Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

[EN] Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems' view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.Gonzales-Zurita, Ó.; Clairand, J.; Peñalvo-López, E.; Escrivá-Escrivá, G. (2020). Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies. 13(13):1-29. https://doi.org/10.3390/en13133483S1291313Ross, M., Abbey, C., Bouffard, F., & Joos, G. (2015). Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation. IEEE Transactions on Sustainable Energy, 6(4), 1306-1314. doi:10.1109/tste.2015.2428676Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5(1). doi:10.1186/s41601-019-0147-zKatircioğlu, S., Abasiz, T., Sezer, S., & Katırcıoglu, S. (2019). 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© 2019 Elsevier Ltd Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids

Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik

Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. Swarm quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu swarm yang baik, sesuai dengan kriteria swarm oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan swarm. Pengontrolan swarm quadrotor memiliki tingkat kerumitan yang tinggi karena melibatkan banyak agen. Riset pengembangan swarm quadrotor masih belum banyak dilakukan dan masih membuka peluang untuk meneliti dengan metoda lain yang lebih baik dalam menghasilkan swarm. Makalah ini mengusulkan pengontrolan swarm quadrotor yang terdiri dari dua tingkat lup kontrol. Lup pertama adalah pengontrol sistem model swarm untuk membangkitkan lintasan swarm dan lup kedua merupakan pengontrol pada quadrotor untuk melakukan penjejakan lintasan swarm. Pengontrol pertama menggunakan pengontrol proporsional derivatif (PD), sedangkan pengontrol kedua menggunakan regulator linier kuadratik (RLK). Pengontrol yang dirancang memiliki parameter yang banyak, sehingga pemilihan parameter yang optimal sangat sulit. Pencarian parameter optimal pada pengontrol model swarm quadrotor membutuhkan teknik optimasi seperti algoritma genetik (AG) untuk mengarahkan pencarian menuju solusi yang menghasilkan kinerja terbaik. Pada makalah ini, penalaan dengan optimasi AG hanya dilakukan pada pengontrol PD untuk menghasilkan lintasan swarm terbaik, sedangkan matrik bobot RLK dilakukan secara uji coba. Hasil simulasi swarm pada model quadrotor menunjukkan parameter , . , dan  yang diperoleh menggunakan AG menghasilkan pergerakan swarm yang baik dengan kesalahan RMS pelacakan 0,0094 m terhadap fungsi obyektif. Sedangkan ketika parameter ,  dan  dicari menggunakan AG, tidak berpengaruh banyak dalam memperbaiki hasil simulasi swarm quadrotor. AbstractThe quadrotor is a type of take-off or vertical landing unmanned aerial vehicles with a cross shape and has one rotor at each end of its arm with high maneuverability. A quadrotor swarm consisting of a group of quadrotors leads to a good swarm, according to Reynold's swarm criteria, which accomplishes collision avoidance, velocity matching, and flock centering. Quadrotor swarm control has a high level of complexity because it involves many agents. Research on the development of quadrotor swarm has received insignificant attention and it still opens opportunities to research other methods that are better at producing swarm. The paper proposes the control of a quadrotor swarm consisted of two levels of control loops. The first loop controls the swarm model system to generate the swarm trajectory and the second loop is the controller on the quadrotor to track the swarm path. The first controller uses a proportional derivative controller (PD), while the second controller uses the linear quadratic regulator (LQR). The controller that is designed has many parameters, so the optimal parameter selection is very difficult. The search for optimal parameters in the swarm model controller requires optimization techniques such as the genetic algorithm (GA) to direct the search for solutions that produce the best performance. In this paper, tuning with the optimization of GA is only done for the PD controller in order to produce the best swarm trajectory, while the weight matrices of the LQR are done on a trial error basis. Swarm simulation results of a quadrotor model system show the parameters , . , and  obtained using GA produce a good swarm movement with RMS error 0.0094 m of the objective function. Whereas when parameters ,  and  are searched using GA, it does not have much effect in improving the quadrotor swarm simulation results

Quasilinear Control: Multivariate Nonlinearities, Robustness and Numerical Properties, and Applications

