Wind energy has the biggest market share of renewable energy around the world. High growth and development rates of wind energy lead to a massive increase in complexity and scale of wind energy conversion systems (WECSs). Therefore, it is required to upgrade methods and strategies for design and implementation of WECS. Considering WECS as cyber-physical systems (CPSs) will enable wind energy for the Internet of Energy (IoE). IoE is a cloud network where power sources with embedded and distributed intelligence are interfaced to smart grid and mass of consumption devices like smart buildings, appliances, and electric vehicles. Research trends of CPS for energy applications are mainly focusing on smart grids and energy systems for demand-side management and smart buildings with less attention given to generation systems. This paper introduces potentials of cyber-physical (CP) integration of next-generation WECS. In addition, this paper surveys the advances and state-of-the-art technologies that enable WECS for IoE. Challenges and new requirements of future WECS as CPS like abstractions, networking, control, safety, security, sustainability, and social components are discussed.

A Survey of Cyber-Physical Advances and Challenges of Wind Energy Conversion Systems: Prospects for Internet of Energy

Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Travelling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behaviour of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

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Discrete Spider Monkey Optimization for Travelling Salesman Problem

The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions

The southwest summer monsoon contributes the bulk of India’s rainfall. Consequently, almost all the geomorphic work by the rivers is carried out during the monsoon season in general and the monsoon floods in particular. Indian rivers are characterized by high average flood discharges and large temporal variability. There is also significant spatial variation in the magnitude, frequency and power of floods, on account of regional variations in monsoon rainfall, basin characteristics and channel geometry. As a result, the channel responses and the geomorphic effects also vary spatially. This paper describes the hydrological and geomorphological aspects, as well as the geomorphic effects of monsoon floods in the Indian rivers. The geomorphic effects of floods are most impressive only in certain areas — the Himalaya, the Thar Desert, and the Indus-Ganga-Brahmaputra Plains. There are numerous instances of flood-induced changes in the channel dimension, position and pattern in these areas. In the Ganga-Brahmaputra Plains, the annual floods appear to be geomorphologically more effective than the occasional large floods. In comparison, the rivers of the Indian Peninsula are, by and large, stable and the geomorphic effects of floods are modest. Only large-magnitude floods that occur at an interval of several years to decades are competent to modify the channel morphology in a significant way. A synthesis of the various case studies available from the Indian region indicates that often the absolute magnitude of a flood is not as important with respect to the geomorphic effects as the flow stress and competence.

Geomorphic effects of monsoon floods on Indian rivers

By exploiting the vulnerabilities in cyber components, an attacker could intrude in the wind farm supervisory control and data acquisition (SCADA) system and energy management system (EMS), and maliciously trip one or multiple wind turbines. The reliability of the overall power system could thus be impacted by the performance of wind farms. In this paper, cyber attack scenarios concerning cyber components or networks are considered in the integrated wind farm SCADA/EMS system architecture. Two Bayesian attack graph models are adopted to represent the procedures of successful cyber attacks, and a mean time-to-compromise model is used by considering different attack levels and various vulnerabilities. Frequencies of successful cyber attacks on the wind farm SCADA/EMS system are estimated. A procedure for evaluating the power system reliability is proposed by considering wind turbine trips caused by various cyber attacks. Simulations are conducted based on a typical IEEE reliability test system. Simulation results indicate that the overall system reliability decreases when the frequency of successful attacks on the wind farm SCADA/EMS system and skill levels of attackers increase.

Power System Reliability Assessment Incorporating Cyber Attacks Against Wind Farm Energy Management Systems

Transmission network expansion planning (TNEP) problem is a large-scale, complex mixed integer nonlinear programming problem. The solution of TNEP problem is essential to fulfill the load demand in an economical manner. A grey wolf optimization (GWO) algorithm which is a nature-inspired metaheuristic algorithm is used to solve the TNEP problem. Further, a modified GWO is developed, and to validate its result, it is tested on 20 standard benchmark test functions. The basic and modified version of GWO algorithms is applied to solve TNEP problem for Graver’s six-bus and Brazilian 46-bus systems. The obtained results are compared with other state-of-the-art algorithms. The results demonstrate the accuracy as well as proficiency of the proposed algorithm.

Modified Grey Wolf Optimization Algorithm for Transmission Network Expansion Planning Problem

The power system is a complex interconnected network which can be subdivided into three components: generation, distribution, and transmission. Capacitors of specific sizes are placed in the distribution network so that losses in transmission and distribution is minimum. But the decision of size and position of capacitors in this network is a complex optimization problem. In this paper, Limacon curve inspired local search strategy (LLS) is proposed and incorporated into spider monkey optimization (SMO) algorithm to deal optimal placement and the sizing problem of capacitors. The proposed strategy is named as Limacon inspired SMO (LSMO) algorithm. In the proposed local search strategy, the Limacon curve equation is modified by incorporating the persistence and social learning components of SMO algorithm. The performance of LSMO is tested over 25 benchmark functions. Further, it is applied to solve optimal capacitor placement and sizing problem in IEEE-14, 30 and 33 test bus systems with the proper allocation of 3 and 5-capacitors. The reported results are compared with a network without a capacitor (un-capacitor) and other existing methods.

Optimal placement and sizing of capacitor using Limaçon inspired spider monkey optimization algorithm

The nature-inspired algorithms NIAs have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation SMO algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO PLSMO. The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.

Power law-based local search in spider monkey optimisation for lower order system modelling

Optimal power flow OPF is the most requisite tool in the power system analysis. The OPF is relatively a difficult constrained optimisation problem and broadly solved by conventional as well as modern intelligent methods. In this paper the authors proposed a Levy flight spider monkey optimisation LFSMO algorithm to solve the standard OPF problem for IEEE 30-bus system, which is proposed to improve the exploitation capability of spider monkey optimisation SMO algorithm. The performance of LFSMO is evaluated by testing it over 25 benchmark functions.

Optimal power flow analysis using Lévy flight spider monkey optimisation algorithm

Induction motors are widely used in power system due to their robustness, even though induction motors are highly non-linear and their control is quite complicated. Proportional integral derivative acceleration PIDA controller is used with induction motor for improving its efficiency. But, finding optimal PIDA controller parameters is a complex task and cannot be easily dealt by the available analytical strategies. As the nature inspired algorithms NIAs have shown their accuracy and efficiency to solve such complicated optimization problems, in this paper, a recent NIA, namely Spider Monkey Optimization SMO algorithm is used for finding the optimal PIDA controller's parameters for controlling the induction motor. As per the best of authors knowledge, it is the first attempt to design optimal PIDA controller's parameters for induction motor through any swarm intelligence motivated algorithm.

Annapurna Bhargava

Papers

Modified Grey Wolf Optimization Algorithm for Transmission Network Expansion Planning Problem

Optimal placement and sizing of capacitor using Limaçon inspired spider monkey optimization algorithm

Power law-based local search in spider monkey optimisation for lower order system modelling

Optimal power flow analysis using Lévy flight spider monkey optimisation algorithm

Optimal design of PIDA controller for induction motor using Spider Monkey Optimization algorithm