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Optimization of the combustion control process in a tangentially fired pulverized coal boiler, to achieve uniform temperature distribution, is discussed in the paper. This issue is even more critical in those thermal power plants which are not equipped with modern systems for combustion enhancement, such as low NOx burners. Research has shown that the temperature distribution inside the boiler of such power plant can be controlled by adjusting firing, through coal redistribution among the mills. Furthermore, disturbed flame symmetry (i.e. non-uniform temperature distribution in the boiler) is reflected in a large difference between the output temperatures measured on the left and right sides of the boiler. Given the non-stationary conditions typical of thermal power plant boilers, an adaptive control approach is proposed, based on PI controllers which are very popular in industry and widely accepted. Self-tuning of the PI controllers is based on dynamic model parameters derived applying the weighted recursive least squares (WRLS) method to real data recorded at Nikola Tesla B thermal power plant in Serbia, whose nominal power is 650 MW. The same model was later used to test the proposed control approach.

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller.

This paper proposes a decentralized current-sharing control strategy to endow fast transient response to paralleled dc–dc converters systems, such as dc microgrids or distributed power systems. The proposed controller consist of two main control loops: an external voltage droop control for current-sharing proposes and an internal current loop. The external droop control loop is designed as a voltage loop with embedded virtual impedance, which avoids the use of a slow voltage loop and a separate extra virtual impedance loop that may limit the system bandwidth. The internal current loop, thanks to the external control loop simplification, plays a major role in the system bandwidth, so that an adaptive proportional-integral (PI) controller is proposed for this matter. In the paper, two different droop control methods have been modeling, designed, simulated, and tested: The conventional virtual-impedance-loop based V–I droop and the proposed embedded-virtual-impedance based I–V droop. In order to compare the dynamic response performances between two droop controllers, their state-space models have been developed and analyzed in this paper. The results show that the dynamic response of the I–V droop control is faster than that of the conventional V–I droop control. Furthermore, by analyzing the effects from I–V droop control parameters, the errors can be reduced faster by enlarging the proportional terms, but with no fluctuations, and then completely eliminated by restoring back to small proportional values. Meanwhile, there exists a tradeoff phenomenon between the fast dynamic response and good steady-state performance; thus, an adaptive PI controller is proposed to both improve dynamic response and guarantee good steady-state performance simultaneously. Experimental results are shown to verify the accuracy of the models and the effectiveness of the proposed control framework.

Owing to the intermittent nature of the renewable energies employed in smart grids, large frequency fluctuations occur when the load frequency control (LFC) capacity is not enough to compensate for the imbalance of generation and demand. This problem may become intensified when the system is working in an island operation mode. Meanwhile, electric vehicles (EVs) are growing in popularity, being used as dispersed energy storage units instead of small batteries in the systems. Accordingly, the vehicle-to-grid (V2G) power control can be applied to compensate for the inadequate LFC capacity and thereby to improve the frequency stability of smart grids, especially in the island operation mode. On the other hand, large scale and complex power systems encounter many different uncertainties. In order to handle these uncertainties, this study proposes a combination of the general type-2 fuzzy logic sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique, as a novel heuristic algorithm, to adaptively tune the proportional-integral (PI) controller for LFC in islanded MicroGrids (MGs). Although implementing general type-2 fuzzy systems is generally computationally cumbersome, by using a recently introduced plane representation, GT2FLS can be regarded as a combination of several interval type-2 fuzzy logic systems (IT2FLS), each with its own corresponding α level and linguistic rules can directly be incorporated into the controller. This paper further presents a new modified optimization algorithm to tune the scaling factors and the membership functions of general type-2 fuzzy PI (GT2FPI) controller and thereby to minimize the frequency deviations of the MG system against load disturbances more effectively. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of the proportional integral derivative (PID), Fuzzy-PID (FPID), and Interval Type II fuzzy based PI (IT2FPI) controllers, which are the most recent methods applied in this respect. Simulation results demonstrate the perfection and efficacy of proposed controller.

Generally, the power of a solar photovoltaic panel is strongly related to climate conditions. Hence, a grid-connected system should have a good response over variations in the solar radiation and temperature. Consequently, this work describes the application of the control based general type-II fuzzy system for the grid-connected solar power generation system. In this study, general type-2 fuzzy logic sets (GT2FLS) and the Modified Backtracking Search Algorithm (MBSA) techniques for the adaptive tuning of the most popular existing proportional-integral (PI) controller is integrated in order to tackle these uncertainties. The achieved results are compared with conventional PI controller and Optimal Fuzzy PI (OFPI) controller results, which are the most recent researches into in the present issue to evaluate the proficiency of the proposed controller. Finally, the extensive studies and hardware-in-the-loop simulations are presented to show the effectiveness of the proposed controller.