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Society 2.0; with the help of recent advancements in the internet and web 2.0 technology, makes the social media-based platform the most popular source for big data research. Big Data Analytics contributes by adjusting, analyzing, and forecasting insightful recommendations from this huge source of noisy & mostly unstructured “Big Social Data”. We present 10 mostly used big data analytics in the working domain of social media-based platforms. Different popular techniques or algorithms related to each big data analytic are also listed in this study. We show that “Text Analytics” is the most popular big data analytics in social media data analysis. Through this research, we try to explain the 10 Bigs of big data and introduce the “Sunflower Model of Big Data”. We also explain the reason why the social media-based platform is so significant and popular source of big data by analyzing the most recent statistics. This study will be a handful for all other researchers who want to work with big data in social media and in advance; make their work easy for selecting the best big data analytics method suitable for their research work.

Today, due to the widespread penetration of renewable energy sources (RESs) and distributed generators (DGs), a new power system stability issue has emerged. This issue is the reduction and variation of inertia in the power system and is triggered by the utilization of power electronics interfaces to connect the RESs and DGs into the system, leading to a higher system uncertainty that needs more complex system operation and control. To maintain system reliability and providing efficient use of RESs and DGs, the synthesis and control of virtual inertia should be a key technology to achieve a flexible operation in today and future power systems. This chapter provides an introduction to the fundamental aspects of synthesis and control of virtual inertia for the purpose of the power system controls. An overview of the low inertia issue in the system with a high share of RESs and the role of virtual inertia are highlighted. The concept of virtual inertia emulation is briefly presented. Finally, the past achievements in the synthesis of virtual inertia respect to power system stability and control are briefly reviewed.

In the previous chapters, to deal with the disturbances including high integration of distributed generators (DGs)/renewable energy sources (RESs), the virtual inertia constant, which is the crucial factor in emulating additional inertia power into the system, is fixed at one value. In the application of virtual inertia control, improper selection of its control value may result in a higher frequency deviation, slower recovery time, and instability. To overcome this problem, in this chapter, the basic proportional-integral (PI) or proportional-integral-derivative (PID) controllers, which are widely used in the real-practice in the industrial systems, are applied to the virtual inertia control to generate proper virtual inertia constant for imitating the effective inertia power and improving system frequency stability. This chapter provides the synthesis of a new decentralized PI/PID-based virtual inertia control to evaluate the virtual inertia power under different levels of RESs/DGs penetration and load disturbances. The uses of the PI/PID controllers for frequency stability enhancement are briefly discussed. Then, the optimal setting of PI/PID parameters using the classical and modern tuning techniques are described in detail to obtain the sufficient virtual inertia constant with respect to the additional power, assuring stable grid operation. Finally, the proposed method is tested in a control area power system with different levels of RESs/DGs, loads, and system inertia and damping reduction.

Nowadays, a new concept of modern power systems (i.e., smart/micro-grids), which includes various components such as smart meters, smart appliances, renewable energy sources (RESs), distributed generators (DGs), and controllable loads has increased attention worldwide due to its energy efficiency and environmental concerns. Such a modern system requires the employment of real-time application and intelligent control. To properly utilize the virtual inertia control regarding an intelligent ability in future predictions, the model predictive control (MPC) is necessary. The MPC has a fine performance in delivering fast dynamic response with robustness against disturbance and uncertainty, while keeping future control variables in account. Thus, it has been implemented in a wide range of industrial applications, including real-time measurement and control. In this chapter, the design of decentralized MPC-based virtual inertia control is introduced to emulate the suitable virtual inertia power, while predicting the future behavior or event regarding inertia control-based frequency regulation. The MPC controller applies a feedforward control technique to reject the disturbances from RES/DG and load penetration as well as system parameter uncertainty, ensuring rapid dynamic response with the robustness of system operation. The proposed MPC-based virtual inertia control is verified through a nonlinear control area system with high RESs/DGs penetration including the extended communication delay time.

In the previous chapter, the regulation of the interchange power and the implementation of multiple virtual inertia controls are not considered. In real practice, most of the power systems are interconnected to enable the power transfer between each area in the system for more efficient use of the available resources. With the increasing penetration of renewable energy sources (RESs)/distributed generators (DGs), the inertia of some areas will decrease and could lead to power system oscillations. In this regard, the regional inertia property is critical to managing the oscillations and avoiding system instability. To overcome this problem, this chapter investigates the coordination of multiple virtual inertia control systems to improve the frequency stability and responses of the inertia power and tie-line power in the interconnected power systems with RESs/DGs. The dynamic performance and frequency characteristics of an interconnected system with multiple virtual inertia, primary, and secondary control loops are explained. A dynamic response model of the interconnected system is linearized and investigated in detail. The role of interchange power between control areas is briefly described. The effects of control parameters affecting the system frequency response with respect to the implementation of multiple virtual inertia units in the interconnected system are discussed via the state-space representation.

The integration of the significant number of virtual inertia control systems could create new challenges in the implementation of virtual inertia control systems in today and future power systems. Since the large amounts of RESs/DGs have been integrated by the electrical industry into power systems, considerable effort is required to effectively manage the installed virtual inertia control units. This chapter presents the technical challenges and further research needs regarding the virtual inertia control system. The key aspects of the challenges are how to manage changes in system topology created by various virtual inertia control systems as new control units in the network along with the resulted change in system dynamics and how to make the robust power grid by taking the advantage of the potential flexibility of the decentralized/distributed virtual inertia control units. The challenges such as the optimization of the virtual inertia control system and real-time inertia estimation are emphasized for supporting the reliable operation of virtual inertia control units for their application in low inertia power systems with high penetration of RESs. Finally, some important challenges related to the market for inertia service and the regulation of the virtual inertia control system are discussed for further research.

