Self-tuned Artificial Neural Network Controller ANN

G. Self-tuned Artificial Neural Network Controller ANN

The neural network used in this application is the simplest one that uses three layers, which is widely used in the control of the electrical machines. Each layer is composed of neurons. Each neuron is connected via weights to the previous layer. The first layer is connected to the input variables. The second one is connected via weights to all the neurons of the previ- ous layer, and the last one is composed of one neuron given the output value. The weights and the biases of the ANN net- work’s are updated to ensure that the global error of the system is minimized. The proposed ANN regulator is tuned on-line using the back-propagation algorithm. The on-line ANN rule- based algorithm is used to update the ANN network weights and biases to ensure continuous effective dynamic response while keeping the motor inrush current under specified tol- erable limits. The input vector with 3-layer ANN as shown in Fig. 35.22 is

¯X = {e t (k), e t (k − 1), e t (k − 2), e t (k

c (k − 1)} (35.43)

H. Self-tuned Fuzzy Logic Controller (FLC) As shown in Fig. 35.23, the FLC system consists of three subsystems which are the fuzzification, rule base, and defuzzification. Fuzzifica- tion subsystem converts the exact inputs to fuzzy values using five membership functions: Positive Big (PB), Positive Small (PS), Zero (ZZ), Negative Small (NS), and Negative Big (NB). The rule base unit processes these fuzzy values with fuzzy rules. The defuzzification unit converts the fuzzy results to exact val- ues. The FLCs input values are the global error, e t and change in global error, de t . According to these variables, a rule table is produced in the FLCs rule base unit as shown in Table 35.4.

35.8.2.2 Digital Simulation Results

The integrated micro grid for PMDC driven Electric Vehicle scheme using the Photo Voltaic PV, Fuel Cell FC, and backup Diesel generation with battery backup renewable generation system performance is compared for two cases, with fixed and self-tuned type controllers using either GA or PSO. The sec- ond case is to compare the performance with ANN controller and Fuzzy Logic Controller (FLC) with the self-tuned type con- trollers using either GA or PSO. The Tuned Variable structure sliding mode controller VSC/SMC/B–B has been applied to the speed tracking control of the same EV for performance com- parison. There are three different speed references. In the first speed track, the speed increases linearly and reaches the 1 PU at the end of the first 5 s, and then the reference speed remains speed constant during 5 s. At the tenth second, the reference speed decreases with same slope as at the first 5 s. After 15s, the motor changes the direction and EV increases its speed through the reverse direction. At the twentieth second, the reference speed reaches the −1 PU and remains at a constant speed at

1014 A. M. Sharaf and A. A. A. El-Gammal

FIGURE 35.23 Main structure of the FLC-incremental scheme.

TABLE 35.4 Fuzzy rules decision table Digital simulations are obtained with sampling interval Ts =

20μs. Dynamic responses obtained with GA are compared with ones resulting from the PSO for the seven proposed self-tuned

controllers. The dynamic simulation conditions are identical

for all tuned controllers. To compare the global performances

of all controllers, the Normalized Mean Square Error (NMSE)

deviations between output plant variables and desired values,

and is defined as

DC −bus −V DC −bus−ref

NMSE V DC −bus =

DC −bus−ref 2

the end of the twenty-fifth second and then the reference speed m −ω −ref

2 (35.45) decreases and becomes zero at 30 s. The second reference speed

NMSE ω m =

m −ref

waveform is sinusoidal and its magnitude is 1 PU and the The digital simulation results validated the effectiveness of period is 12 s. The third reference track is constant speed ref- both GA- and PSO-based tuned controllers in providing effec-

erence starting with an exponential track. In all references, the tive speed tracking minimal steady-state errors. Transients are system responses have been observed. Matlab–Simulink Soft- also damped with minimal overshoot, settling time, and fall ware was used to design, test, and validate the effectiveness time. The GA- and PSO-based self-tuned controllers are more of the integrated micro grid for PMDC driven Electric Vehi- effective and dynamically advantageous in comparison with the cle scheme using Photo Voltaic PV, Fuel Cell FC, and backup ANN controller, the FLC, and fixed type controllers. The self- Diesel generation with battery backup renewable generation regulation is based on minimal value of absolute total/global system with the FACTS devices. The digital dynamic sim- error of each regulator shown in Figs. 35.17–35.20. The control ulation model using Matlab/Simulink software environment system comprising the three dynamic multi-loop error driven allows for low cost assessment and prototyping, system param- regulator is coordinated to minimize the selected objective eters selection, and optimization of control settings. The use of functions. SOO obtains a single global or near optimal solution PSO-search algorithm is used in online gain adjusting to mini- based on a single weighted objective function. The weighted SO mize controller absolute value of total error. This is required function combines several objective functions using specified before full scale prototyping which is both expensive and time or selected weighting factors as follows: consuming. The effectiveness of dynamic simulators brings on detailed sub-models selections and tested sub-models Matlab weighted objective function library of power system components already tested and vali- dated. The common DC bus voltage reference is set at 1 PU.

=α 1 J 1 +α 2 J 2 +α 3 J 3 +α 4 J 4 +α 5 J 5 (35.46)

35 Novel AI-Based Soft Computing Applications in Motor Drives 1015 TABLE 35.5 Selected objective functions versus the tuned variable structure sliding mode controller based SOGA and MOGA control schemes

J 4 Minimize J 5 Minimize generator

The Diesel The GPFC

The PMDC

The α-

J 1 Minimize the

J 2 Minimize the

J 3 Minimize the

the (Maximum the (Rise Time) Regulator

– DC side

motor

controller

(Total Error)

(Steady State

(Settling Time)

Overshoot) ×10 α g ⎤

Error) (PU)

Where α 1 = 0.20, α 2 = 0.20, α 3 = 0.20, α 4 = 0.20, α 5 = cases, with GA and PSO tuning algorithms and traditional

