Optimization of harmonics with active power filter based on ADALINE neural network

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Optimization of Harmonics with Active Power Filter Based On ADALINE Neural Network M. Sujith , S. Padma PII: DOI: Reference:

S0141-9331(19)30469-7 https://doi.org/10.1016/j.micpro.2019.102976 MICPRO 102976

To appear in:

Microprocessors and Microsystems

Received date: Revised date: Accepted date:

23 September 2019 20 December 2019 26 December 2019

Please cite this article as: M. Sujith , S. Padma , Optimization of Harmonics with Active Power Filter Based On ADALINE Neural Network, Microprocessors and Microsystems (2019), doi: https://doi.org/10.1016/j.micpro.2019.102976

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Optimization of Harmonics with Active Power Filter Based On ADALINE Neural Network M.Sujith* S.Padma** *

Associate Professor, IFET College of Engineering, Villupuram, [email protected] **Professor, Sona College of Technology, Salem [email protected]

Abstract: Mostly the power quality issues in the distribution line system happen due to the presence of harmonics. Especially, the nonlinear loads such as power electronic converters, high-speed semiconducting switches, and solid state drives were the major causes for harmonics in distorted power system signals. Moreover, the estimation of magnitude and the phase of this harmful harmonic interference are necessary. By taking in to consideration of all the above factors, this paper develops an efficient technique for harmonic estimation and detection of the renewable wind energy resources and elimination of these harmonics will also be done accordingly for getting desired output from wind energy. 8 bit inputs (4+4) are collected and used to generate the intended input set for ANN training. The proposed work develops an Adaptive Linear Neural Network (ADALINE) for the estimation of harmonics which is the novelty of this work. For making the harmonics content more negligible and to enhance the load power quality, an Active Power Filter (APF) is used. The novel control design is developed with a Pulse Width Modulation (PWM) control. In addition, feed forward networks (trained by back propagation algorithm) works like a hysteresis band comparator. An APF control design is developed with ADALINE network in which the load and current along with voltage will be analyzed and then the controller will be calculating the control signal by considering the reference compensation current. Afterwards, the power system is injected with compensating current. The simulation is carried out with Matlab- Simulink to analyze the proposed control designs efficiency. The proposed work consummation is compared with conventional PI controller method comprising Shunt Active Power Filters (SAPF) with ADALINE for the performance perspectives. This method was found to be effective in terms of many parameters such as load voltage, load current, voltage, reactive power, real power and especially THD value than those of the existing works which are considered. Keywords: Harmonics estimation, wind energy, neural network, non-linear load, Active Power Filter, and Adaptive linear neuron. 1. INTRODUCTION The increased power electronic components caused distortion in the transmission line and made the load nonlinear. This caused voltage flicker, variation, and imbalance in threephase lines received from wind energy. This resulted in power quality issues and harmonics in these signals. The presented harmonics will flow in the electrical network which affects the process of the component, or else it resulted in damage. Thus, the calculation of system frequency and the variation rates help in monitoring, protection, and control of electrical power system equipment [1, 2]. Especially the estimation of magnitude and phase of harmful harmonics inference was necessary for analysis and design of the equipment. In this situation, a progressive technique of signal processing was essential for accurate harmonics estimation parameters. However maintenance of this accuracy remained as a tough task in the power system. Popular techniques for estimation of harmonics were Fast Fourier transformation (FFT) of these signals. But applying FFT, the phenomena of leakage, picket-fence, and aliasing effect reduced the accuracy. To overcome these drawbacks, the Recursive Least Square (RLS) and least square (LS) algorithms have been

commonly applied in this scenario which would be effective in frequency estimation. The Artificial Neural Network had the ability to deliver an enhanced methodology for deriving nonlinear models which provided more advantages over other conventional techniques. But the data availability was a crucial issue for ANN in real time applications. Based on these network conditions, phase angle, power factor, and the inner harmonics, current and voltage signals would vary. To overcome all these challenges & drawbacks, an ADALINE structure for harmonics estimation of wind energy is developed. In the ADALINE network, generally, weights are updated online by using LMS (Least Mean Square), RLS and KF (Kalman Filter) recursive algorithm. The output of the ADALINE will be compared with harmonic current and then it creates a modulating signal for generating the PWM pulse to the filter. Harmonics reduction is the important process in power system. In existing works, a passive filter was deployed to decrease the harmonics distortion at the distribution line. But due to its large structure with less durability, it caused resonance with series impedance. The recent trends have introduced an APF to improve power quality which carries

the advantages over passive filter like smaller, cheaper, more versatile, and less prone to failure. The basic principle of APF was to generate the current component that would be removing the harmonics current produced. In traditional methods, the control approach was based on frequency domain. But these frequency domains require large memory, computation power, and imprecise results under transient condition. To overcome all these limitations, time domain approach like d and q transformation, p and q transformation, symmetrical components transformation, are introduced.

