**3. Real-Time Experimentation for Non-Intrusive Identification of Load Pattern (NIILP) Using Percentage THD Measurement**

In this section, real-time experimentation for NIILP using percentage THD measurement is described. A National Instruments (NI) compact reconfigurable input–output system (cRIO) 9082-based virtual instrumentation experimental setup is developed for validating the proposed NIILP. It is one of the potential real-time hardware tools for percentage THD measurement, as per the requirements of international standards such as IEEE 519,1159 and IEC 61000-4-60. The NI-cRIO 9082 has a Field-Programmable Gate Array (FPGA) architecture which is equipped with an Intel Core-i7 dual-core Central Processing Unit (CPU) with a frequency of 1.33 GHz, 2 GB of DRAM (Dynamic random access memory), 32 GB of ROM (read only memory), and a Xilinx Spartan-6 LX150 FPGA. It consists of a reconfigurable embedded chassis with an integrated intelligent real-time controller and data acquisition modules for analog signal acquisition [41,42]. EDLIFFT with 4 MSW was deployed in the LabVIEW-configured host computer and interfaced to the NI LabVIEW-powered NI-cRIO 9082 through the TCP/IP interface, as illustrated in Figure 3. Most of the typical real-world loads are CFL, LED, fan and PC. Hence, the CFL, LED, exhaust fan and SMPS of the personal computer, which is connected to the single-phase 230 V, 50 Hz utility supply mains, are considered for computing the percentage THD using EDLIFFT with a 4MSW. The load current waveforms are acquired from supply mains and processed to the NI-cRIO 9082 using the NI-9227 current input module.

**Figure 3.** Hardware schematic for percentage THD computation.

A detailed flow-chart of the percentage THD measurement method is described in Figure 4. The percentage THD is measured by using the data obtained from EDLIFFT with 4 MSW. The individual switches turn the loads ON and OFF to verify the different load combinations. Thereby, the percentage THD of each combination is computed by the algorithm given in Figure 4.

**Figure 4.** Flow chart for percentage THD computation.

#### **4. Results and Discussion**

The real-world load current waveforms acquired by the NI-cRIO 9082 for various load combinations are depicted in Figures 5–7. Initially, single-load operation is acquired and the individual load current waveforms of the CFL, LED, exhaust fan and SMPS of the PC are illustrated in Figure 5. From the individual load waveforms depicted in Figure 5a–d, it can be observed that no two waveforms are found to be the same shape due to the harmonic pollution. Moreover, these waveforms are highly nonlinear. Therefore, the percentage THD values of these loads are found to be unique in nature.

**Figure 5.** Individual single loads. (**a**) CFL load waveform; (**b**) LED load waveform; (**c**) exhaust fan waveform; (**d**) SMPS of the PC waveform.

**Figure 6.** Any two loads. (**a**) CFL + LED load waveform; (**b**) CFL + exhaust fan load waveform; (**c**) CFL + SMPS of the PC load waveform; (**d**) LED + exhaust fan load waveform; (**e**) LED + SMPS of the PC load waveform; (**f**) exhaust fan + SMPS of the PC load waveform.

Any two real-world load combinations are monitored by turning ON their corresponding load switches. The waveforms acquired from the NI-cRIO 9082 for any two real-world load combinations are shown in Figure 6. From the figures, it is observed that no two waveforms are found to be the same shape due to the harmonic distortion. Therefore, the percentage THD values of these load patterns are found to be unique in nature.

Any three load combinations are monitored by turning ON their corresponding load switches and the three load combination waveforms of the CFL, LED, exhaust fan and SMPS of the PC are depicted in Figure 7. It is observed from Figure 7 that no two waveforms are found to be the same due to the harmonic pollution.

**Figure 7.** Any three loads. (**a**) CFL + LED + exhaust fan load waveform; (**b**) CFL + LED + SMPS of the PC load waveform; (**c**) LED + exhaust fan + SMPS of the PC load waveform; (**d**) CFL + exhaust fan + SMPS of the PC load waveform.

The load waveform at the supply mains when all the loads are active is illustrated in Figure 8. This waveform is also found to be quite different from the other load pattern waveforms.

**Figure 8.** All four loads. CFL + LED + exhaust fan + SMPS of the PC load waveform.

From single-load operation to four-load operation, the load current waveforms are unique, indicating that the percentage THD is different for all load combinations. Therefore, percentage THD can be safely used for load identification effectively. The percentage THDs measured by EDLIFFT with a 4MSW using an NI-cRIO 9082 data acquisition system described in Figure 4 are tabulated in Table 2.


**Table 2.** Percentage THD for all 15 load patterns in real time.

Our experiment was conducted and established that percentage THD uniquely identifies all combinations of loads. The standard deviations of the percentage THD values indicate that they are all uniquely different. Hence, they are used in the primary key of a lookup table to discern the loads in operation based on the percentage THD. A load power versus percentage THD plot is depicted in Figure 9, where the percentage THDs are relatively different for the different load powers. The actionable recommendations are based on the possible shifting of load demand from the red region to the yellow region and from the yellow region to the green region for a better response in order to reduce harmonic pollution to the extent possible in the load consumption scenarios.

**Figure 9.** Load power versus percentage THD of the demand.

The recommendation chart for DR management in Table 3 establishes the opportunity to reduce percentage THD within premises. The consumer benefits from the increased life of his appliances and utilities benefit from the increased life of their equipment in the distribution system.



*Energies* **2020** , *13*, 4628

1 NR = no

recommendation.

The change in power consumption and the change in percentage THD columns show these direct benefits by responding positively to the proposed recommendations, as appropriate.

C. Nalmpantis et al. [15] proposed qualitative and quantitative metrics for NILM, and the authors of this study applied the same metrics, which are presented in Table 4 below. The quantitative metrics clearly demonstrate the effectiveness of the proposed experimental approach over non-deterministic NILM methods.


**Table 4.** Quantitative metrics for NILI–percentage THD.

<sup>1</sup> TP = true positive; <sup>2</sup> FP = false positive; <sup>3</sup> TN = true; <sup>4</sup> FN = false negative.