Currently, renewable energy sources (RESs) and distributed generators (DGs) are highly integrated into power systems regarding energy crisis, environmental concerns, and economic growth. The RESs/DGs penetration brings more complexity to the power system, since its systems are decentralized, and power outputs are intermittent or unpredictable against time-varying. Nevertheless, the RESs/DGs may not participate in stability regulation (e.g., frequency/voltage control), which causes the lack of inertia and damping property to the system, resulting in the weakening of grid stability. This situation can lead to system instability, cascading failures, and power blackouts. To deal with this problem, the system requires high-level (advanced) inertia controllers in tracking various levels of RESs/DGs penetration. Fuzzy logic control can be considered as one of the solution techniques due to the high reliability in nonlinear modeling with the fast processing time. In this chapter, a fuzzy logic technique is integrated into a virtual inertia control loop to enable the self-adaptive ability of virtual inertia constant against the different levels of RESs/DGs penetration regarding frequency control. As a result, the virtual inertia control unit can automatically adjust itself in emulating different amounts of inertia and damping responding the integrated levels of RESs/DGs at the specific time. At the beginning, the fundamental of fuzzy logic is discussed, and the recent achievements in fuzzy applications for frequency control problems are briefly reviewed. Then, a decentralized fuzzy controller in scheduling virtual inertia control constant is designed. Lastly, the effectiveness of the proposed control scheme is demonstrated through a nonlinear simulation under wide ranges of critical RESs/DGs penetration regarding system inertia and damping variations.

The inertia power generated by the rotating mass (rotor) of a synchronous generator plays a significant role in slowing down the frequency oscillation and has an active role in the system frequency stability during a disturbance. Lower system inertia could lead to a significantly faster change in the system frequency, resulting in the degradation of system frequency stability. A rapid frequency deviation can lead to system instability, collapse, and power blackout. Thus, by understanding the concept of inertia and its role in the power system, it would give a better insight on how to deal with the frequency problem caused by low system inertia. To give a clear understanding of the inertia compensation and frequency control, this chapter elaborates on the subject of active power-based inertia compensation regarding power system frequency control, including its basic concept and definition. Afterward, the primary, secondary, tertiary, and emergency control loops for power system frequency control are discussed in detail. A frequency response model is provided and its utilization for the sake of dynamic analysis and simulation regarding virtual inertia is elaborated. Finally, the past achievements regarding the inertia power compensation for system frequency control are discussed.

Regarding the previously elaborated inertia control techniques, they are not explicitly designed to deal with the effect of high uncertainty and disturbance. Thus, it is difficult to achieve a suitable trade-off between robust and nominal performances. In adaptive control techniques, the uncertainty formulation may not be appropriately included in the control design process. As a result, it is difficult to ensure the simultaneous robust performance and stability of the virtual inertia control in the presence of bounded modeling errors. Due to the capability of uncertainty formulation in its control synthesis, the robust control technique successfully resolves the concerned problem. Compared with the adaptive control theory, the robust control theory is static rather than adapting to measurements of variations. Subsequently, the robust controller is specially designed to operate assuming that certain varibles will be unknown but bounded. This chapter presents the application of robust uncertainty modeling theory for designing the H∞ robust virtual inertia control system in the presence of high renewable energy sources (RESs)/distributed generators (DGs) penetration. Practical constraints and system uncertainties are appropriately considered during the robust synthesis process. The H∞ robust control is used via a developed linear matrix inequalities (LMI) algorithm to reach an optimal solution between nominal and robust performances for design objectives. The robustness and performance of the H∞-based virtual inertia controller are executed along with different sets of severe parametric uncertainty and external disturbance. The closed-loop system is verified through a nonlinear control system under the critical operating scenarios of uncertain control parameters with high RESs/DGs penetration.

The inertia property is one of the most critical aspects to maintain the frequency stability in a single (islanded) power system. Therefore, this chapter explains the dynamic performance and frequency characteristics of a single-area system with the deployment of virtual inertia control in addition to the primary and secondary control loops. The linearized frequency response model for virtual inertia, primary, and secondary controls is presented by using the state-space representation (i.e., mathematical model of a physical system). Dynamic and static performances of the virtual inertia response model are explained in terms of small-signal (dynamic) and state-space analysis. The effects of various parameters over inertia control-based frequency response are emphasized. A dynamic model of virtual inertia control is verified by a well-tested classical load-frequency control model in the varying operating conditions of the power system. Moreover, some experimental studies with a practical virtual inertia control in the laboratory environment are also demonstrated.

The use of Internet of Things (IoT) for plant growth and environmental management is a promising new field of research. Here a network of seamlessly connected sensors is used to feed data aimed at providing healthier plant growth and a better environment. In this chapter, we present a system where eight types of sensors are used to measure the air and soil quality. Our design utilizes cloud storage for keeping the collected sensor data which then gets sorted online in order to create accurate forecasts on the environment and plants using an autoregressive integrated moving average algorithm. Additionally the system has been designed with web interface and data visualization, enabling people to obtain the real-time environmental information to take better decisions for plant growth and environmental management. Finally we highlight the accuracy of results of prediction data which is approximately 99.13%.

In the Order Preserving Pattern Matching (OPPM) problem, we have a text T and a pattern P on an integer alphabet as input. And the goal is to locate a fragment which is order-isomorphic with the pattern. Two sequences over integer alphabet are order-isomorphic if the relative order between any two elements at the same positions in both sequences is the same. In this paper we present an efficient algorithm to construct an interesting and useful data structure, namely, prefix table, from the order preserving point of view.