0.20 are selected weighting factors. J 1 ,J 2 ,J 3 ,J 4 ,J 5 are the controllers with constant controller gains results shown in selected objective functions. On the other hand, the MO finds Table 35.9, ANN controller in Table 35.10 and Figs. 35.30– the set of acceptable (trade-off) Optimal Solutions. This set

35.32, and FLC in Table 35.11 and Figs. 35.33–35.35, it is of accepted solutions is called Pareto front. These acceptable quite apparent that the GA and PSO tuning algorithms highly trade-off multi-level solutions give more ability to the user to improved the PMDC-EV system dynamic performance from a make an informed decision by seeing a wide range of near general power quality point of view. The GA and PSO tuning optimal selected solutions.

algorithms had a great impact on the system efficiency improv- Table 35.5 shows the optimal solutions of the main objec- ing it from 0.906631 (constant gains controller), 0.928253 tive functions versus the Tuned Variable structure sliding mode (ANN controller), and 0.937334 (FLC) to around 0.948156 controller Gains based SOGA and MOGA control schemes. (GA-based tuned controller) and 0.930708 (PSO-based tuned On the other hand, Table 35.6 shows the optimal solutions controller) which is highly desired. Moreover, The Normal-

of the main objective functions versus the Tuned Variable ized Mean Square Error (NMSE-V DC −Bus ) of the DC bus structure sliding mode controller Gains based SOPSO and voltage is reduced from 0.08443 (constant gains controller), MOPSO control schemes. Table 35.7 shows the DC bus behav- 0.04827 (ANN controller), and 0.03022 (FLC) to around ior comparison using the GA-based Tuned Variable structure 0.007304 (GA-based tuned controller) and 0.005854 (PSO- sliding mode controller for the three selected reference tracks. based tuned controller). In addition the Normalized Mean In addition, Table 35.8 shows the system behavior using the Square Error (NMSE-ω m ) of the PMDC motor is reduced PSO-based Tuned Variable structure sliding mode controller. from 0.053548 (constant gains controller), 0.02627 (ANN con- Figures 35.24–35.29 show the effectiveness of MOPSO and troller), and 0.02016 (FLC) to around 0.0076308 (GA-based MOGA search and optimized control gains in tracking the tuned controller) and 0.006309 (PSO-based tuned controller). PMDC-EV motor three reference speed trajectories. Compar- Maximum Transient DC Voltage Over/Under Shoot (PU) is ing the PMDC-EV dynamic response results of the two study reduced from 0.054604 (constant gains controller), 0.04186

1016 A. M. Sharaf and A. A. A. El-Gammal TABLE 35.6 Selected Objective Functions versus the Tuned Variable structure sliding mode controller Gains based SOPSO and MOPSO control

schemes The Diesel

J 4 Minimize J 5 Minimize generator

The GPFC

The PMDC

The α-

J 1 Minimize the

J 2 Minimize the

J 3 Minimize the

the (Maximum the (Rise Time) ⎡ Regulator

– DC side

motor

controller

(Total Error)

(Steady State

(Settling Time)

Overshoot) ×10 −2 K α g ⎤

Error) (PU)

TABLE 35.7 DC bus behavior comparison using the GA-based Tuned Variable structure sliding mode controller VSC/SMC/B–B

The Third Speed Track DC bus voltage (PU)

The First Speed Track

The Second Speed Track

0.964182 DC bus current (PU)

0.613914 Maximum Transient DC Voltage

0.008437 Over/Under Shoot (PU) Maximum Transient DC Current –

0.0028122 Over/Under Shoot (PU) DC System Efficiency

0.94302 NMSE V DC −bus

0.008248 NMSE ω m

0.008558 PMDCM total controller Error e tm

0.007663 DC side GPFC Error e td

0.004337 The diesel engine gen set total

0.004377 controller Error e tg The diesel engine converter total

0.002328 controller Error e tR

(ANN controller), and 0.03126 (FLC) to around 0.009302 (GA- 0.00292 (GA-based tuned controller) and 0.005987 (PSO- based tuned controller) and 0.007259 (PSO-based tuned con- based tuned controller). DC bus voltage (PU) is improved troller). Maximum Transient DC Current – Over/Under Shoot from 0.917020 (constant gains controller), 0.932736 (ANN (PU) is reduced from 0.087336 (constant gains controller), controller), and 0.94745 (FLC) to around 0.97417 (GA-based 0.07355 (ANN controller), and 0.04383 (FLC) to around tuned controller) and 0.974602 (PSO-based tuned controller).

35 Novel AI-Based Soft Computing Applications in Motor Drives 1017 TABLE 35.8 DC bus behavior comparison using the PSO based Tuned Variable structure sliding mode controller VSC/SMC/B–B

The Third Speed Track DC bus voltage (PU)

The First Speed Track

The Second Speed Track

0.950341 DC bus current (PU)

0.605076 Maximum Transient DC Voltage

0.008571 Over/Under Shoot (PU) Maximum Transient DC Current –

0.0047885 Over/Under Shoot (PU) DC System Efficiency

0.9327892 NMSE V DC −bus

0.0052055 NMSE ω m

0.007071 PMDCM total controller Error e tm

0.0052238 DC side GPFC Error e td

0.007769 The diesel engine gen set total

0.0071823 controller Error e tg The diesel engine converter total

0.0052152 controller Error e tR

Speed curve

×10 1 −4 Speed error curve

Speed (PU) −0.5

Speed error −1

Time (s)

Time (s)

×10 −4 Current error curve

×10 −4 5 Global error curve 4

Current error

Global error

Time (s)

Time (s)

FIGURE 35.24 EV-PMDC Motor speed response for the first speed track using GA-based tuned Tri-loop variable structure sliding mode controller VSC/SMC/B–B.

