## 5 Conclusions  
In this work, a novel module-level performance evaluation algorithm of a PV system with the advanced fault detection monitoring is developed under field conditions. The proposed method can distinguish faultless, cracks, static shading and broken sensors under changeable climatic conditions for the individual PV module, which is the most innovative feature of this study. Moreover, the simple structure, low complexity and low costs of the proposed method are key merits to implement performance evaluation for the smart PV system in this study. Furthermore, the experimental results successfully demonstrate the high feasibility of the proposed method to evaluate performance and locate the anomalies of the smart PV system at the module level based on the data matrix signature. The experimental results also show a good linear agreement between the PV module temperature estimated by the thermal model and the actual measured module temperature with an \(R^{2}\) value of 0.922 to prove the validity of thermal model. The similar \(PI_{\Delta I}\) and \(PV_{\Delta V}\) are calculated through the module temperature from the thermal model and measured module temperature to justify the proposed method being effective in the faultless case. The performance indicators in current and voltage detect and locate the faulty PV modules in the smart PV system because of both \(PI\) bringing more differences caused by cracks in 0.22 of \(PV_{\Delta V}\), partial shading in 0.47 of \(PV_{\Delta V}\) and broken sensors in 0.17 of \(PV_{\Delta I}\) and 1 of \(PV_{\Delta V}\) separately. Meanwhile, when the output power of a cracked module is similar to the output power of a normal module, the proposed method is more effective in identifying the cracks than monitoring only the power. Furthermore, it reflects the high validity of the developed performance evaluation methods based on the intelligent PV modules in the smart PV system. Although the proposed method has successfully achieved the expected performance evaluation and fault location objectives at the module level, an improved fault-location method will still need to be proposed to accurately identify the fault type when the faults cause the voltage of faulty modules to be lower than the critical voltage for sensors during \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Faults** &
\begin{tabular}{c} **Performance** \\ **Indictors** \\ \end{tabular} & **System ID** & **String ID** & **Module ID** & **Detection** \\ \hline \hline Cracks & \(PI_{\Delta V}=0.22\) & PVS1 & S5 & BG033404 & \(\surd\) \\ Shadow & \(PI_{\Delta V}=0.47\) & PVS1 & S5 & BG033061 & \(\surd\) \\ Broken & \(PI_{\Delta I}=0.17\), & PVS1 & S6 & BG033269 & \(\surd\) \\ sensors & \(PI_{\Delta V}=1\) & & & & \\ Faultless & \(PI_{\Delta V}=0.09\), & PVS1 & S5 and S6 & Others & – \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of faults detection in PV strings. normal operation. At the same time, the proposed method should be further applied to more commercial systems in order to improve the accuracy of the method in the future. Conceptualization, L.F.; methodology, L.F.; software, L.F. and J.Z. ; validation, L.F. and J.Z. ; formal analysis, L.F. and J.Z. ; investigation, L.F.; resources, K.D. ; data curation, L.F.; writing--original draft preparation, L.F., N.A. and F.U.H. ; writing--review and editing, N.A. and F.U.H. ; visualization, L.F. and J.Z. ; supervision, F.U.H. and N.A. ; project administration, L.F., F.U.H. and N.A. ; funding acquisition, L.F., F.U.H. and N.A. All authors have read and agreed to the published version of the manuscript. This research is financially supported by the grant code of LRGS/1/2019/UKM-UNITEN/6/2 from the Ministry of Higher Education (MoHE) of Malaysia. Moreover, the publication support will be received by the BOLD funding from the iRMC of Universiti Tenaga Nasional (@UNITEN, The National Energy University, Malaysia).