Moreover, ADALINE based technique is utilized to extricate basic sinusoidal from a distorted load current waveform that makes it a simpler and faster method for current extraction. The main motive of this research work is to estimate and eliminate the harmonics in the power system. The main subject of the research work is optimizing the harmonics based on ADALINE network. The harmonics are optimized by controlling the entire design using active power control. The proposed work contains an APF which is connected with the distribution system for harmonics reduction and ADALINE, a version of ANN is introduced as a new harmonic detection technique. The active power control depends on neural network techniques. Initially, the current signals from the distribution system is fed to the ADALINE, based on that the PWM generates the switching signal to the active filter. The versatile neural network calculates the fundamental and harmonic components from non-linear load current signal. Then the active filter output current will be fed to the distribution line. In between that a hysteresis current, controller will gather the signals, inject the compensating signal to make the line current as sinusoidal. The major contribution of this research work is:  To estimate the magnitude and phase of the harmonics with the method of FFT-Fast Fourier Transform using ADALINE for renewable energy resources.  To propose a new control technique by using an Active power filter for harmonics reduction. Other sections of this paper are organized as follows: section 2 presents recent techniques related to harmonics estimation and reduction. Section 3 provides the design procedure and t mathematical equation of the proposed work. Section 4 comprises performance analysis and its comparative results with the proposed techniques are related. The conclusion is provided in section 5. 2. LITERATURE REVIEW In this section, the different harmonic detection methods with harmonics compensators are discussed.

Harmonics estimation [3] asserted the harmonics estimation based on trained ANN with a time-dependent power supply signals. This work contributes better estimation accuracy by ESPRIT Estimation of Signal Parameters via Rotational Invariance Technique which helps ANN to update these parameters continuously based on input signal variation which delivered more accuracy and reliability of harmonics amplitudes. This method provided a practical estimation for every half cycle. This ESPRIT was good in accuracy but it lags with computational time. [4] presented a Fast Transverse Recursive Least Square (FT-RLS) which was used for harmonics estimation. This algorithm helped in calculation of amplitude, frequency, and phase in time-varying power signals. This approach was mainly developed for accuracy and fast estimation of harmonics signals under a power system frequency. Initially, the choice of the covariance matrix for the input signals was considered to be more critical. Moreover, the computation time and estimation error rate would be more under an improper choice of a covariance matrix. Although, the ANN-based estimators were very fast as compared with other earlier techniques.[5]reviewed the application of neural networks in wind energy and its applications were categorized into different topics. Based on the survey, the performance prediction of the wind energy system can be effectively done using ANN. As far as the wind availability in India is concerned, India holds the record of being the largest wind power capacity in the world right after other giants like China, U.S and Germany [6]. We all know that wind is one of the cleanest renewable resources available globally. Wind power has also captured the global researcher’s attention due to its latest developments. Hence, future research on neural networks is also discussed in this work like wind energy optimization. Perfect data selection was required for the ANN set up. The review analyzed that the ANN approach had performed better than the conventional approach.[7]worked with a single-phase standalone wind energy translation system that utilized a two winding Self-Excited Induction Generator (SEIG). The controller utilized in this work consists of an Intelligent Neural Network-Based Control (INNBC) algorithm which controls a Battery Energy Storage System (BESS) and two leg Voltage Source Converter (VSC) is contained in it. An excellent dynamic and steady-state response of the system was achieved by the degree of load current that was modified using the proposed algorithm. Constant voltage and frequency were maintained in all types of load conditions which were proved by the experimental and simulation test respectively. [8] came up with a new strategy to estimate harmonics distortion based on ADALINE network in which the decomposition depended on the LMS training algorithm and the Fourier series analysis of current signals. The output of ADALINE was checked with the line current to generate the modulating signal from PWM for active line conditioner. From this work, the switching power supply was improved and an Insulated Gate Bipolar Transistor (IGBT) locked up

