## Article
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Abbreviation** & **Location** & **Date** & **Duration** & **Affected** & **Causes** & **Ref.** \\  & & **(h)** & **(Million)** & & **Causes** & **Ref.** \\ \hline IT\_2003 & Italy & 28 September 2003 & 14 & 60 & Power system failure & [8] \\ \hline DK\_SE\_2003 & Denmark, Sweden & 23 September 2003 & 1 & 5 & Power system failure & [9] \\ \hline GR\_2004 & Greece & 12 July 2004 & 3.5 & 5 & Power system failure & [10] \\ \hline DE\_2005 & Germany (Münsterland) & 25 November 2005 & 100 & 0.25 & Power system failure & [11] \\ \hline EUR\_2006 & Europe & 4 November 2006 & 2 & 15 & Dispatching mistake & [12; 13] \\ \hline AN\_2007 & The Netherlands & 12 December 2007 & 0.2 & 72 & \begin{tabular}{c} Crash of an army \\ helicopter into HV \\ power lines \\ \end{tabular} & [14] \\ \hline PL\_2008 & Poland & 8 April 2008 & 0.4 & 24 & \begin{tabular}{c} Extreme weather \\ conditions \\ \end{tabular} & [15] \\ \hline UK\_2009 & \begin{tabular}{c} South East \\ London and \\ North Kent, \\ U.K. \\ \end{tabular} & 20 July 2009 & 0.4 & 0.5 & \begin{tabular}{c} Fire near a HV power \\ cable started \\ deliberately by vandals \\ \end{tabular} & [16] \\ \hline AN\_2015 & \begin{tabular}{c} The \\ Netherlands \\ \end{tabular} & 27 March 2015 & 1.5 & 1 & \begin{tabular}{c} Extreme weather \\ conditions \\ \end{tabular} & [17; 18] \\ \hline TR\_2015 & Turkey & 31 March 2015 & 4 & 70 & \
\begin{tabular}{c} Lack of N-1 safety \\ criterion \\ \end{tabular} & [19; 20] \\ \hline UA\_2015 & Ukraine & 23 December 2015 & 6 & 0.5 & Cyber-attack & [21; 22; 23; 24] \\ \hline GR\_2021 & Greece & 8 February 2021 & 1 & 1 &
\begin{tabular}{c} Fire from a short circuit \\ in an autotransformer \\ \end{tabular} & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of major recorded power outages around Europe in the past 20 years. also presented. At the end of this work a new restoration strategy is proposed, using the A* Algorithm. The proposed strategy has been validated both in the IEEE-39 and IEEE-68 bus systems. The objectives of restoration are to reduce losses and restoration time, minimize negative effects on society, and enable the power system to return to normal conditions safely and quickly. To achieve the aforementioned objectives, numerous non-structured approaches, technologies, and object-oriented expert systems have been used in the creation of restoration schemes [37]. Some heuristic algorithms, such as genetic algorithms [38], artificial neutral networks [39], and fuzzy logic [40], are used for system restoration with the growth of computational intelligence. Multi-agent technologies [41; 42; 43; 44] have been developed based on the regional distribution features in space. Decision support systems [45] have been successfully proven as an extension of expert systems and heuristic rules. The backbone network must be built step by step, and each step's restoration path optimization must reduce the cost of forming the target network while taking limits into account. Each study has a distinct element in this area. In [46], the weight coefficient is modeled and evaluated with respect to each transmission line, using the data envelopment analysis method that incorporates preference information. Two reliable optimization models are proposed in [47] to reduce overall line restoration risk, while considering the risk of transmission line failure. These models are applied to the series and parallel restoration stages, respectively. A network reconfiguration technique based on the weighted complex network model is suggested in [48]. At the same time, the ideal skeleton network and restoration path sequence can be achieved. According to the characteristics of hierarchical scheduling in the power system, Ref. [49] provides a hierarchical collaborative optimization approach. The goals are described as total power generation and network reconfiguration level.