an analysis and approach to prediction of poor power

An Analysis and Approach to Prediction of Poor Power Quality in

Electrical Drives – A Machine Learning Case Study

*Vishnu Murthy K1, Ashok Kumar L 2(IEEE Member) 1 Research Scholar, Department of Electrical and Electronics Engineering, PSG College

of Technology, Coimbatore

2 Professor, Department of Electrical and Electronics Engineering, PSG College of

Technology, Coimbatore

*[email protected]

Abstract

In recent times, electrical drives are the prime source of movers in any industrial sector.

Since the industrial population is keep on increasing, quality power at the input of the

same is not easily available. Since due to non-linearity in the load section, higher usage

of power converters makes the power quality a matter of deep concern. To this scenario,

the electrical drives performance also come into spotlight. Even though lot of study

portraits the key issues with regard to the same, almost every research highlights the

drive output performance and load side changes due to these poor qualities of power.

Also, even after most advanced technologies enhanced in operating drive, these

vulnerabilities play a key role in their performance and oncourse their failure too. In this

research article, the above said key issues were taken up for the case study and based on

the analysis of the same, artificial intelligence method of prediction learning is done.

Upon having the machine learning as a tool and using the case study data of electrical

drives for training the algorithm and validated with the real time data. The result shows

the exact prediction before the particular time frame for safer operation of electrical

drives thereby can minimize the failure rate of the drives as well as from future data

collection the possible enhancements too can take up by the manufacturing sector.

Keywords: Voltage Sag/Swell, Voltage Unbalance, Machine Learning, Inverter Drives,

Artificial Intelligence

1. Introduction

In this rapid changing industrial productivity procedures and demand in innovative

products, the prime mover for the same is keep in evolving at must rapid phase. In this

regard, most of the prime mover rating purely depends on type of load in which it is been

operated and accordingly the voltage range it has to be operated. Every industry in the

world which is been producing societal needs heavily depends on electricity. And if we

look at this electricity power deeply, it has much practical problems it associated with

due to many factors like nonlinear load, frequency change, harmonics, real and reactive

power compensation and so on. Inspite of these disturbances, industries using different

levels of voltages for their productivity. Mostly this voltage levels used will be

determined by the local governing body or government agency.

Upon having the standard voltage levels, various electrical drives manufacturers

around the globe make use of those same according to the local region standards. And

most of the drive manufacturers make use of the voltage ranging from 230 V to 690 V to

operate their load condition in low to medium enterprises industries [1]. Regardless of

the load condition in which the drives are installed, the good quality of power is must

and foremost in every instant of time. The good power quality means nominal voltage is

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the first and foremost thing to kick start any electrical drives operation. But during the

operating conditions of the majority electrical drives, will it experience the good quality

of power is a big question. Even though, drives manufacturers have their safety features

inbuilt with the system, in practical conditions it fails its operation because of various

other deteriorating factors associated with the voltage quality as per the IEEE and IEC

standards[2][3].

This research article clearly investigates the poor voltage quality at the input section of

any electrical drives, thereby fetch the voltage patterns in different time frames and

analyses the performance and possible prediction methodology using artificial

intelligence. Even though basic fuzzy and neural network algorithms are fundamental

prediction methods for various specific application, in this case study also can fit in the

said methods. But latest technological adoption makes the prediction schemes even

better [4][5]. Inspite of neural network and fuzzy logic systems implemented for safety

features and performance improvement of any system, it cannot predict by its own

learning algorithm as artificial intelligence do [6][7][8]. Also, the drives manufacturers

give at most protection from variation in load side conditions, though it experience

different environmental conditions and thereby either the system gets damage or failure

in severe cases. Since we are in Industry 4.0, artificial intelligence will be best suited to

implement along with the causes and effects of fault pattern recorded from the field test

data[9]. The various parameters affecting electrical drives performance will be cited in

the below section to understand the key learning parameters for prediction algorithm.

2. Review on Key Parameters in Poor Quality of Power

For any industrial problem investigation, upon considering good input quality power to the system, engineers proceed with other possible problems associated with. If the input voltage itself finds unsatisfactory note, the careful study into the same is very much essential in all aspects. This voltage issues can be categorized as poor quality is based on IEEE and IEC standards [2][3]. Also, voltage sad and their severity with regard to the industrial equipment like that of electrical drives, which lead to any cases of tripping, failure, normal operation of the same. Based on the duration of the voltage sag, severity in terms of their magnitudes will get determined with respect to the equipment’s behavior [2]. The voltage sag conditions also can be classified due to short circuit faults, transformer energizing, due to large induction motor starting, lighting faults, self-extinguishing faults, combination of starting heavy load and short circuit faults[10]. Temperature also one of the key parameters and as a byproduct of this poor voltage quality on VFD drives, thereby affecting the insulation parameters on electrical machines and so its reliability. So, careful selection of VFD plays a vital role in minimizing these effects with respect to the motor [11]. Voltage swell, sag, as well as distortion effects and their timely detection into the system using special methods called GOST which is based on root mean square value of the signal over the period of time. By using this method, the above said events may be minimizing the damage to the industry system. The detection methods are selected on account of their operating speed [12].