problem was solved. Moreover, ZVS-PWM - Zero Voltage Switching - Pulse Width Modulation had better efficiency. [9] presented a Bilinear Recursive Least Square (BRLS) for estimating the frequency, phases, and amplitudes based on time variance of power signals. The stationary harmonics signal and the dynamic harmonics signals were analyzed in this paper. To prove the performance a variable frequency drive panel was used to control the induction motor in a large paper industry. [10]uses fractional order repetitive control method under a fixed sampling rate, which will handle the variable frequency of all periodic signals. A Lagrange-interpolationbased Fractional Delay (FD) filter was utilized for certain fractional delay items. This technique offers a fast online tuning of fractional delay. This work provides a fast update of the coefficient. Harmonics elimination [11] presented a harmonic detection method based on Artificial Neural Network (ANN) with Hybrid Active Power Filter (HAPF) for reducing the issues in enabling the design of current controller. This method and design is utilized in ADALINE network. By this approach, the harmonic current can be satisfied based on neural network utilized. But the conventional Pi controller based P-Q theory regulated the capacitor DC (Direct Current) voltage of APF. When this was compared with the ANN, the PI controller incorporated with P-Q theory had low settling time and rising time. [12] analyzed APF that suppressed the harmonics current in the distributed network. Two different control strategies were provided namely, independent current control strategy and harmonics elimination. In this p and q methods control each phase individually. The main objective of this proposed work was to develop an algorithm based on Synchronous Reference Frame (SRF) based controller. From this technique, it was possible to control each phase of the 3 PH (Phase) 4 wire method. But in this work, negative fundamental and zero sequence compensation were not considered. [13] presented a comparative study on involved harmonics characteristics using reference generation methodologies with shut power filter which was active. This work considered instantaneous power theories (p and q theories) and ADALINE network control techniques based on Least Mean Square - Equalizer (LMSE) and Recursive Inverse Algorithm (RIV) were taken and considered for their comparison. The harmonics estimation was calculated based on the online mode and individual mode so that APF could realize compensation. The author had concluded that a neural network could effectively estimate the harmonics individually. The paper had also concluded that implementation of ADALINE was simple enough for practical applications considered by them. [14] proposed a reference compensation current extraction scheme for APF. The main motive was to minimize the harmonics level of 5 % at the point of common coupling. The PI controller effectively minimized the DC link voltage regulation and these gains were optimized by Particle Swarm Optimization

(PSO) in a more enhanced manner. In this, the APF contained the voltage source, inverter, pulse width modulation technique. [15] proposed a Model Reference Adaptive Sliding Mode Control (MRASMC) using a Radical Basic Function (RBF) for controlling the single-phase ACF. This RBF with NN helped to estimate the nonlinear functions to rectify modeling error at the APF system. The main advantage of this problem was that the asymptotic stability and the line of the system had the ability to adjust the weight of the neural network. Thus the tracking performance was improved by the sliding mode controller at the DC side. [16] proposed derivate loss controller based on a nonlinear load, which helped to control the SAPF. In this work, some base frames were introduced by replacing it with stationary frame. Both the current dynamic inner loop and the voltage dynamic outer loop were utilized in this work. But when compared with SAPF, Shunt Hybrid Power Filter (SHPF) minimized the active parts power rating to the better part. So, this SAPF was not up to mark for meeting the day-to-day requirements. [17] presented a comparative analysis on the weight updating adaptive algorithm based on fuzzy logic based variable step size, Least Mean Square - LMS and LMS based ADALINE for current harmonics detection. These techniques were compared with Distribution Static Synchronous Compensator (DSTATCOM). [18] proposed an enhanced self-charging algorithm by including a step size error cancellation in SAPF. The step size cancellation had some additional features to the self-charging algorithm with dynamic and steady operations. [19] reviewed a problem based on reference signal generation for SAPF to eliminate Total Harmonics Distortion (THD) in the distribution line. The problems related to the adaptive control methods like steady-state error, speed, and stability were addressed in this paper. From the results, this paper had delivered the control method SAPF with ADALINE that performs well in dynamic conditions. This technique provided satisfying results in harmonics reduction and reactive power compensation, but some systems were subjected to errors and time-consuming processes. 3. PROPOSED WORK In this section, different methods involved in the proposed work will be explained. First of all, 8 bit sources (4+4) are gathered and utilized for generating the desired input set for ANN training. In this proposed method, as a initiating step, estimation of the harmonics’ magnitude and its respective phase is proceeded by using the method of FFT-Fast Fourier Transform in our novel ADALINE for the energy resources which are renewable. Then, the harmonics reduction is facilitated by the usage of a new control methodology deploying the Active power filter. This work had made use of only one harmonics patterned spectra. The general concept of this work is to connect an APF to the distribution system, in order to minimize the harmonics of the wind energy and estimate the harmonics present in it. In this proposed work, an ADALINE based network is also included

for accurately estimating the harmonics which will be providing perfect compensation to the power lines. The combination of using the APF with the ADALINE makes this proposed method a novel one. As a result, the power received from the wind energy is getting converted into three-phase signals with the aid of Permanent Magnet Synchronous Generator (PMSG). In the above process, the signal of the line current is obtained from the distribution system which is in turn passed on to ADALINE. All the requisites are shown in the figure 1 with the help of a frame work. In this framework, wind acts as the driving force for harmonics

elimination which is going to be executed after appropriate estimation of the same.