DC bus current (PU) is reduced from 0.769594 (constant (GA-based tuned controller) and 0.0074294 (PSO-based tuned gains controller), 0.67464 (ANN controller), and 0.64712 controller). The diesel engine gen set total controller Error (e tg ) (FLC) to around 0.614695 (GA-based tuned controller) and is reduced from 0.067513 (constant gains controller), 0.04507 0.607674 (PSO-based tuned controller). PMDCM total con- (ANN controller), and 0.02964 (FLC) to around 0.005121 troller Error (e tm ) is reduced from 0.095145 (constant gains (GA-based tuned controller) and 0.007013 (PSO-based tuned controller), 0.04200 (ANN controller), and 0.02154 (FLC) to controller). The diesel engine converter total controller Error around 0.009167 (GA-based tuned controller) and 0.0048638 (e tR ) is reduced from 0.086233 (constant gains controller), (PSO-based tuned controller). DC side GPFC Error (e td ) is 0.03978 (ANN controller), and 0.0260 (FLC) to around reduced from 0.70746 (constant gains controller), 0.03416 0.003265 (GA-based tuned controller) and 0.0053836 (PSO- (ANN controller), and 0.02416 (FLC) to around 0.004618 based tuned controller).

1018 A. M. Sharaf and A. A. A. El-Gammal

Speed curve

1 −4 ×10 Speed error curve

Speed (PU) −0.5

Speed error 0

Time (s)

Time (s)

Current error curve ×10 2 −4 ×10 Global error curve 4

Current error

Global error 0

Time (s)

Time (s)

FIGURE 35.25 EV-PMDC Motor speed response for the first speed track using PSO-based tuned Tri-loop variable structure sliding mode controller VSC/SMC/B–B.

Speed curve

×10 −4 Speed error curve

Speed (PU) −0.5

Speed error −2

Time (s)

Time (s)

×10 −4 Current error curve

×10 −4 Global error curve

Current error

Global error −2

Time (s)

Time (s)

FIGURE 35.26 EV-PMDC Motor speed response for the Second speed track using GA-based tuned Tri-loop variable structure sliding mode controller VSC/SMC/B–B.

35 Novel AI-Based Soft Computing Applications in Motor Drives 1019

Speed curve

×10 −4 Speed error curve

Speed (PU) −0.5

Speed error −2

Time (s)

Time (s)

×10 −4 Current error curve

×10 −4 Global error curve

Global error 0

Current error

Time (s)

Time (s)

FIGURE 35.27

EV-PMDC Motor speed response for the Second speed track using PSO-based tuned Tri-loop variable structure sliding mode controller VSC/SMC/B–B.

Speed curve

×10 −4 Speed error curve

Speed (PU)

0 Speed error

Time (s)

Time (s)

×10 −4 Current error curve

×10 −4 Global error curve

Global error Current error 0 −4

Time (s)

Time (s)

FIGURE 35.28 EV-PMDC Motor Speed response for the third speed track using GA-based tuned tri-loop variable structure sliding mode controller VSC/SMC/B–B.

1020 A. M. Sharaf and A. A. A. El-Gammal

Speed curve

×10 −4 Speed error curve

Speed (PU)

0 Speed error

Time (s)

Time (s)

×10 −4 Current error curve

×10 −4 Global error curve

Current error −4

Global error

Time (s)

Time (s)

FIGURE 35.29 EV-PMDC Motor speed response for the third speed track using PSO-based tuned Tri-loop Variable structure sliding mode controller VSC/SMC/B–B.

TABLE 35.9 DC bus behavior comparison using the constant parameters variable structure sliding mode controller VSC/SMC/B–B

The Third Speed Track DC bus voltage (PU)

The First Speed Track

The Second Speed Track

0.895291 DC bus current (PU)

0.769731 Maximum Transient DC Voltage

0.053089 Over/Under Shoot (PU) Maximum Transient DC Current –

0.081906 Over/Under Shoot (PU) DC System Efficiency

0.895666 NMSE V DC −bus

0.084672 NMSE ω m

0.053953 PMDCM total controller Error e tm

0.09422 DC side GPFC Error e td

0.703462 The diesel engine gen set total

0.069606 controller Error e tg The diesel engine converter total

0.085740 controller Error e tR

35.8.3 Novel Self-Regulating Green Plug/

filter scheme is controlled by a dynamic PSO and GA error

Energy Management/Energy Economizer

driven self-adjusting controller to ensure voltage stabilization,

GP–EM–EE Schemes for Wind Driven

minimum impact of the electric load excursions, and wind

Induction Generation

variations on terminal voltage. The sinusoidal pulse width modulation complementary switching scheme is dynami-

The application presents a novel self-adjusting wind energy cally controlled using on-line minimal global error search utilization scheme using a modified single phase operation of that continuously adjusts and modifies the controller gains. the three-phase induction generator supplemented by a voltage The application presents a family of novel switched smart stabilization switched filter compensation scheme developed filter compensated devices using Green Plug/Energy Man- by the First Author. The series-parallel switched capacitor agement/Energy Economizer (GP–EM–EE) devices for small

35 Novel AI-Based Soft Computing Applications in Motor Drives 1021 TABLE 35.10 DC bus behavior comparison using ANN Controller

The Third Speed Track DC bus voltage (PU)

The First Speed Track

The Second Speed Track

0.91131 DC bus current (PU)

0.64627 Maximum Transient DC Voltage

0.05541 Over/Under Shoot (PU) Maximum Transient DC Current –

0.06083 Over/Under Shoot (PU) DC System Efficiency

0.926261 NMSE V DC −bus

0.05231 NMSE ω m

0.03146 PMDCM total controller Error e tm

0.04639 DC side GPFC Error e td

0.02440 The diesel engine gen set total

0.05522 controller Error e tg The diesel engine converter total

0.03463 controller Error e tR

Speed curve

Speed error curve

Speed (PU)

− 0.5 Speed error

Time (s)

Time (s)

Current error curve

Global error curve

Current error

Global error − 0.02

Time (s)

Time (s)

FIGURE 35.30 EV-PMDC Motor Speed response for the first speed track using ANN-based controller.

single phase induction motors (SPIM) used in residential/ scale applications of wind energy utilization in the range from commercial motor drives used in water pumping, ventilation,

5 to 25 KVA.

