## 6 Cascading Failures, Co-Ordination and Cyber Security Issues in Smart Grids  
Since a smart grid is a huge interconnected system, if there is any failure in any part of the grid, it can trigger the failure of other parts as well. This phenomenon, called the cascading failure occurs commonly in computer networks and power systems. The fundamental architecture of a smart grid is different from the conventional grid as many advanced technologies are incorporated into the smart grid. Distributed generation, which forms the crux of the smart grid, transforms it into a decentralized mechanism and at the same time. However, any failure occurring at any part of the grid, can propagate to other regions and give rise to massive scale black-outs and interruptions. This is more dangerous when the number of local generators is increased in the smart grid. Therefore, there is a necessity to equip the smart grids with infrastructure to predict, prevent, and mitigate the effects of cascading failures. This includes condition monitoring and intelligent control of the grid. In addition, the robustness and resilience of the smart grid can be improved by means of risk assessment models where, in the event of a cascading failure, the power network degradation is quantified and utilizing this disintegration state, the optimal dispatch and configuration is determined at every restoration stage. There are several data driven approaches to risk assessment of cascading failures in smart grids. Several of them depend on historical and simulation-based data (statistical data), which define the features of the cascading effects. The smart power grid is considered as a multi-agent system and complex network. Data driven algorithms are developed for uncertain regions in the multi-agent network. Preventive control methods make use of reinforcement learning and artificial intelligence, which make use of the available data to overcome low accuracy. There are also instances where the time series data samples are utilized from time domain simulations. The data from dynamic behavior of the system and the sensitivity is used to determine the most effective set of generators for improving the power system security. There are several deterministic and risk-based tools utilized in the power system operation and planning to analyze, mitigate and prevent cascading failures. Several are  
Figure 12: Sample chain code. commercially available and several are available in the development stages. However, the complexity in risk evaluation of cascading failures is high. Any event which has occurred will generate a sequence of reactions that need to be modeled. Since the events may occur individually or as a combination, most of these risk-based tools adopt a probabilistic strategy. Table 6 provides a brief overview of certain popularly used commercial as well as research grade tools used for cascading failure analysis [34]. Grid inverters play a significant role in voltage regulation by modulating the real and reactive power components. Interfacing solar photovoltaic system (PV) with the smart grid creates challenges to the operation, owing to its seasonal and intermittent nature. The fluctuations in the voltage will result in grid-voltage violation beyond the operating limits. With the advent of PV smart inverters, which have a fast response to voltage regulation, the inherent problems have been partially addressed. However, there exist issues due to co-ordination as the existing control strategies for smart inverters are based on local information. Several algorithms based on deep learning and artificial intelligence has tried to address this sub-optimal performance and the co-ordination of multiple smart inverters. The reactive power of the smart inverters is effectively utilized for ensuring voltage operation limits. However, there exists several technical restrictions on the volt-var curves for grid-connected solar PV systems employing smart inverters.