finding optimal PID parameters. The effectiveness of variation weight summation is observed in order to find the optimal performances of system. The system dynamic model is derived using Lagrange equation. A combination of five parameters (PID and PD) controllers are utilized for positioning and oscillation control of the system. System responses including trolley displacement and payload oscillation are observed and analyzed. Simulation is conducted within Matlab environment to verify the performance of the controller. It is demonstrated that implementation of linear weight summation approach in controlling system is useful in order to find the required performances according to the needs and circumstances.

Smart grid communication concept for frequency control in distribution system

The advancement of electrical device technologies can be used to balance the frequency of the system by adjusting their power consumption. However, this can only be achieved by having good data communication management. The introduction of Smart Home System (which consists of Smart Appliance (SA) and Smart Meter (SM)), Smart Regional Server (SRS) and Smart Server (SS) will be used to manage data communication in smart distribution system. The communications between these devices will be via Power Line Communication (PLC), and WiMAX will be used as a medium to transfer the data to the SRS. Transport Control Protocol (TCP) will increase the reliability of transferring data. By having good communication and architecture, as well as good management and decision making by the servers and smart metering, the use of load-balancing control to maintain the frequency of the system within the acceptable limit in smart distribution grid system will be achieved.

Optimum multi DG units placement and sizing based on voltage stability index and PSO

Optimum DG placement and sizing is one of the current topics in restructured power system. Most of the authors have worked out on their optimum placement base on the power losses reduction concept. However, the improvement on power losses value in the network will not guarantee to the planner to have lower voltage stability index (VSI) for the system. This paper proposes a new approached for multi DG placement and sizing for distribution systems which is based on a voltage stability index. The most optimum DG size will be found out using several types of PSO optimization algorithm. The output results will also compared with EPSO, REPSO, and IPSO. The proposed algorithm is tested on 12-bus, modified 12-bus and 69-bus radial distribution networks.

Gravitational Search Algorithm and Selection Approach for Optimal Distribution Network Configuration Based on Daily Photovoltaic and Loading Variation

Network reconfiguration is an effective approach to reduce the power losses in distribution system. Recent studies have shown that the reconfiguration problem considering load profiles can give a significant improvement on the distribution network performance. This work proposes a novel method to determine the optimal daily configuration based on variable photovoltaic (PV) generation output and the load profile data. A good combination and coordination between these two varying data may give the lowest power loss in the system. Gravitational Search Algorithm (GSA) is applied to determine the optimum tie switches positions for 33-Bus distribution system. GSA based proposed method is also compared with Evolutionary Programming (EP) to examine the effectiveness of GSA algorithm. Obtained results show that the proposed optimal daily configuration method is able to improve the distribution network performance in term of its power loss reduction, number of switching minimization and voltage profile improvement.

Conceptual data management and communication for smart distribution system

Load adjustment is a new concept in smart grid system that can be used to balance the frequency of the system by adjusting their power consumption. However, the system required to have effective data communication management. The introduction of Smart Home System (which consists of Smart Appliance (SA) and Smart Meter (SM)), Smart Regional Server (SRS) and Smart Server (SS) as well as the messaging sequence between these entities will be used to manage data communication in smart distribution system. The communications between these devices will be via Power Line Communication (PLC) and WiMAX as a medium to transfer the data to the SRS. Transport Control Protocol (TCP) will increase the reliability of transferring data. By using the propose communication and data management technique, the use of load-balancing control to recover the frequency in a smart distribution grid system will be achieved.