air conditioning, compressors, refrigeration applications. The Wind Energy Conversion Systems (WECS) are increasing GP–EM–EE devices are equipped with a dynamic online error their number as an environmental friendly power generation driven optimally tuned controller that ensures improved power system. Small sized wind turbine systems in the output power factor, reduced feeder losses, stabilized voltage, minimal cur- range of 5–25 KVA to be used for the residential houses or rent ripples, and efficient energy utilization/conservation with small sized business complex are extending their application minimal impact on the host electric grid security and relia- fields. There are various advantages of the small size wind bility. The proposed schemes can enhance the power quality; turbine system. The rotational speed of the smaller sized extend induction motor life span by reducing overheating due wind turbines can more rapidly follow the change of the to inrush currents and harmonics. They prevent overheating wind speed than the large-scale wind turbines system due to and possible motor damage. The scheme is suitable for small their smaller inertia. This feature contributes higher power

1022 A. M. Sharaf and A. A. A. El-Gammal

Speed curve

Speed error curve

Speed (PU) −0.5

Speed error −0.01

Time (s)

Time (s)

Current error curve

Global error curve

Current error

Global error −0.01

Time (s)

Time (s)

FIGURE 35.31 EV-PMDC Motor Speed response for the second speed track using ANN-based controller.

Speed curve

Speed error curve

Speed (PU)

0 Speed error −0.02

Time (s)

Time (s)

Current error curve

Global error curve

Current error

0 Global error −0.02

Time (s)

Time (s)

FIGURE 35.32 EV-PMDC Motor Speed response for the third speed track using ANN-based controller.

generation efficiency of the wind turbines in the case that Small scale induction motors drives consume over 50% of the the wind speed frequently changes [34]. Induction generators total electrical energy generated in the developed countries are being increasingly utilized in a WECS since they are rel- [36]. The electric utility industry and consumers of electrical atively inexpensive, rigid, and require low maintenance [35]. energy around the world are facing new challenges for cutting

35 Novel AI-Based Soft Computing Applications in Motor Drives 1023 TABLE 35.11 DC bus behavior comparison using FLC Controller

The Third Speed Track DC bus voltage (PU)

The First Speed Track

The Second Speed Track

0.930581 DC bus current (PU)

0.630216 Maximum Transient DC Voltage

0.02065 Over/Under Shoot (PU) Maximum Transient DC Current –

0.05014 Over/Under Shoot (PU) DC System Efficiency

0.937334 NMSE V DC −bus

0.02129 NMSE ω m

0.02024 PMDCM total controller Error e tm

0.02852 DC side GPFC Error e td

0.07781 The diesel engine gen set total

0.02928 controller Error e tg The diesel engine converter total

0.02051 controller Error e tR

Speed curve

Speed error curve

Speed (PU) −0.5

Speed error −0.005

Time (s)

Time (s)

Current error curve

× 10 −3 Global error curve 0.01 15

Current error −0.02

Global error

Time (s)

Time (s)

FIGURE 35.33 EV-PMDC Motor Speed response for the first speed track using FLC-based controller.

electric energy cost, improving energy utilization, enhancing losses due to excessive inrush currents and severe voltage sags. energy-efficiency, demand-side management, improving sup- The extended use of power electronic switching conveners ply waveform-power quality, reducing safety hazards to per- and devices in motor drives, process-industries: Mining. Oil sonnel, and protecting sensitive computer and automatic-data and Gas Industries and industrial DC and AC arc type fur- processing networks [37]. There is a mushrooming use of naces have resulted in a polluted grid and unreliable radial nonlinear electric loads especially in large motor drives, arc distribution/utilization system with serious inherent voltage furnaces, and power electronic converter loads. All these non- and power quality problems [39]. These nonlinear type elec- linear loads are byproduct of analog (saturation or Limiter tric loads are used with ventilation, air conditioning, water type) or Digital (converter, solid state switching type) nonlin- pumping, and low power factor industries such as sewing, earities [38]. Nonlinear type loads cause severe waveform dis- printing, shear and press machinery, and food processing tortion, power quality problems interference, and extra feeder plants. These nonlinear loads also fall in the category of inrush

1024 A. M. Sharaf and A. A. A. El-Gammal

Speed curve

Speed error curve

Speed (PU) −0.5

Speed error −0.01

Time (s)

Time (s)

Current error curve

Global error curve

Current error

Global error −0.01

Time (s)

Time (s)

FIGURE 35.34 EV-PMDC Motor Speed response for the second speed track using FLC-based controller.

Speed curve

Speed error curve

Speed (PU)

0 Speed error

Time (s)

Time (s)

Current error curve

Global error curve

Current error −0.02

Global error

Time (s)

Time (s)

FIGURE 35.35 EV-PMDC Motor Speed response for the third speed track using FLC-based controller.

or arc type motorized loads and combined with fluorescent motors. But direct starting will result in severe voltage sags and lighting can cause waveform distortion, harmonic interfer- extra heating. When starting large induction motors, exces- ence, and voltage flickering [40–42]. Generally, direct online sive voltage dips result in overheating and loss of motor life motor starting is an economical method for starting induction expectancy [43].

35 Novel AI-Based Soft Computing Applications in Motor Drives 1025

Source

GP-EE-EM and speed control

Motor load

3-Phase induction machines operating as

a single-phase induction generator

Single-phase induction motor SPIM

Gear box

Phase A

Phase B

Phase C

Wind turbine

FIGURE 35.36 The proposed switched smart filter compensated devices using GP–EM–EE devices for Single Phase Induction Motor (SPIM) drive system.

