A review of maintenance scheduling approaches in deregulated power systems

Traditionally, the electricity industry is fully regulated with a centrally controlled structure. The power system operator has full technical and costing information as well as a full control over the operation and maintenance of power system equipment. Recently, many countries have gone through privatization of their electricity industries unbundling the integrated power system into a number of separate deregulated business entities. The preventive maintenance of power system equipment in the restructured electricity industries is no longer controlled centrally, and none of these entities currently have explicit accountability for maintenance activities. The approaches used to schedule the maintenance activities in the centralized system are not ideal for addressing the new deregulated environments. In recent years a few research publications has been reported in this area. This paper presents a review and analysis of these reported maintenance scheduling approaches for power system equipment in the changed environment

Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Generational and steady state genetic algorithms for generator maintenance scheduling problems

The aim of generator maintenance scheduling (GMS) in an electric power system is to allocate a proper maintenance timetable for generators while maintaining a high system reliability, reducing total production cost, extending generator life time etc. In order to solve this complex problem a genetic algorithm technique is proposed here. The paper discusses the implementation of GAs to GMS problems with two approaches: generational and steady state. The results of applying these GAs to a test GMS problem based on a practical power system scenario are presented and analysed. The effect of different GA parameters is also studie

A survey on portfolio optimisation with metaheuristics.

A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation

Generator maintenance scheduling of electric power systems using genetic algorithms with integer representations

The effective maintenance scheduling of power system generators is very important to a power utility for the economical and reliable operation of a power system. Many mathematical methods have been implemented for generator maintenance scheduling (GMS). However, these methods have many limitations and require many approximations. Here a Genetic Algorithm is proposed for GMS problems in order to overcome some of the limitations of the conventional methods. This paper formulates a general GMS problem using a reliability criterion as an integer programming problem, and demonstrates the use of GAs with three different problem encodings: binary, binary for integer and integer. The GA performances for each of these representations are analysed and compared for a test problem based on a practical power system scenario. The effects of different GA parameters are also studied. The results show that the integer GA is a very effective method for GMS problems

A review of generator maintenance scheduling using artificial intelligence techniques

New Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS) problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic algorithm to a test system based on a practical power system scenario

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

YesProposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problem

A GA-based technique for the scheduling of storage tanks

YesThis paper proposes the application of a genetic algorithm based methodology for the scheduling of storage tanks. The proposed approach is an integration of GA and heuristic rule-based techniques, which decomposes the complex mixed integer optimisation problem into integer and real number subproblems. The GA string considers the integer problem, and the heuristic approach solves the real number problems within the GA framework. The algorithm is demonstrated for a test problem related to a water treatment facility at a port, and has been found to give a significantly better schedule than those generated using a heuristic-based approach

Intelligent Active Vibration Control for a Flexible Beam System

YesThis paper presents an investigation into the development of an intelligent active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used for AVC system design. The system is then implemented, tested and its performance assessed for GAs and ANFIS based design. Finally a comparative performance of the algorithm in implementing AVC system using GAs and ANFIS is presented and discussed through a set of experiments