Due to these events in the power system sector, the behavioral study on induction motor with capacitor states, as the sag between 10% to 90% value over the duration of half cycle to less than a minute and the electrical machines are at serious risk. Even though the capacitor bank presence, it couldn’t meet out the transients arise due to the events occurring, also capacitor cannot overshoot the reactive power which need to compensate during the event. Voltage surge with higher reactive power scenarios which damages the capacitor itself [13]. The study of this effects on consumer electronics equipment either gives failure of the product, reduction of life span, or poor performance. When the level of signal falls below the standards as specified, it has to

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withstand the stress exerted on the product and the manufacturer has to take of those factors while designing [14]. As this study is fine to have with small rated domestic electronic products, but on large scale industrial environment, it becomes very tedious job to carry out. Also, when three phase system is been used for industry, it is highly impossible to have a identical voltage magnitude in all phases due to many technical reason and these uneven unbalanced voltages will definitely have a impact on the industrial motors too. As per NEMA standards, for every change in percentage of voltage levels in all three phases, there is a corresponding deteriorating performance efficiency in the motors too [15]. For this sag condition, compensation methods also used to nullify the sag effect, but up to about 50% only can be done [16]

In this article also [17], again the sag condition plays important role in their research

and it discuss about the energy losses due to the same. The nominal RMS deflection and

distortion forms a primary sag or swell condition and in turn this reflects in variation in

the electrical drive and the methods to solve these problems were discussed. It also gives

a solution to this issue by either DVR or limiting the voltage deflection at primary side or

limiting the current rise in the induction motor due to maximum torque reduction.

Harmonics induced due to usage of more and more power electronics devices cause

significant effects on the power system network. Even though manufacturers make

power circuitry with different topologies with utmost care in reduced harmonic

generation, still there is a prominent effect on the network as per IEC standards [18]. Due

to this harmonics, thermal stress gets increased at the prime mover section, thereby

leading to sparking at the motor shaft. Due to this current harmonic injection, motor

surface faces stringent heating effect and this led to sparking from motor shaft to ground

which will be drive common mode voltage (CMV). Key parameter to reduce this issue is

distortion in both voltage and current parameter, CMV and taking proper design factors

this can be marginally reduced to optimum operating conditions. Temperature and CMV

should be monitored throughout the operating of electrical drives to ensure both are

within the limits [19]. The key parameter which either the drive performance or prime

mover performance at the industrial environment is mainly due to this voltage sag and

swell condition. Voltage sag prevalent in the power system will generate numerous

troubles to performance of the electrical drives[20].

3. A Case Study on Unbalance Voltage Levels in Industrial Sector

The voltage as the prime factors for every possible problem in which any electrical

drives it associated in the industry. When having the test condition in various industries

using PQube meter for better understanding of the DNA of the industrial manufacturing.

To study the various kinds of voltages patterns which prevails in the industrial sector, the

above said meter is fixed at the input section of the electrical drives setup. From the

months of monitoring the same, it is identified that, in various industries, the input

voltage remains the main focus and if any poor quality of this parameter may result in

adverse effects and thereby further deteriorating effects in electrical drive system. When

looking at the voltage patterns in which it was recorded on the testing resembles a

nominal and optimum quality of power to the electrical drive system. But at certain

uncertain time of instant, there were few glitches in the power quality side which will be

discussed below with the graphical representation.

Over the month of observation, often the voltage values range between nominal value

of RMS 415 V, this can be represented in the below table with percentage of time the

various voltage patterns recorded for the duration of operation.

In the above said table, in almost entire operation of the drive system, the nominal

voltage levels prevail in the systems with respect to the voltage levels vs percentage of

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times. Also, the Line to Line voltage over normal operation without any fault event

triggered is given below figure 1 along with the log event table

Table 1. Line to Line Voltage Range during the Month of Observation on the input section of Electrical Drives

Channel

Percent

of

Time

Between

L-L

RMS

50% 408.0V - 416.8V

95% 403.2V - 422.4V

99% 401.6V - 423.2V

99.50% 401.6V - 423.2V

Table 2. Line to Line Voltage Events

Channel Min Avg Max

L-L

RMS

RMS (10-cyc) 394.0V 412.4V 423.9V

RMS 1/2 (1-cyc) 372.4V --- 424.1V

L1-L2 RMS 1/2 (1-cyc) 373.1V 412.7V 424.1V

L2-L3 RMS 1/2 (1-cyc) 372.4V 412.4V 423.5V

L3-L1 RMS 1/2 (1-cyc) 373.0V 412.2V 423.6V

Figure 1. Nominal Voltage Range during the Drive Operation

Similarly, the Flicker patterns with respect to power instantaneous values and voltage

THD is shown below figure 2 during the same instant where triggering event not took

place.