ω mref

Speed loop

Speed tracking loop

V C α Dynamic momentum loop

Cos × −1

Optimal tunned

controller

1 1+ T p S

Current loop

e Im

I mbase

1+ T I S

Im

Current limiting loop

D FIGURE 35.37 Tri-loop error driven self-regulating dynamic controller for control of SPIM drive.

to regulate the DC bus voltage and minimize inrush cur- Figure 35.36 depicts the block diagram of the utilization rent transients and load excursions and (2) the SPIM drive (single-phase induction motor SPIM) and the connection of with the speed regulator that ensure speed reference tracking switched smart filter compensated device using GP–EM–EE with minimum inrush conditions and ensure reduced voltage devices and the speed control drive system to the SPIM Load. transients and improved energy utilization. Figures 35.39– Figures 35.37 and 35.38 show the proposed tri-loop dynamic

35.8.3.1 Sample Study Motorized System

35.43 depict the proposed family of switched smart filter tracking controller to ensure both objectives of (energy/power) compensated devices using GP–EM–EE devices. All filters saving as well as power quality enhancement of the supply objectives can be either: (a) Harmonic reduction and power system current and load bus voltage. The novel PSO and GA quality (PQ) enhancement or (b) Electric power/energy savings self-tuned multi-regulators and coordinated controller are used and dynamic reactive compensation for the SPIM loads. The for the following purposes: (1) Green plug filter compensator proposed utilization scheme is fully validated using the Mat- GPFC–SPWM regulator for pulse width switching scheme lab/Simulink software environment under normal conditions,

1026 A. M. Sharaf and A. A. A. El-Gammal

D = 10 mSec D

Current limiting Loop

e Is

I S Is I dbase 1 +T s S

f SW

Muliply S 1

Optimal tuning

Voltage stabilization loop e Vs

V CS

1 1 +T

V dbase

vs

V CSref FIGURE 35.38 Tri-loop error driven self-regulating dynamic controller for the GP–EM–EE scheme.

C Triac

2-pulse diode

bridge

FIGURE 35.39 Low cost switched power filter compensator scheme-A. N FIGURE 35.40 Low cost switched power filter compensator scheme-B.

load excursion, SPIM motor torque changes to assess the con-

A. Dynamic Error driven Control The proposed control sys- trol system robustness, effective energy utilization, and speed tem comprises two sub-regulators or controllers named as DC reference tracking.

side Green Plug Filter Compensator GPFC–SPWM regulator The common concerns of power quality are the long and the SPIM drive speed controller. Figures 35.37 and 35.38 duration voltage variations (overvoltage, under-voltage, and depict the proposed multi-loop dynamic self-regulating con- sustained interruptions), short duration voltage variations trollers based on MOO search and optimization technique (interruption, sags, and swells), voltage imbalance (voltage based on soft computing PSO and GA. The global error is unbalance), waveform distortion (DC offset, harmonics, the summation of the three loop individual errors including inter-harmonics, notching and noise), voltage fluctuation voltage stability, current limiting, and synthesize dynamic (voltage flicker), and power frequency variations. To prevent power loops. Each multi-loop dynamic control scheme is used the undesirable states and to reduce the power consumption, a to reduce a global error based on a tri-loop dynamic error GPF scheme is used to stabilize the system.

summation signal and to mainly track a given speed reference

35 Novel AI-Based Soft Computing Applications in Motor Drives 1027

2-pulse

2-pulse

C g diode bridge

Triac

diode bridge

FIGURE 35.43 Low cost switched power filter compensator scheme-E.

FIGURE 35.41 Low cost switched power filter compensator scheme-C.

2-pulse diode bridge

The total or global error e ts (k) for the GP–EM–EE side scheme at a time instant:

2-pulse diode bridge

e ts ( k) =γ vs e vs ( k) +γ is e is ( k) +γ ps e ps ( k) (35.50)

S 1 In the same manner, the (per-unit) three dimensional-error vector (e ω m ,e Im ,e pm ) of the SPIM motor scheme is governed

C f by the following equations:

1 1 FIGURE 35.42 Low cost switched power filter compensator scheme-D.

trajectory loop error in addition to other supplementary motor

(35.51) current limiting and dynamic power loops are used as auxiliary

−ω m ( k)

1 + ST m

loops to generate a dynamic global total error signal that consists of not only the main loop speed error but also the cur-

e Im ( k) =I m ( k)

1 + SD conditions.

rent ripple, over current limit and dynamic over load power

1 + ST m

(35.52) shown in Fig. 35.38. The (per-unit) three dimensional-error

The global error signal is input to the self-tuned controllers

−I m ( k)

1 + ST m

vector (e vs ,e Is ,e ps ) of the diesel engine controller scheme is governed by the following equations:

1 + ST s And the total or global error e tm (k) for the MPFC scheme at

1 1 1 a time instant:

(35.48) ( e tm k) =γ ω m e ω m ( k) +γ im e im ( k) +γ pm e pm ( k) (35.54)

e Ps ( k) =I s ( k) ×V s ( k)

A number of conflicting objective functions are selected to

1 + ST s

1 + SD

optimize using the PSO algorithm. These functions are defined

1028 A. M. Sharaf and A. A. A. El-Gammal by the following:

K I ∫ J 1 = Minimize the Total Harmonic Distortion

++ + J 2 = Minimize the Total Harmonic Distortion

V of the Load current (THDi) c (35.55)

K D d/dt J 3 = Maximize the electric energy efficiency

of the Load Voltage (THDv)

(35.57) FIGURE 35.44 Optimally tuned conventional PID controller block

diagram.

