Souad Chebbi

Fault detection and classification approaches in transmission lines using artificial neural networks

This paper studies a new approach based on the artificial neural networks (ANN) for the fault detection and classification, in real time, in transmission lines to extra high voltage (EHV) which can be used in the production system digital protection. This approach is based on the treatment of each phase current and voltage. The outputs of the ANN indicate the fault presence and it type. The ANN detector and classifier are tested in various fault types, various locations, different fault resistances and various inception angle. All the test results show that the fault suggested detector and classifier can be used to support a new system generations of protection relay at high speed.

Improvement of the integration of a grid-connected wind-photovoltaic hybrid system

This paper presents control strategies for the integration of a grid-connected wind-photovoltaic hybrid system via adaptation converters connected to a common DC bus. For both wind and solar system, adequate control algorithms have been implemented for the maximum power extraction. The grid side converter has been connected to the point of common coupling (PPC) through an RL filter. The control of the grid side converter (GSC) was made in order to control the power quality and quantity of the feed power to the grid. At the DC bus, a PI-regulation control was adopted to overcome the ripples caused by the power flow. Performances of the adopted control laws have been evaluated by MATLAB/Simulink simulations.

Optimal energy management strategy for wind photovoltaic hybrid system with battery storage

This paper proposes control strategies for a grid connected Hybrid System (WPVHS) composed of wind turbine photovoltaic array and storage system. The aim is to ensure the service continuity despite the fluctuating behavior of the renewable sources by using a batteries storage system. The proposed control scheme is based on the power balancing in the common DC-link. Hence, the Hybrid System is able to inject desired active and reactive power to the electrical grid. MATLAB software simulation results are provided in order to demonstrate the performance and to show the feasibility of the proposed control.

Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic

As a perspective to ensure the power system stability and to avoid the vulnerability leading to the blackouts, several preventive and curative means are adopted. In order to avoid the voltage collapse, load shedding schemes represent a suitable action to maintain the power system service quality and to control its vulnerability. In this paper, we try to propose an intelligent load shedding strategy as a new approach based on fuzzy controllers. This strategy was founded on the calculation of generated power sensitivity degree related to those injected at different network buses. During the fault phase, fuzzy controller algorithms generate monitor vectors ensuring a precalculated load shedding ratio in the purpose to reestablish the power balance and conduct the network to a new steady state.

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.

Neural Network Approach to Fault Location for High Speed Protective Relaying of Transmission Lines

Fault location and distance protection in transmission lines are essential smart grid technologies ensuring reliability of the power system and achieve the continuity of service. The objective of this chapter is to presents an accurate algorithm for estimating fault location in Extra High Voltage (EHV) transmission lines using Artificial Neural Networks (ANNs) for high speed protection. The development of this algorithm is based on disturbed transmission line models. The proposed fault protection (fault detection/classification and location) uses only the three phase currents signals at the one end of the line. The proposed technique uses five ANNs networks and consists of two steps, including fault detection/classification and fault location. For fault detection/classification, one ANN network is used in order to identify the fault type; the fault detection/classification procedure uses the fundamental components of pre-fault and post-fault sequence samples of three phase currents and zero sequence current. For fault location, four ANNs networks are used in order to estimate the exact fault location in transmission line. Magnitudes of pre-fault and post-fault of three phase currents are used. The ANNs are trained with data under a wide variety of fault conditions and used for the fault classification and fault location on the transmission line. The proposed fault detection/classification and location approaches are tested under different fault conditions such as different fault locations, different fault resistances and different fault inception angles via digital simulation using MATLAB software in order to verify the performances of the proposed methods. The ANN-based fault classifier and locator gives high accuracy for all tests under different fault conditions. The simulations results show that the proposed scheme based on ANNs can be used for on-line fault protection in transmission line.

On the Convergence of Constrained Particle Filters

The power of particle filters in tracking the state of nonlinear and non-Gaussian systems stems not only from their simple numerical implementation but also from their optimality and convergence properties. In particle filtering, the posterior distribution of the state is approximated by a discrete mass of samples, called particles, that stochastically evolve in time according to the dynamics of the model and the observations. Particle filters have been shown to converge almost surely toward the optimal filter as the number of particles increases. However, when additional constraints are imposed, such that every particle must satisfy these constraints, the optimality properties and error bounds of the constrained particle filter remain unexplored. This letter derives performance limits and error bounds of the constrained particle filter. We show that the estimation error is bounded by the area of the state posterior density that does not include the constraining interval. In particular, the error is small if the target density is “well localized” in the constraining interval.

EEG dynamic source localization using constrained particle filtering

We consider the dynamic EEG source localization problem with additional constraints on the expected value of the state. In dynamic EEG source localization, the brain sources, also called dipoles, are not stationary but vary over time. Moreover, given our specific EEG experiment, we expect the dipoles to be located within a certain area of the brain (here, the visual cortex). We formulate this constrained dynamic source localization problem as a constrained non-linear state-estimation problem. Particle filters (PFs) are nowadays the state-of-the-art in optimal non-linear and non-Gaussian state estimation. However, PFs cannot handle additional constraints on the state that cannot be incorporated within the system model. In this case, the additional constraint is on the mean of the state, which means that realizations of the state, also called particles within the PF framework, may or may not satisfy the constraint. However, the state must satisfy the constraint on average. This is indeed the case when tracking brain dipoles from EEG experiments that try to target a specific cortex of the brain. Such constraints on the mean of the state are hard to deal with because they reflect global constraints on the posterior density of the state. The popular solution of constraining every particle in the PF may lead either to a stronger condition or to a different (unrelated) condition; both of which result in incorrect estimation of the state. We propose the Iterative Mean Density Truncation (IMeDeT) algorithm, which inductively samples particles that are guaranteed to satisfy the constraint on the mean. Application of IMeDeT on synthetic and real EEG data shows that incorporating a priori constraints on the state improves the tracking accuracy as well as the convergence rate of the tracker.

Power quality improvement using VF-DPC-SVM controlled three-phase shunt active filter

The main focus of this paper is to present a novel and simple direct power control of shunt active power filter with constant switching frequency using space-vector modulation (DPC-SVM). Also, the AC line voltage sensors with a virtual flux (VF) estimator are replaced. This control method is applied to eliminate harmonic pollution and compensate the reactive power in the presence of nonlinear loads and unbalanced sources. The control system is resistant to the majority of line voltage disturbances using by the idea of virtual flux and phase locked loop (PLL) approach. The superior advantages of this method are simple algorithm, good dynamic response, constant switching frequency and resistant to the majority of line voltage disturbances. The simulation results, using Matlab/Simulink, are presented to show the validity of the proposed model, and to evaluate the performance of the control strategy.

Fuzzy logic based switching state selection for virtual flux DPC of shunt active filter

Active filters are effective for eliminating harmonic currents and compensating reactive power in the presence of non-linear loads and unbalanced sources. In this paper, a new strategy for control of shunt active filter is proposed. Fuzzy logic based switching state selection Direct Power Control (DPC) is employed for this control scheme. In addition, the AC line voltage sensors with a virtual flux (VF) estimator are replaced. To retrieve the phase and the frequency information, we have integrated a phase locked loop (PLL) control method. The advantages of this strategy are the simple algorithm, good dynamic response and resistance to the majority of the line voltage disturbances. Indeed, the operation of a proposed control strategy is implemented and verified in Matlab/Simulinks simulation environment. The results show that the control algorithm of the active filter is effective for eliminating harmonic currents and improving reactive power injected from nonlinear loads, which allowed us to confirm the robustness of the proposed strategy.