Main parts such as PMSG, PWM generator, a band comparator, active power filter, ADALINE based network and DC regulator will be working together for improving the some such as current, voltage, etc. if any towards achieving the productive harmonics elimination

i

DPMSG

Machine Side Converter (MSC)

Pulse

Load Side Converter (LSC)

Non Linear Loads

Pulse Adaline Network

PWM Generator

PWM Generator

Hysterias Band Comparator

Hysterias Band Comparator

Filter Output

Filter Output

Load Voltage

v DC Regulator

Fig. 1: Overall frame work of the proposed work

Reference Current

This framework will use the fundamental components from line current signals based on Fourier series. This kind of new current signal disintegration will aid in defining the input for the neural network that is being used here. The weight training will be carried out by LMS algorithm. Then these fundamental components outputs are compared with distorted line current. This kind of process will be helping in generating the modulating signal. This modulation switching pattern is deployed for producing the PWM switching pattern for the power switch and then, the active filters output current is injected into the power line. The hysteresis current controller will be helping to support the harmonics detection process by comparing the DC side voltage control and current signals. The ANN uses the fundamental and the harmonic components from the non-linear load current signal for further calculation. For regulation of voltage (DC) of inverter constant involved, the outputs of the proportional and integral controller are used. Afterwards, comparisons are carried out using the output value to generate the output compensating current is produced by the inverter. Thus, the differences between these above values are given as an input to hysteresis band comparator. At the same time, hysteresis current controller will be gathering these signals and it injects the compensating signal to make the line current as sinusoidal. A. Active power filter with neural network The involved nonlinear load will be creating many power and characteristic issues like voltage flicker, harmonics, and voltage sag, swell. For reducing the harmonics in the distribution line, a filter will be added for compensation. In this proposed work, an APF will be generating the compensation current. The main motive is to obtain a source current without any harmonics. The perfect compensation current is injected by the APF will enable to resemble the load current as a non-active component. This proposed filter is developed with inverter circuit on the DC side. In this paper, an active or a dynamic power filter consisting a hysteresis band comparator is added to eliminate the harmonics in the nonlinear loads. Most importantly, the major aim of deploying the controller is to maintain the compensation current. In this process, the 3ph inverters’ switching strategy will keep the current under hysteresis band. Meanwhile, the load current will be calculated and then it will be analyzed with non-active components and reference currents.

Fig. 2: An active power filter Block diagram

The design of the proposed APF control technique by an ADALINE is depicted in fig 2. in which the load voltage and current were sensed by the controller and it measured the trigger signals of IGBT power circuits from the reference current. Injection of the compensating current on to the distribution system will be done by using utilized filter. This work contains different control blocks in which one of the blocks is an ADALINE network-based control block which helps online estimation and a back propagation network. As shown in fig.3, once the training process is completed by training it in a neural network, the comparator will be making a comparison with the reference waveforms and actual compensation current. Then, the controls of the switching logic for the transistors are analyzed by the compensation currents that are flowing all through. Based on all these happenings, the proposed control will deliver with excellent filtering dynamic response. In addition, for any kind of load current, this compensation current can be adapted quickly. Figure 3 shows the working flow of the algorithm that is preceded here in this work. B. Control of compensation current In the feed-forward neural network control principle, the neural network contains many strongly connected elements. Thus, the input data of i(l), i(2), i(3), ..., i(n) will go through weights which will be collected in a node and get characterized as a circle. Before their addition, these weights will be modifying the input signals in either way by magnifying or devitalizing. If the process is completed, then the data goes through the output through the transfer function. The neural architecture depends on three layers: the input layer, hidden layer, and an output layer. Thus, the feedback architecture computes the input data in a parallel way rather than computer sequential algorithm. To supply the output target, the network can be trained based on the appropriate input. The back propagation algorithm is most widely utilized in this scenario. Under this methodology, the random value will be assigned initially and afterwards, the current output will be compared with the initial output pattern. Until the error rate is minimized, the utilized algorithm will keep on adjusting the weights.