Quasilinear Control (QLC) is a theory with a set of tools used for the analysis and design of controllers for nonlinear feedback systems driven by stochastic inputs. It is based on the concept of Stochastic Linearization (SL), which is a method of linearizing a nonlinear function that, unlike traditional Jacobian linearization, uses statistical properties of the input to the nonlinearity to linearize it. Until now in the literature of QLC, SL was applied only to feedback systems with single-variable nonlinearities that appear only in actuators and/or sensors. In this dissertation, my recent contributions to the literature of QLC are summarized. First, the QLC theory is extended to feedback systems with isolated multivariate nonlinearities that can appear anywhere in the loop and applied to optimal controller design problems, including systems with state-multiplicative noise. Second, the numerical properties of SL, particularly, the accuracy, robustness, and computation of SL, are investigated. Upper bounds are provided for the open-loop relative accuracy and, consequently, the closed-loop accuracy of SL. A comparison of the computational costs of several common numerical algorithms in solving the SL equations is provided, and a coordinate transformation proposed to improve most of their success rates. A numerical investigation is carried out to determine the relative sensitivities of SL coefficients to system parameters. Finally, QLC is applied to the optimal primary frequency control of power systems with generator saturation, and control of virtual batteries in distribution feeders. The expected impacts of this work are far-reaching. On the technical front, this work provides: i) a new set of theoretical and algorithmic tools that can improve and simplify control of complex systems affected by noise, ii) information to control engineers on accuracy guarantees, choice of solvers, and relative sensitivities of SL coefficients to system parameters to guide the analysis and design of nonlinear stochastic systems in the context of QLC, iii) a new computationally efficient method of addressing saturation in generators or virtual batteries in modern electric power systems, resulting in efficient utilization of resources in providing grid services. On the societal front, this work: i) enables technologies that rely on computationally-efficient algorithms for automation of complex systems, e.g., control of soil temperature for agriculture, which depends on multiple factors like soil moisture and net radiation, ii) allows effective coordination of controllable smart devices in people’s homes, so as not to hamper their quality of service, and iii) provides a stepping stone towards key societal challenges like combating climate change by facilitating reliable operation of the grid with significant renewable penetration

Control and estimation techniques applied to smart microgrids : a review

DATA AVAILABILITY : No data was used for the research described in the article.The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network.http://www.elsevier.com/locate/rserhj2023Electrical, Electronic and Computer Engineerin

Modelling and digital control of two-phase interleaved coupled-inductor non-isolated DC-DC converters

This thesis focuses on the complete mathematical modelling and digital closed-loop control of two-phase interleaved coupled-inductor non-isolated dc-dc converters. Coupled-inductors have been shown to reduce the cost, size, and weight of high-power magnetic components while increasing efficiency. The complete large-signal model of the coupled-inductor boost converter is presented and compared to the traditional single-phase and two-phase discrete inductor dc-dc converters. The CCM-DCM mode maps are presented and discussed for the coupled-inductor boost converter. Sample analyses of several different CCM and DCM modes of operation are also presented. The different CCM and DCM waveforms are experimentally produced by a 1 kW laboratory prototype. Following on from the large-signal model, the complete small-signal model of the coupledinductor boost converter is presented. The method of solving for the small signal models is discussed, and sample analyses of several different CCM and DCM modes of operation are presented. Calculated and experimental frequency sweeps for several of the CCM and DCM modes of operation are produced and compared to verify the accuracy of the small-signal models. Controllers for the 1 kW prototype are designed from the transfer functions derived from the small-signal models. The control strategy of average-current-mode control is digitally implemented, which uses an outer voltage loop and an inner current loop to eliminate any error between the output and the desired output. The FPGA used in testing is the Altera Cyclone III FPGA. Initially, PI controllers are developed and compared to simulated results. In order to improve the closed-loop performance of the converter, the inner current loop PI controllers are replaced with Type II compensators. Several compensators are designed as examples for a number of CCM and DCM modes of operation. Finally, to increase the stability of the converter, bumpless PI control and forced-output control utilizing the Type II compensators are introduced and implemented