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Figure 2. Nominal THD voltage and Power Flickering Effect during not Triggering Event Phase

As against this graph shown above without any event trigger, the below said graphical

entries depicts the fault event trigger effect in the input of the electrical drive system.

The below figure shows there is the fault event trigger flagged between 18.00 pm to

20.00 pm. During the same period of time, the effect on Flickers and voltage THD

graphical entries are also shown below for the understanding.

Figure 3. Triggering Event Occurring Phase during the operation of electrical drives (Line to Line Voltage graph

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Figure 4. Triggering Event Occurring Phase during the operation of electrical drives (THD voltage and Power Flickers)

Figure 5. Three Phase Harmonic Content during Event Trigger Phase

The above figure 5 shows the H1 harmonics content with regard to voltage is 240 V as

against the remaining harmonic contents. Also, event for under frequency is also been

recorded on the same day resulting for 131.25 second and the graphical representation

for the same is given below for the references.

Table 3. Under-Frequency vs Other Parameter Impact on the Drive Operation.

Channel Min Max Min

During Event Only

Max

During Event Only

L1-L2 406.0V 409.6V 406.0V 409.6V

L2-L3 406.9V 410.3V 406.9V 410.3V

L3-L1 406.0V 409.6V 406.0V 409.6V

L1 Amp 67.3A 72.8A 67.3A 72.0A

L2 Amp 70.3A 76.0A 70.3A 75.5A

L3 Amp 72.5A 81.9A 72.5A 81.9A

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E Amp 8.2A 11.4A 8.4A 11.4A

Frequency 49.748Hz 49.856Hz 49.748Hz 49.852Hz

Power 46.2kW 49.6kW 46.2kW 49.4kW

Figure 6. THD patterns recorded during the Operation of Drives

Figure 7. Under Frequency Event for definite time period during drive operation

The input voltage waveforms correspond to the under-frequency period is shown

below in figure 7. The below waveform shows there is no significance waveform

distortion takes place during the said event. Also, the similar waveform during the

harmonic content peak also resembles the same type of voltage waveform from the

figure 8.

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Figure 8. Voltage Waveform during Under-frequency Phase

Figure 9. Voltage Waveform during high THD harmonic Phase

Similarly, the further period of studying the test data, it reveals that suddenly at any

particular instant of time, there occurs a definite event triggering takes place and

consequently the corresponding waveforms and voltage values gets disturbed in a very

small-time frame. This can be understood based on the voltage cycles which is been

recorded via a testing device to know how much amount of quality power been recorded

for the entire duration of 24 hours’ time frame. In this figure 9, the voltage for

continuous 10 cycles are been taken as single count of data capture. So, it is almost

evident that, voltage has not been continuously for the underanged or below nominal

value, but definitely for less than 10 cycles it prevails often.

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Figure 10. Voltage Range Count for 10 Continuous Cycles of Drive Operation.

So, after having this conclusion, still deeper version of data capture has been carried

out and from that field study it is finally come to know that, there is a voltage of any one

particular phase line to neutral rms voltage is goes below the nominal value (i.e., almost

zero or underanged values for fractional period of time say in 0 to 10 seconds. This can

understand from the below figure. based on this figure, it is understood that, voltage of

any one phase is getting weaker for sudden interval of time and again it is restoring to the

nominal value of the voltage standards. So due to this interruption of any one phase in

the electrical drive system, the percentage or rate of failure of the same in kept

increasing.

When this fault event occurring phase alone be concentrated, for explanation purpose,

another week of event data is given in the figure below. form this figure it is understood

that, any one phase is experiencing the extreme voltage sag condition, there by one phase

voltage levels reaches up to almost zero. Due to this occurrence, the inverter drives

which is been connected to the load, is experiencing the high level of current drawn in to

the system, there by some damages to the inverter possible in severe conditions the

failure of the said is occurred. Since this event is occurred with purely uncertain patterns,

the solution to this problem may be achieved through eh artificial intelligence methods.

Another important behavioral pattern it recorded was, there is no any abnormality or

deteriorating factor in the other parameter of the electrical drive system like current,

THD, CO2 emission etc., Of course there are some poor quality of the later said data

recorded, but that doesn’t look promising for the failure of the data when voltage tripping

in one phase alone be analyzed. The parameters which earlier said in figure 7 , 8 are just

recorded only at some point of time and this aids the quality of the problems which the

voltage interruptions do. In order to have checks and balances to these scenarios, an

artificial intelligence may be the right solution, also it can give a futuristic prediction

based on the recorded events in the past.

