Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm

Abstract

A non-revisiting genetic algorithm (NrGA) was used to determine distribution network configuration for loss reduction. By advocating binary space partitioning (BSP) to divide the search space and employing a novel BSP tree archive to store all the solutions that have been explored before, NrGA can quickly check for revisits by communicating with BSP tree archive when a new solution is generated by genetic algorithm (GA), and can mutate an alternative unvisited solution through a novel adaptive mutation mechanism that based on BSP tree while a revisit has occurred, which achieves no duplicates in the entire search. A method for getting independent loops of distribution network was realized using breadth-first search algorithm. Furthermore, the extended intermediate crossover mode, which requires no tuning parameter such as crossover rate and extends the crossover results, is employed for improving the performance of NrGA in solving distribution network configuration problem. The proposed approach has been successfully tested on three sample systems and three practical systems. Numerical studies have revealed its accuracy and efficient performance.

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

“…The results showed that when reducing purchased energy cost, the operation costs are also diminished. Similar outcomes were achieved in [60] and [61].…”

Performance analysis of a new algorithm for power distribution system reconfiguration

“…Table II compares the initial population si generations needed for convergence for th (first row) with the corresponding results recent publications on the subject. All of the works summarized in Table II converge to power loss solution (139.49 kW) as in t However, three of them [35]- [37] satisfied on objective, and one [34] satisfied the power lo loading index objectives, while the FNSGA objectives simultaneously.…”

Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

“…Figure 12 describes the voltage at all buses of the network before and after reconfiguration. e results obtained from the proposed CSFSA including the power loss, voltage deviation, and minimum voltage are compared to those from other methods in the literature such as AGA [45], UVDA [46], GA [39], MICP [47], and NRGA [48] as summarized in Table 5. After reconfiguration, the obtained power loss from the proposed CSFSA is 278.9 kW, which decreases 41.76 kW compared to that from the initial case.…”

“…To verify the effectiveness of the proposed method, four distribution networks have been used for testing including the 33bus, 84-bus, 119-bus, and 136-bus systems. e obtained results from the proposed CSFSA have been compared with those from other methods reported in the literature such as HSA [10], Firework Algorithm (FWA) [29], Runner-root Algorithm (RRA) [30], CSA [31], Hybrid Big Bang-Big Crunch Algorithm (HBB-BCA) [32], Fuzzy Shuffled Frog-Leaping Algorithm (Fuzzy-SFLA) [33], Multiobjective Invasive Weed Optimization (MOIWO) [34], Improved Adaptive Imperialist Competitive Algorithm (IAICA) [35], Improved Mixed-integer hybrid Differential Evolution (IMI-DE) [36], Plant Growth Simulation Algorithm (PGSA) [37], GA [38,39], hybrid Artificial Immune Systems-Ant Colony Optimization (AIS-ACO) [40], Heuristic method [41], improved Tabu search (ITS) [42], modified Tabu search (MTS) [43], hybrid Ant Colony Optimization-Harmony Search Algorithms (ACO-HAS) [44], adaptive GA (AGA) [45], Uniform Voltage Distribution-based constructive reconfiguration algorithm (UVDA) [46], Mixed-Integer Convex Programming (MICP) [47], Non-revisiting Genetic Algorithm (NRGA) [48], Feasibility-preserving Evolutionary Optimization (FPEO) [49], a hybridization of Grey Wolf Optimizer (GWO) and PSO method (GWO-PSO) [50], and Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) [51] which are available in the literature. e remaining organization of the paper is represented in the order as follows.…”