István Erlich

Simultaneous coordinated tuning of PSS and FACTS damping controllers in large power systems

This work deals with the simultaneous coordinated tuning of the flexible AC transmission systems (FACTS) power oscillation damping controller and the conventional power system stabilizer (PSS) controllers in multi-machine power systems. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among FACTS controller and PSS controllers are considered. Furthermore, the parameters of the damping controllers are optimized simultaneously. Simulation results of multi-machine power system validate the efficiency of this approach. The proposed method is effective for the tuning of multi-controllers in large power systems.

Enhanced Fault Ride-Through Method for Wind Farms Connected to the Grid Through VSC-Based HVDC Transmission

This paper describes a new control approach for secure fault-ride through of wind farms connected to the grid through a voltage source converter-based high voltage DC transmission. On fault occurrence in the high voltage grid, the proposed control initiates a controlled voltage drop in the wind farm grid to achieve a fast power reduction. In this way overvoltages in the DC transmission link can be avoided. It uses controlled demagnetization to achieve a fast voltage reduction without producing the typical generator short circuit currents and the related electrical and mechanical stress to the wind turbines and the converter. The method is compared to other recent FRT methods for HVDC systems and its superior performance is demonstrated by simulation results.

Online optimal management of PEMFuel cells using neural networks

A novel two-phase approach to manage the daily operation of proton exchange membrane (PEM) fuel cells for residential applications is presented in this paper. Conventionally, the performance optimization is carried out offline since it is a time-consuming process and needs high computational capabilities. To simplify the management process and to enable online parameter updating, the paper suggests a new technique using artificial neural networks (ANNs). First, a database is extracted by performing offline optimization processes at different load demands and natural gas and electricity tariffs using a genetic algorithm (GA). Then, the obtained results are used for the offline training and testing of the ANN, which can be used onsite to define the settings of the fuel cell. The tariffs and load demands as inputs of the ANN can be easily updated online to enable the ANN to estimate new optimal or quasioptimal set points after each variation in operating points. The agreement between ANN decisions and optimal values as well as the achieved reduction in operating costs encourage the implementation of the proposed technique to achieve both fast online adaptation of settings and near optimal operating cost. This technique is applicable for different distributed generating units (DGUs), which are expected to spread within the power systems in the near future.

Strategies for Wind Power Trading in Competitive Electricity Markets

The emergence of independent generators and new technologies over past decades has led to the competition in the electricity sector to encourage development of a more market-based electricity industry. The success of wind energy under new market structures will depend, to a large degree, on the market rules on technical and financial aspects. This paper proposes a suitable trading option for wind power in the emerging electricity market for its sustainable development. Market clearing price with and without wind power has been analyzed in both supply side and demand side bidding scenarios for linear bid and block bid trading models. Several other aspects of wind power in electricity market have also been highlighted. This paper could be a guideline for the policy makers and market operators to promote the wind power with system reliability and security.

Evaluating the Mean-Variance Mapping Optimization on the IEEE-CEC 2014 test suite

This paper provides a survey on the performance of the hybrid variant of the Mean-Variance Mapping Optimization (MVMO-SH) when applied for solving the IEEE-CEC 2014 competition test suite on Single Objective RealParameter Numerical Optimization. MVMO-SH adopts a swarm intelligence scheme, where each particle is characterized by its own solution archive and mapping function. Besides, multi-parent crossover is incorporated into the offspring creation stage in order to force the particles with worst fitness to explore other sub-regions of the search space. In addition, MVMO-SH can be customized to perform with an embedded local search strategy. Experimental results demonstrate the search ability of MVMO-SH for effectively tackling a variety of problems with different dimensions and mathematical properties.

Simultaneous coordinated tuning of PSS and FACTS controller for damping power system oscillations in multimachine systems

This work deals with the simultaneous coordinated tuning of the FACTS (flexible AC transmission systems) POD power oscillation damping) controller and the conventional PSS (power system stabilizer) controllers in multimachine power systems. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among FACTS controller and PSS controllers are considered. Furthermore, their parameters are optimized simultaneously. Simulation results of multimachine power system validate the efficiency of this new approach. The proposed algorithm is an effective method for the tuning of multicontrollers in large power systems.

Hybrid Mean-Variance Mapping Optimization for solving the IEEE-CEC 2013 competition problems

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the emerging field of heuristic optimization algorithms, which has been quite successful in solving a variety of power system optimization problems. This paper introduces a hybrid variant of MVMO (MVMO-SH) for solving the IEEECEC 2013 competition test suite. MVMO-SH is based on a swarm scheme of MVMO with embedded local search and multi-parent crossover strategies to increase search diversity and solution quality. Numerical results attest to the promising prospect of MVMO-SH to become a general purpose optimization algorithm.

Improving the Reactive Power Capability of the DFIG-Based Wind Turbine During Operation Around the Synchronous Speed

The doubly fed induction generator (DFIG) equipped with self-commutated insulated gate bipolar transistor (IGBT) voltage source converter (VSC) is one of the most popular topologies used in wind power systems. It has the ability to control active and reactive power independently. The reactive power capability is subject to several limitations which change with the operating point. Around synchronous operating point, a special attention is needed since the limitation of maximum junction temperature of the IGBTs cause a reduction on maximum permissible output current at the rotor side. This paper investigates the thermal behavior of the converter using semiconductor losses and thermal model based on the IGBT manufacturer datasheet. Different pulse-width modulation (PWM) types, including continuous and discontinuous types are applied and the results of reactive power capability are compared. Simulation results show that appropriate selection of PWM type is necessary at around synchronous speed to increase the maximum permissible rotor current as well as reactive power capability.

Generic Model of MMC-VSC-HVDC for Interaction Study With AC Power System

This paper presents a generic rms model of voltage source converter (VSC) in modular multilevel converter (MMC) topology for high-voltage direct current (HVDC) application. The generic rms model is suitable for stability analysis of a MMC-VSC-HVDC interconnected to ac power system. Based on the rms model, a linearized model for analytical model analysis is also developed and described. The parameters of the generic rms model are identified via an established averaged EMT model. The dynamic performance of the proposed generic model is tested in both time and frequency domain analysis using a typical high-voltage transmission system.

Enhanced Dynamic Voltage Control of Type 4 Wind Turbines During Unbalanced Grid Faults

The fully rated converter of type 4 wind turbines is capable of providing dynamic voltage control during grid faults by injecting controlled reactive currents. This paper describes three different dynamic voltage control options during unbalanced grid faults: 1) the positive sequence voltage control with only a positive sequence reactive current injection and suppression of the negative sequence current; 2) the positive sequence voltage control with limitation of the positive sequence reactive current injection and suppression of the negative sequence current; and 3) the positive and negative sequence voltage control with both a positive and a negative sequence reactive current injection. These different control options are compared in simulations of a wind power plant connected to a meshed power system, including synchronous generators. It is shown that both the positive sequence voltage control with limitation and the positive and negative sequence voltage control can overcome the voltage rise and voltage distortion that can occur with pure positive sequence voltage control without limitation. Both of these options have a distinct fault response, where the positive and negative sequence voltage control results in a fault response that resembles the fault response of a synchronous generator with higher fault current contributions in the faulted phases.