# Grid Nodes Selection Strategies for Power Quality Monitoring
## 4 Problems and Future Prospects
### Sophisticated Attention to Every PQ Event
Currently, the suitability of many algorithms for PQ evaluation has not been sufficiently investigated and it remains an open research field with traditional methods such as FT modifications for signal components filtering (discrete Fourier transform--DFT, fast Fourier transform--FFT, short-time Fourier transform--STFT), and newer methods for data processing and specific features extraction, for example, Kalman filter--KF, wavelet transform--WT, discrete Stockwell transform--DST, Hilbert-Huang transform--HHT, Gabor transform--GT, time-frequency representation--FFR, chirp z-transform--CZT, space-phasors--SP, morphology method--MM, slant transform--SLT, Teager energy operator--TEO, principle curves--PC, spectral kurtosis--SK, amplitude and frequency demodulation--AFD. KF is applicable for the high accuracy evaluation of harmonic signal amplitude, phase angle, and frequency in case of noise presence, and together with other methods (e.g., discrete WT, fuzzy expert system--FES) can be used for data PQ deviations identification and classification. In addition, WT can be used for data compression, noise reduction, to extract various signal array characteristics such as amplitude, mean, standard deviation, probability density function, entropy, etc. ST is an extension of either WT or STFT with varying window length, and can output better time-frequency representation of the signal. HHT is a combination of empirical mode decomposition and Hilbert transform, and suitable for nonstationary signal processing. GT is an advanced signal processing tool which it is possible to apply for short duration transient analysis. Furthermore, many AI methods are also appropriate: well-known neural networks--NN (multilayer, radial basis function, probabilistic), support vector machine--SVM, fuzzy c-means--FCM, k-nearest neighbor algorithm--k-NN, hidden Markov model--HMM. For instance, a combination of fully informed particle swarm and adaptive probabilistic NN can be employed for PQ disturbances' identification, when ST and time-time transform (extension of ST) are used for probabilistic NN training. FCM, DWT-based SVM and many other techniques can be used for PQ disturbances' classification [28]. Figure 8: Perspective block diagram of a remote PQ monitoring process. In regard to PQ monitors' allocation, from the literature review, it is seen that none of the above-mentioned AI methods have been applied in [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] or included in standards guidelines (see Chapter 2): for example, in [7], the branch-and-bound algorithm is used (Figure 2); in [16; 17; 18], matrix operations which are based on Ohm's law; in addition, some classical statistical methods have been proposed. Each method has its own advantages and disadvantages. For example, the advantage (perhaps the only one) of the branch-and-bound algorithm is the provision of an optimal solution (basing on input data), but the increase in task amount by 1 unit doubles the convergence time (in the case of [7]--see Figure 2); thus, in order to avoid enormous computational times, the task must be strictly defined (limited) considering capabilities of modern computers. In our opinion, the main reasons for the absence of AI in reviewed papers are described in this subsection. First of all, the further development and approach unification of PQ evaluation methods together with technical progress of PQ-measuring devices capabilities are the prerequisites for further progress in PQ monitors' allocation strategies. In the future, the monitoring aim and general strategy will be selected by a human (interested party) but AI algorithms will deal with the remainder of the task--determine required quantity, monitoring places, and will prioritize them. Feedback signal along with various information flows will be used for decision making, for example, to determine the expediency of the selected node and evaluate displacement need.