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4. Inverter Drives Predictive Machine Algorithm (IDPMA)

Since the voltage sag condition prevails in the power system and thereby to the

electrical drives, and upon taking the field test data as in the figure is used for training

into the machine learning model for predictive operation. Once the training of the data is

been done, it is been given a live data from the cloud-based server to predict the said

process and the result were discussed below for better understanding. From the test result

of machine learning algorithm, it has been found that, whenever the fault inducing event

occurs into the power system and in the electrical drives system, the algorithm can able

to detect how frequent the voltage sag/swell occurs in different levels of magnitude as

shown in the figure 10.

Figure 11. Fault Occurrence Probability during the Machine Learning Prediction Algorithm

From the above figure it is clear that, the nominal value of voltage in terms of RMS

value is dominant, but below the threshold value too, events occurred and their

percentage of occurrence is given for future prediction process. In addition to this the

prediction patterns are also done using the same as shown in the figure 11.

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Figure 12. Prediction Result of Fault Event Triggering of IDPMA

In the above figure, the steep dip in the waveform depicts the fault event occurrence

exactly matches with the field test data. As per the recorded events, the machine learning

algorithm finds the exact prediction pattern as against the recorded date. Since this

algorithm nearest neighbors can detect the fault event exactly by the occurred sequence.

Also, when the fault triggering event occurs only other noticeable changes in the drives

system as well as in the power system network is the normal with regard to frequency,

THD and the current value as shown in the below figure 12.

Figure 13. Comparative Parametric Analysis during the Prediction of IDPMA

Figure 14. Fault Prediction Phase vs Frequency Behavior in IDPMA

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Figure 15. Fault Prediction Phase vs THD Voltage Behavior in IDPMA

Figure 16. Fault Prediction Phase vs Current Behavior in IDPMA

Also, when other machine learning algorithms were also taken for prediction testing,

based on the test result, comparative graph of the four different algorithms were given in

the below figure for better understanding. As compared to nearest neighbors, random

forest and adaboost machine learning algorithm were almost nearly predicting like that

of former, but it is taking the pre and post dataset which is allied during the fault

triggering event. So, Compared to the former, the two latter prediction algorithm fetches

below par accuracy with regard to Nearest Neighbors algorithm. But when we look at the

Naïve Beyes algorithm, it looks completely different in their prediction pattern as it is

shown in the figure 16. So, naïve beyes technique mostly won’t fit into the prediction

schemes, as it included the very minor changes in the voltage levels. Since the voltage

levels is not a constant value, and reasonable changes are permitted this algorithm can’t

give a satisfactory result.

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Figure 17. Comparative Analysis of Different Machine Learning Algorithm in IDPMA

Considering these four algorithms, which will be suited to field data, Nearest

Neighbors find a suitable algorithm for the prediction of the fault event occurring in the

power system network as well as electrical drives system. Also, with the help of these

algorithm result, it can be easily identifying the pattern on future fault event and in turn

makes the manufactures to improve the design constraints to withstand to these

vulnerable faulty issues.

Figure 18. Live Fault Occurrence Prediction of Different Machine Learning Algorithm in IDPMA

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By incorporating the live feeds from the inverter drive section to the trained

algorithm, the following prediction pattern is inferred as shown in figure 17. From the

prediction patterns it clearly indicates the fault scenarios as and when it experienced in

the drive section shows that the machine learning algorithm is predicting the faulty

section and delivering to the dashboard in IoT. Also, since the fault tracking of the

machine learning algorithm is trained with uncertainty data set, their fault prediction also

will be of same as above, in that case fault prediction will be perfectly fine, but the time

of prediction may be slightly in delayed sequence. So, to improve the accurate and fast

response to this uncertainty of data to the drive section, the modified training algorithm

is to be developed and incorporated in the above algorithm in future work.

5. Conclusion

From the various patterns of simulated prediction algorithm for IDPMA through

machine learning, the case study of the power grid input to the electrical drives section is

analysed. Of the many algorithms, these four forms better prediction schemes due to the

type and pattern of test data set obtained through the case study, each algorithm gives its

prediction pattern satisfactorily. Out of four algorithms testes, Nearest neighbors suits to

have the satisfactory result compared with other three and Adaboost and Random forest

algorithms having a slight below par performance as the pre and post event sequence

also been considered by the latter two methods. Also, if we examine even deep into this

prediction schemes, still a further separate subroutine may be given for this prediction

schemes for the enhanced and better result in this regard for the drive safety aspects. This

separate routine analysis and testing will be performed in future research article as an

extended version of this research. As of now, this prediction will give a operating

personal to have a some brief time to which he can perform the safety precautions to the

drives thereby end customer equipment is obtained.

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an analysis and approach to prediction of poor power

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