Reza Kazemi

The increasing contribution of wind turbine generators (WTGs) in power grids requires the control of wind generators and their impacts on the load frequency control (LFC). In this study, a detailed strategy for the control of WTGs in a smart power grid for LFC is presented. The WTGs are controlled by applying fuzzy logic controller with three inputs from wind velocity, frequency deviations, and wind velocity changes per second. The control takes into account for each WTG, an improved pitch angle controller to assist in fast damping of frequency deviations due to sudden high wind. Also, a feedback signal from frequency deviations, with the participation factor determined by a smart learning‐based intelligent controller (BELBIC) is used to determine rotor speed deviations. The controller facilitates frequency stability, and lower variations in the output power of conventional units. Simulation results, performed on a test system with three interconnected zones, validate the effectiveness of the proposed control strategy.

The static excitation system of a turbo generator, supplied by a rectifier, can cause induced voltages on the generator's shaft. This paper proposes a complete distributed model of a turbo generator, regulated by a static excitation system, to investigate the shaft voltage and bearing current. The complete modeling of all components is proposed by considering the couplings between them in a static excitation system. When shaft voltages exceed the dielectric breakdown voltage of the lubricating oil film in journal bearings, electrical discharge current flows through the bearings. This will further reduce the life expectancy of the dielectric gradually. Two different approaches are explained and discussed in detail to keep the shaft voltages within the safe region: a passive-filter and a DC-DC converter solution. Both methods along with the initial system are simulated and compared accordingly. Additionally, frequency domain analysis has been performed for evaluating the compensating solutions. Moreover, experimental tests were carried out on a 200 MVA turbo generator, and results of the suggested passive filter with those of the proposed compensation method are compared. Both experiments and simulations confirm the mentioned issue and show the effectiveness of the proposed compensation approaches in this regard.

Frequency-dependent impedance scans, or simply frequency scans, are an integral part of the sub-synchronous control interaction (SSCI) study process for assessing stability of wind power plants near series compensated transmission lines. Frequency scans are often utilized as an important screening tool to evaluate small-signal stability and identify contingency cases requiring more detailed evaluation. Small-signal stability is the foundational element required to evaluate SSCI stability, as it encompasses the largest realm of potential events and likelihood. Although a small-signal approximation is typically valid, physical systems, and modern wind turbine generators in particular, are ultimately non-linear and the harmonic excitation signal used to conduct the scan itself must be carefully constructed to maintain validity of the small-signal approximation. Otherwise, the results obtained can be inaccurate and misleading. In this paper, two common frequency scanning methods are compared and validated using detailed time-domain models of wind turbines. Recommendations and guidelines are provided for conducting frequency scans and for interpreting results.

Estimation of solar energy generation in the integrated power systems must be performed for power system the long-term planning and the short-term control. Irradiation is one of the main factors affecting the out coming energy of solar system. Hence, investigation of solar irradiation on the horizontal surface helps in proper and efficient implementation of solar energy system. Capturing much more energy from the irradiation, by the Photovoltaic (PV) cells, results in enhancement of out coming energy of PV station. This paper is going to study the impact of implementation two topologies for energy harvesting in PV stations. The topologies are as follows: (a) PV station with multilevel boost converters and inverters, (b) PV station with only multilevel inverters. Studies have been performed on 80 KW PV test station, located in Kabudrahang, Iran. The comparison between the results demonstrates much more solar energy harvesting in the case of applying multilevel converters in the output of PV arrays. Simulation results show the promising result of the proposed topology in harvesting maximum power from a PV station.

Nowadays, regarding high percentage of wind power penetration in power systems, operating conditions of power system necessitates wind turbine generators (WTGs) to contribute in frequency regulation of the system, similar to the conventional units. To reach this goal, active power output of WTGs must be controlled, but this issue distances the operating conditions of WTGs from maximum power point tracking (MPPT) mode. This paper initially presents a completed model of doubly fed induction generator (DFIG) for dynamic studies in frequency stability analysis. Next, a coordinated control strategy to regulate active power command set point (Pcmd) for individual WTGs in a wind farm (WF) and a control strategy to regulate wind power output of DFIG upon operator’s request is presented. Stability is assured under different wind conditions in the proposed control strategy. Individual WTGs set point allocation is performed applying fuzzy logic controller (FLC), while emotional learning based intelligent controller is used in regulation of the coefficient in the DFIG to reach the best stability conditions. Simulation results, performed on a test system consisting of both conventional units and WTGs, validate the effectiveness of the proposed control strategy in comparison with other mentioned solutions.

This paper presents the effective siting and sizing of the Wind Farms (WF) and Flexible AC Transmission Systems (FACTS) devices in a power system applying the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The objectives which are simultaneously considered in NSGA-II are: a) reduction of costs, and b) improvement of voltage profile of the buses in the system under study. In the simulations, the optimal siting and sizing of WF has been investigated along with FACTS devices' best siting and setting in the power system under study. K-means clustering algorithm is applied in classifying the data related to the WF output power and load power demand in 8760 hours of a year. The simulation results are compared with each other and at last, best solutions are introduced. IEEE Reliability Test System (IEEE-RTS) is used as the test system for investigation of system parameters and for comparison of the proposed solutions with each other.