J 4 = Maximize the Power factor

add the “Derivative” mode to stabilize the overshoot, then add J 5 = Minimize the KWh Consumption (35.59) more “Proportional,” and so on. The PID controller has the following form in the time domain as shown in Fig. 35.44:

In general, to solve this complex optimality search problem, there are two possible optimization techniques based on PSO:

de(t) Single aggregate SOO, which is explained and MOO. The main

e(t)dt +K d (35.60) procedure of the SOO is based on selecting a single aggregate

u(t) =K p e(t) +K i

dt

objective function with weighted SO parameters scaled by a number of weighting factors. The objective function is opti-

Where e(t) is the selected system error, u(t) the control mized (either minimized or maximized) using either GA or variable, K p the proportional gain, K i the integral gain, and

PSO search algorithm (PSO) methods to obtain a single global K d is the derivative gain. Each coefficient of the PID con- or near optimal solution. On the other hand, the main objective troller adds some special characteristics to the output response of the MO problem is finding the set of acceptable (trade- of the system. Because of this, choosing the right parameters off) Optimal Solutions. This set of accepted solutions is called becomes a crucial decision. In this scheme, the Tri-loop Error Pareto front. These acceptable trade-off multi-level solutions Driven Controller is utilized with traditional PID controller.

give more ability to the user to make an informed decision by PID controller gains (K P ,K I ,K D ) are dynamically self-tuned seeing a wide range of near optimal selected solutions that are using the PSO and GA dynamic search and optimization cri- feasible and acceptable from an “overall” standpoint. SO opti- terion based on total error minimization, steady-state error, mization may ignore this trade-off viewpoint, which is crucial. maximum overshoot, settling time, and rising time. The main advantages of the proposed MOO method are: it doesn’t require a priori knowledge of the relative importance of

35.8.3.2 Digital Simulation Results

the objective functions and it provides a set of acceptable trade- off near optimal solutions. This set is called Pareto front or The family of (GP–EM–EE) devices system performance is optimality trade-off surfaces. Both SOO and MOO searching compared for two cases; without (as shown in Table 35.12) algorithms are tested, validated, and compared.

and with the (GP–EM–EE) devices, the second case is studied The dynamic error driven controller regulates the con- with fixed (as show in Table 35.13) and self-tuned type con-

trollers’ gains using the PSO and GA to minimize the system trollers using either GA or PSO. In addition, the second case is total error and the selected objective functions. The proposed studied to compare the performance with ANN controller (as dynamic Tri-Loop Error Driven controller, developed by the show in Table 35.14) and FLC (as show in Table 35.15) with the First Author, is a novel advanced regulation concept that oper- TABLE 35.12 System behavior without (GP–EM–EE) schemes ates as an adaptive dynamic type multi-purpose controller capable of handling sudden parametric changes, load and/or

Without (GP–EM–EE)

source excursions. By using the Tri-Loop Error Driven con- RMS Motor voltage (PU) troller, it is expected to have a smoother, less dynamic over- 0.8578

RMS Motor current (PU)

shoot, fast, and more robust controller when compared to Maximum Transient Voltage Over/Under Shoot (PU)

those of classical control schemes.

Maximum Transient Current – Over/Under Shoot (PU) 0.1875

System Efficiency

NMSE V

B. Self-tuned conventional PID controller Fundamentally, NMSE ω m

the conventional PID controller comprises three basic con- 0.4238

NMSE I

THDv Bus L (%)

trol actions. They are simple to implement and they provide THDi Bus L (%)

good performance. The tuning process of the gains of PID con- THDv Bus M (%)

trollers can be complex because is iterative: first, it is necessary THDi Bus M (%)

35 Novel AI-Based Soft Computing Applications in Motor Drives 1029 TABLE 35.13 System dynamic behavior comparison using the constant parameters conventional PID controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9690 RMS Motor current (PU)

0.6821 Maximum Transient Voltage

0.0846 Over/Under Shoot (PU) Maximum Transient Current –

0.0740 Over/Under Shoot (PU) NMSE V

0.0907 NMSE ω m

0.0773 NMSE I

0.0823 THDv Bus L (%)

9.5253 THDi Bus L (%)

9.5659 THDv Bus M (%)

9.2456 THDi Bus M (%)

8.7230 System Efficiency

0.8579 Motor Power Factor

0.9083 Reduction in KWh Consumption (%)

TABLE 35.14 System dynamic behavior comparison using ANN controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9719 RMS Motor current (PU)

0.7077 Maximum Transient Voltage

0.0830 Over/Under Shoot (PU) Maximum Transient Current –

0.0869 Over/Under Shoot (PU) NMSE V

0.0714 NMSE ω m

0.0704 NMSE I

0.0811 THDv Bus L (%)

9.9567 THDi Bus L (%)

8.8514 THDv Bus M (%)

10.8888 THDi Bus M (%)

10.9988 System Efficiency

0.8632 Motor Power Factor

0.8734 Reduction in KWh Consumption (%)

TABLE 35.15 System dynamic behavior comparison using the FLC controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9584 RMS Motor current (PU)

0.6929 Maximum Transient Voltage

0.0866 Over/Under Shoot (PU) Maximum Transient Current –

0.0797 Over/Under Shoot (PU) NMSE V

0.0757 NMSE ω m

0.0818 NMSE I

0.0710 THDv Bus L (%)

8.2990 THDi Bus L (%)

9.7391 THDv Bus M (%)

10.2212 THDi Bus M (%)

10.3043 System Efficiency

0.8990 Motor Power Factor

1030 A. M. Sharaf and A. A. A. El-Gammal TABLE 35.16 System dynamic behavior comparison using the SOGA-based Tuned conventional PID controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9799 RMS Motor current (PU)

0.6428 Maximum Transient Voltage

0.0380 Over/Under Shoot (PU)

0.0495 Over/Under Shoot (PU) NMSE V ×10 −1

Maximum Transient Current –

0.0531 NMSE ω m ×10 −1

0.0477 NMSE I ×10 −1

0.0533 THDv Bus L (%)

4.7221 THDi Bus L (%)

5.3056 THDv Bus M (%)

3.7171 THDi Bus M (%)

5.7022 System Efficiency

93.5954 Motor Power Factor

91.4794 Reduction in KWh Consumption (%)

self-tuned type controllers using either GA or PSO. The self- The control system comprising the three dynamic multi- tuned type controllers based either GA or PSO is Tuned con- loop error driven regulator is coordinated to minimize the ventional PID controller, has been applied to the speed tracking selected objective functions. SOO obtains a single global or control of the same system parameters for performance com- near optimal solution based on a single weighted objective parison. Matlab–Simulink Software was used to design, test, function. The weighted SO function combines several objec- and validate the effectiveness of the (GP–EM–EE) devices for tive functions using specified or selected weighting factors as small motors used in household appliances, washers, dryers, follows: fans, water pumps, ventilation systems, air-conditions, and other applications in dispersing machines, actuators, and small

weighted objective function

converters with induction motor size from 5 to 25 KVA. The digital dynamic simulation model using Matlab/Simulink soft-

