Geev Mokryani

An overview on the smart grid concept

Smart Grid is a concept for transforming the electric power grid by using advanced automatic control and communications techniques and other forms of information technology. It integrates innovative tools and technologies from generation, transmission and distribution all the way to consumer appliances and equipment. This concept integrates energy infrastructure, processes, devices, information and markets into a coordinated and collaborative process that allows energy to be generated, distributed and consumed more effectively and efficiently. This paper reviews some researches and studies on Smart Grids (SGs) technology.

Probabilistic Assessment of the Impact of Wind Energy Integration Into Distribution Networks

Combined Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) considering different combinations of wind generation and load demand over a year are used to evaluate wind turbines (WTs) integration into distribution systems. MCS is used to model the uncertainties related to the stochastic variations of wind power generation and load demand while the social welfare is maximized by means of market-based OPF with inter-temporal constraints. The proposed probabilistic methodology allows evaluating the amount of wind power that can be injected into the grid as well as the impact of wind power penetration on the social welfare and on distribution-locational marginal prices. Market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The effectiveness of the proposed probabilistic method in assessing the impact of wind generation penetration in terms of both technical and economic effects is demonstrated with an 84-bus 11.4-kV radial distribution system.

Assessing Wind Turbines Placement in a Distribution Market Environment by Using Particle Swarm Optimization

A hybrid optimization method for optimal allocation of wind turbines (WTs) that combines particle swarm optimization (PSO) and market-based optimal power flow (OPF) with security constraints is proposed in this paper. The method maximizes the net present value (NPV) associated with WTs investment in a distribution market environment. The PSO is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis considering different combinations of wind generation and load demand. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

Improving Fault Ride-Through Capability of Variable Speed Wind Turbines in Distribution Networks

In this paper, a fuzzy controller for improving the fault ride-through (FRT) capability of variable speed wind turbines (WTs) equipped with a doubly fed induction generator (DFIG) is presented. DFIGs can be used as reactive power sources to control the voltage at the point of common coupling (PCC). The controller is designed to compensate for the voltage at the PCC by simultaneously regulating the reactive and active power generated by WTs. The performance of the controller is evaluated in different case studies considering a different number of wind farms in different locations. Simulations carried out on a real 37-bus weak distribution system confirmed that the proposed controller can enhance the FRT capability.

Application of wavelet transform and MLP neural network for Ferroresonance identification

In this paper an efficient method for detection of ferroresonance in distribution transformer based on wavelet transform is presented. Using this method ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Multi Layer Perceptron (MLP) neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying ferroresonance from other transients.

Evaluating the Benefits of Optimal Allocation of Wind Turbines for Distribution Network Operators

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

Identification of ferroresonance based on S-transform and support vector machine

Abstract The aim of this paper is to develop a method based on S-transform and Support Vector Machine (SVM) for ferroresonance detection. Using this method ferroresonance can be discriminate from other transients such as capacitor switching, load switching and transformer switching. S-transform (ST) is used for decomposition of signals, feature selection is done by Kernel Principal Component Analysis (KPCA). SVM is used for classification. Ferroresonance data and other transients were obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying ferroresonance from other events.

Detection of inrush current based on wavelet transform and LVQ neural network

Transformer inrush currents are high magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. In this paper an efficient method for detection of inrush current in distribution transformer based on wavelet transform is presented. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. Wavelet transform is used for decomposition of signals and Learning Vector Quantizer(LVQ) neural network used for classification. Inrush current data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying inrush current from other events.

Identification of ferroresonance based on wavelet transform and artificial neural networks

A novel method for Ferroresonance detection is presented in this paper. Using this method Ferroresonance can he discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and competitive neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.

A Robust Optimization Approach for Active and Reactive Power Management in Smart Distribution Networks Using Electric Vehicles

This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. First, based on duality theory, the max min form of the model is converted to a max form. Second, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.