Sahbi Marrouchi

Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches

The development of the industry and the gradual increase of the population are the main factors for which the consumption of electricity increases. In order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of unit commitment problem (UCP). The decisions are which units to commit at each time period and at what level to generate power meeting the electricity demand. Therefore, in a robust unit commitment problem, first stage commitment decisions are made to anticipate the worst case realization of demand uncertainty and minimize operation cost under such scenarios. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 h to 1 week. However, each production unit has some constraints that make this problem complex, combinatorial and nonlinear. In this chapter, we have proposed two strategies applied to an IEEE electrical network 14 buses to solve the UCP in general and in particular to find the optimized combination scheduling of the produced power for each unit production. The First strategy is based on a hybrid optimization method, Gradient-Genetic algorithm, and the second one relies on a Fuzzy logic approach. Throughout these two strategies, we arrived to develop an optimized scheduling plan of the generated power allowing a better exploitation of the production cost in order to bring the total operating cost to possible minimum when it’s subjected to a series of constraints. A comparison was made to test the performances of the proposed strategies and to prove their effectiveness in solving Unit Commitment problems.

Combined use of Particle Swarm Optimization and genetic algorithm methods to solve the Unit Commitment problem

Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination of the Particle Swarm Optimization method and the genetic algorithm applied to an IEEE electrical network 14 buses containing 5 production units to solve the Unit Commitment problem in one side and to find an optimized combination scheduling in the other side leading to minimize the total production cost.

Active and reactive power compensation through a preventive defense strategy based on FACTS devices

The excessive increase in the number of subscribers in the electrical grid requires a flexible control of this electrical network and a better reliability of the offered power quality. Thus, several defense plans are omnipresent to ensure stability, continuity of service and to overcome some disturbances which can affect the electrical grid. In this context, we present in this paper the development of a new control strategy used a compensator UPFC based on fuzzy controllers. We simulated this strategy in the IEEE test 14 buses network and the results were promising.

A fault classification scheme with high robustness for transmission lines using fuzzy-logic system

This paper present a new and accurate scheme for fault classification of EHV transmission lines using fuzzy logic system. The fault classification scheme is developed by using only the post-fault magnitude of three phases current and its symmetrical components. The proposed fault classification technique is able to classify all faults type can be affect a transmission line such as the single-phase to ground faults, two-phases faults, two-phases to ground faults and three-phases faults with high accuracy under wide variety of fault conditions. The proposed scheme has a good performance in high fault conditions such as high fault resistances, high fault inception angles and high fault distances to fault from relaying point. Large numbers of test cases are generated to verify the performance of proposed scheme. The simulation studies have been carried out by using Matlab software and Matlab fuzzy-logic toolbox.

Novel flexible algorithm for the operation of renewable source grid interface VSCs under unbalanced voltage sags

This study deals with a newly-conceived control strategy for three-phase grid connected VSIs used for distributed generation (DG) system with renewable energy sources. This strategy has been called SCCA (Symmetrical Component Control Algorithm). The SCCA aims that DG power plants can avoid overcurrent while helping to mitigate the adverse effects of unbalanced voltage sags for continuous operation. For this, it accomplished simultaneously the control of the injected currents and active and reactive power flow. A simulation comparison among five published methods and SCCA is presented in terms of quality of the injected current and ripple of the injected powers. The extensive MATLAB/Simulink simulation studies of SCCA concept demonstrate that the proposed strategy outperforms the majority of already published strategies in terms of the above criteria.

New strategy based on combined use of Particle Swarm Optimization and Gradient methods to solve the unit commitment problem

This paper presents a new approach based on the combination of the Particle Swarm Optimization and the gradient method to solve the unit commitment (UC) problem. The proposed strategy optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption during a well specific duration. Each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. The resolution of the UC Problem is conducted to several constraints that take into account the minimum up and minimum down time constraints, start-up cost and spinning reserve. The adopted approach was applied to an IEEE electrical network 14 buses containing 5 production units and the simulation results have clearly proven that the Gradient-PSO method was very competent in optimizing the UC problem in comparison to other existing methods.

An accurate fault location algorithm for transmission lines with use of two-end unsynchronized measurements

This paper present a novel fault location algorithm for Extra High Voltage transmission lines based on unsynchronized data generated from two-end of the line. Only post-fault currents and voltages at both ends of the line are required to estimate the synchronization angle and the exact distance to fault. First, a mathematical development is presented for estimated the synchronization angle. Then, the fault location is determined for balanced and unbalanced faults. The proposed algorithm is independent of pre-fault measurements data and all fault conditions. The performance evaluation of the proposed algorithm is tested under different fault scenarios using digital simulation through Matlab software. Simulation results indicate the high accuracy of the proposed algorithm.