# Grid Nodes Selection Strategies for Power Quality Monitoring
## 2 Literature Review
### Scientific Papers and Practical Reports  
Despite there being no legislation dedicated to PQ monitors' allocation, the absence does not reduce the importance of the issue which can improve a project's cost-effectiveness. Many studies have been carried out to investigate how to optimize PQ monitors' allocation. In addition, different PQ monitoring campaigns have been executed over the past few decades in various countries. For this paper, only those researches were selected which are directly related or can be indirectly examined through a prism of PQ monitors' allocation strategy. Most of the practical and theoretical research is described in scientific papers, but other sources can also be found (e.g., grid operator report). In this subchapter, the review of 17 scientific papers, 1 state-of-the-art review, and 1 grid operator report is given: the oldest reference dates back to 2005, the latest--2021. In [6] (2005), PQ monitoring campaigns are described, which have been carried out since the 1990s by Belgian DSOs. Fixed monitors were connected to MV busbars in HV/MV substations, starting with the most critical: 427 monitors were installed in 218 substations. In the MV grid, phase-to-phase voltages were registered, excluding some areas with many overhead lines where phase-to-ground measurements were taken (to investigate operation of protection systems). In addition, temporary measurements with 64 mobile monitors were taken in order to respond to customer's complaints, issue an advice, investigate disturbing effects of chosen LV load types. Approximately 1500 campaigns were executed until 2004. In contrast to [6;7] (2008) focuses on TSO grid. A branch-and-bound algorithm is developed for optimal PQ monitoring nodes allocation, i.e., to find the minimum number of required monitors and localize them. A casual problem is raised--optimization between the monitor installation cost, bus voltage monitoring, and line current monitoring. The algorithm was developed using MATLAB. The objective function is defined as following:  
\[f=\sum_{i=1}^{n}c_{i}x_{i}=\boldsymbol{c}\;\boldsymbol{x}=[c_{1}\;c_{2}\; \cdots\;c_{n}]\cdot[x_{1}\;x_{2}\;\cdots\;x_{n}]^{\mathrm{T}}\rightarrow\min, \tag{1}\]where \(e\)--cost vector; \(x\)--existence vector (binary value which defines whether monitor is installed on the bus or not). The branching (decision) tree diagram of the proposed algorithm (based on Equation (1)) is shown in Figure 2. Branching is a division of the original problem into smaller (specific) cases. The total number of possible solutions is equal to \(2^{n-1}\). The algorithm has been tested on IEEE 14, IEEE 30, IEEE 57, IEEE 118, and CEMIG (the Brazilian test system with 48 buses) test schemes. The results and test schemes' features are summarized in Table 1. Optimal amounts of PQ monitors were found considering different installation cost. Thus, the final solution depends on the case. Moreover, it is considered that \"the redundancy in the measurements is desirable\" for system reliability enhancement in anticipation of data loss due to errors or noise. However, new challenges arise: despite data redundancy potentially being advantageous, optimal oversize must be determined in the case of large power system monitoring in anticipation of communication channel jamming, for data processing simplification purposes, etc. In the paper, the redundancy is estimated with Data Redundancy Factor (DRF) which is defined as follows:  
\[\text{DRF}=\frac{\text{Sumof observed state variable}}{\text{Total number of state variable}}. \tag{2}\]  
In [8] (2009), a PQ monitoring campaign was initiated in 2005 by the Italian Regulatory Authority for Electricity and Gas. The aim was to investigate the present performance of MV network in terms of PQ. During 3 years, 600 instruments were installed on 400 MV busbars (11% of the MV network) of HV/MV substations. The analyzers were connected to the LV side of pre-existing voltage transformers.