Service Restoration In Distribution Systems Research Articles

A Genetic Algorithm Based Multi Objective Service Restoration in Distribution Systems

Problem statement: A Genetic Algorithm (GA) used here to find exact or approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection and crossover. Approach: GA is a method for search and optimization based on the process of natural selection and evolution. In this approach, several modifications are done for effective implementation of GA to solve the Electric Power Service Restoration Problem. Results: The problem statement includes all the objectives and constraints required for a practical supply restoration scheme. GA is used here to obtain the better result compared with other methods. GA starts with number of solutions to a problem, encoded as a string of status of sectionalizing and tie switches. Conclusion: The status of the switch ‘1’ and ‘0’ has been considered as ‘close’ and ‘open’ condition of the switch. The string that encodes each string is ‘chromosome’ and the set of solutions are termed as population. Obtained results are good and this technique is recommended here for future study.

Evolution of radial basic function neural network for fast restoration of distribution systems with load variations

This paper proposes a new algorithm to construct the optimal radial basic function (RBF) neural network for fast restoration of distribution systems with load variations. Service restoration of distribution systems is to restore power to the blacked out but unfaulted area. Basically, it is a stressful and urgent task that must be performed by system operators. In this paper, a new algorithm which combines orthogonal least-squares (OLS) and enhanced differential evolution (EDE) methods is developed to construct the optimal RBF network that shall further achieve the fast restoration of distribution systems. The proposed scheme comprises training data creation phase and network construction phase. In the training data creation phase, a heuristic-based fuzzy inference (HBFI) method is employed to build the restoration plans under various load levels. Then an optimal RBF network is constructed by OLS and EDE algorithms in the network construction phase. Once the RBF network is constructed properly, the desired restoration plan can be produced as soon as the inputs are given. The proposed method was tested on a typical distribution system of the Taiwan Power Company (TPC). Results show that the proposed method outperforms the existing methods in terms of convergence performance and forecasting accuracy.

Application of BDI‐based intelligent multi‐agent systems for distribution system service restoration planning

AbstractBy changing the status of switches on distribution feeders, service to de‐energized customers can be restored after an occurrence of a fault. In this paper, multi‐agent system (MAS) is applied for the power distribution system service restoration task using local information by utilizing the characteristics of information sharing. Each software agent is running autonomously and is able to exchange information within limited distance. Every agent is capable of performing service restoration such that service restoration can be performed in parallel when a multiple fault scenario occurs. As a result, the outage period can be reduced. BDI (believe–desire–intention) agent architecture is implemented in this paper for group and zone service restoration for power distribution systems. Zone/switch relation table is built and used by agents in order to identify adequate switching actions during the service restoration planning stage. The proposed MAS is implemented on JADE (Java Agent Development) platform. The simulation results show that the proposed BDI‐based service restoration agent system is capable of providing adequate restoration switching plan in several scenarios. Copyright © 2010 John Wiley & Sons, Ltd.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

A Fast Restoration Strategy in Distribution Systems Using an Enhanced Differential Evolution Approach

This paper proposes a fast restoration strategy of distribution systems using an enhanced differential evolution (EDE) approach. Service restoration of distribution systems is an emergent task that must be performed rapidly by the system operators. Basically, it is a complicated combinatorial optimization problem, often having many candidate solutions to be evaluated by the operators. To improve the efficiency of restoration and reduce the burden on the operators, this paper proposes an EDE method combining variable scaling differential evolution (VSDE) algorithm and ant system (AS) to solve the combinatorial optimization problem. To verify the effectiveness of the proposed method, a typical distribution system of the Taiwan Power Company (TPC) was tested and compared with the existing methods. The results show the proposed method was superior to the existing methods in terms of convergence time and the obtained restoration plan.

Service restoration method considering simultaneous disconnection of distributed generators by one‐bank fault of distribution system

AbstractDistributed generators (DGs) such as fuel cells and solar cells are going to be installed in the demand side of distribution systems. The DGs can reduce distribution loss by appropriate allocation. However, there are several problems installing DGs such as service restoration of distribution system with DGs and so on. When one bank fault of distribution substation occurs in distribution system, since DGs are simultaneously disconnected from the system, it is not easy to restore isolated load by one bank switching in distribution substation. Therefore, a service restoration method to determine restoration configuration and restoration procedures (switching procedure from normal configuration to restoration configuration) taking into account simultaneous disconnection of DGs is needed. In this paper, the authors propose a computation method to determine the optimal restoration configuration and the restoration procedure considering simultaneous disconnection of DGs by one bank fault of distribution system. In the proposed algorithm, after all of the restoration configuration candidates are effectively enumerated under the operational constraints, the optimal configuration to restore the isolated load is selected among enumerated configuration candidates. After determining the optimal restoration configuration, the optimal restoration procedures are obtained by greedy algorithm. Numerical simulations are carried out for a real scale system model with 237 sectionalizing switches (configuration candidates are 2237) and 21 DGs (total output is 5250 kW which is 3% of total load) in order to examine the validity of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(8): 44–55, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10133

