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Smart Grids
Published in Dimitris Al. Katsaprakakis, Power Plant Synthesis, 2020
More options and choices are provided for the system's operator to handle system stability and security issues. For example, in case of power supply interruptions, the capacity for direct (e.g., dispersed production) or indirect (e.g., load curtailments) power supply through DSM strategies can be employed to contribute towards the recovery of the electrical system to precontingency levels. For the same reason, DSM strategies can also be considered as alternative means of delivering ancillary services for system operators, such as voltage support, active and reactive power balance, frequency regulation, and power factor correction [28]. This is easily conceivable if we consider load as a virtual (or negative) spinning reserve. If load is reduced (e.g., through energy efficiency, load curtailments, or load shifting strategies) the available spinning reserve in the system increases, and vice versa. This can be achieved if the power demand is associated in a smart way with the grid state (i.e., a droop control).
Elements of Computational Intelligence for Network Management
Published in Witold Pedrycz, Athanasios Vasilakos, Computational Intelligence in Telecommunications Networks, 2018
Particular aspects of the overall IGI problem domain: The domains of interest are network-based, involving both telephone and power network applications.A domain simulator will be used to fulfill the role of the control system. This will supply alarm, status, and analog data.A simple system database will be built to handle system data.Existing commercial tools will be used to implement the key components: RT works for the real-time expert system and VAPS for the graphic display system.
IoT and the Need for Data Rationalization
Published in Diego Galar Pascual, Pasquale Daponte, Uday Kumar, Handbook of Industry 4.0 and SMART Systems, 2019
Diego Galar Pascual, Pasquale Daponte, Uday Kumar
Service-oriented architecture (SOA) offers a powerful framework for supporting the connectivity, interoperability, and integration in IoT systems; it forms the backbone of present-day IoT frameworks. While SOA goals are to primarily enhance IoT application interoperability, its monolithic usage in recent IoT frameworks amplifies the problem of scalability, especially with the enormous number of predicted “things.” IoT systems tend to expand, and with time, a capable SOA framework becomes too immovable to handle system extensibility. Micro service aims to fragment different IoT systems based on the system of systems paradigm to accommodate system evolution and extensibility (Uviase and Kotonya, 2018).
Processor in the Loop Verification of Fault Tolerant Control for a Three Phase Inverter in Grid Connected PV System
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Nasim Ullah, Irfan Sami, Abdulrahman Jamal Babqi, Hend I Alkhammash, Youcef Belkhier, Ahmed Althobaiti, Asier Ibeas
Classical PID controllers are widely utilized in the industry due to its simplicity and implementation ease. Several variants of PID controllers have been reported in the literature for grid-connected PV converter system such as digital PI control (Selvaraj, Rahim, and Krismadinata 2008), optimal PID control (Arzani, Arunagirinathan, and Venayagamoorthy 2015), and fuzzy PI controller (Karbakhsh et al. 2016). However, classical PID controllers are not robust enough to handle system stability under grid faults. In order to overcome the shortcomings of classical PID controllers, artificial intelligence-based control methods have been reported in the literature such as fuzzy PI (Karbakhsh et al. 2016), fuzzy neural network (Lin et al. 2015), and ANFIS control (Lakshmi and Naik 2018). However, such methods are dependent on user experience, and under severe grid faults, system stability can be compromised due to lack of prior knowledge about a specific fault.
Dynamic event-triggered distributed filtering design for interval type-2 fuzzy systems over sensor networks under deception attacks
Published in International Journal of Systems Science, 2021
Since most of the practical control systems are often nonlinear, which make the system analysis and design extremely difficult (Pan et al., 2020; Zhang, Liu, Dai, et al., 2020; Zhang, Liu, & Wang, 2020). Fortunately, the Takagi-Sugeno (T-S) fuzzy model can approximate the nonlinear system by connecting multiple local linear systems with nonlinear MFs (Lam, 2018). Because of its powerful modelling ability for nonlinear systems, it has been widely concerned by scholars (Su et al., 2013; Yang et al., 2012; Zhang et al., 2016; Zheng et al., 2002). However, the mentioned T-S fuzzy method cannot effectively capture the uncertainty of nonlinear systems. To overcome this difficulty, the IT2 fuzzy logic model is developed to handle system uncertainty based on the footprint of uncertainty. Intuitively, IT2 fuzzy logic system can be considered as a collection of T-S fuzzy ones, in which the upper and lower MFs involve uncertainty bound information. Due to its uncertainty processing ability, it has achieved considerable results from different aspects, such as stability analysis (Lam & Seneviratne, 2008; Sheng & Ma, 2014), output-feedback tracking control (Kavikumar et al., 2020; Xiao, Lam, Yu, et al., 2020), filter design (Gao et al., 2019; Liu et al., 2017), and so on. Moreover, sensor networks are also gradually being applied in IT2 fuzzy systems. In Liu et al. (2017), the reliable filter is constructed for IT2 fuzzy systems over sensor networks with random link failures. Furthermore, the fault detection issue is handled in Gao et al. (2019) by designing a distributed filter for IT2 fuzzy stochastic systems.
Robust adaptive fuzzy control for single-chamber single-population microbial fuel cell
Published in Systems Science & Control Engineering, 2021
Li Fu, Xiuwei Fu, Hashem Imani Marrani
To date, various controllers have been used based on the mathematical model of the MFC system. Input-output linearization methods are limited in properly representing the correct dynamics of nonlinear systems in the wide operating range (Chen et al., 1995; Dochain, 1992). Nonlinear control schemes can handle system uncertainty and online parameter estimation. A fuzzy PID controller for constant output voltage of MFC is discussed in Yan and Fan (2013). Lipping et al. presented a predictive model control scheme (MPC) for a two-chamber microbial fuel cell (Fan et al., 2015). Lately, the adaptive control technique has taken considerable attention to overcome the limitation that the nonlinearities of considered systems are required to be identified or can be linearly parameterized. A new adaptive sliding mode control scheme for covering disturbances and uncertainty effects in single-chamber microbial fuel cells is presented in Fu et al. (2020). In addition, uncertain nonlinear systems can be modelled by combining fuzzy set theory with fuzzy equations. Because the uncertainty in nonlinear systems can be turned into fuzzy set theory, fuzzy systems are good models for systems with uncertainty. Fuzzy models are made according to fuzzy rules, and these rules provide information about uncertain nonlinear systems. Each nonlinear system can be approximated by several piecewise linear systems (Takagi–Sugeno fuzzy model) or known nonlinear systems (Mamdani fuzzy model) (Mamdani, 1974; Takagi & Sugeno, 1985). Uncertain nonlinear systems can be modelled by fuzzy models with simple linear or nonlinear models.