Fig. 4: Artificial neuron model

Fig. 3. Flow of algorithm Firstly, input-output data is obtained from the electric power generated by using the wind as the medium which is then fed to in-built layered ANN for computing the errors. Then, the decision is made after computing the errors in total after which decision will be required whether to update the weights or not. After this decision making operation, if there is no error, the weights would be updated accordingly. If there is error, again the training will be initiated for re-computing the errors encountered until now. Finally, the testing will be done and then the algorithm ends.

Typical construction of an artificial neural network is depicted in the figure 4. This ANN network is utilized for the training the dataset efficiently because of its in-built layers for smooth conduct of the further processes. Generally, there are three layers presented in the ANN such as the Input layer, Hidden layer, and an output Layer which had been discussed already. The i(1) to i(n) is given as input data on to the ANN. The weight value of this value is reckoned in the layer which is hidden and it gets summed up. Then, the weight value is altered based on the learning rate and the partial derivatives of Loss Function. After the transfer function, the output layer will give rise to output. Below figure 5 shows the Simulation pictorial of the devised work which is being utilized for our proceedings. This simulation comprises the generator and its subsidiary components such as controllers, pilot operated open and closure doors, 3 phase lines to make use of the wind power to give rise to voltage, current, etc. for the purpose of the harmonics elimination via the simulation. Below figure 6 illustrates the design of the Generator that is being deployed in our work which makes use of the wind energy to produce the required power. In this work, we are just using the generator for the purpose of testing the outputs raised form it for the efficient elimination of the harmonics in the wind. In later sections, we will be discussing the various components along with its function as and when required.

Fig. 5: Simulation pictorial of the devised work

Fig. 6: Generator Design Thus, it will be able to increase the efficiency by minimizing the error as a result of all the above procedures that had been followed.

C. Adaptive linear neural network The Fourier analysis will expand the periodic waveform by the sum of sine and cosine frequency components. In this process, Gradient descent- an iterative mitigation technique is being added for the betterment. The route of the steepest ascent of the error function will be always indicated with the error functions’ gradient. Initially, it will be starting with a random weight vector and then follows the negative gradient as shown below, (1) Efficiency (

( )

) at

(2)

It helps in repeating the gradient descent procedure for number of random initial states because of the local minimum, which is as follows: ( for 0 ≤ α≥ 1

) *efficiency (

( )

) at (3)

The neural network will process with its input layer, an output layer, and hidden layer. The weight training process will be carried out by the hidden layer. Consider a two layer neural network, namely an output layer and hidden layer, we get ( )

∑

( )

(

( ) )

( )

(4)

( )

(5)

( ) ( ) as the weight matrix of the hidden With layer and the jth row contains the weights of neuron, ( )( ( ))

/

( )

( )( ( ))

(6)

( )

Z=(

( ) ( )

( ( (

) )) )

(7)

([ ( )( ( ))])

( )

( )

(8)

The output layer is given as, ∑

( )

( )

( )

( ) a (2) = ( ( ) ) ( ) ( )(

( )

o=(

( ) ( )

( )

( )(

( ( (

( ))

(12)

) )) )

= - /2 = /2

(20) (21)

Maintain the same rate, where m = a, b, c three phases, I is the load current, and the Vs is the dc link voltage of the inverter. Even then, the frequency in the PWM method will not be constant so that the output will be giving rise to nonoptimum harmonics. Compute Peak detector *( ) Let, source

(

(

)=

(

=( )

)*π/2) (22) // maximum voltage (23)

Let

=

Let

=

Let

=

Let

=

(

( (

)

(

)

(

)

))

⁄

(24) (25)

(13) ( ))])

( )

(14)

The network has d inputs, m hidden neurons and n output neurons, therefore the transfer function is given by, ( ) (∑

> , < ,

(11)

([ ( )(

( )

(10)

( ))

( )

/

(9)

If If

( ) ( )(∑

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(26)

⁄

(27)

(15) =(

In the matrix form, we get O = h (2) (w (2) h (1) (w (1) x)

⁄

)+(

)+(

)

(28)

(16)