Application of parallel tabu search‐based hierarchical optimization to distribution system service restoration

AbstractThis paper proposes a hybrid method of hierarchical optimization and parallel tabu search (PTS) for distribution system service restoration with distributed generators. The objective is to evaluate the optimal route to recover the service. The improvement of power quality makes the service restoration more important. Distribution system service restoration is one of the complicated combinational optimization problems that are expressed as nonlinear mixed integer programming. In this paper, an efficient method is proposed to restore the service in a hierarchical optimization with PTS. The proposed method is tested in a sample system. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(4): 15–23, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ eej.20463

On-line Network Reconfiguration and Service Restoration in Radial Distribution Systems Using Genetic Algorithm

On-line Network Reconfiguration and Service Restoration in Radial Distribution Systems Using Genetic Algorithm

PC Cluster based Parallel Adaptive Evolutionary Algorithm for Service Restoration of Distribution Systems

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel ABA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After ABA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 ㎓ Pentium Ⅳ CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

배전계통 광역 정전복구 알고리즘 개발

This paper presents the service restoration in electric power distribution systems. The aim of the service restoration is to control an emergency taken place in distribution systems and to restore out-of service areas as soon as possible when a fault occurs in distribution systems. For this reason, new service restoration strategies in multi-outage area are suggested in this paper. The suggested algorithm consist of two schemes. One is an individual restoration scheme, the other is an integrated restoration scheme. The former determines a restoration order of outage areas based on a restoration index. Unless the former scheme can generate a feasible restoration plan, the latter one will try to find out a new configuration without an overloaded section through tie exchange method.

Application of Parallel Tabu Search Based Hierarchical Optimization to Distribution System Service Restoration

This paper proposes a hybrid method of hierarchical optimization and Parallel Tabu Search (PTS) for distribution system service restoration with distributed generators. The objective is to evaluate the optimal route to recover the service. The improvement of power quality makes the service restoration more important. Distribution system service restoration is one of complicated combinational optimization problems that are expressed as nonlinear mixed integer programming. In this paper, an efficient method is proposed to restore the service in a hierarchical optimization with Parallel Tabu Search. The proposed method is tested in a sample system.

Service Restoration Method Considering Simultaneous Disconnection of Distributed Generators by One Bank Fault of Distribution System

Distributed generators (DGs) such as fuel cells and solar cells etc. are going to be installed in demand side of distribution systems. The distributed generators can reduce distribution loss by appropriate allocation. However, there are several problems to install DGs such as service restoration of distribution system with DGs and so on. When one bank fault of distribution substation occurs in distribution system, since DGs are simultaneously disconnected from the system, it is not easy to restore isolated load by one bank switching in distribution substation. Therefore, a service restoration method to determine restoration configuration and restoration procedures (switching procedure from normal configuration to restoration configuration) taking into account simultaneous disconnection of DGs is needed. In this paper, the authors propose a computation method to determine the optimal restoration configuration and the restoration procedure considering simultaneous disconnection of DGs by one bank fault of distribution system. In the proposed algorithm, after all of restoration configuration candidates are effectively enumerated under the operational constraints, the optimal configuration to restore the isolated load is selected among enumerated configuration candidates. After determining the optimal restoration configuration, the optimal restoration procedures are obtained by greedy algorithm. Numerical simulations are carried out for a real scale system model with 237 sectionalizing switches (configuration candidates are 2237) and 21DGs (total output is 5250kW which is 3% of total load) in order to examine the validity of the proposed algorithm.

Service restoration in distribution system using non-dominated sorting genetic algorithm

In this paper, a non-dominated sorting genetic algorithm-II (NSGA-II) based approach is presented for service restoration in power distribution systems. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors required for conversion of multi-objective function into an equivalent single objective function. Based on the simulation studies carried out in four different systems, the performance of the proposed scheme has been found to be better than the performance of conventional GA technique based approaches. Moreover, by including the string representing the pre-fault configuration of the distribution system in the initial population, the speed of convergence is enhanced significantly.

