Under‐voltage load shedding (UVLS) is an important technique to maintain the voltage stability and frequency of a power system network. UVLS has been applied widely in transmission systems to avoid system blackouts. However, with increasing penetration of distributed generation such as photovoltaic (PV) systems, the application of UVLS becomes important for islanded distribution systems. Under this condition, the network does not have a frequency reference as when it is connected to the grid. In this condition, when the load demand exceeds the PV capacity, UVLS is the only option to stabilize the system by shedding the load based on the changes of the voltage magnitude. In this work, a new UVLS scheme based on voltage stability indices is proposed. Four voltage stability indices are used as indicators for load shedding. Based on the stability indices, the loads that have the highest tendency of voltage collapse shall be the first ones to be shed. The proposed scheme is tested on a practical distribution network energized by a grid, a mini hydro generator, and a PV system. The test results on various scenarios prove that the proposed method is able to restore the system stability. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Power system network is an interconnected web of crucial electrical elements and system monitoring. It is imperative in ensuring system integrity and stability. In regard to this, state estimation is commonly employed to obtain the best estimate of the power system condition based on limited number of measurements. The placement of meters at appropriate locations is crucial in determining the accuracy of the state estimation. Hence, this paper presents a new optimal meter placement strategy for state estimation. A new evolutionary strategy for discrete optimization problem is proposed so that the location of additional meter placement will improve the accuracy of state estimation. The minimization of sum covariance error of state estimation is selected as the objective function to be minimized. Simulation results on the IEEE 30-bus system clearly shows that the proposed approach is able to outperform the conventional heuristic method in determining the optimal meter placement, which enhances the state estimation accuracy.
System state monitoring is crucial in order to maintain the system security, reliability and quality of a power system. Considering the limitation of monitoring devices in distribution network, state estimation technique could be employed. State estimation is an action of appraising an unknown system variables based on limited number of realtime measurements. The main objective of this paper is to find the suitable state estimation that able to estimate the state of the islanded distribution network. In this paper, state estimation for a distribution network is developed and enhanced by incorporating composite load model. By incorporating this model, the calculated state variables of the network will be more accurate (to obtain the most practical value of the system state variables). Moreover, the proposed state estimation results are compared with backward-forward sweep load flow technique to justify the system state variables. Index Terms-Distributionstate Estimation, Distribution network, Load Modeling, Backward-forward sweep load flow.
Conventional methods are commonly used to solve optimal power flow problems in power system networks. However, conventional methods are not suitable for solving large and non-linear optimal power flow problems as they are influenced by initialization values and more likely be trapped in local optimum. Hence, heuristic optimization methods such as Firefly Algorithm have been widely implemented to overcome the limitations of the conventional methods. These methods often use random strategy that can provide better solutions to avoid being trapped in the local optimum while achieving global optimum. In this study, the load flow analysis was performed using the conventional method of Newton-Raphson technique to calculate the real power loss. Next, Firefly Algorithm was implemented to optimize the control variables for minimizing the real power loss of the transmission system. Generator bus voltage magnitudes, transformer tap settings and generator output active power were taken as the control variables to be optimized. The effectiveness of the proposed Firefly Algorithm was then tested on the IEEE 14-bus and 30-bus system using MATLAB software. The simulated results were then analyzed and compared with Particle Swarm Optimization's results based on the consistency and execution time. Implementation of the Firefly Algorithm has successfully produced minimum real power loss with faster computational time as compared to Particle Swarm Optimization. For the IEEE 14-bus system, the active power loss for the Firefly Algorithm is 6.6222 MW and the calculation time is 18.2372 seconds. Therefore, the application of optimal power flow based on Firefly Algorithm is a reliable technique, in which the optimal settings with respect to power transmission loss can be determined effectively.
This paper presents an effective method based on Particle Swarm Optimization (PSO) to identify the optimal measurement placement of power system state estimation. The main objective is to simplify the complexity in finding the best measurement placement and provide a high accuracy level of estimated state. The effectiveness of the proposed method is tested using the IEEE 30 bus system. Pseudo measurements of load injection are included as measurement data in assisting the state estimation computation.
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