Thereby determining the output “o” function of the input x is also represented as forward sweep in the back propagation algorithm. D. Hysteresis band controller The feed-forward neural network works as a hysteresis band comparator under the PWM control. This network is developed with 2 hidden layers along with 14 neurons and one output layer along with one neuron. The activation functions are linear in the layer of the output and log sigmoid in the hidden layer. The patterns are trained by the back propagation algorithm. The output of the comparator will be based on their evolution and inputs respectively. The network weight issues can be fixed under these happenings. Thus, there is a need for the comparison of the network output with the real electrical system output. After which, the hysteresis band comparator will be delivering the output pulses to the inverter, The algorithm for this controller is, ( ) Upper band Lower band

( )

( ) ( ) ( )

Where, = hysteresis band limit,

(17) (18) (19)

Fig. 7: Hysteresis current controller design In these processes, the source voltage and current are considered as the input. In order to find the peak detector value, the least mean square of all the three-phase voltage is calculated and to measure the maximum voltage source, the gain value will be multiplied with the voltage source. The current value will be calculated by raising the peak detector value along with the individual voltage values. Finally, the current in all three phases will be calculated by adding and subtracting the individual phase current values. Based on these values, the signal will be generated accordingly. Figure 7 & 8 illustrates the design of the hysteresis current controller deployed in this work and the pulse generated

Amplitude

during the hysteresis operation respectively towards the effort of the harmonics elimination in this work.

Fig. 10: Time window when current was active 4. PERFORMANCE ANALYSIS The performance or fulfillment of the proposed system with different parameters like voltage, current, load, and total distortion occurring in terms of harmonics were analyzed in this section. Moreover, to show the improvement of the outcome of the proceeded work, a comparison is made with the proposed work and the existing PI controller.

Time (sec) Fig. 8: Hysteresis controller pulse Any Harmonic estimation will be always subjected to the time windows [20] for lessening the severity caused due to the leakage of spectra which might be caused or being at the verge of the leakage scenario. Likewise, for the harmonic estimation, the general equation for time window for the FFT is given by, ( )

∫ ( ( )

(

)

)

(29)

Where, f (t) be the signal which is band limited necessarily, FFT ( ) be the entity defined by the well sampled information, e is indicating the exponential operation, and k is just a constant that is being introduced for better correlation.

Table 1. Simulation specifications Parameters Input voltage Output power Wind Speed Inductor DC Capacitor Load resistance Nominal frequency

Values 440 V 2482 watts 20 m/s 1.2e-3mH f 10ohms 50HZ

The designated data resolution [20] for FFT can be found by using the below devised relation as follow: ΔS =

( )

(30)

Fig. 9: Time window when voltage was active Fig. 11: THD value for the source voltage waveforms by FFT The above Fig. 11 depicts the THD value for the corresponding source voltage waveforms that is being used throughout the performance analysis of our work. This value indicates the various inputs utilized and standards available in

to realize the various inputs utilized and standards available in the exported place of the FFT inbuilt which had been taken from the scope and arrays that had been generated earlier.

Current

Voltage

the exported place of the FFT inbuilt which had been taken from the scope and arrays that had been generated earlier.

Time (sec)

Fig. 12: Three phase waveform for source voltage Fig. 12 represents the source voltage waveform of the proposed circuit in our work, in which a three-phase source is taken as an input in order to get a valid output. From the waveform, it is clear that the voltage sources are more stable than being unstable in the worst cases.

Time (sec)

Fig. 15 (a): Three phase waveform for load current Fig. 15 (a) represents the three phases of the current waveform at the load side which is depicted above. As we already know that, due to the power electronics components the distribution line will be more disturbed. The graph also resembles in the same way which depicts that the nonlinear load creates distortion in all these three phase lines.

Fig. 13: Three phase waveform for source current Fig. 13 represents the source current waveform of the proposed circuit in our work, in which a three-phase source is taken as an input.

Fig. 15 (b): THD value for the load current waveforms by FFT Fig. 15 (b) depicts the THD value for the corresponding load current waveforms that is being used throughout the performance analysis of this proposed work. This value helps to realize the various inputs utilized and standards available in the exported place of the FFT inbuilt which had been taken from the scope and arrays that had been generated earlier. Fig. 14: THD value for the source current waveforms by FFT Fig. 14 depicts the THD value for the corresponding source current waveforms that is being used throughout the performance analysis of this proposed work. This value helps

A. ADALINE network performance

Fig. 18 depicts the fulfillment of the devised control technique. From the above graph, we can infer that all the testing, training, and validation values were above the best or benchmarked values.

Fig. 16: Training data output Fig. 16 illustrates the training data input, error output, plant output, and neural network output. Figure 17 represents the training, testing, validation, and total response of the data for neural network strategy. The R value represents the association between targets and its corresponding outputs. If the value of R equals to 1, then there will be a linear association between the targets and its corresponding outputs. The graph below in fig. 17 shows the output tracks are performing very well for training, testing, and validation.

