Optimal Shedding Against Voltage Collapse Based on Genetic Algorithm

The distribution system acts as a conduit between the consumer and the bulk power grid. Due to characteristics such as a high resistance/reactance ratio, distribution networks cannot be solved using conventional methods, such as the Gauss-Seidel and Newton–Raphson. This research proposes a method for the calculation of the power flow in radial networks that considers their wide range of resistance and reactance values, PV generator characteristics, and radial structure. An iterative methodology is employed, with each iteration beginning with the branch that has the highest accurate power flow solution. The procedure is reliable and effective over various workloads and network configurations. To confirm the effectiveness of the suggested technique on the simple and IEEE 33-bus radial distribution system, simulations were carried out in MATLAB. The implications of including a renewable energy source, such as a PV generator, in the network under consideration are investigated by simulation result comparison. The optimal location of the PV generator was also determined using an Artificial Neural Network (ANN) controller. The results of the identification process improve the already exceptional efficacy and performance of the ANN controller.

Section: Introductionmentioning

confidence: 99%

Load Flow Analysis and the Impact of a Solar PV Generator in a Radial Distribution Network

Zdiri

Dhouib

Alaas

et al. 2023

“…In [2], under-frequency load shedding is discussed with specific provisions for acceptable parameters, including the number of load shedding steps, the percentage of load shedding in each step, and the accuracy of the frequency measurement conducted in protective relays. Optimization techniques using algorithms such as Improved PSO (EPSO), Artificial Bee Colony PSO (ABC-PSO), Artificial Neural Network-PSO (ANN-PSO), and Genetic Algorithm (GA) [3][4][5] are proposed to address under-voltage load shedding issues. These methods rely on the concept of voltage stability margin and its sensitivity at the maximum load ability point.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Power System Operation Problem towards minimizing Generation and Damage Costs due to Load Shedding

Le,

Nguyen,

Hoang

et al. 2023

Optimizing the operational parameters and control of the power system in steady-state conditions is a crucial issue in reducing the costs of power generation and operation. In the case of long-term operation of a power system, besides aiming to minimize power generation costs, the cost of damage caused by load shedding also needs to be considered. This paper presents the optimization of the total cost of a power system including minimizing the generation cost function of power plants or power companies and minimizing the damage cost function caused to customers due to load shedding or power outages. At the same time, the objective function must also ensure the constraints on the operating conditions of the power system. This contributes to maintaining the continuity of the power supply to critical loads and minimizing damage. Base loads, priority loads, or loads that are not allowed to be shed are considered as constraints. The optimization problem is addressed by using the Particle Swarm Optimization (PSO) algorithm and the Cuckoo Search Algorithm (CSA). The IEEE 30-bus test system is applied to validate the reduction in total cost. The result comparison shows that when applying the CSA, the total cost is significantly reduced by 3.75% in comparison with the PSO algorithm. The algorithms are implemented in Matlab to demonstrate the efficiency and accuracy of the proposed method.

“…The ANN is used to estimate the total amount of active power that causes unbalance over the attenuation frequency period, equivalent from the rated value to the threshold value. In [10], the Genetic Algorithm (GA) is also applied to optimal shedding against voltage collapse. However, the GA algorithm has the disadvantage of a very long training time compared to PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

Nhung

Phung²,

et al. 2022

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.