Genetic algorithm for supply restoration in distribution system with priority customers

Abstract

service area as efficiently as possible and the burden of Abstract-In this paper an algorithm based on genetic operators, a computer aided decision supports assist the algorithm is proposed to solve service restoration problem in operators. The researchers have developed many methods to power distribution systems with priority customers. In genetic solve the service restoration problem in distribution systems algorithm new solutions are found in every next generation with probability of getting the better solution till the optimal solution [1]- [12]. Heuristic techniques [1]- [4] have been developed is not found. Service restoration problem is formulated as multi-using heuristic rules. Because heuristic techniques rely on the objective multi-constraint combinatorial optimization problem. knowledge of operator of distribution systems, a compromiseThe integer permutation scheme is used to generate the offspring between knowledge acquisition and performance of the in genetic algorithm. Integer permutation does not allow the solution is made. Expert systems [5]- [8] have been developed hamming cliff problem. In case of partial restoration, including to quickly determine restoration plans and build look-up tables capacitor control action that increases the power transfer capacity reduces out-of-service area. The operating time of fredition persona sthe ory [9], networ manual and automatic controlled switches is significantly reductiontechnique [10] andthe rankingbased searchmethod different. Therefore both type of switches are considered [11] have also been developed. The petri-net approach [12] separately. The comprehensive simulation results of proposed has been developed. In [12] the knowledge or the method including single fault and multi faults are presented for configuration of the concern system in the form of graphic five test systems of different size. representation is expressed through a structured model. In [11] genetic algorithm is used to solve the service restoration Before automation in distribution systems, only manual priority customers. controlled switches were used in distribution system. After development of automatic controlled switches for the purpose of automation, these manual controlled switches started to be I. INTRODUCTION replaced by automatic controlled switches. As a result, three AULTED events are unavoidable in the huge and complex categories of presently existing distribution systems can be relectrical power distribution systems. These faults affect seen. 1. Those have only manual controlled switches. 2. Those the system's reliability and customer's satisfaction. So the have both manual and automatic controlled switches. 3. Those reduction in the effect of fault is necessary to maintain the have automatic controlled switches only. The operating time system's reliability and customer's satisfaction by restoring of both types of switches is different. Therefore, both type of the service quickly in the area left unsupplied due to fault. switches should be considered separately. After o...

Permutation-based optimization for the load restoration problem with improved time estimation of maneuvers

“…Kumar et al [52] model the entire network (including the CC switches) with a binary structure. In order to prevent radiality violations, they adopt before each evaluation a breadth first search starting from each source node, which artificially opens all edges that are visited twice (Figure 3.5).…”

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“…In the process alternate routes for the power flow are created. This can also include capacitor switches [6].…”

MILP‐based technique for smart self‐healing grids

“…The techniques discussed in the pertinent literature aimed at solving the restoration optimisation problem can be broadly categorised into three types, i.e. those based on heuristic or expert systems (ESs) [2, 3], soft computing (SC) [4–7], and mathematical programming (MP) [8–11]. The heuristic approach incorporates a search strategy that utilises the operators’ knowledge and practical experience to find a final restoration configuration.…”

“…They can learn in a supervised or unsupervised manner, whereby the former strategy relies on the provision of pairs of inputs and outputs to facilitate knowledge acquisition [12]. Several SC techniques have been utilised in solving the restoration problem such as genetic algorithm [4], fuzzy logic [5], and particle swarm [6]. Further, a recent study presented by Souza et al [7] proposed two SC approaches to solve the distribution system reconfiguration problem with variable loads.…”