=α 1 J 1 +α 2 J 2 +α 3 J 3 +α 4 J 4 +α 5 J 5 (35.64) ware environment allows for low cost assessment and prototyp- ing, system parameters selection, and optimization of control Where α 1 = 0.20, α 2 = 0.20, α 3 = 0.20, α 4 = 0.20, α 5 = 0.20 settings. The use of GA and PSO- search algorithms are used are selected weighting factors. J 1 ,J 2 ,J 3 ,J 4 ,J 5 are the selected in online gain adjusting to minimize controller absolute value objective functions. On the other hand, the MO finds the set of of total error. This is required before full scale prototyping acceptable (trade-off) Optimal Solutions. This set of accepted which is both expensive and time consuming. The effectiveness solutions is called Pareto front. These acceptable trade-off of dynamic simulators brings on detailed sub-models selec- multi-level solutions give more ability to the user to make tions and tested sub-models Matlab library of power system an informed decision by seeing a wide range of near optimal components already tested and validated. The dynamic sim- selected solutions. Table 35.16 shows the system behavior using ulation conditions are identical for all tuned controllers. To traditional controllers with constant controller gains for the compare the global performances of Tuned conventional PID (GP–EM–EE) schemes. In addition, Table 35.10 shows System controller, the NMSE deviations between output plant vari- behavior comparison using the SOGA-based Tuned conven- ables and desired values, and is defined as

tional PID controller and Table 35.17 shows the system behav-

2 ior comparison using the MOGA-based Tuned conventional

S −V S −ref

NMSE VS =

2 (35.61) PID controller. Finally, Tables 35.18 and 35.19 show system

S −ref

behavior comparison using the SOPSO- and MOPSO-based

2 tuned conventional PID controller, respectively. Comparing

the system dynamic response results of the two study cases, NMSE ω m =

m −ω m −ref

with GA and PSO tuning algorithms and traditional con-

−ref

trollers with constant controller gains results, ANN controller

and FLC, it is quite apparent that the GA and PSO tun- NMSE I S =

S −I S −ref

2 (35.63) ing algorithms highly improved the system dynamic perfor-

S −ref

mance from a general power quality point of view. The GA

35 Novel AI-Based Soft Computing Applications in Motor Drives 1031 TABLE 35.17 System dynamic behavior comparison using a selected solution from the MOGA Pareto Frontbased Tuned conventional PID controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9939 RMS Motor current (PU)

0.6252 Maximum Transient Voltage

0.0365 Over/Under Shoot (PU) Maximum Transient Current –

0.0502 Over/Under Shoot (PU) NMSE V ×10 −1

0.0564 NMSE ω m ×10 −1

0.0599 NMSE I ×10 −1

0.0372 THDv Bus L (%)

4.0436 THDi Bus L (%)

5.9842 THDv Bus M (%)

5.6249 THDi Bus M (%)

4.2902 System Efficiency

91.4767 Motor Power Factor

91.8090 Reduction in KWh Consumption (%)

TABLE 35.18 System dynamic behavior comparison using the SOPSO-based Tuned conventional PID controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9809 RMS Motor current (PU)

0.6446 Maximum Transient Voltage

0.0429 Over/Under Shoot (PU) Maximum Transient Current –

0.0506 Over/Under Shoot (PU) NMSE V ×10 −1

0.0448 NMSE ω m ×10 −1

0.0482 NMSE I ×10 −1

0.0575 THDv Bus L (%)

4.8095 THDi Bus L (%)

3.7976 THDv Bus M (%)

3.9065 THDi Bus M (%)

4.1457 System Efficiency

92.6432 Motor Power Factor

94.6423 Reduction in KWh Consumption (%)

TABLE 35.19 System dynamic behavior comparison using a selected solution from the MOPSO Pareto Frontbased Tuned conventional PID controller

GP–EM–EE Scheme D GP–EM–EE Scheme E RMS Motor voltage (PU)