05/02561 Innovative service restoration of distribution systems by considering short-term load forecasting of service zones

05/02561 Innovative service restoration of distribution systems by considering short-term load forecasting of service zones

Innovative service restoration of distribution systems by considering short-term load forecasting of service zones

A rule-based expert system with a colored Petri net (CPN) inference model is developed for service restoration of distribution systems with fault contingency. The CPN models of distribution components such as four-way line switches are proposed to derive the proper restoration plan after the faulted location has been identified and isolated. To assure the restoration plan can comply with the operation regulation, heuristic rules based on the standard operation procedures of Taipower distribution system are included in the best first search of the CPN. The unfaulted but out-of-service zones will be restored as much as possible with the proposed switching operation plan. The short-term load forecast of each service zone is also included in the restoration strategy. A Taipower distribution system with 68 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed CPN model to solve the service restoration of distribution system.

SERVICE RESTORATION IN DISTRIBUTION AUTOMATION SYSTEM USING PARALLEL HYBRID GENETIC ALGORITHM-TABU SEARCH

This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) in searching the optimal service restoration solution in an electric power distribution system, a discrete optimization problem. In this investigation, parallel hybrid GA-TS was developed for the service restoration of a distribution system. For parallel computing, a PC-cluster system consisting of 8 PCs was developed. Each PC employed a 2 GHz Pentium IV CPU, and was connected with others through Ethernet-switch-based fast Ethernet.The newly developed algorithm was tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal fault restoration strategy. The obtained results were the same as those of the explicit exhaustive search method (Wu, Tomsovic, and Chen, 1991). Also, it was found that the proposed algorithm is efficient and robust for distribution system service restoration in the solution quality, speed-up, efficiency, and computation time.

Practical Application of Tabu Search to Distribution System Service Restoration in Korea

This paper develops an efficient Tabu Search (TS) for service restoration in electric power distribution systems. The service restoration problem is formulated as a constrained multi-objective optimization problem. The proposed algorithm begins with an initial solution obtained by dividing and transferring the out-of-service area into several subnetworks according to the margins of adjacent tie feeders. This procedure makes it converge fast to the global optimal state. In addition, we also designed a neighborhood structure and tabu list for distribution system restoration. The final output of the algorithm is presented in the form of a list of ranked alternate supply variants and switching operations. The final choice of the restoration strategy for the out-of-service area is made by the operator on the basis of the list of ranked variants. The effectiveness of the proposed method has been demonstrated by applying it to a practical distribution system in Korea. From the simulation results, the proposed method found the optimal fault restoration strategy. The obtained results were the same as that of the explicit exhaustive search method[8].

Multiobjective service restoration of distribution systems using fuzzy cause-effect networks

This paper proposes the use of a fuzzy cause-effect (FCE) network to achieve the multiobjective service restoration of distribution systems. Service restoration is a complicated combinatorial optimization problem that often has a great number of candidate solutions to be evaluated by the operators. To tackle the problem of service restoration with multiple objectives, the weighted sum strategy is employed to convert these objectives into a single objective function by giving relative weighting values. The weighting factor of each objective is assessed via the analytical hierarchy process (AHP) approach. Since the operators' needs and heuristic rules are often expressed in imprecise linguistic terms, the FCE networks are then built to represent the related knowledge and inference scheme regarding the task of restoration. The proposed FCE networks were practically tested on a typical distribution system of the Taiwan Power Company (TPC). The flexibility and effectiveness of the FCE networks have validated it for use as decision support in restoration scheduling of distribution systems.

A rule-based expert system with colored Petri net models for distribution system service restoration

A rule-based expert system with a colored Petri net (CPN) inference model is developed for service restoration of distribution systems. The CPN models of distribution components such as four-way line switches are proposed to derive the proper restoration plan after the faulted location has been identified and isolated. The unfaulted but out-of-service areas are restored by applying parallel-like reasoning in the CPN for multiple contingencies simultaneously. To assure the restoration plan can comply with the operation regulation, heuristic rules based on the standard operation procedures of the Taipower distribution system are included in the best first search of the CPN. For the fault contingency during summer peak season, the load shedding is executed and the CPN models are designed to restore service to as many key customers and loads as possible. The priority indices of each feeder and service zone are determined according to the key customers within the service territory. A Taipower distribution system with 18 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed methodology. It is found that the service restoration of distribution system can be obtained very efficiently by applying the proposed CPN model.

A Rule-Based Expert System with Colored Petri Net Models for Distribution System Service Restoration

A Rule-Based Expert System with Colored Petri Net Models for Distribution System Service Restoration