Fig. 19 Number of Epochs Vs. training state parameters The performance or fulfillment of the training state with respect to a gradient, Mu, and validation are explained with help of the above Fig. 19. This shows how much iteration has been carried out in this process in the considered number of epochs 6. The values for the training, Mu and validation checks under epochs 6 are 0.0001005, 1e-07, and 6 respectively. Also, the accuracy of the harmonics estimation was found to be higher somewhat the with ANN even before the utilization of filters for harmonics reductions wherein testing input comprising very significant harmonic spectra than it was utilized for the training the data in ANN. B. After applying filter The main contribution is to estimate the number of harmonics present in the power system. It illustrated that the current outputs from the load as well as the DC source measurement. After applying the filter to the distribution system, the harmonics present in the line are eliminated and then it attains a pure sine waveform. This shows how the harmonics content are being eliminated by our proposed work. The DC signal used for the controller is also illustrated by depicting the waveforms.

Voltage

Fig. 17 (a) Training (b) Validation (c) Testing, and (d) Total response.

Fig. 18 Performance of proposed ANN control

Time (sec) Fig. 20 (a) Three phase waveform for load voltage

The above Fig. 20 (a) is a graph plotted by taking the time and Voltage in x-axis and y-axis respectively. It shows the behavior of the involved load voltage after applying the devised filter.

Fig. 20 (d) DC voltage for the controller The above Fig. 20 (d) is a graph plotted by taking the time and Reactive Power in x-axis and y-axis respectively. It shows the behavior of the involved load voltage after applying the devised filter. C. Simulation result for the proposed controller Fig. 20 (b): THD value for the load voltage waveforms by FFT

The steady state analysis based on pro-active power filter is given in Table 2. The analysis is done on the basis of setting time and overshoot. As a result, before adding the filter circuit, the setting time and overshoot seemed to be high. But, after the APF introduction, the output value is found to be significantly reduced.

The above Fig. 20 (b) depicts the THD value for the corresponding load voltage waveforms that is being used throughout the performance analysis of our work. This value indicates the various inputs utilized and standards available in the exported place of the FFT inbuilt which had been taken from the scope and arrays that had been generated earlier.

Table 2 Steady-state analysis of the proposed filter

Setting Time Overshoot

Steady state analysis Without APF With APF 7.4915% 0.503% 2.8% 1.928 %

The load currents’ THD is monitored to analyze the fulfillment or performance of devised controller in this work. The below Fig. 21 illustrates the total harmonics level which is measured only after connecting the filter.

Fig. 20 (c) Three phase current waveform after APF The above Fig. 20 (c) depicts a graph which is plotted by taking the time and Real Power in x-axis and y-axis respectively.

Active Power

Time (sec)

Fig. 21. Load current output with an Active Power Filter (APF)

Reactive power

By this way, the selected signals represent the voltage source, current active power filter, and dc voltage which is being used for the controller. Then, the fundamental frequency is taken as 50 HZ - normal value and distortion rate in the total harmonics are also calculated simultaneously. The THD obtained after using the filter is 3.84%, which is considered to be lower harmonics when it is under a nonlinear load. Thus the performance or fulfillment of the active power filter based on ADALINE which performs well in terms of harmonics elimination. Fig. 21 indicates that all the three phases of the output load current are maintained in equal values without any fluctuations by the implementation of active power filter (APF).

Time (sec) Fig. 23. (b) Generator reactive power D. Total Harmonics Distortion The distortion in terms of the total harmonics is an important parameter which helps to analyze the number of harmonics presented in the voltage or current waveform. Any repetitive waveform can be represented in mathematical form as pure sine waves’ series in the harmonic analysis that we make. The multiplying factors of underlying frequency is contained in the sine waves are called Harmonics. Fig. 22. Load current output without an active power filter Fig. 22 shows the total harmonics level which is measured before connecting the active power filter (APF). It can be seen that the three phases of output load current varies with each other and as a result, fluctuation occurs in the current.

Real power

Below fig. 23 (a) & (b) shows the yielded generators’ real and reactive powers respectively.

Fig. 24 (a) Total Harmonics Distortion before APF Time (sec) Fig. 23. (a) Generator real power

Thus the Fig. 24 (a) & (b) illustrate the harmonics presented in the distribution is considered, before connecting with the filter and after connecting to the filter. In this proposed work, the filter used here is an APF based one. Before being connected to the filter, the THD value is noted as 30.32% with the proposed controller and 46% with the PI controller. At that moment, after being connected to the APF, the harmonics level drops to 3.80% and 6.2 % with the proposed and PI controller respectively. This seemed to be a clear proof of the proposed filter performing well in the harmonics elimination.