GP–EM–EE Scheme A

GP–EM–EE Scheme B

GP–EM–EE Scheme C

0.9847 RMS Motor current (PU)

0.6278 Maximum Transient Voltage

0.0359 Over/Under Shoot (PU) Maximum Transient Current –

0.0528 Over/Under Shoot (PU) NMSE V ×10 −1

0.0373 NMSE ω m × 10 −1

0.0535 NMSE I ×10 −1

0.0581 THDv Bus L (%)

6.4921 THDi Bus L (%)

3.7277 THDv Bus M (%)

4.8263 THDi Bus M (%)

6.5793 System Efficiency

95.5501 Motor Power Factor

92.4638 Reduction in KWh Consumption (%)

1032 A. M. Sharaf and A. A. A. El-Gammal and PSO tuning algorithms had a great impact on Motor

RMS voltage (PU) is improved from 0.8578 (without the (GP–EM–EE) device), 0.9762 (constant gains controller), 0.9790 (ANN controller), and 0.9598 (FLC) to around 0.9804 (SOGA-based tuned controller), 0.9865 (MOGA-based tuned controller), 0.9887 (SOPSO-based tuned controller), and 0.9852 (MOPSO-based tuned controller). Motor RMS cur- rent (PU) is reduced from 0.8724 (without the (GP–EM– EE) device), 0.7029 (constant gains controller), 0.6837 (ANN controller), and 0.6863 (FLC) to around 0.6247 (SOGA- based tuned controller), 0.6304 (MOGA-based tuned con- troller), 0.6358 (SOPSO-based tuned controller), and 0.6471 (MOPSO-based tuned controller). Maximum Transient Motor Voltage Over/Under Shoot (PU) is reduced from 0.1797 (with- out the (GP–EM–EE) device), 0.0905 (constant gains con- troller), 0.0769 (ANN controller), and 0.0788 (FLC) to around 0.0472 (SOGA-based tuned controller), 0.0394 (MOGA-based tuned controller), 0.0441 (SOPSO-based tuned controller), and 0.0365 (MOPSO-based tuned controller). Maximum Tran- sient Motor Current – Over/Under Shoot (PU) is reduced from 0.1875 (without the (GP–EM–EE) device), 0.0701 (constant gains controller), 0.0871 (ANN controller), and 0.0828 (FLC) to around 0.0431 (SOGA-based tuned controller), 0.0533 (MOGA-based tuned controller), 0.0412 (SOPSO-based tuned controller), and 0.0563 (MOPSO-based tuned controller). Moreover, the Normalized Mean Square Error (NMSE-V) of the Motor voltage is reduced from 0.4672 (without the (GP– EM–EE) device), 0.0828 (constant gains controller), 0.0790 (ANN controller), and 0.0844 (FLC) to around 0.00396 (SOGA-based tuned controller), 0.00429 (MOGA-based tuned controller), 0.00489 (SOPSO-based tuned controller), and 0.00440 (MOPSO-based tuned controller). In addition the (NMSE-ω m ) of the SPIM motor is reduced from 0.6476 (with- out the (GP–EM–EE) device), 0.0809 (constant gains con- troller), 0.0715 (ANN controller), and 0.0838 (FLC) to around 0.00458 (SOGA-based tuned controller), 0.00608 (MOGA- based tuned controller), 0.00608 (SOPSO-based tuned con- troller), and 0.00513 (MOPSO-based tuned controller). The (NMSE-I) of the Motor current is reduced from 0.4238 (with- out the (GP–EM–EE) device), 0.0722 (constant gains con- troller), 0.0837 (ANN controller), and 0.0860 (FLC) to around 0.00361 (SOGA-based tuned controller), 0.00499 (MOGA- based tuned controller), 0.00602 (SOPSO-based tuned con- troller), and 0.00465 (MOPSO-based tuned controller). Total Harmonic Distortion THD (%) of the supply voltage is reduced from 19.647 (without the (GP–EM–EE) device), 7.8228 (con- stant gains controller), 10.4023 (ANN controller), and 8.2175 (FLC) to around 6.2535 (SOGA-based tuned controller), 4.8408 (MOGA-based tuned controller), 4.2581 (SOPSO- based tuned controller), and 4.1081 (MOPSO-based tuned controller). THD (%) of the supply current is reduced from 20.346 (without the (GP–EM–EE) device), 7.3532 (constant gains controller), 9.3573 (ANN controller), and 7.5181 (FLC)

to around 4.1530 (SOGA-based tuned controller), 4.0103 (MOGA-based tuned controller), 5.2433 (SOPSO-based tuned controller), and 5.1378 (MOPSO-based tuned controller). THD (%) of the motor voltage is reduced from 21.675 (with- out the (GP–EM–EE) device), 8.0566 (constant gains con- troller), 8.5408 (ANN controller), and 9.9806 (FLC) to around 5.7887 (SOGA-based tuned controller), 4.9313 (MOGA-based tuned controller), 4.5873 (SOPSO-based tuned controller), and 4.3629 (MOPSO-based tuned controller). THD (%) of the motor current is reduced from 21.675 (without the (GP–EM– EE) device), 10.8589 (constant gains controller), 9.3120 (ANN controller), and 11.0353 (FLC) to around 3.9572 (SOGA- based tuned controller), 3.9998 (MOGA-based tuned con- troller), 5.1673 (SOPSO-based tuned controller), and 5.1216 (MOPSO-based tuned controller). The system efficiency is improved from 0.7853 (without the (GP–EM–EE) device), 0.8577 (constant gains controller), 0.8943 (ANN controller), and 0.9195 (FLC) to around 95.4132 (SOGA-based tuned controller), 95.4882 (MOGA-based tuned controller), 95.4999 (SOPSO-based tuned controller), and 90.7467 (MOPSO- based tuned controller). Motor power factor is improved from 0.7167 (without the (GP–EM–EE) device), 0.9082 (con- stant gains controller), 0.8609 (ANN controller), and 0.9169 (FLC) to around 95.1040 (SOGA-based tuned controller), 90.8137 (MOGA-based tuned controller), 91.7450 (SOPSO- based tuned controller), and 91.4433 (MOPSO-based tuned controller). Reduction in KWh Consumption (%) is reduced from 0.000 (without the (GP–EM–EE) device), 14.4937 (con- stant gains controller), 12.3129 (ANN controller), and 11.4634 (FLC) to around 18.3913 (SOGA-based tuned controller), 16.5388 (MOGA-based tuned controller), 15.9691 (SOPSO- based tuned controller), and 16.5328 (MOPSO-based tuned controller).

The application validated the operation of a novel wind driven-squirrel cage induction generator fed from single phase supply. The scheme utilizes a simple but effect series-parallel switched capacitor–filter to ensure dynamic terminal voltage stabilization, effective energy utilization, and minimal impact of electric load switching and dynamic excursions and wind velocity changes. The novel error driven self-regulating multi- loop dynamic controller utilizes random search optimization algorithms using PSO and GA to continuously search for effective and optimized controllers gains that can cope with specified objective functions restrictions of minimum voltage deviations, limited generated current and power excursions due to terminal load changes and wind velocity excursions and gusting. The application presents a family of novel low cost green plug electricity saving devices/Energy Manage- ment/Energy Economizer (GP–EM–EE) devices equipped with

a dynamic online error driven optimally tuned controller using

a dynamic online error driven optimally tuned controllers. The GP–EM–EE are a small low cost energy conservation devices in the form add-on dynamic switched capacitor filter

35 Novel AI-Based Soft Computing Applications in Motor Drives 1033 compensator schemes for low horse power motors used in

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6. R. Eberhart and Y Shi, “Particle swarm optimization: Developments, over the estimated motor life span of 7–10 years. This trans-

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