Table 3 shown below indicates the overall comparison of the THD for all three phases with the existing LMS and RLS in this work [19]. While comparing with the existing work, the proposed control method of APF with ADALINE network performs well. The Harmonics in the distribution line was reduced to a lower value. Table 3: Comparative analysis based on Total Harmonics Distortion[19] Proposed ADALINE LMS RLS

Phase 1

Phase 2

Phase 2

3,84%

4,00%

3,80%

14,37% 6,27%

18,94% 7,71%

15,74% 6,76%

18.94

Fig. 24 (b) Total Harmonics Distortion after APF E. Comparative analysis of the proposed work The overall performance of the novel controller is validated by comparing the proposed controller with the existing PI controller deployed in the work of [21]. In this exiting work, the induction motor is considered to be a nonlinear load. The comparative graphs shows the total harmonics value before and after adding the filter. In the considered existing method, the THD value obtained was 6.2% but in the proposed work, the THD value is found to be reduced to 3.8%. Even though, the initial value of the harmonics before adding filter in the proposed work was higher than the existing work. It can be inferred that the proposed works’ performance is found to be much more superior to the existing PI controller. Before adding the Filter, the existing PI controller showed THD value as 46% and the proposed controller exhibits THD value as 30.32%. Before the filtering case, the value of harmonics is found to be reduced when compared with the existing PI controller.

Values in %

20 Phase 1 Phase 2 Phase 3

15 10 5

14.37

15.74

7.716.76 6.27

3.84 4 3.8

0 Proposed ADALINE

LMS

RLS

Technquies

Fig. 26 Comparative analysis based on Total Harmonics Distortion Table 4: Before and after (Voltage, current) compensation Source Voltage Current (v) (amps) After compensation Before compensation

Nonlinear Load Voltage Current (v) (amps)

440

33

220

33

440

45

50

13

6. CONCLUSION 60

THD %

50

6.2 46

40 3.8 30.32

30 20 10 0 PI controller Before filter

Propsoed controller After filter

Fig. 25: An analysis of Total harmonic distortion with comparison

To achieve a better harmonics elimination and accurate estimation, an APF control method has been presented in this work. To increase the accuracy in the performance, a PWM control with ADALINE network was developed in this proposed work. The method for obtaining the reference current and the fundamental active currents are also described. A hysteresis band comparator was developed with a feed-forward well-trained neural network with the devised back propagation algorithm. Thus the proposed work effectively eliminates and accurately calculates the harmonics. The results obtained from Matlab Simulink shows voltage and current. As compared with the traditional controller technique, the proposed controller has reduced

harmonics more effectively. Further, the simulation can be carried out with different nonlinear loads under different controller techniques. Conflict of Interest An APF control design is developed with ADALINE network in which the load and current along with voltage will be analyzed and then the controller will be calculating the control signal by considering the reference compensation current. Afterwards, the power system is injected with compensating current. The simulation is carried out with Matlab- Simulink to analyze the proposed control designs efficiency. The proposed work consummation is compared with conventional PI controller method comprising Shunt Active Power Filters (SAPF) with ADALINE for the performance perspectives. This method was found to be effective in terms of many parameters such as load voltage, load current, voltage, reactive power, real power and especially THD value than those of the existing works which are considered REFERENCES [1]

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M.Sujith was born in Namakkal, Tamilnadu in 1987.He received the B.E. degree in Electrical and Electronics Engineering from K.S.R.College of Engineering, Tiruchengode, in 2008 and the M.E. degree in Applied Electronics from the Annai Mathammal Sheela Engineering College, Namakkal, in 2012. He is working as Associate Professor in the Department of Electrical and Electronics Engineering at I.F.E.T College of Engineering, Villupuram. His main research interests include power electronic converters, compensators, power quality issues, and active power filters. S.Padma was born in Salem, Tamilnadu in 1969. She received the BE from Government College of Engineering, Salem, in 1990 and ME degree from Annamalai univeristy, Chidambaram, in 2002. She received her PhD in 2011 from Anna University, Chennai. She is working as Professor in the Department of Electrical and Electronics Engineering at Sona College of Technology, Salem, India from 2011. Her research interests include power electronics converters, drives, energy

storage, power quality issues, and active power filters.